Bioanalytical Chemistry: From Biomolecular Recognition to Nanobiosensing 9783110589160, 9783110589092

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Paolo Ugo, Pietro Marafini, Marta Meneghello Bioanalytical Chemistry

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Paolo Ugo, Pietro Marafini, Marta Meneghello

Bioanalytical Chemistry From Biomolecular Recognition to Nanobiosensing

Authors Prof. Paolo Ugo Department of Molecular Sciences and Nanosystems University Ca’Foscari of Venice via Torino 155 30172 Venezia Mestre, Italy [email protected] Dr. Pietro Marafini CS Genetics Limited 50–60 Station Road CB1 2JH Cambridge, United Kingdom [email protected] Dr. Marta Meneghello Laboratoire de Bioénergétique et Ingénierie des Protéines CNRS 31 Chemin Joseph Aiguier 13009 Marseille, France [email protected]

ISBN 978-3-11-058909-2 e-ISBN (PDF) 978-3-11-058916-0 e-ISBN (EPUB) 978-3-11-058929-0 Library of Congress Control Number: 2020946138 Bibliographic information published by the Deutsche Nationalbibliothek The Deutsche Nationalbibliothek lists this publication in the Deutsche Nationalbibliografie; detailed bibliographic data are available on the Internet at http://dnb.dnb.de. © 2021 Walter de Gruyter GmbH, Berlin/Boston Cover image: Paolo Ugo Typesetting: Integra Software Services Pvt. Ltd. Printing and binding: CPI books GmbH, Leck www.degruyter.com

This book is dedicated to the memory of Prof. Karel Vytras (1944–2019) and Prof. Valeria Guzsvany (1975–2019), two great colleagues and friends.

Preface In recent years, bioanalytical chemistry has been playing an increasingly important role and this led it to occupy a unique and autonomous position within the analytical chemistry domain. Bioanalytical chemistry operates, indeed, in a decidedly interdisciplinary area where the basic concepts and typical methodologies of analytical chemistry strictly intersect with biochemistry, molecular biology and biotechnology, together with nanoscience and nanotechnology. Interestingly, such an impressive development of bioanalytical methods has followed two main paths, which are briefly summarised below. On the one hand, there is the application and refinement of increasingly sophisticated and sensitive instrumental techniques that are based on the skilful use of advanced, but also very expensive, instrumentation providing bioanalytical information primarily based on the chemical–physical properties of biomolecules. In fact, remarkable bioanalytical applications have been recently made possible by applying advanced instrumental techniques such as chromatography, electrophoresis, spectroscopy and mass spectrometry. On the other hand, new horizons have been opened by the equally impressive development of techniques and devices exploiting molecular recognition principles together with advanced transduction modes and unexpected miniaturisation capabilities offered by nanotechnologies. These two faces of bioanalytical chemistry do not compete with each other but cover different specificities, showing different but complementary application capabilities. The former includes purely instrumental methods aimed at maximising laboratory analysis capabilities, offering detection limits, accuracy and precision unthinkable until a few years ago. The second gathers biorecognition methods aimed at the development of low-cost analytical devices suitable for easy and rapid use, with high sensitivity and specificity. Such devices are made especially for decentralised analysis, as needed for point-of-care diagnostics in the biomedical field, for in-field environmental analysis or for online monitoring in the food and biotechnology production industry. It is fascinating to see how the development of the analysis and sequencing of polynucleotides (DNA and RNA) often integrated the two paths mentioned earlier, since a continuously evolving combination of physical instrumental tools with sophisticated molecular processing and interactions led to significant advancements in our biological and biomedical knowledge. The role of technologies based and derived from the polymerase chain reaction is a really good example of this. In light of the complexity and the growing importance of bioanalytical techniques, it is fundamental to provide future bioanalytical chemists with adequate knowledge and training tools. The importance of the availability of efficient, reliable and quick molecular diagnostic tools has been dramatically highlighted during https://doi.org/10.1515/9783110589160-202

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Preface

the recent (and current at the time of writing) global crisis caused by the Covid-19 pandemic. This notwithstanding, in 2020 there is still a scarcity of didactic texts specifically dedicated to this sector. While a couple of excellent textbooks cover didactic needs concerning the bioanalytical application of what could be defined as “classical” instrumental techniques, the situation is completely different with regard to the bioanalytical applications of techniques based on molecular recognition and bio-nanotechnologies. In this specific field, a wide range of advanced texts aimed at expert researchers is indeed available; however, what is missing is the offer of didactic texts suitable for students. This deficiency looks particularly deleterious given the necessity of developing and validating a common interdisciplinary language, to be shared by the professionals who will be working in this area. In order to try to fill this gap, this textbook is intended for graduate students attending courses in chemistry, biotechnology or material science, and the focus is mainly on giving an overview of the new technologies based on molecular recognition, sequencing and biosensing. In order to limit the number of topics covered in the already content-rich six chapters that compose this book, it is taken for granted that the reader is already familiar with the basic concepts of analytical chemistry, from the knowledge of analytical data validation (such as calibration, accuracy, precision, sensitivity, detection and quantification limits) to the basic principles of modern instrumental techniques (i.e. spectroscopy, mass spectrometry, electrophoresis, electrochemical techniques, chromatography). In order to help the reader to fill eventual knowledge gaps or to facilitate a more in-depth study of specific topics of interest, a list of selected further readings is provided at the end of each chapter. Despite the limits and specificity of the selected topics, we hope that this book, which does not want to be an exhaustive treatise of bioanalytical chemistry, may contribute to providing a clear foundational knowledge for the bioanalytical chemistry students, allowing them to understand the fascinating challenges behind the progress in this continuously evolving field. Paolo Ugo, Pietro Marafini and Marta Meneghello, June 2020

Contents Preface

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About the authors Abbreviation list Symbol list 1 1.1 1.1.1 1.1.2 1.1.3 1.1.4 1.1.5 1.2 1.2.1 1.2.2 1.2.3 1.2.4 1.2.5 1.2.6 1.3 1.3.1 1.3.2 1.3.3 1.3.4 1.4 1.4.1 1.4.2 1.4.3 1.4.4 1.4.5 1.4.6

2 2.1 2.1.1

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Biomacromolecules in analytical chemistry 1 Nucleic acids 1 Structure of nucleotides 1 Structure of oligonucleotides and duplexes 3 DNA and genetic information: the central dogma of molecular biology 5 Binding of oligonucleotides and thermodynamics 10 Synthetic DNA as a biochemical tool 16 Introduction to proteins 19 Amino acids 21 Zwitterionic character, pK and pI 22 The peptide bond 24 The structure of proteins 26 Protein folding and denaturation 32 The biological function of proteins 33 Enzymes 34 Substrate specificity 36 Active sites, coenzymes and cofactors 37 Oxidoreductases 40 Kinetics of enzymatic reactions 43 Antibodies and antigens 47 Methods to produce antibodies 47 Antibody structure 49 Classification of immunoglobulins 52 Antigen–antibody interaction 53 Factors influencing antigen–antibody interactions 55 Quantitative evaluation of antibody properties 56 Further readings 59 Introduction to bioanalytical assays and biosensors Molecular biorecognition and analytical assays Example: bioassays for lactate 63

61 61

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2.2 2.3 2.4 2.4.1 2.4.2 2.5 2.6 2.6.1 2.6.2 2.6.3 2.6.4 2.6.5 2.6.6 2.6.7 2.6.8 2.7 2.7.1 2.7.2 2.7.3 2.7.4 2.7.5 2.8 2.8.1 2.8.2 2.8.3

3 3.1 3.1.1 3.2 3.2.1 3.2.2 3.2.3 3.2.4 3.3 3.3.1 3.3.2 3.3.3 3.3.4 3.3.5

Contents

Principles of kinetic analytical methods 66 Introduction to biosensors 71 Classification of biosensors according to the receptor 72 Catalytic biosensors 72 Affinity biosensors 73 Classification of biosensors on the basis of the transducer Immobilisation of biomolecules 75 Physical entrapping within dialysis membranes 77 Physical adsorption of biomacromolecules 78 Electrostatic adsorption 79 Physical entrapment within a polymeric gel 79 Cross-linking with polyfunctional reagents 80 Non-specific covalent bonds 81 Specific covalent bonds 81 Biospecific adsorption 82 Immobilisation of biomolecules in practice 83 Entrapment within dialysis membranes 83 Entrapment within polymeric matrices 84 Encapsulation in bilayer lipid membranes 89 Cross-linking 90 Covalent bonding 92 Functionalisation of transducer surfaces 96 Self-assembled monolayers 96 Silanisation 98 Functionalisation of surfaces with diazonium salts 100 Further readings 101 Enzymatic biosensors 103 Properties of immobilised enzymes 103 Effects of the thickness of the enzymatic layer 105 Electrochemical biosensors 105 Potentiometric biosensors 106 Principles of dynamic electrochemical techniques 115 Electron transfer between enzymes and electrodes 128 Amperometric biosensors 136 Optical biosensors 144 Optical fibres 145 Light source and detector 148 Optical phenomena employed in biosensors 149 Principles of the most common optical sensors 152 Enzymatic optodes 156 Further readings 161

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4 4.1 4.2 4.3 4.4 4.4.1 4.4.2 4.5 4.5.1 4.5.2 4.5.3 4.5.4 4.5.5 4.6 4.7 4.7.1 4.7.2 4.7.3 4.8 4.8.1 4.8.2

5 5.1 5.2 5.3 5.3.1 5.3.2 5.3.3 5.3.4 5.3.5 5.4 5.5 5.5.1 5.5.2

Immunochemical assays and immunosensors 163 Introduction 163 Immunoprecipitation and radioimmunoassay 164 Enzyme immunoassays (EIA and ELISA) 167 Lateral flow immunoassays 172 Pregnancy test 174 Strip tests for antibodies and antigens related to SARS-CoV-2 Western blotting 177 Electrophoretic separation 177 Transfer to solid membrane 178 Blocking unspecific binding 179 Incubation with antibodies 179 Detection 180 Enzyme label immunosensors 181 Microbead-based immunoassays 183 Multiplexed microbead immunoassays based on flow cytometry 184 Magnetic beads electrochemiluminescence assays 186 Mechanism of electrochemiluminescence 188 Label-free immunosensors: SPR and QCM 189 Surface plasmon resonance immunosensors 190 Quartz crystal microbalance: immunosensors based on piezoelectric effect 193 Further readings 197

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Analysis of nucleic acids 199 DNA extraction 199 Southern blotting 199 Amplification and detection of specific DNA sequences: PCR 201 The polymerase chain reaction 201 Analytical PCR: quantitative PCR or real-time PCR 203 Signal generation in PCR 206 PCR in action: the coronavirus disease (COVID-19) 210 Digital PCR 213 DNA microarrays 216 DNA sequencing 219 Sanger sequencing 219 Illumina sequencing: an example of short-read next-generation sequencing 220

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5.5.3

6 6.1 6.2 6.2.1 6.2.2 6.2.3 6.2.4 6.3 6.3.1 6.3.2 6.4 6.5 6.6 6.6.1 6.6.2 6.6.3 6.6.4 6.7 6.7.1 6.7.2 6.8

Index

Contents

Pacific Biosciences and Oxford Nanopore Technologies sequencing: examples of long-read third-generation sequencing 230 Further readings 234 Nanotechnologies for bioanalysis 237 Introduction 237 Classification of nanomaterials 237 Zero-dimensional nanomaterials 237 One-dimensional nanomaterials 238 Two-dimensional nanomaterials 241 Three-dimensional nanomaterials 243 Synthesis of nanomaterials 244 Approaches based on physical changes 245 Approaches based on chemical trasformations 249 Functionalisation of nanomaterials 260 Analytical techniques for nanomaterial characterisation 262 Bioanalytical applications of nanomaterials 262 Optical properties of metal nanoparticles 262 DNA–AuNP sensors 264 Colorimetric detection of proteins with nanoparticles 267 SERS assay for DNA denaturation 268 Electrochemical nano-biosensors 271 Voltammetry with nanoelectrode arrays 272 Bioelectroanalysis with nanoelectrode arrays 276 Final remarks 278 Further readings 278 281

About the authors Paolo Ugo is a former Professor of Analytical and Bioanalytical Chemistry and, currently, honorary Senior Researcher at Ca’ Foscari University of Venice (Italy). Paolo earned his doctorate degree in Industrial Chemistry in 1980. After working for two years in a pharmaceutical company, he started his academic carrier as Assistant Professor of Analytical Chemistry at the University of Venice in 1983. In 1987–88, he worked as visiting associate at the California Institute of Technology, Pasadena. Prof. Ugo has been the coordinator of research projects concerning electrochemistry, chemical sensors and biosensors for environmental, food and health control, collaborating actively with several research institutions in Italy and abroad. He has authored more than 150 peerreviewed scientific research articles and served as guest editor for various special issues of international journals on topics related to electroanalytical chemistry and biosensors. Pietro Marafini studied Chemistry at Ca’ Foscari University of Venice (Italy) and, as a part of an Erasmus Lifelong Learning Programme placement at the University of Southampton, he worked on nanostructured surfaces for DNA detection by SERS (surface-enhanced Raman scattering). He then completed a DPhil in Chemical Biology at the University of Oxford where he specialised in Nucleic Acid Chemistry, focusing on biophysical studies of chemically modified oligonucleotides. After his studies, Pietro joined Illumina where he worked on novel reagents and consumables for DNA sequencing as part of the Product Development and the Research and Technology Development teams. Pietro is currently Principal Scientist at CS Genetics, where he leads Chemistry and Chemical Biology R&D. Marta Meneghello obtained her Bachelor and Master degrees in Chemistry at the Ca’ Foscari University of Venice (Italy). After an Erasmus exchange at the University of Southampton (UK), she started a PhD programme, supported by an ITN Marie Curie fellowship, at the same university. Here, under the supervision of Prof. P. N. Bartlett, she worked on enzymatic biofuel cells and enzyme immobilisation for amperometric electrodes. After her PhD, in 2018 she started a postdoctoral research activity at the French National Centre for Scientific Research (CNRS), in the group of C. Leger and V. Fourmond in Marseille, to study metalloenzymes using electrochemical methods.

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Abbreviation list A AA Ab ABTS Ach AChE ACP ADH AFM Ag AgNP ALP AMP APTS AR ATP AuNP AuNW BHQ bp bpy BSA C CA CCD cCVD CDC CDH cDNA CE CGM CMOS CNT CoA COVID-19 CPG CTP CuAAC CV DAB ddNTP DET DLS DMAPN DMF DNA DNB

Adenine Ascorbic acid Antibody 2,2ʹ-Azino-bis(3-ethylbenzothiazoline-6-sulphonic acid) Acetylcholine Acetylcholinesterase Acyl carrier protein Alcohol dehydrogenase Atomic force microscope Antigen Silver nanoparticle Alkaline phosphatase Adenosine monophosphate 3-Aminopropyl-triethoxy-silane Aspect ratio Adenosine triphosphate Gold nanoparticle Gold nanowire Black hole quencher Base pair Bipyridyl Bovine serum albumin Cytosine Cellulose acetate Charge-coupled device Catalytic chemical vapour deposition Centres for Disease Control and Prevention Cellobiose dehydrogenase Complementary deoxyribonucleic acid Counter electrode Continuous glucose monitor Complementary metal-oxide semiconductor Carbon nanotube Coenzyme A Coronavirus disease 2019 Controlled pore glass Cytidine triphosphate Copper-catalysed azide–alkyne cycloaddition Cyclic voltammetry 3,3ʹ-Diaminobenzidine 2′,3′-Dideoxynucleoside triphosphate Direct electron transfer Dynamic light scattering Dimethylaminopropionitrile Dimethylformamide Deoxyribonucleic acid DNA nanoball

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dNHP DNP dNTP dPCR DPN dsDNA DTT EBL EC ECL EDC EDX EIA ELEC ELISA EMF EQCM ESR EXAFS FAD FAM FE-SEM FITC fMet FMN FRET FTIR G GA GC GDH GFP GGBP GLDH GLYMO GOx GPT GTP hBN hCG His HIV HOPG HPLC HPTS HRP HR-TEM HSQ ICP-MS

Abbreviation list

Deoxynucleoside hexaphosphate 2,4-Dinitrophenol or dinitrophenyl group Deoxynucleoside triphosphate Digital polymerase chain reaction Dip-pen nanolithography Double-stranded deoxyribonucleic acid Dithiothreitol Electron-beam lithography Enzyme Commission (number) Electrochemiluminescence 1-Ethyl-3-(3-dimethylaminopropyl)carbodiimide Energy-dispersive X-ray spectroscopy Enzyme immunoassay Enzyme-labelled electrochemical (immunosensor) Enzyme-linked immunosorbent assay Electromotive force Electrochemical quartz crystal microbalance Electron spin resonance Extended X-ray absorption fine structure Flavin adenine dinucleotide Fluorescein Field-emission scanning electron microscopy Fluorescein isothiocyanate Formylmethionine Flavin mononucleotide Fluorescence/Förster resonance energy transfer Fourier-transform infrared spectroscopy Guanine Glutaraldehyde Guanine–cytosine base pair Glucose dehydrogenase Green fluorescent protein Galactose glucose binding protein Glutamate dehydrogenase 3-Glycidoxypropyl-trimethoxy-silane Glucose oxidase Glutamate-pyruvate transaminase Guanosine triphosphate Hexagonal boron nitride Human chorionic gonadotropin Histidine Human immunodeficiency viruses Highly oriented pyrolytic graphite High-performance liquid chromatography Hydroxypyrene trisulphonic acid (pyranine) Horseradish peroxidase High-resolution transmission electron microscopy Hydrogen silsesquioxane Inductively coupled plasma mass spectrometry

Abbreviation list

Ig IR ISE ISFET ITO IUPAC LDH LED LFIA LOD LSPR MALDI-TOF MES MET MGB MIP miRNA mRNA MS MT MW MWCNT NAD NADP NEA NEE NGS NHS NIL NIR NMR NP NTP NW ONT PacBio PAGE PANI PAP PBE PCR PD PDH PEDOT PEGDGE pI PMMA PMT POx

Immunoglobulin Infrared Ion-selective electrode Ion-sensitive field-effect transistor Indium tin oxide International Union of Pure and Applied Chemistry Lactate dehydrogenase Light-emitting diode Lateral flow immunoassay Limit of detection Localised surface plasmon resonance Matrix assisted laser desorption/ionization time-of-flight mass spectrometry 2-(N-Morpholino)ethanesulphonic acid Mediated electron transfer Minor groove binder Molecularly imprinted polymer Micro-ribonucleic acid Messenger ribonucleic acid Mass spectrometry Mutant Molecular weight Multi-wall carbon nanotube Nicotinamide adenine dinucleotide Nicotinamide adenine dinucleotide phosphate Nanoelectrode array Nanoelectrode ensemble Next-generation sequencing N-Hydroxysuccinimide Nanoimprint lithography Near infrared Nuclear magnetic resonance spectroscopy Nanoparticle Nucleoside triphosphate Nanowire Oxford Nanopore Technologies Pacific Biosciences Polyacrylamide gel electrophoresis Polyaniline p-Amino phenol Partially blocked electrode Polymerase chain reaction Photodiode Pyranose dehydrogenase Poly(3,4-ethylenedioxythiophene) Polyethylene glycol diglycidyl ether Isoelectric point Poly(methylmethacrylate) Photomultiplier Pyranose oxidase

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PPi PPy PQQ PSA PVC PVDF QCM qPCR qRT-PCR RCA RDE RE redox RI RIA RNA rRNA SAM SARS-CoV-2 SAXS SBS SCE SDS sec-Ab SEC-UV-vis SEM SERS SHE SMRT SNP SP SPAAC SPL SPR ssDNA SSNMR ssRNA SSV SWCNT T TCEP TEM TIR TMD TPA Tris tRNA Tween U

Abbreviation list

Pyrophosphate anion Polypyrrole Pyrrolo-quinoline quinone Prostate-specific antigen Polyvinylchloride Polyvinylidene fluoride Quartz crystal microbalance Quantitative polymerase chain reaction Quantitative reverse transcription PCR Rolling circle amplification Rotating disc electrode Reference electrode Reduction–oxidation Refractive index Radioimmunoassay Ribonucleic acid Ribosomal ribonucleic acid Self-assembled monolayer Severe acute respiratory syndrome – coronavirus – 2 Small-angle X-ray scattering Sequencing by synthesis Saturated calomel electrode Sodium dodecyl sulphate Secondary antibody Size-exclusion chromatography with UV–vis detection Scanning electron microscope Surface-enhanced Raman spectroscopy or scattering Standard hydrogen electrode Single-molecule real time Single-nucleotide polymorphism Sequencing primer Strain-promoted azide–alkyne cycloaddition Scanning probe lithography Surface plasmon resonance Single-stranded deoxyribonucleic acid Solid-state nuclear magnetic resonance spectroscopy Single-stranded ribonucleic acid Sphere segment void Single-wall carbon nanotube Thymine Tris(2-carboxyethyl)phosphine Transmission electron microscopy Total internal reflection Transition metal dichalcogenide Tripropylamine Tris(hydroxymethyl)aminomethane Transfer ribonucleic acid polysorbate-type non-ionic surfactant Uracil

Abbreviation list

UTP UV UV–vis WE WT XANES XPS ZMW

uridine triphosphate Ultraviolet Ultraviolet–visible Working electrode Wild type X-ray absorption near-edge structure X-ray photoelectron spectroscopy Zero-mode waveguide

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Symbol list A A Aact Ageom Å B/F C CT or Cq D Da δ Δ e e− E E1=2 Ea Ec Em Epa Epc E0 E 0′ ε η F G h H i ilim imax ip ipa ipc I J k kcat kET K Ka KA KD Keq KM l

Ampere, electrical current unit Area or (in spectroscopy) absorbance Active area Geometric area Ångström, unit of length (10−10 m) Bound versus free antigen ratio Concentration Threshold or quantification cycle Diffusion coefficient Dalton, atomic mass unit Diffusion layer Variation Euler’s number Electron Electrode potential Half-wave potential Anodic potential Cathodic potential Membrane potential Anodic peak potential Cathodic peak potential Standard potential Formal potential Molar extinction coefficient or absorbivity Viscosity Faraday constant Gibbs energy Planck’s constant Enthalpy Electrical current (current intensity) Limiting current Maximum current Peak current Anodic peak current Cathodic peak current Light intensity Flux Reaction rate constant Catalytic constant Electron transfer rate constant Kelvin, temperature unit Acid dissociation constant Association constant for complexation reactions Dissociation constant for complexation reactions Equilibrium constant Michaelis–Menten constant Light path length

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ln log λ λmax m M μep μq ν ν EM ν EX q Q r R RU ρ S S/N t T Tm v vmax V

Symbol list

Natural logarithm Base-ten logarithm Wavelength Wavelength of maximum absorption Mass Molar (mol L–1) Electrophoretic mobility Quartz crystal shear modulus Wave frequency Frequency of emission photon Frequency of excitation photon Ionic charge Electrical charge Radius Ideal gas constant Response unit Density Entropy Signal-to-noise ratio Time Temperature Melting temperature Reaction rate or (in electrochemistry) potential scan rate Maximum reaction rate Volt, electrical potential unit

1 Biomacromolecules in analytical chemistry 1.1 Nucleic acids The discovery of the substance that would later be known as deoxyribonucleic acid (DNA) was made by Johann Friedrich Miescher in 1869. He extracted the nuclei of leucocytes, taken from the pus on fresh surgical bandages, and he obtained a precipitate called “nuclein”. When the acidic properties of “nuclein” were discovered, the name was changed to “nucleic acid” by Richard Altmann in 1889. Most importantly, in 1944 Oswald, MacLeod and McCarty suggested that DNA carries genetic information, and their finding was confirmed in 1952 by Alfred Hershey and Martha Chase. A year later, James D. Watson and Francis H. C. Crick proposed the double helix structure for DNA.

1.1.1 Structure of nucleotides DNA and RNA are biological polymers built up from monomers called nucleotides that are linked together by phosphodiester linkages. Nucleotides have three main components: a nitrogen-containing heterocyclic base, a pentose sugar (2′-deoxy-Dribose in DNA and D-ribose in RNA) and a phosphate (Figure 1.1). There are five different fundamental nitrogenous bases and only four are present in DNA. These are divided into two groups (Figure 1.2): – the purines: adenine (A) and guanine (G); – the pyrimidines: cytosine (C), thymine (T) and uracil (U).

Figure 1.1: Structure of nucleotides. The pentose here depicted is 2′-deoxy-D-ribose (Adapted from P. Marafini, Biophysical Studies of Oligonucleotides Containing Duplex Stabilising Modifications, DPhil Thesis, University of Oxford, 2017. Reproduced with permission from the author).

The nucleobase is linked to the pentose sugar via an N-glycosidic bond connecting the C-1′ of the sugar with N-9 of purines or N-1 of pyrimidines. The nitrogenous base can be located on the same side of the pentose ring as its 5′-hydroxyl group (β-anomer) or on the opposite side (α-anomer), with naturally occurring oligonucleotides presenting nucleosides in the β-configuration (Figure 1.3).

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1 Biomacromolecules in analytical chemistry

Figure 1.2: The five nitrogenous bases present in DNA and RNA. Thymine is present in DNA only, while uracil is present only in RNA. The numbering of the nucleobases is in light blue (Adapted from P. Marafini, Biophysical Studies of Oligonucleotides Containing Duplex Stabilising Modifications, DPhil Thesis, University of Oxford, 2017. Reproduced with permission from the author).

Figure 1.3: α- and β-anomers of a DNA nucleoside (Adapted from P. Marafini, Biophysical Studies of Oligonucleotides Containing Duplex Stabilising Modifications, DPhil Thesis, University of Oxford, 2017. Reproduced with permission from the author).

The nucleobase can rotate around the N-glycosidic bond, having two extreme conformations (Figure 1.4). The syn conformer has the bigger N-3 (purine) or O-2 (pyrimidine) above the pentose ring, and it is therefore more sterically hindered than the anti conformer that has the smaller H-8 (purine) or H-6 (pyrimidine) above the sugar. For this reason, naturally occurring nucleic acids prefer the anti conformation.

Figure 1.4: Anti and syn conformations of deoxyadenosine (Adapted from P. Marafini, Biophysical Studies of Oligonucleotides Containing Duplex Stabilising Modifications, DPhil Thesis, University of Oxford, 2017. Reproduced with permission from the author).

In order to reduce electronic and steric interactions between the substituents of furanose, the ring is twisted out of plane generating the so-called sugar puckering. There are two forms of sugar pucker, identifiable by the position of the 2′ and 3′ carbons of the pentose ring (Figure 1.5). The South (S) configuration presents the C-2′

1.1 Nucleic acids

3

on the same side of the ring (endo) as the C-5′, while the North (N) configuration presents the C-3′ in the endo position. In DNA, 2′-deoxy-D-ribose usually adopts the C-2′ endo (S) conformation, while in RNA, D-ribose usually prefers the C-3′ endo (N) conformation.

Figure 1.5: C-2′ endo and C-3′ endo sugar conformations (Adapted from P. Marafini, Biophysical Studies of Oligonucleotides Containing Duplex Stabilising Modifications, DPhil Thesis, University of Oxford, 2017. Reproduced with permission from the author).

1.1.2 Structure of oligonucleotides and duplexes One of the fundamental steps in understanding DNA structure was made by Erwin Chargaff who noted that the ratio of purines to pyrimidines in cells is equal (i.e. 1:1). This was followed by X-ray diffraction studies by Rosalind Franklin and Maurice Wilkins, which showed that the secondary structure of DNA is helical and humidity dependent. The key discovery in consolidating this information was published in 1953 by James Watson and Francis Crick, who proposed the structure of B-DNA as a double helix with hydrogen bonds joining the DNA nucleobases of the two different strands in what are now known as Watson–Crick base pairs (Figure 1.6).

Figure 1.6: Watson–Crick base pairing. The G≡C base pair stabilises DNA more than the A =T base pair because of the additional hydrogen bond (Adapted from P. Marafini, Biophysical Studies of Oligonucleotides Containing Duplex Stabilising Modifications, DPhil Thesis, University of Oxford, 2017. Reproduced with permission from the author).

In an oligonucleotide, for example a single strand of DNA, nucleotides are covalently linked through the phosphate groups forming a phosphodiester linkage. As shown in Figure 1.7A, the 5′-phosphate group of one nucleotide is joined to the 3′-hydoxyl group of the next nucleotide. The B-DNA double helix (Figure 1.7B) is made by two

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1 Biomacromolecules in analytical chemistry

(A)

(B)

Axial rise: 3.4 Å

5′ End

5′

Phosphodiester linkage

Minor groove

Helix pitch: 36 Å

3′

Major groove

3′ End

Helix diameter: 20 Å

Figure 1.7: DNA structure. (A) Single-stranded DNA (ssDNA) with a phosphodiester linkage highlighted. (B) Schematic representation of the B-double helix; there are 10.5 base pairs per turn of the helix (3.6 nm). 1 Å = 0.1 nm. (Adapted from P. Marafini, Biophysical Studies of Oligonucleotides Containing Duplex Stabilising Modifications, DPhil Thesis, University of Oxford, 2017. Reproduced with permission from the author).

antiparallel single strands that interact through hydrogen bonding between nitrogenous bases, as proposed by Watson and Crick (Figure 1.6). The base pairs that are preferentially formed are always the same: cytosine binds specifically to guanine (G≡C), and adenine to thymine (A=T). Two major conformations of DNA are known: A- and B-DNA (Figure 1.8). Both B-DNA and A-DNA are right-handed helices, but while the B-form of DNA, which is the most biologically important and common conformation, is formed in environments with a high humidity level, A-DNA is present when the humidity is low. The A-form is also the major conformation adopted by RNA–RNA and RNA–DNA duplexes. B-form duplexes (Figures 1.7B and 1.8) present two grooves of similar depth, but one (major groove, about 12 Å) is wider than the other (minor groove, about 6 Å). The nucleobases are stacked upon each other, with an interbase distance of 3.4 Å, and they are almost perpendicular to the helix axis. There are 10.5 bases for each 36 Å helix pitch and the helix diameter is 20 Å. The furanose sugars are in the C-2′ endo conformation with anti nucleobases.

1.1 Nucleic acids

5

Figure 1.8: Side and top views of the structure of B-DNA and A-DNA. The two structures have the same sequence; the models were produced using Hyperchem 7. (Adapted from P. Marafini, Biophysical Studies of Oligonucleotides Containing Duplex Stabilising Modifications, DPhil Thesis, University of Oxford, 2017. Reproduced with permission from the author).

A-form duplexes (Figure 1.8) are slightly wider (23 Å of helix diameter) and more compact, with 11 bases for each 28 Å helix pitch. Therefore, the nucleobases are closer to each other, with an interbase distance of only 2.6 Å. Because of that, in order to maintain the normal van der Waals separation, the nucleobases are significantly tilted compared to the B-form. Another major difference with B-form nucleic acids is the fact that base pairs are almost centred over the helical axis in B-form, while in A-form they are displaced away from the central axis creating a 3 Å wide hollow core. The furanose sugars are in the C-3′ endo conformation with anti nucleobases. As a result of these features, the minor groove is wide and shallow and the major groove is narrow and deep. The overall stability of double-stranded DNA (dsDNA) depends on the sequence, which determines the base pair composition. The higher the G≡C/A=T ratio, the more stable the resulting dsDNA, since the G≡C base pair has an additional hydrogen bond compared to the A=T base pair. Another factor that stabilises dsDNA is the stacking of the nitrogenous bases when the double helix is formed, as the overlapping π-orbitals of neighbouring base pairs interact stabilising the molecule. The environment has a significant effect on the stability of a duplex as well, for example cations help masking the negative charges of the backbones, reducing so that destabilising repulsive effect. It is also important to notice that divalent cations like Mg2+ have a more positive stabilisation effect than monovalent cations such as Na+.

1.1.3 DNA and genetic information: the central dogma of molecular biology 1.1.3.1 Genetic information in a biological system In a biological system, three major types of biopolymers carry information encoded in a sequence of monomers: DNA, RNA (both nucleic acids) and proteins. As stated by Francis H. C. Crick in 1958, the “central dogma of molecular biology” says that “the

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transfer of information from nucleic acid to nucleic acid, or from nucleic acid to protein may be possible, but transfer from protein to protein, or from protein to nucleic acid is impossible” (Figure 1.9). The central dogma is also known in a simplified, albeit incorrect, version popularised by James D. Watson that essentially sees the information as moving unidirectionally from DNA to RNA and finally ending in a protein. Given the scope of this book, just this simplified version is going to be briefly outlined further. DNA

RNA Transcription

Protein Translation

Reverse transcription DNA replication

RNA replication

Figure 1.9: A schematic representation of the central dogma of molecular biology. Information can flow between nucleic acids and from those to proteins, but not the other way around.

The human genome contains about 3 × 109 DNA base pairs that are divided into 23 chromosomes stored in the nucleus of cells. About 20,000–21,000 proteins are encoded in the genome and the nucleic acid sequence storing the information for a protein is called a gene. The first step in moving the information from DNA to a protein is called transcription (Figure 1.10). During this, a DNA-dependent RNA polymerase transcribes the sequence of the gene of interest to an RNA molecule using four ribonucleoside 5′-triphosphates (NTPs: ATP, GTP, UTP and CTP). The polymerase builds the RNA in the 5′ to 3′ direction and to have access to the template strand it needs to locally unwind the DNA forming a so-called transcription bubble.

Figure 1.10: Simplified schematic representation of an RNA polymerase during the transcription step. The polymerase (in pink) transcribes the template strand of the DNA (in red) into the nascent RNA (green) that is elongated from 5′ to 3′. The active site is at the 3′-end of the RNA strand.

Interestingly, only a portion of most of eukaryotic genes codes for proteins and the sequences that do so are called exons. The segments that do not code for the protein of interest are instead called introns. After a process called splicing (Figure 1.11), all

1.1 Nucleic acids

7

Figure 1.11: RNA splicing. During splicing, the introns are removed from the pre-mRNA forming so the final mRNA.

the introns are removed from the precursor messenger RNA (pre-mRNA) to form the mature messenger RNA (mRNA). The subsequent step mentioned by the central dogma is called translation, which is essentially an mRNA-directed protein synthesis process. At this point, it is important to notice that, while during transcription the “language” is essentially maintained (the only change from DNA to RNA is the replacement of T with U), the same is not true for the translation step. Since there are 20 amino acids, there cannot be a one-nucleotide one-amino acid equivalence. At least three nucleotides are necessary to code for each amino acid, giving 43= 64 different combinations (two nucleotides would not be sufficient because 42= 16 combinations, which is less than the 20 amino acids). Table 1.1 reports the standard genetic code, which is nearly universal with the exception of some small variations in some bacteria, single-celled eukaryotes and mitochondria. As it is noticeable from that table, the genetic code is degenerate, which means that several mRNA codons are expressed with the same amino acid. However, this degeneracy is not uniform, for example, Arg, Leu and Ser are all coded by six different codons, while Met and Trp correspond to only one codon (names and abbreviations of amino acids are listed in Figure 1.23). In 1958, Francis H. C. Crick hypothesised that “the amino acid is carried to the template by an ‘adaptor’ molecule, and that the adaptor is the part which actually fits on to the RNA” and he also speculated that the adaptor “might contain nucleotides. This would enable them to join on to the RNA by the same ‘pairing’ of bases as is found in DNA”. Crick’s hypothesis turned out to be mostly correct and the adaptor he mentioned is a special kind of RNA called transfer RNA (tRNA) to which amino acids are attached (Figure 1.12A). tRNA is a single-stranded RNA molecule that folds in a cloverleaf secondary structure, and on its bottom loop there is a nucleotide triplet called anticodon that pairs with the corresponding codon on the mRNA. The synthesis of the polypeptide chain occurs in a supramolecular machine called ribosome, which is composed of two subunits (one large and one small) and that has a mass made of RNA (rRNA) for almost two-thirds and of proteins for the

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Table 1.1: The standard genetic code. Codons are written in the 5′ to 3′ direction. AUG signals the initiation of translation, while UAA, UAG and UGA signal the termination. 1st codon letter (5′-end)

U

C

A

G

2nd codon letter U

C

A

UUU UCU UAU Phe Tyr UUC UCC UAC Ser UUA UCA UAA Stop UUG UCG UAG Stop CUU CCU CAU Leu His CUC CCC CAC Pro CUA CCA CAA Gln CUG CCG CAG AUU ACU AAU Asn AUC Ile ACC AAC Thr AUA ACA AAA Lys AUG Met ACG AAG GUU GCU GAU Asp GUC GCC GAC Val Ala GUA GCA GAA Glu GUG GCG GAG

3rd codon letter G (3′-end) UGU U Cys UGC C UGA Stop A UGG Trp G CGU U CGC C Arg CGA A CGG G AGU U Ser AGC C AGA A Arg AGG G GGU U GGC C Gly GGA A GGG G

remaining one-third. Ribosomes read through the mRNA in the 5′ to 3′ direction and they synthesise the polypeptides starting from the amino-terminal amino acid by adding subsequent residues to the carboxyl-terminal end. In bacteria, the start AUG codon codes for a modified form of Met called Nformylmethionine (fMet). Because of the formyl group, fMet can be incorporated only at the N-terminus of the polypeptide chain, as no peptide bond can be formed on that side. After the synthesis is initiated with the formation of the ribosome/mRNA/ fMet–tRNA complex, the elongation phase begins (Figure 1.12B). During this, a second tRNA complementary to the next codon enters the ribosomal complex and a peptide bond is formed between the two amino acids, with fMet attaching to the new residue. Elongation cycles continue until a stop codon is encountered, as no tRNA recognises that. Instead, proteins called release factors intervene and the ribosomal complex is disassembled ending with the release of the polypeptide. 1.1.3.2 DNA and the genetic information: mutations in DNA A permanent change in the nucleotide sequence of DNA is called mutation. Mutations are important because they are responsible for genetic variation and the source of

9

1.1 Nucleic acids

(A)

(B)

fMet

Arg

5′

Ser

U A C A U G G C A

3′ mRNA

5′p C GU

tRNA

Phe

5′ Anticodon

5′

mRNA

U A C

fMet Arg

AGC UCG

fMet

Arg

U A C C G U A U G G C A

3′

G A A

3′ Codon 5′

C G U G C A U U C

3′

Figure 1.12: tRNA and the protein synthesis process. (A) Schematic representation of a tRNA molecule (in red) pairing with an mRNA codon for Ser. (B) Simplified scheme of the elongation phase of the protein synthesis. The mRNA (in green) is translated by the ribosome (in black) in the 5′ to 3′ direction.

evolutionary change. Mutations can be induced by exogenous or endogenous agents (mutagens) or occur spontaneously. Spontaneous mutations are much less frequent than induced mutations, with the former having a frequency of one in 105 to 108 cells. Nevertheless, spontaneous mutations are more important than induced mutations from an evolutionary point of view. Induced mutations usually reduce gene function and rarely enhance it, with cells evolving a sophisticated system to repair damaged DNA. When a mutation alters a single base pair of DNA, it is called point mutation. There are three main types of point mutation in DNA (Figure 1.13): – Transitions, in which a pyrimidine is replaced by a different pyrimidine, or a purine is replaced by a different purine; – Transversions, in which a pyrimidine is replaced by a purine, or a purine is replaced by a pyrimidine; – Indel mutations (for insertion–deletion), in which one (or more) base pair of DNA is inserted or deleted from the nucleotide sequence.

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(A)

(C) A=T

T=A

↕

↕

G≡C (B)

A=T ↕ C≡G

C≡G ↔

TCCAGGGACT →

TCCAGGGAACT

Insertion

T=A

TCCAGGGACT → TCAGGGACT

↕

Deletion

↔ G≡C

Figure 1.13: Point mutations: (A) transition, (B) transversion and (C) indel. Pyrimidines are depicted in grey while purines are depicted in light blue, inserted or deleted bases are in red.

Point mutations have a direct impact on the phenotype (i.e. the set of observable characteristics of a living organism). As explained earlier, DNA is transcribed to mRNA, which in turn is translated into proteins and each amino acid is encoded by three bases (codon). Therefore, DNA-level mutations have protein-level effects. Examples of such mutations include (Figure 1.14): – Synonymous or silent mutations, in which the nucleotide mutation produces the same amino acid due to codon degeneracy; – Missense mutations, in which the nucleotide mutation alters the codon and produces a different amino acid. “Conservative” substitutions mean that the new amino acid is chemically similar to the original one, with “non-conservative” mutations giving amino acids with different chemical properties. For the former, protein function is not severely changed, whereas for the latter, the effect can be more severe; – Nonsense mutations, in which the nucleotide mutation generates a stop (translational termination) codon. This results in truncated or completely inactive proteins.

1.1.4 Binding of oligonucleotides and thermodynamics As explained in the previous paragraphs, the sequence of an oligonucleotide is of utmost importance, as it carries the genetic information. To form the famous double helix, the correct G≡C/A=T base pairing is very important; however, this does not mean that all nucleobases will always be paired with the correct one. In fact, as a consequence of mutations, a nucleobase can pair with the wrong partner (e.g. a G with a T) giving rise to a mismatch. The introduction of such mismatches changes the hydrogen bond pairing pattern and, because of this, the DNA duplex is locally

1.1 Nucleic acids

11

Figure 1.14: Scheme of the consequences of point mutations.

destabilised. This means that there is a difference in binding affinity towards a specific target region between a fully complementary wild-type strand and a mutant one containing a mismatch. From a bioanalytical point of view, it is important to notice that this difference in stability can be exploited to design probes that will allow to recognise whether a region of interest has the correct sequence or not. A useful measurement of the thermal stability of a duplex is the melting temperature (Tm ), which is the temperature at which half of the oligonucleotides are in double-stranded form and half are in single-stranded form. The higher the Tm , the less likely it is to have a double helix that opens at room temperature. A simple way of studying the stability of a short DNA or RNA duplex is a technique commonly referred to as UV (ultraviolet) melting. The capacity of oligonucleotides to absorb light in the

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Absorbance

UV region arises mainly from the pyrimidine and purine nucleobases and their n–π* and π–π* transitions. Each nucleotide has a different extinction coefficient (ε) and a different absorption maximum (λmax ), and therefore, oligonucleotides have an average λmax around 260 nm. The intensity and the maximum of absorption not only depend on the sequence of the oligonucleotide under study, but are also influenced by, for example, the salt concentration, the pH of the solution and importantly base–base stacking. In fact, interactions between nucleobases stacking on each other cause a decrease in the extinction coefficient. This hypochromic effect is very useful for following the transitions from duplexes to single strands of oligonucleotides, as the absorption intensity of the former is lower than the latter. By monitoring the UV absorption while increasing the temperature, it possible to record a melting curve and derive from it the melting temperature. If there is only one melting transition, the Tm will correspond to the midpoint of the sigmoidal curve (Figure 1.15).

Tm Temperature (°C) Figure 1.15: UV melting curve of a transition from dsDNA to ssDNA when increasing the temperature. Tm corresponds to the midpoint of the sigmoidal curve. (Adapted from P. Marafini, Biophysical Studies of Oligonucleotides Containing Duplex Stabilising Modifications, DPhil Thesis, University of Oxford, 2017. Reproduced with permission from the author).

While the melting temperature is a good measurement of the thermal stability of a duplex, it does not provide a complete picture of the affinity one strand has for the other. Performing a thermodynamic study allows to obtain a more comprehensive understanding of the system and it is possible to derive the thermodynamic parameters needed from UV melting experiments.

1.1 Nucleic acids

13

The two thermodynamic relations defining the variation of Gibbs energy of a reaction Δr G = − RT Keq ln

(1:1)

Δr G = Δr H − TΔr S

(1:2)

can be combined to give the following van ’t Hoff plot equation: ln Keq =

Δr S Δr H − R RT

(1:3)

where Keq is the equilibrium constant, R is the ideal gas constant (8.314 J K–1 mol–1), T is the temperature in kelvin, Δr S is the entropy variation of the reaction and Δr H is the enthalpy variation of the reaction. Rearranging this equation allows to obtain the following relation: 1 Δr S R − ln Keq = T Δr H Δr H

(1:4)

that can be plotted as a line when graphed as 1/T versus ln Keq (Figure 1.16). This allows the values of the variation of enthalpy and entropy to be obtained after the intercept (q = Δr S=Δr H) and the slope (m = R=Δr H) are known:
 R R (1:5) Δr H = − and Δr S = q Δr H = q − m m

1/T (1/K)

y=m

x+q

In Keq Figure 1.16: 1/T versus ln Keq plot to determine thermodynamic parameters. A linear regression (red) is performed on the experimental data (light blue) to obtain intercept (q) and slope (m). (Adapted from P. Marafini, Biophysical Studies of Oligonucleotides Containing Duplex Stabilising Modifications, DPhil Thesis, University of Oxford, 2017. Reproduced with permission from the author).

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The value of the equilibrium constant Keq depends on the nature of the duplex, that is, on whether the two strands forming it are non-self-complementary or selfcomplementary. Given two non-self-complementary oligonucleotides, A and B, having the same concentration (CT =2) with a total concentration of ssDNA CT (CT = [A] + [B], with [A] = [B] = CT =2), it is possible to write the process of the formation of a duplex AB as follows: A + B Ð AB (1:6) At equilibrium, the reaction quotient is equal to the equilibrium constant which is Keq =

½AB α ðCT =2Þ 2α = 2 = ½ A ½B CT CT ð1 − αÞ2 CT 2 −α 2

(1:7)

where α is the extent of association, that is, the fraction of single strands in the duplex state. Since at the melting temperature (Tm ) α = 1/2, eq. (1.7) can be rewritten as Keq = KTm = −

4 CT

(1:8)

Therefore, if eq. (1.8) is substituted into eq. (1.4), the following is obtained: 1 R CT Δ r S = ln + 4 Δr H Tm Δr H

(1:9)

Equation (1.9) can be fitted as a straight line when plotted as 1=Tm versus lnðCT =4Þ (Figure 1.17). Once the intercept (q = Δr S=Δr H) and the slope (m = R=Δr H) are measured, it is possible to obtain the values of the variation of enthalpy and entropy:
 R R (1:10) Δr H = − and Δr S = q Δr H = q − m m It possible to obtain the variation of Gibbs energy of the formation of a duplex (Δr G) at a temperature T by substituting the values found in eq. (1.10) into eq. (1.2):
 R R (1:11) Δr G = − T q m m The binding constant (Kb ) of the process at the same temperature T can also be easily derived by combining the result obtained from eq. (1.11) with eq. (1.1): Kb = e − ðΔr G=RT Þ

(1:12)

Finally, the dissociation constant (Kd ) can be derived from eq. (1.12): Kd =

1 = eðΔr G=RT Þ Kb

(1:13)

1.1 Nucleic acids

15

0.00314 0.00312 y = –2.10–5 x + 0.003 1/Tm (1/K )

0.00310 0.00308 0.00306 0.00304 0.00302 –16

–15

–14

–11

–12

–13

–10

In(CT/4) Figure 1.17: Example of a 1=Tm versus lnðCT =4Þ plot to determine thermodynamic parameters. The linear regression is in red. (Adapted from P. Marafini, Biophysical Studies of Oligonucleotides Containing Duplex Stabilising Modifications, DPhil Thesis, University of Oxford, 2017. Reproduced with permission from the author).

When working with self-complementary DNA, the thermodynamic parameters can be derived in a similar manner as in eqs. (1.9) to (1.13), with the only difference being the value of the equilibrium constant Keq . Given a self-complementary oligonucleotide A having concentration CT , the process of the formation of a duplex AA is 2A Ð AA

(1:14)

Therefore, the equilibrium constant is Keq =

½AA ½ A

2

=

α ðCT =2Þ 2

ð C T − α CT Þ

=

α 2 CT ð1 − αÞ2

(1:15)

Since at the melting temperature (Tm ) α = 1/2, Keq can be rewritten as Keq = KTm =

1 CT

(1:16)

which when substituted in eq. (1.4) yields the following: 1 R Δr S = ln CT + Tm Δr H Δr H

(1:17)

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1.1.5 Synthetic DNA as a biochemical tool 1.1.5.1 Solid-phase DNA/RNA synthesis In order to use DNA as a biochemical tool, it is important to be able to obtain pure oligonucleotides of a known sequence. This is possible thanks to the solid-phase synthesis method invented in the 1960s by Bruce Merrifield, for which he won the Nobel Prize in 1984. With this method, the synthesis is carried out on a solid support, allowing the use of large excesses of reagents to drive the reactions to completions in short times, while the surplus reagents and the impurities are washed away in cycles which in turn makes it possible to avoid purification after each step. The solid support is usually made of controlled pore glass (CPG) or polystyrene and the synthesis of the oligonucleotides occurs in the pores, often 500 or 1 000 Å wide, with the size depending on the desired length of the oligonucleotide. Different chemistries have been used over the years; however, the phosphoramidite method developed by Marvin Caruthers in the early 1980s is the one still used in modern solid-phase DNA synthesisers. With these instruments, the synthesis proceeds in the 3′ to 5′ direction with only one nucleotide phosphoramidites added per cycle. Various chemical steps are needed to complete a cycle (Figure 1.18), with the first being the detritylation with trichloroacetic acid necessary to deblock the 5′-OH of the initial nucleotide attached to the resin. The following step is the activation of the phosphoramidite with a tetrazole catalyst, followed by coupling with the nucleotide on the surface. Although this chemistry is extremely efficient, no reaction achieves 100% yield and, for this reason, a capping step is performed in order to block all the unreacted 5′-OHs by acetylating them. This is an important step as it avoids the synthesis of side-product oligonucleotides with one or more deletions in their sequence. The phosphite-triester (P(III)) linkage obtained after the coupling is not stable to the acidic conditions of the next step (detritylation) and so it needs to be oxidised to a phosphortriester (P(V)) by using iodine. After this, all the steps are repeated until the desired oligonucleotide sequence is obtained. The final product is cleaved from the solid support and the protecting groups on nucleobases and phosphate backbone are removed by using a solution of ammonia in water. The final product needs to be purified and to do so different techniques can be used. Gel filtration is the simplest form of chromatographic purification that can be used, it is based on size separation principles and it therefore allows to separate small molecules from the bigger oligonucleotides. This means that only small impurities (like the protecting groups cleaved by the ammonia treatment) can be removed, while anything that is similar in size to the desired product cannot be separated. Gel filtration can also be used to remove salts from oligonucleotides, which is very important as the type and concentration of the salts will affect the binding and folding properties of the oligonucleotides. Even though the chemistry used to synthesise oligonucleotides is very efficient, several impurities are present in the crude mixture. These can be oligonucleotides of

1.1 Nucleic acids

17

Figure 1.18: The phosphoramidite oligonucleotide synthesis cycle.

different lengths than the desired one (e.g. a deletion product, one base shorter than desired) or oligonucleotides that, although correct in sequence, still present some of the protecting groups that were not cleaved by the ammonia treatment. These impurities have a similar size to the desired product and, since gel purification is inadequate in this scenario, high-performance liquid chromatography (HPLC) and polyacrylamide gel electrophoresis (PAGE) are used to obtain the high purity needed for numerous applications. Two types of HPLC are routinely used to purify

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short oligonucleotides: reversed-phase HPLC separates the various products based on their hydrophobicity, while anion-exchange HPLC separates them based on charge differences. PAGE is commonly used to purify longer oligonucleotides, separating them thanks to their electrical charge and hydrodynamic properties. 1.1.5.2 Modified oligonucleotides With the discovery of the solid-phase DNA/RNA synthesis, the introduction of unnatural modifications into oligonucleotides has become increasingly common. These modifications have been designed to expand the possible applications of nucleic acids by enhancing their natural properties and introducing new ones as well. One of the areas where nucleic acids are frequently modified is for antisense applications. Antisense agents are short oligonucleotides capable of binding their complementary sites on mRNA, preventing the expression of that particular genetic information (Figure 1.19). Antisense oligonucleotides have been chemically modified in order to improve their cellular uptake and to increase their biostability by increased resistance to nucleases. Additionally, strong hybridisation of the antisense oligonucleotides to their targets is fundamentally important for their function, and modifications increasing the thermal stability of the duplexes formed are therefore extremely useful. In fact, the correlation between antisense activity with hybridisation affinity and melting temperature has been observed.

Figure 1.19: Action of antisense oligonucleotides. Antisense oligonucleotides (red) interact with mRNA (yellow) preventing its expression.

Another area where the modification of oligonucleotides is highly beneficial is the design and production of probes for specific DNA sequences, which could be used for diagnostic or forensic applications. Probes need to be long enough to be able to bind tightly to their target, while also being specific for it and being able to differentiate a mutated sequence from the original one. It is also necessary to have methodologies for detecting and differentiating among different probes and their responses. For example, the modification of oligonucleotides with moieties that are fluorescent is very useful for real-time polymerase chain reaction (PCR) probes like HyBeacons (Figure 1.20), while the use of dyes that are Raman active is essential to follow the electrochemical denaturation on a nanostructured gold surface via surface-enhanced Raman spectroscopy (SERS).

1.2 Introduction to proteins

19

Figure 1.20: HyBeacon probes. When the HyBeacon probe (in blue) is in its single-stranded form, it is dark (non-fluorescent). After it hybridises with its target (in yellow), the probe fluoresces.

As a final example of the use of modified oligonucleotides, it is worth mentioning the nanostructure field. Modified oligonucleotides have been used to stabilise, study, control, conjugate and enhance the properties of different simple nanoconstructs. 1.1.5.3 Post-synthetic labelling of modified oligonucleotides Highly modified oligonucleotides are in constant demand for a wide variety of applications in biology, genetics, forensics or nanotechnology and some examples were presented in the previous section. These modifications often involve either changing the structure of the backbone, the sugar or the nucleobases as well as the addition of functional moieties such as fluorophores. For example, in the case of PCR probes, functionalisation with different dyes allows their use in more complex multiplex assays. Modifications to DNA or RNA strands are commonly added using phosphoramidite monomers in solid-phase oligonucleotide synthesis. However, the synthesis of such phosphoramidite monomers can be challenging and the modifications need to be stable to the conditions used during the solid-phase synthesis cycle. Alternatively, it is possible to follow a different approach by post-synthetically labelling the oligonucleotides. This involves the addition of simpler phosphoramidite monomers in the DNA or RNA strands which have a chemical handle (e.g. a primary amine and an alkyne), which can react with an appropriate label. Three commonly used methodologies are the coppercatalysed azide and alkyne cycloaddition reaction (CuAAC), the strain-promoted azide and cycloalkyne cycloaddition reaction (SPAAC) and the amine and active ester coupling reaction (Figure 1.21). The linkers formed with these three labelling methods are uncharged and confer a certain rigidity to the structure via the resultant triazole or amide bond.

1.2 Introduction to proteins Proteins are large biomolecules, or macromolecules, consisting of one or more long chains of amino acid residues. Like other biological macromolecules, such as polysaccharides and nucleic acids, proteins are essential parts of all living organisms and participate in virtually every process within cells. They can be classified according to their biological function as follows: – enzymes, which catalyse almost all biochemical reactions within cells and are vital to metabolism;

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Figure 1.21: Examples of post-synthetic labelling. (A) Copper-catalysed azide and alkyne cycloaddition reaction. (B) Strain-promoted azide and cycloalkyne cycloaddition reaction. (C) Amine and active ester coupling reaction. (Adapted from P. Marafini, Biophysical Studies of Oligonucleotides Containing Duplex Stabilising Modifications, DPhil Thesis, University of Oxford, 2017. Reproduced with permission from the author).

– transport proteins, which serve the function of moving other species from one location to another within an organism, for example, haemoglobin that transports oxygen from the lungs to other organs and tissues; – structural proteins, which confer stiffness and rigidity to biological components. Some examples are keratin (the main component of hair, nails and some animal shells), collagen and elastin (present in connective tissues such as bones, tendons or cartilages), and the proteins of the cytoskeleton, which form a system of scaffolding that maintains cell shape; – antibodies, defence proteins used by the immune system to recognise and neutralise pathogens such as viruses, pathogenic bacteria and substances extraneous to the organism. There are also many other proteins with different functions, for example, cell signalling and signal transduction, and DNA replication. However, the most interesting proteins in bioanalytical chemistry are enzymes and antibodies. So, in this chapter, we mainly focus on these two types of macromolecules.

1.2 Introduction to proteins

21

From a chemical point of view, proteins are polymers of amino acids bound together by peptide bonds. Therefore, we begin our description of proteins with an overview on such fundamental molecules.

1.2.1 Amino acids Amino acids are the building blocks for peptides and proteins. Although 500 natural amino acids are known, only 20 are found in living organisms and form millions of different proteins, just like only 26 letters of our alphabet can form millions of different words in many different languages. The 20 amino acids present in the living organisms have all the same general structure shown in Figure 1.22. It consists of a tetrahedral carbon atom connected to four groups: an amino group (–NH2), a carboxylic group (–COOH), a hydrogen atom (–H) and a side chain (–R). The latter is the only part that varies from one amino acid to another. Since the amine is attached to the first (alpha) carbon atom relative to the carboxylic group, they are called α-amino acids. Amino acids are chiral molecules, with the exception of glycine (where the R substituent is a second hydrogen atom). All natural amino acids, with only few exceptions, have the same absolute configuration: they are L-isomers (“left-handed” isomers) in the Fischer convention. COOH C

H 2N

R H

Figure 1.22: General structure of an α-L-amino acid.

Amino acids can be classified according to their R side chain in: – basic amino acids (R containing a further amino group), – acidic amino acids (R containing a further carboxylic group), – aliphatic amino acids, – aromatic amino acids, – hydroxyl-containing amino acids, – sulphur-containing amino acids and – secondary amino acids. For convenience, the names of the amino acids are often abbreviated to either a three-symbol or a one-symbol short form, as reported in Figure 1.23.

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Figure 1.23: Structure of the 20 common α-L-amino acids present in the living organisms and relevant three- and one-letter symbols.

1.2.2 Zwitterionic character, pK and pI In the physiological fluids, at the physiologic pH (which is approximately 7.4, but can slightly vary from one organ to another), amino acids would not be in the neutral form represented in Figure 1.23. The α-carboxylic acid group is a weak acid,

1.2 Introduction to proteins

23

with a pKa between 1.8 and 2.5 for the 20 common amino acids, so that at neutral pH, it is deprotonated and negatively charged (−COO−). On the other hand, the amino group is a weak base, with a pKa between 8.7 and 10.7, so that at the physiologic pH, it is mostly protonated, becoming a positively charged ammonium group (−NH3+). Since all amino acids contain both a basic and an acidic functional group, they are amphoteric molecules. In protic media, therefore in all physiological fluids, the neutral form represented in Figure 1.23 is really rare. Below pH 2.5, the predominant form will have a neutral carboxylic acid group and a positive ammonium ion (net charge = +1). Above pH 8.7, the predominant form will present a negative carboxylate group and a neutral amine (net charge = −1). Finally, at pH between 2.5 and 8.7, therefore at the physiologic pH, the predominant form of amino acids usually presents both a negatively charged carboxylate and a positively charged ammonium group (see Figure 1.24), so that the net charge is null. This molecular form is known as zwitterion, from the German word zwitter meaning “hermaphrodite” or “hybrid”.

Figure 1.24: Charge of an amino acid at different pH values.

In addition to the α-carboxylic acid and the α-amino group, some amino acids present other charged functional groups, so that even at neutral pH the net charge will be positive or negative. The positively charged amino acids are the three basic amino acids (lysine, histidine and arginine), whereas two acidic amino acids (aspartic and glutamic acid) are negatively charged. All the other amino acids have no net charge at neutral pH, but they can be polar or non-polar depending on the side chain R (see Table 1.2). At pH values between the pKa of the carboxylic group and the pKa of the amino group, the zwitterion form predominates, but coexists in a dynamic equilibrium with small amounts of positive and negative ions. However, at the exact midpoint between the two pKa values, the trace amounts of positive and negative ions exactly balance, so that the net charge of all forms present is null. Such pH value is known as isoelectric point (pI). At its isoelectric point, an amino acid remains stationary in an applied electric field, not moving towards the positive or negative pole. Since the amino acids have different pKa values, their isoelectric points will be slightly

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Table 1.2: pI values for the 20 common amino acids. Non-polar amino acids

pI

Polar amino acids

pI

Charged amino acids

pI

Alanine

.

Glycine

.

Lysine

.

Valine

.

Asparagine

.

Histidine

.

Leucine

.

Glutamine

.

Arginine

.

Isoleucine

.

Tyrosine

.

Aspartic acid

.

Phenylalanine

.

Cysteine

.

Glutamic acid

.

Tryptophan

.

Serine

.

Methionine

.

Threonine

.

Proline

.

different. The isoelectric point of each amino acid can be estimated through the Henderson–Hasselbach equation: 1 pI = ðpK1 + pK2 Þ 2

(1:18)

where pK1 and pK2 are the dissociation constants of the ionisation steps involved. This calculation is straightforward for amino acids presenting only one carboxylic group and one amino group, where pK1 and pK2 are the pKa values of such functional groups. For amino acids with ionisable side chains, the calculation of the pI value is more complex as we need to consider also the pKa of the R substituent. The pI values of the 20 common amino acids are listed in Table 1.2. Given that proteins are polymers of amino acids, they also present negatively and positively charged groups in aqueous solutions. Therefore, each protein will have a specific isoelectric point, which is the pH value at which all its positive and negative functional groups are exactly balanced.

1.2.3 The peptide bond Amino acids are the structural units, or monomers, that make up proteins. They bind together to form short polymer chains called peptides or oligopeptides, or longer chains called polypeptides or proteins. Such polymers are linear and unbranched, with each amino acid within the chain linked to two neighbouring amino acids. The amino acids are connected to each other in a head-to-tail manner via peptide bonds, the condensation of the α-carboxylic acid group of an amino acid with the α-amino group of another amino acid, via elimination of a water molecule (Figure 1.25). The C–N bond formed through condensation of two amino acids presents a partial double

1.2 Introduction to proteins

25

–

COO –

O

COO

O +

+

+

C

H 3N H

C

H 3N

+

C

H 3N H

Amino acid 1

C R2

H R2

H

R1

H N

O

Amino acid 2

+ H2O

R1

Peptide bond

Figure 1.25: Formation of a peptide bond between two amino acids.

bond character due to its two resonance structures shown in Figure 1.26. For this reason, it cannot rotate and the peptide unit NH–CO is planar and rigid. Instead, the bonds to the neighbouring α-carbons can rotate, within steric constraints, and play an important role in protein folding.

O H R

N NH3

O

COO H R

R H

H

COO N

NH3

R H

H

Rigid O COO H R

N NH3

H

R H Rotates

Figure 1.26: Partial double bond character of the peptide bond.

The formation of a peptide bond consumes energy, which in living organisms is taken from ATP. Organisms use enzymes and ribosomes, which are proteins themselves, to bind amino acids together and make up peptides and proteins. On the other hand, a peptide bond can be broken through hydrolysis by addition of a water molecule, releasing energy (about 8–16 kJ/mol). However, given that the peptide bond is relatively unreactive under physiological conditions thanks to its resonance stabilisation, this process is extremely slow, with the half-life at 25 °C of about 350–600 years. Fortunately, in living organisms, this process is normally catalysed by enzymes such as peptidases or proteases, which can carry it out in milliseconds! The peptide bonds together with the α-carbons form the backbone of proteins, while the R substituents are the side chains. An example of a peptide consisting of

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five amino acid residues (Phe-Ala-Glu-Cys-Leu) is given in Figure 1.27. To be unambiguous about the start and the end of a sequence, the first amino acid residue is always the one with the free amino group, the N-terminus, and is written to the left. The last amino acid residue is the C-terminus with the free carboxyl group and is written to the right. Peptides can also have a circular structure, so that they do not present any N- or C-terminus. Phe O H NH3

Ala

Glu

H CH3 H N N H

O

Cys O

Leu

HS H

H

H

H

COO

N

N O

H C-terminus

COO N-terminus

Figure 1.27: A peptide with the amino acid sequence Phe-Ala-Glu-Cys-Leu.

With the 20 natural L-amino acids, it is possible to form an immense number of combinations and permutations. For a dipeptide, there are already 202 = 400 possible arrangements, and for a tripeptide, 203 = 8 000 different combinations. A relatively small protein with 100 amino acid residues can be arranged in 20100 = 1.27 × 10130 different ways, which is an enormous number, especially when considering that scientists have calculated that there are “only” 1078 atoms in the whole universe.

1.2.4 The structure of proteins Proteins are not just randomly coiled chains of amino acids. A variety of intramolecular interactions enables the amino acid chain to fold in a specific way to give the protein a three-dimensional structure and shape. This structure is critical for its activity and function. Several amino acid strings can be entangled and connected to each other via disulphide bridges. Parts of the amino acid chain can be organised into helices or sheets. Globular proteins such as enzymes and antibodies are more folded and coiled, whereas fibrous proteins are more filamentous and elongated. To describe the complex structure of proteins, four levels of organisation can be distinguished: primary, secondary, tertiary and quaternary structures. Primary structure The primary structure of a protein is determined by its amino acid sequence. For example, the hormone insulin has two polypeptide chains (A and B), shown in

1.2 Introduction to proteins

27

Figure 1.28. In the primary structure, the disulphide bridges between cysteine residues are highlighted; however, we will describe this type of interactions later on.

Figure 1.28: Primary structure of the cow insulin with the polypeptide chains A and B.

The amino acid sequence of a protein is determined by the DNA of the gene that encodes the protein (or a portion of it for multi-subunit proteins). A change in the DNA sequence of the gene may lead to a change in the amino acid sequence of the protein. Changing just a single amino acid in a critical position of the protein can significantly alter its activity and function, and be the cause of diseases and disorders. Secondary structure The secondary structure of a protein refers to regular elements that are formed within a polypeptide between relatively small portions of its amino acid sequence, thanks to interactions between atoms of the backbone. These local folded structures, in fact, are determined by the polypeptide chain (peptide bonds plus αcarbons), not taking into account the R groups. The most common types of secondary structures are the α-helix and the β-pleated sheet. Both structures are held in shape by hydrogen bonds between the C=O of one amino acid and the N–H of another amino acid, as shown in Figure 1.29. In an α-helix, the C=O of one amino acid forms a hydrogen bond with the N–H of the amino acid residue that is four down the chain (e.g. the carbonyl of amino

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O

C H

H N

N

C

R HC

O N

O

C

HC

N N

R

H

H

N

O

N

C N

N R

C

O

N

R

H N

N

C

O

R HC

C O

N N

HC R

H

O

CH

R

C

H

R CH

C H

H

C

C H

H

CH

O

H

O

CH

R

HC

H

CH

C R

O

R

H N

N

R

O

R

C CH

C CH

C

O

H

HC

H

O

CH

R

C

R

O

O

H

C

R C H

Figure 1.29: Chemical structure of an α-helix (left) and an antiparallel β-pleated sheet (right). The hydrogen bonds are highlighted in red.

acid 1 would be bonded to the N–H of amino acid 5). This bonding pattern pulls the polypeptide chain into a helicoidal structure that looks like a curled ribbon (Figure 1.30), with each turn of the helix containing 3.6 amino acid residues. The side chains of the amino acids point outwards from the helix, where they are free to interact with the R groups of other amino acids. In a β-pleated sheet, two or more segments of a polypeptide chain line up next to each other forming a sheet-like structure held together by hydrogen bonds between the C=O and N-H groups of the backbone, while the side chains point outwards to the top or bottom of the sheet. The adjacent chains of a β-pleated sheet may be aligned either in the same direction (parallel β-sheet, with the N- and C-termini in the same side), or in opposite directions (antiparallel β-sheet, with the N-terminus of one strand located next to the C-terminus of the other one, see Figure 1.31).

1.2 Introduction to proteins

29

Figure 1.30: Schematic drawing of an α-helix. (Left) cartoon drawing and (right) stick drawing with the single amino acid residues reported in different colours.

Figure 1.31: Schematic drawing of an antiparallel β-pleated sheet. (Left) cartoon drawing and (right) stick drawing with the single amino acid residues reported in different colours.

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Tertiary structure The overall three-dimensional structure of a protein is called tertiary structure, and is mainly due to interactions between the side chains of the amino acid residues. Such interactions can be classified as covalent bonds and non-covalent interactions, as given in Table 1.3. Table 1.3: Interactions between the side chains of amino acid residues that contribute to the tertiary structure of a polypeptide.

The most common covalent interaction between amino acids within a protein is the disulphide bond (or disulphide bridge), which is formed between the side chains of two cysteine residues. Disulphide bridges are much stronger than other types of interactions between R groups, so that they act as molecular “safety pins”, keeping parts of the protein firmly attached one to the other. Other covalent interactions are “peptide bonds” formed between side chains containing carboxylic acid groups (aspartic or glutamic acid) and amino groups (lysine or arginine). However, these interactions are less common since the reaction between a carboxylic acid and an

1.2 Introduction to proteins

31

amine needs particular conditions to occur, such as the presence of a catalyst or coupling reagents. Non-covalent interactions include hydrogen bonds, ionic bonds between charged R groups, dipole–dipole interactions and London dispersion forces. Other important structures are hydrophobic interactions between non-polar side chains that cluster together in the inner part of a protein leaving the hydrophilic amino acids on the outside to interact with the surrounding water molecules. Thanks to these interactions, the polypeptide chain is folded like a ball of yarn, so that amino acid residues that are very far from each other in the primary structure (in the amino acid sequence) can be found in close contact in the tertiary structure, as shown in Figure 1.32.

Figure 1.32: Example of the tertiary structure of a polypeptide with the interactions between side chains of amino acid residues.

Quaternary structure Many proteins are constituted of a single polypeptide chain and have only three levels of structure: primary, secondary and tertiary structures. However, some proteins are made up of multiple polypeptide chains, called subunits. When these subunits come together, they give the protein its quaternary structure. In general, the quaternary structure is held together by the same type of interactions that contribute to the

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tertiary structure, mostly weak interactions such as hydrogen bonds and London dispersion forces. An example of a protein with a quaternary structure is haemoglobin, the protein that carries oxygen in the blood. Haemoglobin is constituted of four polypeptide chains of two different types (two α chains and two β chains), each one containing a haem active site that binds oxygen (see Figure 1.33). In the structure of haemoglobin, we can see many α-helices: in fact, it is a globular protein, coiled up like a ball of yarn.

Figure 1.33: Quaternary structure of haemoglobin with its four subunits in different colours (the α chains in blue and the β chains in pink/yellow), each one containing a haem group.

1.2.5 Protein folding and denaturation Each protein has its own unique shape and folding structure. If the protein environment is changed, for example, changing the temperature or the pH or by addition of some chemicals, the interactions that contribute to the protein structure may be disrupted, causing the loss of the secondary, tertiary and quaternary structure of the protein. When a protein loses its three-dimensional structure, becoming just an amorphous chain of amino acids, it is said to be denatured. Denatured proteins are usually non-functional. For some proteins, denaturation can be reversed. Since the primary structure of the polypeptide is still intact, it may be able to re-fold into its functional form if it is returned into its natural environment. Other times, however, denaturation is permanent. One example of irreversible denaturation is when we cook eggs: the albumin, a protein present in the egg white, becomes opaque and solid as it is denatured by the heat, and will not return to its original state even when cooled down.

1.2 Introduction to proteins

33

1.2.6 The biological function of proteins Proteins can be classified in different types, such as fibrous, globular and membrane proteins. Fibrous proteins present an elongated structure, are not soluble in water and have high tensile strength and mechanical stability. Their function is to provide structural support to tissues. Collagen, for example, gives connective strength to skin, bones, teeth and tendons. Keratin is the major component of hair and nails. Membrane proteins are inserted in or tethered with biological membranes. They can act as receptors that transmit chemical signals between the inner and outer cellular environment; transport proteins that move molecules and ions across the membranes; membrane enzymes that catalyse specific reactions; cell adhesion molecules involved in binding with other cells or with the extracellular matrix, for instance, tuning immune responses. On the other hand, globular proteins present a compact, spherical structure, soluble in water, with a grooved and jagged surface. Some small molecules can fit into these grooves just like a key fits into a lock. Suck “key–lock mechanism” makes globular proteins very specific to interact or recognise other molecules (Figure 1.34). The best example of such specific proteins is given by enzymes, the biochemical catalysts: an enzyme can only react with a substrate if the locations of its functional groups and hydrogen bonds, as well as its shape, perfectly match the active site of the enzyme. Antibodies are another example of highly specific globular proteins. Thanks to these properties, Enzymes and antibodies are used as molecular recognition elements in bioassays and biosensors: in the following sections, we will make an overview on these two types of biomolecules and their functional mechanisms.

Globular protein

Substrates

Specific substrate

Figure 1.34: Cartoon representation of the key–lock mechanism of globular proteins, such as enzymes, which have specific surface sites able to recognise only specific substrates.

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1.3 Enzymes All living organisms utilise a huge number of biochemical reactions, almost all catalysed by a series of extraordinary biological catalysts, the enzymes. Enzymes make the chemical reactions within cells occur with sufficiently high speed to allow, actually, the existence of life. For example, the spontaneous hydrolysis of sucrose would take 440 years at room temperature, the decomposition of glucose 96 years, and probably the DNA synthesis would never spontaneously happen in the environmental conditions of every living organism. However, all these reactions occur in the timescale of seconds or milliseconds in our cells, thanks to the enzymatic catalysis. Enzymes have been discovered in the nineteenth century during studies about fermentation and digestion processes. In fact, the word “enzyme” (from the Greek en, “in”, and zyme, “yeast”) was coined in 1878 to highlight the fact that in the yeast there was something responsible for the catalysis of the fermentation reactions. Enzymes are catalysts, which means that they increase the rate of chemical reactions by lowering the energy barrier that divides the reagents from the products. However, they differ from the common chemical catalysts, normally metals or metal complexes, in some crucial characteristics: – Higher reaction rates: the rates of the reactions catalysed by enzymes are normally 106–1012 times higher than the ones of the corresponding reactions without catalysis, and at least several magnitude orders higher when compared with chemical catalysis. – Milder reaction conditions: enzymes normally work at atmospheric pressure, neutral (or close to neutral) pH and room or body temperature, contrarily to chemical catalysts that often need high temperature and high pressures, and extreme pH values. – Extraordinary specificity: enzymes are highly specific towards their substrates and products, so that enzymatic reactions do not normally produce unwanted products. – Regulation system: the catalytic activity of many enzymes can vary depending on the concentration of substances other than their substrates. Initially, enzymes have been called with names derived by their provenience or nature, for example the enzyme papain was first found in papayas or the enzyme laccase in the sap of Japanese lacquer trees. However, such names do not tell anything about the reactions catalysed by those enzymes, so that later enzymes have been called with the name of the substrates they are acting on, followed by the suffix – ase, such as urease, esterase, lipase or sucrase. With the increasing number of known enzymes, this nomenclature became ambiguous, so scientists decided to adopt a general system taking into account the nature of the chemical reactions that they catalyse and their substrates. Nowadays, enzymes are classified into seven main

1.3 Enzymes

35

classes of enzymatic reactions (see Table 1.4), in turn divided into subclasses and sub-subclasses. However, many enzymes have also alternative names, often given to them before the systematic nomenclature was introduced.

Table 1.4: Classification of the enzymes according to the type of catalysed reactions. Enzyme class

Type of reaction catalysed

Examples

. Oxidoreductase

Oxidation–reduction (redox) reactions

Glucose oxidase, lactate dehydrogenase

. Transferase

Transfer of functional groups

Acetate kinase, adenosine deaminase

. Hydrolase

Hydrolysis reactions (breakage of bonds by addition of a water molecule)

Lipase, sucrase

. Lyase

Removal of groups of atoms to give double bonds

Oxalate decarboxylase, isocitrate lyase

. Isomerase

Isomerisation reactions: rearrangement of atoms within a molecule

Glucose--phosphate isomerase

. Ligase

Formation of new bonds, normally using energy derived from the hydrolysis of ATP

Acetyl-CoA synthetase, DNA ligase

. Translocase

Movement of ions or molecules across membranes, Transporter or their separation within membranes

The systematic classification of enzymes is called “Enzyme Commission” (EC) number. Such a classification consists of a four-numbers code which specifies the reaction catalysed by the enzyme. If different enzymes, for instance from different organisms or with completely different polypeptide chains, catalyse the same reaction, then they receive the same EC number. The first number of the code can go from 1 to 7 and corresponds to the main enzymatic class reported in Table 1.4. For instance, all enzymes catalysing redox reactions would have a code starting with EC 1, all enzymes catalysing hydrolysis reactions would start with EC 3 and so on. The following three numbers represent a progressively finer classification of the enzymes. For instance, going on with our example, we will have: – EC 1 are all enzymes that catalyse oxidation–reduction reactions; – EC 1.1 are oxidoreductases acting on the CH–OH group of donors; – EC 1.1.3 are those using oxygen as electron acceptor; – EC 1.1.3.4 are glucose oxidases (oxidoreductases using O2 as electron acceptor and acting on the CH–OH group of glucose).

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1.3.1 Substrate specificity Enzymes bind their substrates through the same types of non-covalent interactions used also to stabilise their three-dimensional structure: Van der Waals interactions, electrostatic and hydrophobic interactions and hydrogen bonds. In general, a substrate-binding site is constituted by a small hole on the enzyme surface with a shape complementary to that of the substrate (geometrical complementarity). Moreover, the amino acid residues in the binding site are arranged in order to attract electrostatically the substrate (electronic complementarity). Therefore, molecules that have different shape or different charge disposition when compared with the enzyme substrate would not be able to efficiently bind to the binding site. However, the geometrical specificity varies a lot from one enzyme to another. There are enzymes absolutely specific for only one compound, while other enzymes can bind a series of similar substrates, catalysing the same type of reaction even though with different efficiency. For example, the enzyme alcohol dehydrogenase can catalyse the oxidation of ethanol (CH3CH2OH), but also methanol (CH3OH) and isopropanol [(CH3)2CHOH] at lower rates. Another enzyme involved in digestion/degradation processes, such as the cellobiose dehydrogenase, catalyses the oxidation of cellobiose (a disaccharide produced during the degradation of cellulose), but also of lactose, glucose and other sugars (mono- and disaccharides), although with different efficiency. Another important characteristic of enzymatic active sites is their extraordinary stereospecificity. This is due to the intrinsic chirality of enzymes (constituted of chiral molecular residues such as the amino acids) that generate asymmetrical active sites. For that reason, almost all the enzymes involved in chiral reactions, or reactions with chiral substrates, are absolutely stereospecific. This means that their active site can bind, and catalyse the reaction of, only one enantiomer of a certain chiral compound, for the same reason why a left glove cannot host a right hand (see Figure 1.35). For example, almost all the enzymes that catalyse reactions involving sugars, such as

CH3

d-Lactic acid

C

CH3

COOH

OH H

C H

COOH OH

Enzyme

Figure 1.35: Enzymes are stereospecific towards their substrates: for example, an enzyme that is selective for the L-lactic acid would not react with the other enantiomer of the same compound.

1.3 Enzymes

37

glucose oxidase, glucose dehydrogenase, fructose dehydrogenase and many others, are highly specific for D-sugars (D-glucose, D-fructose, etc.). This is the reason why sugars of the L configuration do not exist in nature, or are very few. Geometrical specificity and, especially, stereospecificity do not occur with any other type of chemical catalysts, and contribute to make enzymes such unique compounds in the fields of catalysis and molecular recognition.

1.3.2 Active sites, coenzymes and cofactors As we have already mentioned, the enzymatic substrates bind and undergo catalytic reactions in specific locations of the enzymes, called active sites. The active site consists of amino acid residues that form temporary bonds with the substrate (binding site) and residues that catalyse the chemical reaction of the substrate (catalytic site). Although the active site is generally small compared to the whole volume of the enzyme (it only occupies the 10–20% of the total volume), it is the most important part of the protein. The enzymatic active sites can be constituted of only amino acid residues, or also other small molecules and ions. In fact, the substrate reaction can be catalysed by the amino acid functional groups, which can be nucleophilic, electrophilic, acidic or basic. Nucleophilic catalysis is commonly observed in enzymes, thanks to the hydroxyl groups (–OH, present in serine, threonine and tyrosine) and sulphydryl groups (–SH, cysteine) that are good nucleophiles. Also acid–base catalysis is common in enzymes, thanks to the carboxyl and amino groups of amino acid side chains (–COOH of aspartic and glutamic acid; –NH2 of lysine and arginine). The amino groups can also participate in electrophilic catalysis. However, except for these three types of catalysis and, in a few cases, radical initiation, amino acid functional groups cannot perform all the types of catalytic reactions observed in enzymology. Due to the limited number of protein functional groups and, therefore, the limited chemistry in which they can be involved, enzymes utilise the unique properties of a variety of non-protein molecules and ions to assist in their catalytic chemistry. Such compounds are known as coenzymes and cofactors. Coenzymes are, in general, organic molecules that contain functionalities not found in proteins, typically derivatives of vitamins. Coenzymes are usually non-covalently bound to enzymes, unlike cofactors that are catalytically essential molecules or metal ions covalently bound or coordinated to amino acid side chains. Here below we will briefly describe some common coenzymes and cofactors.

Coenzymes Coenzymes are used in electrophilic catalysis, redox chemistry, rearrangements, group transfers and other types of reactions. Very common coenzymes are the nicotinamide

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adenine dinucleotide (NAD+) and nicotinamide adenine dinucleotide phosphate (NADP+), which can be reduced by two electrons to generate NADH or NADPH (Figure 1.36). In effect, this type of coenzymes is involved in redox reactions. H

O

O

H

H NH2

NH2

N O OH P O

N

O HO

OH NH2

O HO P O O HO

N O

N OR

NAD+ or NADP+

H+, 2e-

N N R = H (NAD+) R = PO32- (NADP+)

O OH P O

O HO

OH NH2

O HO P O O HO

N O

N

N N

OR

NADH or NADPH

Figure 1.36: Chemical structure of nicotinamide adenine dinucleotide (NAD+) or nicotinamide adenine dinucleotide phosphate (NADP+), which can be reduced to NADH or NADPH.

Other enzymes that catalyse redox reactions are the flavoproteins, which contain the coenzymes flavin adenine dinucleotide (FAD) or flavin mononucleotide (FMN), derivatives of the riboflavin (vitamin B2). These coenzymes can participate in either one- or two-electron redox reactions, as they can exist in an oxidised form (FAD and FMN), a one-electron reduced semi-quinone form (FAD• and FMN•) and a two-electron reduced form (FADH2 and FMNH2, see Figure 1.37). Because of this flexibility in oxidation states, flavin coenzymes are able to participate in a wide variety of redox reactions. Other common coenzymes are: – B12 coenzymes (adenosylcobalamin and methylcobalamin), which contain a cobalt atom and are frequently involved in the catalysis of rearrangement reactions; – thiamin pyrophosphate, the pyrophosphorylated form of thiamin (vitamin B1), which plays a central role in a number of enzymatic reactions that involve the cleavage of C–C bonds; – phosphopantetheine coenzymes, such as the coenzyme A and the acyl carrier protein, which are involved in transfer of acyl groups and carboxylate activation; – lipoic acid, which also serves in the transfer of active acyl groups and, at the same time, acts as a mediator in electron transfer reactions; – biotin, which plays a central role in carboxylation reactions, using mostly bicarbonate and ATP as substrates.

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1.3 Enzymes

NH2

Adenosyl N

N

N O OH O P O HO OH O O P OH O OH

N R

OH N

N

O NH

N O

FAD or FMN

R

O O P OH O H +, e -

HO

R = H (FMN) R = adenosyl (FAD)

HO

O O P OH O H+, eHO

OH OH

N N H

N

O NH

O

FADH or FMNH

N N H

OH OH H N

O NH

O

FADH 2 or FMNH2

Figure 1.37: Chemical structure of flavin adenine dinucleotide (FAD) or flavin mononucleotide (FMN), which can be reduced to the semi-quinone form or the two-electron/two-proton reduced form.

Cofactors Some cofactors are organic molecules covalently attached to amino acids. The most common are quinones, found in quinoproteins that are typically redox catalysts. However, thousands of enzymatic reactions, perhaps a third of all those known, require metal ion cofactors. Metals add new functionalities to enzymes by providing strong electrophilic centres and, often, multiple oxidation states that can facilitate redox and electron transfer reactions. Most of the first-row transition metals, as well as molybdenum, tungsten and magnesium are well-known cofactors in enzymes. Typically, the metal ions are bound to the enzyme via coordination with amino acid residues, although in some cases they are bound to a non-protein ligand (e.g. the haem group is constituted of a Fe ion coordinated to a planar porphyrin ring, see Figure 1.38). Enzymes are not the only type of proteins that use metal ions to increase their functionalities: for example, haem groups are present also in haemoglobin and myoglobin, the proteins that carry oxygen in the blood. The most abundant metal in enzymes and biological systems in general is iron, which is not surprising as Fe is the most abundant transition metal on the crust of the Earth. Iron plays a key role in some of the most central processes in biological systems, including oxygen transport and utilisation, electron transfer, metabolism of nucleic acids, degradation of biological pollutants and many others. Iron can be bound directly to amino acid residues or to other ligands, like in the haem (or heme)

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R

R

R

S

R N

N

N R

Fe

N R

R

S

Fe

Fe

Fe

S

S Fe

R Haem

4Fe–4S cluster

Figure 1.38: Chemical structure of a haem group (constituted of a Fe ion coordinated to the four nitrogen atoms of a porphyrin in a square planar arrangement) and a 4Fe–4S cluster.

groups of cytochrome enzymes. Some Fe ions are found bound to a combination of amino acid side-chains and exogenous ligands, for example in iron–sulphur clusters where two, three, four or more Fe ions are coordinated by inorganic sulphides and amino acid side-chains, usually cysteines (see Figure 1.38). Metalloenzymes containing iron catalyse an enormous variety of reactions, most of which utilise the redox properties of iron, which can be easily found under physiological conditions in two oxidation states, Fe(II) and Fe(III), and involve reactions with oxygen or its reduced species (superoxide and peroxide). Another metal found in redox enzymes is copper that, likewise iron, has two oxidation states, Cu(I) and Cu(II), readily available under physiological conditions. Other cofactors found in metalloenzymes that catalyse redox reactions contain nickel, vanadium, molybdenum, manganese and tungsten. Another transition metal abundant in biological systems is zinc, which has only one oxidation state, Zn(II), accessible under physiological conditions and, therefore, does not participate in catalysis of redox or electron transfer reactions. However, zinc is quite flexible in its preference for ligands and can easily adopt coordination numbers of 4, 5 or 6, so that it can bind to a variety of amino acid residues and substrates in a variety of geometries. Zinc functions widely in Lewis acid catalysis and plays important structural roles in enzymes and proteins.

1.3.3 Oxidoreductases In the previous section, we have focused, in particular, on the coenzymes and cofactors involved in the catalysis of redox and electron transfer reactions, because these are the enzymatic reactions mostly employed in biosensors and bioanalytical chemistry applications. The enzymes that catalyse such reactions belong to the class of oxidoreductases, often called redox enzymes.

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1.3 Enzymes

Oxidoreductases catalyse the oxidation of a chemical species A (called reducing agent or electron donor) with the concurrent reduction of another species B (called oxidising agent or electron acceptor) in the form:

! enzyme

Ared + Box

Aox + Bred

(1:19)

In eq. (1.19), we can see that, in general, two half reactions (one oxidative and one reductive) take place, and at least two substrates are involved, even though usually only one of them is denominated as “enzyme substrate” (the species that undergoes the reduction or oxidation reaction), while the second species serves as electron donor (for reductive enzymes) or electron acceptor (for oxidising enzymes). Figure 1.39 shows the scheme of a simple oxidative catalysis, during which the substrate is oxidised by the enzyme that takes up the electrons and is reduced. Afterwards, the enzyme returns to its oxidised form by transferring the electrons to an electron acceptor that will be reduced. The pathway of the electrons is represented by the red dashed line, going from the substrate to the enzyme, and then to the electron acceptor. For a reductive catalysis, the scheme and electron pathway would be the opposite, with the electrons going from an electron donor, this time, to the enzyme, and from the enzyme to the substrate.

Figure 1.39: Schematic representation of an oxidation reaction catalysed by an enzyme. The red dashed line represents the electron pathway.

Electron acceptors and donors can be small molecules such as O2, CO2 and H2 (commonly used in the metabolism of living organisms), other organic molecules and metal complexes present in the cells, or also other proteins and enzymes that will use the electrons to catalyse other reactions. In biosensors, enzymes can be immobilised on electrodes that serves as electron acceptors and donors, as we will discuss later in Chapters 2 and 3. Oxidoreductases catalyse the oxidation or the reduction of the substrate. In the latter group, we can find enzymes such as reductases and hydrogenases. Concerning the enzymes that catalyse oxidation reactions, they are divided into four main groups: a) Oxidases: they couple the one-, two- or four-electron oxidation of substrates with the two- or four-electron reduction of O2 to hydrogen peroxide or water. Oxidases catalyse a variety of interesting reactions such as oxidation of amines, alcohols

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and sugars. For instance, a well-known enzyme employed in biosensors is glucose oxidase, which contains the coenzyme FAD and catalyses the oxidation of glucose to gluconolactone producing hydrogen peroxide as a coproduct (Figure 1.40).

HO

HO O

HO HO

H + OH O2

Glucose oxidase

O

HO

OH

OH

HO

Glucose

O + H2O2

Gluconolactone

Figure 1.40: Oxidation of glucose catalysed by glucose oxidase.

b) Peroxidases: they catalyse the one-electron oxidation of the substrate usually using hydrogen peroxide or organic peroxides as electron acceptor. Most peroxidases contain a haem active site: an example is horseradish peroxidase that catalyses the oxidation of various organic substrates. Figure 1.41 shows the oxidation of the two hydroxyl groups of ascorbic acid (the vitamin C) in the presence of hydrogen peroxide to produce first a radical, and finally dehydroascorbic acid.

OH O

OH

O OH

+ H2O2

HRP

O

O

O OH

O

OH HO Ascorbic acid

OH

O

OH

+ 2H2O

O

O

O

Figure 1.41: Oxidation of ascorbic acid catalysed by horseradish peroxidase (HRP).

c) Oxygenases: they catalyse the specific introduction of one (monooxygenases) or two (dioxygenases) oxygen atoms from O2 into the substrate. An example is dopamine β-monooxygenase, a copper-containing enzyme that catalyses the oxidation of the neurotransmitter dopamine incorporating one oxygen atom into the product and producing a water molecule as coproduct (Figure 1.42). OH Dopamine + O2 monooxygenase

HO

NH2

HO

HO HO

+ H 2O NH2

Dopamine Figure 1.42: Oxidation of dopamine catalysed by dopamine β-monooxygenase.

1.3 Enzymes

43

d) Dehydrogenases: they typically catalyse reversible hydrogen transfer reactions, for example, the oxidation of substrates by transferring hydrogen to electron ac+ + ceptors such as NAD , NADP or flavin coenzymes. An example is alcohol dehydrogenase that can oxidise ethanol to acetaldehyde (in animals, Figure 1.43), and catalyses the reverse reaction in yeast.

HH H 3C

OH

+ NAD+

H

ADH H 3C

Ethanol

O

+ NADH + H+

Acetaldehyde

Figure 1.43: Oxidation of ethanol to acetaldehyde catalysed by alcohol dehydrogenase (ADH) with NAD+ as electron acceptor.

1.3.4 Kinetics of enzymatic reactions Even though enzymes catalyse different types of reactions, with different substrates and a different number of molecules, all enzymatic reactions can be simply described by a steady-state kinetics based on the work of Leonor Michaelis and Maud L. Menten. This model assumes that the substrate (S) forms a complex with the enzyme (E) (the enzyme–substrate complex, ES) in a reversible step. Afterwards, irreversible breakdown of the enzyme–substrate complex yields the product (P) and the free enzyme: E+S

k1 k–1

[ES]

k2

E+P

(1.20)

Each single reaction in this scheme is characterised by a rate constant: k1 and k − 1 are the rate constants of the formation and dissociation of the enzyme–substrate complex, respectively, and k2 is the rate constant of the complex breakdown to yield the product. The second assumption of this model is that the concentration of the enzyme–substrate complex can be treated using the steady-state approximation. This means that equilibrium is maintained between the enzyme, the substrate and the enzyme–substrate complex, so that the concentration of the latter is constant. Clearly, this is not true immediately after the substrate and enzyme are first mixed together, when the concentration of the complex is building up, and will only be true as long as the concentration of substrate is not significantly consumed by the course of the reaction. For that reason, the substrate concentration should be much higher than the enzyme concentration. In order to describe the kinetics of an enzymatic reaction, likewise every other reaction, we need to find the rate of formation of the product (d½P=dt), which can be

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expressed as the product of the rate constant of the reaction that yields the product (k2 in this case) and the concentration of the reagents of such reaction (ES in this case): v=

d½P = k2 ½ES dt

(1:21)

At this point, to have a good equation that describes the reaction rate v, with parameters that can be experimentally measured (such as the enzyme or substrate concentration), we need to find the concentration of the enzyme–substrate complex [ES]. The formation/dissociation rate of the complex ES can be written as the difference between the rates of the reactions that form it and the rates of the reactions that consume it: d½ES = k1 ½E½S − k − 1 ½ES − k2 ½ES dt

(1:22)

According to the steady-state model discussed above, once the equilibrium is reached, the concentration of the enzyme–substrate complex does not vary with the time, so that its formation rate has to be equal to its consumption rate and we can write d½ES =0 (1:23) dt If we rearrange eq. (1.22), taking into account eq. (1.23) and bringing all the rate constants on the same side and the reagent concentrations on the other side, we will have k − 1 + k2 ½E½S = k1 ½ES

(1:24)

The three rate constants of eq. (1.24) can be unified in one single constant called the Michaelis–Menten constant, KM : KM =

k − 1 + k2 k1

(1:25)

We will discuss the meaning and use of KM later on. As we have already said, in order to be useful, the kinetic expressions need to contain concentrations that can be experimentally measured. In general, the concentrations [E] (free enzyme) and [ES] (enzyme bound to the substrate) are not easy to determine, so that it is more useful to use the total enzyme concentration ½ET , given by ½ET = ½E + ½ES

(1:26)

Note that for the substrate we do not need to make the same distinction since we assume that the substrate concentration is much greater than the enzyme concentration (which is generally the case), so that the concentration of uncomplexed

1.3 Enzymes

45

substrate [S] can be taken as equal to the initial (total) substrate concentration. Therefore, eq. (1.24) can be rewritten by substituting [E] with eq. (1.26), and the rate constants with KM as expressed in eq. (1.25):   ½ET − ½ES ½S KM = (1:27) ½ES Rearranging eq. (1.27) in order to find [ES], we will have ½ES =

½ET ½S KM + ½S

(1:28)

At this point, we have a good equation for [ES], expressed as a function of concentrations that can be experimentally measured, such as the total amount of enzyme and substrate. We can then substitute this expression for [ES] in eq. (1.21) to find the rate of the enzymatic reaction: v = k2 ½ES =

k2 ½ET ½S KM + ½S

(1:29)

When the substrate concentration is very large, much higher than the value of KM , the enzyme is said to be saturated, which means that it is completely in the form ES. In this condition, the reaction rate reaches its maximum (vmax ) and becomes independent of the substrate concentration, in fact we can write ½S  KM ) v =

k2 ½ET ½S = k2 ½ET = vmax KM + ½S

(1:30)

Knowing that the product of k2 with the total enzyme concentration is, in general, a constant parameter (in an enzymatic experiment, we normally vary the substrate concentration, not the enzyme), eq. (1.29) can be rewritten as follows: v=

vmax ½S KM + ½S

(1:31)

Equation (1.31) is the Michaelis–Menten equation, the fundamental equation of the enzymatic kinetics. It describes how the rate of an enzymatic reaction varies with the substrate concentration, having a hyperbolic shape where vmax is the asymptote. As shown in Figure 1.44, KM represents the concentration of substrate for which the reaction rate is half the vmax (we can reach the same conclusion by substituting [S] with KM in eq. (1.31)). We can therefore conclude that (i) KM has the same unit as the substrate concentration, and (ii) KM is an inverse measure of the affinity of the enzyme for the substrate. In fact, small KM values indicate high affinity between the enzyme and the substrate, meaning that the reaction rate will approach its maximum (vmax ) quickly for small substrate concentrations.

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In case the substrate concentration is sufficiently high that the enzyme is completely converted to ES, the rate-determining step of the enzymatic reaction becomes the second step (the breakdown of the enzyme–substrate complex). The rate constant of this step (k2 ) is often referred to as kcat , the catalytic constant or turnover number, defined as the maximum number of substrate molecules converted to product per enzyme molecule per second. Here, we will continue to call it k2 . We can see from eq. (1.30) that k2 determines vmax , which is the maximum velocity of an enzymatic reaction for a certain enzyme concentration. On the other hand, for low substrate concentrations, smaller than the value of KM , the Michaelis–Menten equation (eq. (1.31)) can be approximated as follows: ½S  KM ) v =

vmax ½S vmax ½S = KM + ½S KM

(1:32)

In such a case, the reaction rate increases linearly with the substrate concentration, as we can see in the first part of the curve in Figure 1.44. Thus, for the substrate, the Michaelis–Menten kinetics describes a non-linear situation in which the reaction rate is first order in substrate at low concentration (linear), but zero order at high concentration (independent of substrate). For biosensor applications, therefore, it is recommended to work in the linear region of the response, which is at substrate concentrations < KM .

Figure 1.44: Michaelis–Menten curve: plot of the rate of an enzymatic reaction as a function of the substrate concentration. vmax is the asymptote of the curve, and KM is the concentration value for which the reaction rate is half vmax .

1.4 Antibodies and antigens

47

1.4 Antibodies and antigens Antibodies (Ab) are special proteins produced by the immune system of animals (human included) as specific defence against attack of foreign intruders (bacteria, viruses, toxins, etc.) that have overcome the generic barriers of the body (skin, acidity of mucous membranes and stomach, action of macrophages and proteolytic enzymes, etc.). An antigen (Ag) is defined as any substance or functional group which characterises the intruder, capable of stimulating an immunological response by inducing the production of specific antibodies able to recognise and eliminate that intruder. An antibody possesses indeed recognition sites, named paratopes, suitable to recognise and interact with specific functional groups, named epitopes, present on the antigen (Figure 1.45).

Paratope Recognition event

Epitope

+

Antibody (Ab)

+

Antigens (Ag) mixture

Ag−Ab complex

Unbound antigens

Figure 1.45: Molecular recognition of antibody (Ab) and antigen (Ag) resulting in the formation of an Ag–Ab complex. An antibody will react only with a specific antigen presenting the right epitope, and will not bind other different antigens.

As shown in Figure 1.45, the interaction between the antigen (Ag) and a specific antibody (Ab) produces an antigen–antibody complex. The reaction between Ab and Ag is highly specific and selective: a particular Ab interacts only with the matching Ag, even in the presence of other non-matching molecules. Normally, in the body, the presence of the antigen stimulates the production of the specific antibody.

1.4.1 Methods to produce antibodies Bacteria and viruses contain antigens, either on their surface or (for the former) inside their cells. These antigens can be chemically isolated and inoculated in a living organism to induce the forced production of antibodies for that specific Ag. This is the way by which vaccines and commercial antibodies are produced. Antigens are

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mainly proteins (or protein fragments) that enter a host body through an infection. However, also polysaccharides, lipids and nucleic acids can act as antigens. There are two types of antibodies: monoclonal and polyclonal antibodies. Polyclonal antibodies are produced by multiple clones or cells in complex living organisms (mammals and birds), while monoclonal antibodies are obtained by a single-cell clone line. Polyclonal antibodies are normally generated by B cells of animals (e.g. rabbit, goat, donkey or sheep), inoculated repeatedly with the antigen or infectious agent. The serum of the animal is taken at the peak of antibody production, typically when the specific IgG concentrations is between 1 and 10 mg/mL. Polyclonal antibodies recognise a variety of epitopes on the antigen, and are, therefore, more tolerant to minor changes in the antigen, related to polymorphism, heterogeneity of glycosylation or slight denaturation. Monoclonal antibodies are produced by identical immune cells that are all clones of a unique parent cell. Monoclonal antibodies bind only to one epitope of the antigen. They are produced by the so-called hybridomas, obtained by fusion of B lymphocytes (typically from immunised mice) with immortal cells such as myeloma cells. Advantages of polyclonal antibodies: – Polyclonal antibodies can recognise multiple epitopes and, therefore, are less sensitive to small changes in the structure of the antigen or to its partial denaturation; – Polyclonal antibodies can be produced by different species of animals (e.g. rabbit, mouse, goat, sheep, donkey and chicken) providing a variety of options. For these reasons, polyclonal antibodies provide robust detection, although they can be less specific. Advantages of monoclonal antibodies: – Monoclonal antibodies are highly specific and represent the best choice to be used as primary antibodies in an assay. – Monoclonal antibodies provide more reproducible results since they are chemically and structurally highly homogenous. – Monoclonal antibodies are extremely efficient for binding antigens in complex mixtures or for affinity purification purposes. A particular group of antigens is represented by haptens. These are small molecules (with MW < 10 kDa) able to induce the production of specific antibodies when bound to a carrier with higher molecular weight, typically a protein. For example, dinitrophenyl (DNP) group can act as hapten when bound to a common protein, such as bovine serum albumin (BSA). Dinitrophenylated bovine serum

1.4 Antibodies and antigens

49

albumin (DNP-BSA) can be obtained by reacting activated dinitrophenol derivatives (e.g. 1-chloro-2,4-dinitrobenzene or 2,4-dinitrobenezesulfonic acid) with BSA (Figure 1.46).

BSA

Cl

BSA

HN

NO2

NO2

+ NO2

H 2N

NO2

Figure 1.46: Synthesis of dinitrophenylated bovine serum albumin (DNP-BSA) by reaction of 1-chloro-2,4-dinitrobenzene with lysine residues of the protein.

When inoculated in a host, DNP-BSA (which typically contains 10 DNP groups after functionalisation) stimulates the production of specific antibodies that recognise and react not only with the functionalised protein, but also with the free DNP molecule. A variety of molecules such as drugs, simple sugars, amino acids, small peptides, phospholipids or triglycerides may function as haptens. Since proteins are the biomolecules with the highest antigenic activity, able to stimulate antibody production, they are said to be strongly immunogenic. An important point is that, in a healthy individual (human or animal), the immune system will recognise as antigens only proteins or other molecules coming from a foreign intruder, and not the proteins or other molecules generated by the main organism (named “self”). Failure to distinguish foreign antigens from selfantigens is the cause of autoimmune diseases such as rheumatoid arthritis.

1.4.2 Antibody structure An antibody is a glycoprotein, namely an immunoglobulin (Ig) constituted of polypeptides (82–96% of the weight) and sugars (4–28%). As schematically shown in Figure 1.47, antibodies are Y-shaped. Sometimes, in this kind of representation, the sugar of the glycoprotein is omitted for simplification. An example of molecular structure of an Ab is represented in Figure 1.48. The polypeptide chains start with the N-terminus (free amino group) on the arms of the Y, while the C-termini (free carboxylic groups) are directed towards the basis of the stem of the Y.

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NH3+

NH3+

NH3+

NH3+ Hinge region

Light chain COO–

COO–

Disulphide bridges Heavy chain

COO–

COO–

Figure 1.47: Schematic drawing of a Y-shaped immunoglobulin (Ig) consisting of two identical heavy chains (H) and two identical light chains (L) connected via disulphide bridges.

Figure 1.48: Molecular structure of an antibody IgG. The two heavy polypeptide chains are in dark colours, whereas the light polypeptide chains are in light colours. Immunoglobulins are glycoproteins: they have carbohydrates attached to them (represented in yellow).

The Y structure consists of four polypeptide chains: two identical heavy chains (H) and two identical light chains (L). “Heavy” and “light” refer to the molecular weight of each subunit, which is around 50 and 25 kDa, respectively. The subunits

1.4 Antibodies and antigens

51

are connected to each other via disulphide bridges and non-covalent interactions. As shown in Figure 1.49, each chain consists of homologous units, composed of variable (V) or constant (C) regions. All the antibodies of a certain class (see below) are characterised by the same C regions. Each H chain is composed of four units, one variable (VH) and three constant (CH1, CH2 and CH3), while each L chain is composed of two regions, namely one VL and one CL. The paratopes, the regions that recognise and interact with the epitopes of the antigen, are constituted of the VL and VH regions at the top of the Y: they can be visualised as the claws of a scorpion or a crab. VH

VH

CH1

VL

Paratope

CH1

VL

CL

CL

CH2

CH2

CH3

CH3

Light chain

Heavy chain

Figure 1.49: Constant (C) and variable (V) regions within an immunoglobulin molecule. The variable regions at the N-termini of the light and heavy chains make up the paratopes of the antibody.

As shown in Figure 1.50, proteolytic enzymes can be used to fragment immunoglobulins. The treatment of an Ig with papain determines the cleavage of the immunoglobulin at the level of its hinge region, producing two Fab and one Fc fragments, all with a similar mass of about 50 kDa. The Fc fragment is named with the symbol “c” because it can be crystallised. The enzyme pepsin cuts the antibody below the hinge

Papain

Fc

Fab

Pepsin

Fab Antibody

F(ab)2

Figure 1.50: Cleaving of an immunoglobulin by papain, resulting in two identical Fab fragments and one Fc fragment. Cleaving by pepsin results in an F(ab)2 fragment.

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region, producing a F(ab)2 fragment with the arms of the Y still bound together. Both the Fab and F(ab)2 fragments contain the paratopes, therefore they maintain the molecular recognition properties typical of the Ab. For some purposes including immunoassays, these fragments can be used instead of the intact antibody. In conjugated antibodies, the Fc region is exploited as binding site for labelling enzymes or fluorophores, or for anchoring the Ig on ELISA plates, generally by reacting to the carbohydrate moieties of the Fc stem. This strategy to immobilise or functionalise the Ab is preferred to avoid negative influences on the antigen–antibody interaction. Note that the Fc region acts as recognition site for secondary antibodies in immuneblotting, immune-precipitation and immune-histochemical reactions.

1.4.3 Classification of immunoglobulins There are five classes of immunoglobulins, named IgA, IgE, IgD, IgG and IgM. Each class is characterised by a different type of H chain and/or by being composed of one or more Y-shaped monomeric units. The H chains of each class are named with the Greek letter corresponding to the immunoglobulin class, as given in Table 1.5. The light chains can be of two types, namely λ and κ, and both can be present in immunoglobulins of the same class. The main characteristics of the five classes of immunoglobulins are summarised in Table 1.5 and Figure 1.51.

Table 1.5: Classification of immunoglobulins. Class/ Heavy Light subclass chain chain IgA

α

IgA

MW (kDa)

Structure

Function

α

λ or κ – Monomer, dimer or tetramer

Most produced Ig, protect mucous surfaces, resistant to digestion, secreted in milk

IgD

δ

λ or κ



Monomer

Function unclear, work with IgM in B-cell development, mostly bound to B-cells

IgE

ε

λ or κ



Monomer

Defend against parasites, cause allergic reactions.

IgG IgGa IgGb IgG IgG

γ γ γ γ γ

λ or κ



Monomer

Major Ig in serum, good opsonisers (enhance the action of phagocytes), moderate complement fixer (IgG), can cross the placenta

IgM

μ

λ or κ



Pentamer

First response antibodies, strong complement fixer, good opsonisers

1.4 Antibodies and antigens

53

Figure 1.51: (A) Classification of immunoglobulins: the five classes of Ig differ from each other for the heavy chains H. (B) Unlike the other classes, the antibodies of the class IgM are pentamers constituted of five immunoglobulin subunits linked together by disulphide bonds.

There are also other classes of immunoglobulins, such as immunoglobulin Y (IgY), which is the main antibody in birds, reptiles and lungfishes. IgY has some similarities with mammalian IgG and IgE; however, it presents significant structural and functional differences and does not cross-react with anti-IgG antibodies. Since IgY is present in large quantities in chicken egg yolk, an immunised hen will produce daily large amounts of eggs containing high concentrations of IgY. This is a noninvasive method for the production of antibodies, which differs from traditional methods based on mammals because it does not require extraction of the antibodies from the blood serum of the immunised animal.

1.4.4 Antigen–antibody interaction The interactions between antibody paratopes and antigen epitopes is based on hydrogen bonding, ionic and hydrophobic interactions and Van der Waals forces (see non-covalent interactions in Table 1.3). Individually, these interactions are weak and reversible; however, the cooperation of more than one effect at the same time significantly strengthens the binding between antibody and antigen. The effectiveness of the Ag–Ab interaction can be evaluated quantitatively in terms of affinity, avidity, specificity and cross-reactivity.

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Affinity is a measure of the strength of the interaction between a single epitope and paratope. It is the result of the sum of multiple non-covalent bonds. Quantitatively, considering the following complexation reaction: Ag + Ab ! ½Ag−Ab

(1:33)

The affinity is defined by the association constant KA : KA =

½Ag−Ab ½Ag½Ab

(1:34)

Obviously, if the association constant is high (of the order or 1011–1012 M−1), the dissociation constant KD (equal to 1=KA ) will be small. Indeed, we need to take into account that antibodies have two or more identical binding sites (the paratopes). For instance, IgM antibodies are pentamers (see Figure 1.51), therefore each IgM molecule contains 10 paratopes suitable to bind the antigen. The number of binding sites of the antibody defines the valence. The avidity quantifies the overall strength of the Ag–Ab interaction. Indeed, avidity depends on three factors: i) the paratope–epitope affinity; ii) the valence of the antibody and antigen; iii) the structural arrangement between the interacting parts. Again, a pentameric IgM with 10 binding sites can have a high avidity, even if the individual Ag–Ab interaction is characterised by a low affinity (see Figure 1.52).

A pentamer IgM binds 10 antigen molecules

A dimer of IgA binds 4 antigen molecules

Figure 1.52: Antigen–antibody interaction in the case of a pentamer IgM and a dimer IgA (the antigen molecules are represented in orange).

1.4 Antibodies and antigens

55

The specificity of an Ab is defined as its capability to react only with one specific antigen. The specificity depends on the affinity more than on the avidity, since the avidity in a multimeric antibody can be high even if the strength of the bond between a single paratope and epitope is not so high. In general, an antibody with high specificity can discriminate between the primary structure, isomeric forms, secondary and tertiary structure of similar antigens. Cross-reactivity indicates the ability of an individual antibody to react with more than one antigen. This can occur when avidity or specificity is low, or when different antigens have the same (or structurally similar) epitopes.

1.4.5 Factors influencing antigen–antibody interactions a) Time Taking into account the kinetics of the Ag–Ab complexation reaction, we can observe that equilibrium is reached when the rates of the forward and backward reaction are equal. For the simple case of a monovalent interaction, we can write the reversible complexation reaction as follows: Ag + Ab

kon koff

[Ag–Ab]

(1.35)

where kon is the rate constant of the forward reaction and koff is the rate constant of the backward reaction. The reaction rates for the two reactions can be written as follows: von = kon ½Ag½Ab

(1:36)

voff = koff ½Ag−Ab

(1:37)

where kon is a second-order kinetic constant expressed as L mol–1 s–1, and koff is a first-order kinetic constant expressed as s−1. The association constant KA given by eq. (1.34) can be expressed as KA =

kon koff

(1:38)

For two antigen–antibody couples characterised by comparable forward rate constants kon , the couple with the lower backward rate constant koff will produce the stronger Ag–Ab complex, providing a larger KA value. The time taken to reach the equilibrium is influenced by the diffusion rate and the affinity of the antibody for the antigen. Note that interactions involving multivalences can be more complex, resulting in steric hindrance and rearrangements, so that the overall kinetics is slowed down.

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b) Temperature The optimum temperature for an antigen–antibody reaction depends on the chemical nature of the epitope/paratope interaction. For instance, the formation of hydrogen bonds is exothermic, therefore these bonds are stabilised by lowering the temperature. c) pH The optimum pH for the formation of the antigen–antibody complex is in the range between 6.5 and 8.4, since at lower and higher pH values the interactions between Ag and Ab are inhibited. Indeed, the pH has a strong effect on the ionic interactions between protonated amines and deprotonated carboxylic acid groups (the main charged groups in protein molecules). d) Ionic strength The effect of ionic strength on the antigen–antibody reactions is particularly important since ions derived by salt dissociation (e.g. Na+ and Cl−) tend to form clusters around ionic groups, partially neutralising the charges and decreasing the effect of ionic bonds. This could be a problem when using low-affinity antibodies. Moreover, at relatively high ionic strength, globulins tend to aggregate or to form complexes with the lipoproteins of red blood cells.

1.4.6 Quantitative evaluation of antibody properties Affinity constants can be determined for monoclonal antibodies, but not for polyclonal antibodies since, for the latter, the formation of multiple bonds prevents the univocal characterisation of a specific epitope–paratope interaction. Quantitative measurements of the affinity of an antibody for an antigen can be performed by equilibrium dialysis experiments. Dialysis is a separation process based on the capability of a semi-permeable membrane to allow only relatively small molecules (namely, the antigen molecules) to permeate through, while retaining larger molecules (in this case, the antibody). More details on dialysis membranes are presented in Chapter 2 (Sections 2.6.1 and 2.7.1). Repeated equilibrium dialyses measurements performed with a constant antibody concentration, by varying the antigen concentration, are used to this aim. The experimental approach is schematised in Figure 1.53. The partition of the molecules of an antigen Ag*, labelled with a radioactive isotope or a fluorophore, is followed as a function of time until equilibrium conditions are reached. Two experiments are carried out as shown in Figure 1.53: i) one to determine the partition of the antigen between the sample solution and a buffer solution (control experiment);

1.4 Antibodies and antigens

57

Figure 1.53: Affinity measurements by equilibrium dialysis. (i) Control: the antigen labelled with a radioisotope or with a fluorophore (Ag*) is let equilibrate between the initial or sample solution (B) and a buffer solution (A). The partition of Ag* is followed as a function of time until equilibrium: Ag* equilibrates equally on both sides of the dialysis membrane, so that at the equilibrium both solutions A and B will have the same concentration of Ag*. (ii) Experiment: Ag* equilibrates between the initial solution (B) and a buffer solution (A) containing the antibody Ab. At the equilibrium, solution A will have a higher concentration of Ag* due to the interaction with Ab. The difference Δ in the equilibrium concentration between solutions A and B will provide the amount of Ag* bound to Ab.

ii) the second to determine its partition between the sample solution and the buffer solution containing the antibody of interest. Remember that only the antigen can permeate the dialysis membrane, while the antibody cannot. In the control experiment (i), the partition of Ag* is determined only by the dialysis equilibrium, while in the experiment (ii) the partition is due to the action of both the dialysis membrane and the complexation of Ag* with Ab. The time dependence of the overall changes in concentration of Ag* (including the formation of the complex Ag*–Ab) between the two compartments can be monitored thanks to the label attached to Ag. In this way, the concentration of Ag* can be

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plotted as a function of time, as shown in Figure 1.53 (graphs on the right), and the concentration of bound antigen can be obtained by the segment Δ in experiment (ii). At the end of the 1940s, the chemist George Scatchard introduced the so-called Scatchard equation, demonstrating that there is a linear relationship between the bound/free antigen ratio (called B/F ratio) and the concentration of bound antigen. Application of the Scatchard equation to data obtained with the dialysis method allows the determination of the affinity and valence for the Ag–Ab interaction of interest. Here, we will derive the Scatchard equation for the simplest Ag–Ab interaction that is observed in the case of monovalent bonding. When n = 1, the total antibody concentration can be written as follows: ½AbTOT = ½Ab + ½Ag−Ab

(1:39)

By substituting into eq. (1.34), the association constant becomes KA =

½Ag−Ab  ½Ag ½AbTOT − ½Ag−Ab 

(1:40)

We can define the ratio B/F between bound and free antigen as follows: B=F =

½Ag−Ab ½Ag

(1:41)

By rearranging eq. (1.41) with eq. (1.40), the ratio B/F can be written as B=F = KA ½AbTOT − KA ½Ag−Ab

(1:42)

Figure 1.54: Scatchard plot for the antigen–antibody interaction. The plot of B/F ([Ag–Ab]/[Ag] ratio) as a function of [Ag–Ab] is a straight line, with slope = –KA (the association constant) and intercept on the x-axis equal to ½AbTOT .

Further readings

59

This is the Scatchard equation for the monovalent case. As shown in Figure 1.54, the Scatchard graph obtained by plotting B/F versus [Ag–Ab] is a straight line with slope equal to –KA (the association constant) and intercept on the x-axis equal to ½AbTOT . In the general case of an n-valent antibody, the Scatchard equation becomes B=F = nKA ½AbTOT − KA ½Ag−Ab

(1:43)

Therefore, from the linear plot of B/F versus [Ag–Ab], when [Ab]TOT is known, both KA and n can be obtained. Note that the Scatchard method can be applied when the Ab has independent and identical binding sites (the paratopes). Under these conditions, from the experimental measurement of the concentration of free and bound Ag, it is possible to calculate the affinity and valence of an antibody for its antigen. More recently, alternative methods have been introduced to evaluate KA or KD from kinetics measurements of the forward and backward rate constants (kon and koff ) by means of surface plasmon resonance techniques (see Chapter 4).

Further readings Books and book chapters Blackburn GM, Gait MJ, Loakes D, Williams DM. Nucleic acids in chemistry and biology, 3rd ed. Cambridge, RSC Publishing, 2006. Broderick JB. Coenzymes and cofactors. In: Encyclopedia of Life Sciences, Chichester, UK, John Wiley & Sons, Ltd., 2001. Campbell ID. Biophysical techniques. Oxford, Oxford University Press, 2012. Griffiths AJF, Wessler SR, Lewontin RC, Gelbart WM, Suzuki DT, Miller JH. Introduction to genetic analysis, 8th ed. New York, W.H. Freeman and Co., 2005. Johnson AD, Alberts B, Morgan D, Lewis J, Roberts K, Raff M, Walter P. Molecular biology of the cell, 6th ed. New York, WW Norton and Co., 2014. Manz A, Dittrich PS, Pamme N Iossifidis D. Bioanalytical chemistry, 2nd ed. London, UK, Imperial College Press, 2015. Nelson DL, Cox MM. Lehninger principles of biochemistry, 7th ed. Freeman W.H., and Co., New York, USA, 2017. Voet D, Voet JG. Biochemistry, 4th ed. Hoboken, NJ, USA, John Wiley & Sons, Inc., 2011.

Review and research papers Breslauer KJ, Frank R, Blöcker H, Marky LA. Predicting DNA duplex stability from the base sequence. Proc Natl Acad Sci USA 1986, 83(11), 3746–3750. Crick F. Central Dogma of molecular biology. Nature 1970, 227(5258), 561–563.

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Scatchard G. The attraction of proteins for small molecules and ions. Ann NY Acad Sci 1949, 51, 660–672. Schroeder HW Jr, Cavacini L. Structure and functions of immunoglobulins. J Allergy Clin Immunol 2010, 124, S41–S52. Walker WHC. The Scatchard plot in immunometric assay. Clin Chem 1977, 23, 588–590. Watson JD, Crick FH. Molecular structure of nucleic acids; a structure for deoxyribose nucleic acid. Nature 1953, 171(4356), 737–738.

2 Introduction to bioanalytical assays and biosensors 2.1 Molecular biorecognition and analytical assays An assay can be defined as an analytical test that exploits the occurrence of a molecular recognition event between a reactant and the target analyte. It can be performed in a qualitative or quantitative way, for determining the presence or absence of the analyte and its concentration, respectively. In bioanalytical chemistry, assays are performed exploiting a biorecognition event between a biorecognition element (or bioreceptor) and its substrate (analyte). In instrumental assays, the result of the recognition process is detected by using instrumentation capable of measuring and quantifying the change in a physicochemical quantity associated with the recognition event. This can be a change of colour, absorption or emission, or other interactions with electromagnetic waves (typically UV–visible or infrared radiation, but also surface plasmon resonance), a change in electrical properties (redox potential, faradic current, conductivity), the emission or absorption of heat, changes of mass and so on. The interaction between the receptor and the analyte can occur with both the components initially in solution (homogeneous assay) or with one component immobilised on a surface (heterogeneous assay). Assays are typically performed in two separate steps that are the addition of reagent to develop the recognition event followed by the measurement with a separated instrument, for instance, a colorimeter or a spectrophotometer. This is what differentiates an assay from the detection of the analyte using a sensor or biosensor (see further), which are specialised analytical devices where the recognition and detection steps are performed directly on the surface of only one device, that is, the (bio)sensor itself. In the simplest case, for qualitative purposes, the result of the assay can be detected simply by checking if a coloured product is formed or not. Under such conditions, the “measurement instrument” is indeed the operator’s eye. This approach can be somehow considered as the bioanalytical implementation of the “spot test” used in qualitative chemical analyses. Instead, for quantitative analysis, the detection and quantification of the extent of the recognition event are performed by using established analytical techniques, which employ modern analytical instrumentation such as spectrophotometers, potentiometers, potentiostats or calorimeters. This means that quantitative assays require specialised operators, capable of correctly using the instrumentation and interpreting the results (Figure 2.1).

https://doi.org/10.1515/9783110589160-002

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2 Introduction to bioanalytical assays and biosensors

Assay

Recognition event

Instrument

Reagent

Qualitative assays

Recognition event Operator Eyes

Analyte solution Reagent

Quantitative assays Analyte solution

Change in: – Light absorbance/emission – Fluorescence – Electrical Properties – Mass – Heat

Spectrophotometer Potentiometer Potentiostat Calorimeter

Figure 2.1: Differences between qualitative and quantitative assays.

The biomacromolecular receptors typically used in bioanalytical assays are enzymes, antibodies or antigens, oligo- or polynucleotides. The advantages of using a biological macromolecule as recognition element can be summarised in that: – biomacromolecular receptors recognise their substrate with high selectivity and sensitivity; – they can be employed in complex samples, avoiding or reducing to a minimum the pre-treatment steps; – the reagents are of natural origin and, generally, come from renewable sources; – they are biocompatible and biodegradable; – they are active under mild operative conditions, typically at physiological conditions (between 20 and 37 °C, and at pH values close to neutrality). The main limits are related to the fact that some biocomponents can be expensive or require special treatment and cautions for their disposal. Moreover, the shelf life of some biochemical reagents can be limited, and storage at low temperature is often required. However, the great success of bioanalytical assays is due to the fact that, using one single instrument (e.g. a UV–visible spectrophotometer), a specialised operator can perform hundreds or thousands of different assays, just changing the biorecognition element, or the analyte, or the procedure to detect the latter. Indeed, the number of assays existing nowadays is huge. By using multiwell plates (also known as microplates or microtiter plates), it is possible to perform a large number of assays, especially heterogeneous assays, in a

2.1 Molecular biorecognition and analytical assays

63

short time, or even simultaneously. Microplates (Figure 2.2) are flat plates made of polystyrene, polycarbonate or polypropylene with multiple wells (typically in a number of 24, 48 or 96) used as small test tubes to simultaneously perform the same assay on a large number of samples.

Figure 2.2: Multiple analyses with microplates (luchschen/iStock/Getty Images Plus and Mando19/E +/Getty Images).

In order to explain the functioning principle of bioanalytical assays more in detail, in the following section, we describe, as an example, the enzymatic assay for lactate.

2.1.1 Example: bioassays for lactate Lactate detection is an evergreen research theme since its application spans over a wide range of sectors. In the medical and biological fields, monitoring lactate levels is a good indicator of the balance between tissue’s demand and utilisation of oxygen, since lactate is normally generated under hypoxic or anaerobic conditions. Therefore, lactate is considered an important regulatory molecule of intermediate metabolism involved also in cancer development, diabetes and other diseases. High lactate levels, which can rise up to millimolar concentrations, are associated with not only bacterial infections or short bowel syndrome but also sepsis, ischemia or trauma. From the enzymatic side, it is important to monitor the release of lactate dehydrogenase (LDH), an enzyme that is released from damaged or stressed cells and tissues. High LDH levels in blood are observed in relation to some kinds of cancer, haemolysis, myocardial infarction, acute kidney disease, acute liver disease and others. For these reasons, a large number of assays and bioassays for the detection and quantification of lactate or LDH have been developed, using different strategies. As we will see later, the same principles behind an enzymatic assay can be used to determine the substrate (in this case, lactate) or the enzyme (here LDH),

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depending on which reagent is used in excess with respect to the other (see direct vs. catalytic kinetic methods, Section 2.2). For the sake of simplicity, here we will focus on assays suited to analyse lactate, operating in excess of LDH. The simplest and most linear route for bio-assaying lactate is based on the oxidation of lactate to pyruvate catalysed by the enzyme LDH, which transfers hydrogen atoms to the enzymatic cofactor NAD+ that is reduced to NADH (Figure 2.3).

H

O

O O

+ NAD+

LDH

O

OH

+ NADH + H+

O

Lactate

Pyruvate

Figure 2.3: Conversion of lactate to pyruvate catalysed by lactate dehydrogenase (LDH).

The equilibrium is usually in favour of lactate. However, it can be moved towards pyruvate by introducing a second reaction that consumes pyruvate, pushing the equilibrium to the right. This is achieved by adding a second enzyme, for example, glutamate-pyruvate transaminase (GPT), which catalyses the reaction shown in Figure 2.4.

O

O O

HO NH2 Glutamate

O

O +

O O Pyruvate

GPT

O

O O

HO O α-Ketoglutarate

+

O NH2 Alanine

Figure 2.4: Conversion of pyruvate and glutamate to alanine and α-ketoglutarate catalysed by glutamate-pyruvate transaminase (GPT).

The stoichiometry of the reactions indicates that the amount of NADH produced is directly correlated to the amount of lactate reacted. The production of NADH can be followed and quantified by UV spectroscopy. As shown in the UV spectrum of Figure 2.5, both NAD+ and NADH present a maximum of absorbance near 260 nm, but only the reduced NADH displays a second absorbance peak near 340 nm. Therefore, following the increase in absorbance at 340 nm, one can follow the kinetics of the reaction shown in Figure 2.3 and, by applying suitable kinetic models (see below for details), quantify the lactate concentration in the sample. The quantification is carried out by using a calibration plot obtained with lactate standard solutions, under the same experimental conditions used for the

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2.1 Molecular biorecognition and analytical assays

Figure 2.5: UV spectrum of NADH (blue) and NAD+ (pink).

sample. For both the sample and the lactate standards, the optical absorption at 340 nm is measured at a fixed time after the addition of the reagents (NAD+, LDH, glutamate and GPT). This assay is very sensitive, accurate and precise; however, it requires the use of a UV–visible spectrometer, which is a quite complex and relatively expensive instrumentation. In order to facilitate the detection of the recognition event for the lactate bioassay, other strategies have been implemented. A common approach is represented by the use of suitable chromogenic compounds. Indeed, the NADH produced by the catalytic oxidation of lactate in the reaction shown in Figure 2.3 can be used to produce a dye by reacting with a suitable chromogen. According to this strategy, the reaction in Figure 2.3 is followed by NADH þ Chromogen ! NADþ þ Dye

(2:1)

Note that, in addition to coloured dyes, one can eventually employ fluorescent or chemiluminescent dyes. The quantification of the lactate concentration in the sample is performed by measuring the amount of light absorbed or emitted by the dye, which is directly proportional to the concentration of reacted lactate. Examples of some reagents employed for assaying lactate are listed in Table 2.1. Table 2.1: Examples of optical lactate bioassays. Bioassay

How it works

Technique

Tetrazolium dye MTT, namely -(,-dimethylthiazol--yl),-diphenyltetrazolium bromide

MTT is reduced by NADH to formazan (purple, insoluble)

UV–visible spectroscopy: absorption maximum at  nm

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Table 2.1 (continued ) Bioassay TM

EnzyFluo

(fluorescent probe)

Lactate-GloTM (with LDH, NAD+, reductase, reductase substrate and luciferase)

How it works

Technique

The probe is reduced by NADH to a fluorescent product

Fluorescence: emission intensity measured at  nm

In the presence of NADH, the reductase substrate is converted by reductase to luciferin, which is then used by luciferase to produce light

Luminescence: the luminescent signal is proportional to the lactate concentration

On the market, there is a wide range of kits for lactate assay that contain all the components necessary for the test (LDH, NAD+ and the substrate to be reduced). It is only required to add the kit preparation to the sample and, by using the specific instrumentation (spectrophotometer, fluorimeter, etc.), detect and quantify the final product of the biorecognition event. Assays based on similar principles have been developed and are available for analysing other dehydrogenase or hydrogenase substrates, different from lactate. They utilise a similar approach, but using other enzymes such as aldehyde dehydrogenase for detecting acetaldehyde or formate dehydrogenase for formic acid. This assay strategy finds application for a variety of analytes of biomedical, environmental or food control interest.

2.2 Principles of kinetic analytical methods Kinetic analytical methods allow one to determine the analyte concentration measuring its effect on the reaction kinetics. A wide area of application of kinetic methods is represented by enzymatic analyses. Unlike equilibrium methods, kinetic methods rely on the accuracy in the measurement of time, a parameter that is not always so obvious and easy to control. However, enzymatic methods of analysis present several advantages deriving from: – the high specificity of the enzyme–substrate interaction; – the high sensitivity due to the fact that each enzyme molecule catalyses the reaction of a large number of substrate molecules (chemical amplification); – the capability to provide a quick response, even for irreversible reactions or reactions that would require a long time to reach equilibrium. Enzyme kinetic methods are classified as a) direct methods, when the analyte is the substrate of the enzymatic reaction; b) catalytic methods, when the analyte is the catalyst itself (namely the enzyme).

2.2 Principles of kinetic analytical methods

67

Therefore, enzymatic methods and assays can be used for the analysis of both small molecules (substrates) and large proteins (enzymes). Equation (2.2) (see also Chapter 1) schematises the typical steps of a catalytic reaction between the enzyme E and the substrate S: E+S

k

1 !

k−1

k2

½ES ! E + P

(2:2)

where [ES] is the activated enzyme–substrate complex and P the reaction product. The reaction rate (v), expressed as rate of disappearance of the substrate, can be written as follows: v= −

d½S = k1 ½E½S dt

(2:3)

This is a second-order reaction rate. However, if one of the reactants is in large excess, the change in its concentration is negligible with respect to the other reagent. In this case, the kinetic law can be simplified to a pseudo-first-order reaction rate. Here, we indicate with the subscript “0” the initial concentration of a substance in the sample. When the following condition applies: ½E0  ½S0 ) ½E ffi ½E0 ffi constant

(2:4)

eq. (2.3) can be turned into v = k′½S

(2:5)

where k′ = k1 ½E0 . Vice versa, when the opposite condition is valid ½S0  ½E0 ) ½S ffi ½S0 ffi constant

(2:6)

then eq. (2.3) can be turned into v = k′′½E

(2:7)

where k′′ = k1 ½S0 . With reference to the earlier classification, eq. (2.5) holds for direct methods, while eq. (2.7) is used for catalytic analyses. Therefore, by controlling the excess of enzyme or substrate, one can choose whether to detect the substrate or the enzyme, respectively. Recalling the trend of the Michaelis–Menten plot (see Figure 1.44), it is evident that direct methods exploit the initial linear portion of the plot, where v is directly proportional to ½S. On the other hand, catalytic methods exploit the last part of the plot, where the reaction rate reaches a plateau as the enzyme is saturated by the substrate. In this region, in fact, the reaction rate is equal to vmax , which does not depend on the substrate concentration, but is directly proportional to ½E.

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Kinetic analyses can be performed using different strategies, such as: a) follow the changes in concentration with time and integrate the relevant kinetic equations (integral method); b) measure the reaction rate as the derivative of the kinetic plot at a certain time (derivative method); c) measure the change in the concentration at a certain time (fixed time method); d) measure the time required to reach a pre-set reaction extent (variable time method). Table 2.2 summarises the basic methods employed in kinetic analyses, presenting the relevant equations to be used for the direct and catalytic case, respectively. Table 2.2: Summary of the main operative equations used for different enzymatic kinetic methods, where ½E0 and ½S0 are the initial concentration of enzyme and substrate, respectively, [S] is the substrate concentration at time t, and ½P is the concentration of the detected product. Method

Direct: when ½E0  ½S0

Catalytic: when ½S0  ½E0

Integral

ln[S] = ln[S] – kt

not applied

Derivative

½S0 = const ðd½P=dt Þt = fixed

ðd½P=dt Þt = fixed = const ½E0

Fixed time

½S0 = const Δ½P

Δ½P = const ½E0

Variable time

½S0 = const ð1=Δt Þ

½E0 = const ð1=Δt Þ

As an example, here, we demonstrate the equations used for the direct determination of the substrate S (second column in Table 2.2). The equations for the catalytic case (third column in Table 2.2) can be derived accordingly. Consider a (pseudo) first-order reaction involving a generic analyte S that is converted into the product P with a rate constant k, according to k

S!P

(2:8)

The relative reaction rate is given by v= −

d½S = k½S dt

and the relative kinetic plot is represented in Figure 2.6.

(2:9)

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2.2 Principles of kinetic analytical methods

Figure 2.6: Exponential decay of the substrate concentration [S] with the time. The reaction rate can be determined by the derivative of [S] at the time t (represented by the pink line).

Note that the reaction rate at a certain time t is provided by the derivative of the curve at that time (pink line in Figure 2.6). When using the integral method, eq. (2.9) is integrated as follows: d½S = − k dt ½S ½ð S

½S0

d½S = −k ½S

ln

(2:10)

ðt dt

(2:11)

t0

½S = − kt ½S0

ln½S = ln½S0 − kt

(2:12) (2:13)

As shown in Figure 2.7, the plot of ln[S] versus time provides a straight line, with slope equal to ‒k and intercept on the Y-axis equal to ln[S]0.

Figure 2.7: Integral method, plot of eq. (2.13).

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In the derivative method, we directly measure the reaction rate at a certain time t as the first derivative of the kinetics plot at the chosen time. For the direct method, the reaction rate is always measured operating in excess of enzyme to fulfil the condition: ½E0  ½S0 . For a pseudo-first-order kinetics, the reaction rate at time t, expressed as the rate of product formation, is given by v=

d½P = k½St = k½S0 e − kt dt

(2:14)

Rearranging eq. (2.14), one obtains ½S0 =

d½Pt ekt dt k

(2:15)

Since the measurements are performed at the same fixed time for both the sample and the calibration standards, t can be considered constant as well as the term ekt =k. Therefore, eq. (2.15) becomes ½S0 = const

d½Pt dt

(2:16)

Equation (2.16) shows that there is a direct proportionality between ½S0 and the tangent of the kinetic plot measured at time t, for both the calibration standards and the sample. This is the operative equation reported in the second column of Table 2.2 for the derivative method. Moving from infinitive to finite increments in the product concentration and time, we can write Δ½Pt (2:17) ½S0 = const Δt When the time lag is finite and constant (that is, Δt = const), then eq. (2.17) can be simplified as follows (see second column of Table 2.2 for the fixed-time method): ½S0 = const Δ½Pt

(2:18)

A special case among the derivative methods is represented by the measurement of the initial reaction rate (at t = 0) for both the sample and standards. Consider, for instance, five standard solutions containing five different initial concentrations of S. If all the measurements are performed with an excess of enzyme (condition ½E0  ½S0 ), then the initial rate (the tangent of the curve at t = 0) scales linearly with ½S (see Figure 2.8A). These data can be used to obtain the calibration plot shown in Figure 2.8B and, through interpolation, determine ½S0 in the sample. In the variable time methods (last row in Table 2.2), we measure the time lag required to reach a certain concentration of reagent or product, which means Δ½Pt = const, so that eq. (2.17) becomes ½S0 = const

1 Δt

(2:19)

2.3 Introduction to biosensors

71

Figure 2.8: (A) In blue: instrumental response for five standard solutions containing five different initial concentrations of the substrate S. In red: tangents to the curves at t = 0, representing the initial reaction rate. (B) The slope of the tangents is plotted versus the substrate concentration to obtain a calibration plot.

In this case, the initial substrate concentration scales inversely with the time required to detect this pre-fixed Δ½P value. The equations used for the determination of the enzyme concentration operating in excess of substrate (catalytic methods, when ½S0  ½E0 ) can be derived in the same way and are summarised in the third column of Table 2.2.

2.3 Introduction to biosensors The use and practice of bioanalytical assays, in particular heterogeneous bioassays, together with the modern progress in sensitive and miniaturised analytical instrumentation, has evolved into the development of the so-called analytical biosensors. According to the definition recommended by the International Union of Pure and Applied Chemistry (IUPAC) in 1999, a biosensor is indeed “a self-contained device that is capable of providing specific quantitative or semi-quantitative analytical information using a biological recognition element (biochemical receptor) that is in direct spatial contact with the transduction element”. It is worth reminding that a transduction element (or transducer) is a device that transforms a form of energy into another. The output is typically an electrical signal. For instance, a microphone transforms acoustic waves into an electrical signal; the screen of a computer transforms electronic signals into optical signals; a phototube transforms the energy of photons into an electrical signal and so on. All these devices are transducers. The biological recognition element of a biosensor can be constituted of enzymes, nucleic acids, antibodies, but also tissues or whole cells. The biological element

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provides the sensor with the high degree of selectivity necessary for specifically recognising the analyte, also in the presence of a myriad of other substances. At the same time, the transducer translates the information gained through the biorecognition event into a readable output signal (see Figure 2.9). Note that the bare transducer is substantially unspecific: for instance, a photomultiplier tube transforms any photon into a voltage signal. However, if the photon emitted is the result of a biorecognition event, then the response of the sensor becomes selective and specific for the recognised analyte.

Figure 2.9: Schematic representation of a biosensor.

The majority of biosensors relies directly or indirectly on enzymatic reactions to produce the analytical signal; therefore, the presence or addition of co-substrates is required for determining the analyte. The transducer can be constituted of a variety of different elements such as electrodes, optodes and resonators.

2.4 Classification of biosensors according to the receptor 2.4.1 Catalytic biosensors Catalytic biosensors are based on reactions catalysed by macromolecules. As we have already seen, the catalysts par excellence in biology are the enzymes, so that the most common and well-developed catalytic biosensors utilise an enzyme, or a combination of different enzymes, as recognition system. Some other biosensors may use whole cells, cell organelles (such as mitochondria) or tissues as bioreceptors.

2.4 Classification of biosensors according to the receptor

73

One of the greatest advantages of using enzymes as bioreceptors is that, usually, they are extremely selective towards their substrate, so that they will catalyse the reaction of only one (or few) specific analyte(s). In the basic catalytic biosensor, the substrate S (the analyte) reacts with the enzyme, with or without a co-substrate S′, to yield one or several products, which we can call P and P′, according to the general scheme: enzyme

S + S′ $ P + P′

(2:20)

Different strategies can be used to monitor the consumption of the analyte S by this biocatalysed reaction, therefore revealing its presence or concentration: a) Direct detection of the consumption rate of the analyte S and the corresponding signal decrease. b) Detection of the co-substrate S′ consumption and the corresponding signal decrease from its initial value. In case one molecule of S reacts with one molecule of S′ (ratio 1:1), the rate of consumption of the co-substrate S′ will be equal to the rate of consumption of the analyte S. When one molecule of S reacts with two molecules of S′ (ratio 1:2), the consumption rate of S′ will be double of the one of the analyte S and so on. c) Detection of one of the reaction products, P or P′, and the corresponding signal increase. d) Detection of the state of the biocatalyst active site or cofactor using a mediator that reacts sufficiently fast with the biocatalyst and is easily detected by the transducer. e) Direct electron transfer between the active site of a redox enzyme and the transducer, in the case of electrochemical biosensors.

2.4.2 Affinity biosensors Affinity biosensors are based on the interaction of the analyte with biomolecules. In this case, equilibrium is usually reached and there is no further consumption of the analyte by the immobilised macromolecule. The typical bioaffinity reaction can be schematised as follows: A+B Ð P

(2:21)

where A is the analyte, B is the biorecognition element and P is the product (typically a complex of A and B). The transducer monitors the equilibrium response or, in some cases, utilises a complementary biocatalytic reaction to monitor the state of the affinity reaction. In the latter case, an enzyme label (E) is bound to the biorecognition element, which becomes BðEÞ . When the complex is formed upon reaction between the analyte and

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the biorecognition element, the enzyme will be part of such affinity complex. This reaction can be schematised as follows: A + BðEÞ Ð ABðEÞ

(2:22)

Therefore, we can add a substrate S that specifically reacts with the enzyme label E, so that the total reaction would be ABðEÞ + S Ð ABðEÞ + P

(2:23)

By monitoring, for instance, the formation of the product P, it is possible to determine the amount of the complex ABðEÞ formed, which depends on the concentration of A present in the sample. This strategy is widely used in immunosensors (see Chapter 4 for details). There are different types of affinity interactions that can be exploited for biosensing purposes: a) Antigen–antibody interaction: it is the most common bioaffinity reaction used in biosensors. In this case, the bioreceptor is an antibody and the sensor is normally called immunosensor. b) Receptor–antagonist/agonist interaction: they use ion-channel membrane receptors or binding proteins as receptors. The result of the binding of the analyte (here named “agonist”) to the immobilised channel receptor protein is monitored by changes in ion fluxes through these channels. Typical interactions are based on biotin, a small molecule that can strongly bind proteins such as avidin or streptavidin. c) Oligonucleotide binding: this is a developing field, especially in electrochemical biosensors. It is possible to detect binding of single-strand oligonucleotides (forming the corresponding double strand) by monitoring the signal of the nucleobase guanine (which is electroactive), or the signal of electroactive intercalators (redox molecules that intercalate into the double-helix DNA). Affinity biosensors find fields of application different than catalytic biosensors. In fact, being based on equilibrium reactions, they generally present a narrow linear range of calibration, and are often unsuitable to monitor continuous changes of the analyte concentration.

2.5 Classification of biosensors on the basis of the transducer As schematised in Figure 2.10, depending on the nature of the transducers and their functioning principle, biosensors can be classified into optical, electrochemical, calorimetric and piezoelectric biosensors.

2.6 Immobilisation of biomolecules

Bioreceptor

Signal

Optode, photon counter

Optical sensor

Electron tranfer

Electrode

Electrochemical sensor

Changes in heat

Thermistor

Calorimetric sensor

Other proteins Nucleic acids

Sensor type

Changes in light Enzymes Antibodies

Transducer

75

Cells Tissues

Changes in mass

Quartz microbalance, piezoelectric device

Piezoelectric sensor

Figure 2.10: Classification of biosensors.

Historically, the first biosensors developed have been the calorimetric ones, based on the immobilisation of an enzyme on the surface of a thermistor. Such biosensors were used to measure the heat released by exothermic reactions catalysed by the enzyme, which specifically converts the substrate (analyte) into the product(s). Nowadays, a large part of biosensors on the market relies on electrochemical transduction, in particular, amperometry. However, other types of transducers are also employed, in particular, optical and piezoelectric ones. More details on the assembly and functioning of the different types of biosensors mentioned in Figure 2.10 will be presented in the following chapters.

2.6 Immobilisation of biomolecules As mentioned earlier, a fundamental characteristic of biosensors is that the biological recognition element is in direct spatial contact with the transducer (normally a solid support). Such an intimate contact is achieved by immobilising the bioreceptor on the surface of the transducer, creating a heterogeneous immobilised biosystem. Such procedure presents several advantages such as: – easy separability of the bioreceptor from the sample solution; – possibility of reusing the bioreceptor several times; – possibility of applying the bioreceptor in a continuous flow system; – in some cases, the immobilisation can stabilise the biomolecule structure, making it more robust and resistant to environmental changes, for example, improving its pH tolerance, heat stability or performance in organic solvents; – eventually, the inherent short lifetime of biomacromolecules like enzymes can be increased upon immobilisation.

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The limits of the technique are mainly related to the fact that some immobilisation procedures can denature the biocomponent, with partial or complete loss of its biological activity and selectivity. This can be caused, for example, by altering the secondary or tertiary structure of enzymes, or blocking the epitopes of an antibody. For this reason, it is important to choose the most appropriate immobilisation procedure taking into account the characteristics of the bioreceptor, the chemical nature of the material that constitutes the transducer and the analytical application of the biosensor. We can immobilise different types of biological substrates ranging from peptides or oligonucleotides to whole cells and tissues. Here, we focus on the immobilisation methods for molecular components, leaving the methods of immobilisation of organelles, cells and tissues to other specialised texts. From a chemical point of view, the transducers used to immobilise biological substrates can be constituted of a variety of materials such as: – minerals (such as silica); – metals (gold, platinum, silver, copper, etc.); – oxides (titanium oxide, indium tin oxide (ITO), etc.); – carbon (glassy carbon, graphite, graphene, carbon nanotubes (CNTs), carbon fibres, etc.); – organic polymers, both natural (like cellulose) and synthetic (such as polyamide, polystyrene and polycarbonate). The chemical characteristics of the support are determined by the type of transduction that will be performed: for example, electrochemical biosensors require conductive supports such as metals or carbon-based materials; optical biosensors prefer optically transparent supports such as quartz or ITO films on glass, or some transparent plastics. Once we have selected the solid support and the biomolecule to be immobilised on it, we must choose a suitable immobilisation procedure. The immobilisation procedures can be divided into reversible or irreversible techniques: a) Reversible techniques are fundamentally based on the physical adsorption of the biological substrate on the solid support exploiting non-covalent interactions, such as hydrogen bonds, hydrophobic interactions, Van der Waals forces, affinity binding, ionic binding, chelation or via physical containment. Since the interaction between the bioreceptor and the containment medium is weak, the biomolecules generally retain their native state as well as their molecular recognition capabilities. Thanks to the reversibility of the immobilisation, once the activity of the bioreceptor decays, the support can be easily regenerated and reloaded with fresh biomolecules. Moreover, these procedures offer advantages in terms of facility, time saving and wide applicability with minimal use of reagents. On the other hand, they have some limits in that the biorecognition

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layer can be slowly leaked with time or because of changes in environmental conditions (e.g. ionic strength, pH or temperature). b) Irreversible techniques are based on stronger interactions between the biological substrate and the solid support, such as entrapment in polymers or membranes, cross-linking and formation of covalent bonds. The successful application of such procedures requires a certain amount of manufacturing and knowledge of the chemistry involved. Below we summarise the main techniques employed for the immobilisation of biomolecules (proteins and polynucleotides), furnishing more detailed information in the following section.

2.6.1 Physical entrapping within dialysis membranes This is probably the oldest and simplest methodology for immobilising a molecular recognition layer at a transducer surface. It was applied in 1962 by Clark for the first example of glucose biosensor. This method is based on the use of a dialysis membrane (e.g. a porous cellulose acetate membrane) to trap the biomacromolecules close to the transducer surface (Figure 2.11). Dialysis membranes contain pores of known diameter that allow the retention of large molecules and the passage of small ones. The diameter of the pores determines the “cut-off number” of a certain membrane: it corresponds to the molecular weight of the smallest molecule that is 90% retained by the membrane. Therefore, the membrane will be impermeable to all molecules with molecular weights greater than the cut-off number, and permeable to smaller ones.

Transducer

Figure 2.11: Enzyme molecules trapped within a dialysis membrane.

Advantages: – it is a simple and fast technique, which does not require the use of reagents; – it is a soft method, particularly suitable for soluble proteins, which maintain their native structure; – it is characterised by a very low cost.

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Disadvantages: – for biosensing purposes, it can be applied only when there is a large difference in molecular weight between the bioreceptor (trapped by the membrane) and the analyte (which should permeate through the membrane), like in the case of an enzyme and its substrate; – the dialysis membranes are relatively thick (several micrometres) so that permeation of the analyte can be slow, with consequent slow response times (several minutes); – perfect sealing between the membrane and the solid surface of the transducer can be tricky.

2.6.2 Physical adsorption of biomacromolecules The protein or polynucleotide is adsorbed on the surface of the transducer by noncovalent interactions (Figure 2.12). The method is very simple and, normally, performed by dipping the solid support into the bioreceptor solution, or by drop-casting the bioreceptor solution onto the support. Removal of an eventual excess of unbound biomacromolecules can be performed by simply washing the solid support with water or buffer solution. For instance, enzymes are adsorbed onto hydrophobic surfaces, such as carbon substrates, thanks to their hydrophobic domains.

Transducer

Figure 2.12: Physical adsorption of enzymes on the transducer surface.

Advantages: – soft method, which does not trigger reactions involving the enzyme functional groups; – simple and fast technique; – short response times. Disadvantages: – randomness of the immobilisation: the biomacromolecules can acquire random orientations on the transducer surface, and not all such orientations may be favourable to the interaction with the substrate (analyte);

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– reversibility: the bioreceptor layer formed on the transducer surface is not always stable, as adsorption is a reversible process. In fact, there could be de-adsorption; – low reproducibility, since it is difficult to control the quantity of adsorbed bioreceptor and the thickness of the layer.

2.6.3 Electrostatic adsorption This method is based on the pre-functionalisation of the transducer surface with ion-exchange resins or molecular layers composed of a self-assembled monolayer (SAM) of molecules with ionic pendant groups (Figure 2.13). The resins and the molecular layers of SAM employed for this method are charged, presenting a charge opposite to the one of the biomacromolecule that we want to immobilise. In this way, the bioreceptor would be attracted and retained at the transducer surface thanks to electrostatic interactions. Typical SAMs used for the immobilisation of macromolecules are composed of thiols assembled on the surface of a gold transducer, with pending ionic functionalities. Electrostatic adsorption is a simple and effective immobilisation procedure. However, it is very sensitive to changes in the environmental conditions, in particular, pH and ionic strength. Being based on weak ionic interactions, it is a reversible method.

Transducer Figure 2.13: Electrostatic adsorption of proteins (presenting negative charges in some parts of their surface) on the transducer surface modified with a positively charged layer (ion-exchange resin or self-assembled monolayer, SAM).

2.6.4 Physical entrapment within a polymeric gel A polymeric gel is produced on the surface of the transducer in the presence of the bioreceptor, which remains entrapped within the polymer network (Figure 2.14). Depending on the thickness of the polymeric layer, the response time can be more or less quick.

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Transducer

= Monomer

Figure 2.14: Entrapment of biomolecules within a polymer network.

Advantages: – simple and fast technique; – it requires relatively mild conditions. Disadvantages: – the polymer can undergo degradation over time with concomitant release of the bioreceptor; – entrapment could denature the biomacromolecule; – the film can be permeable to interferences.

2.6.5 Cross-linking with polyfunctional reagents This procedure is based on the use of bi- or polyfunctional reagents that act as cross-linkers for the biomacromolecules (Figure 2.15).

Transducer

= Cross-linking reagent

Figure 2.15: Cross-linking of biomolecules with a bi-functional reagent.

Advantages: – simple and fast procedure; – it can operate in mild conditions; – a molecular recognition layer is created which reduces material loss.

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Disadvantages: – it is difficult to control the inter- and intramolecular cross-linking; – the biomolecules can lose their activity because of the cross-linking.

2.6.6 Non-specific covalent bonds Covalent bonds are formed between the bioreceptor and the transducer surface (Figure 2.16). The choice of the functional groups exploited to anchor the biomacromolecule to the transducer must be carefully evaluated.

Transducer

Figure 2.16: Non-specific covalent bonds between biomolecules and the transducer surface.

Advantages: – stable molecular recognition layers are obtained; – it can operate in relatively mild conditions; Disadvantages: – possible denaturation of the biomacromolecules; – not all the immobilised molecules are active; – it is difficult to protect the enzymatic active sites during the immobilisation reactions.

2.6.7 Specific covalent bonds This procedure is similar to the previous one, but the orientation of the bioreceptor on the transducer surface is carefully controlled in order to facilitate the interaction between the biomolecules and their substrate, as well as the signal transmission to the transducer (Figure 2.17). The main difference with respect to unspecific bonding is given by the advantage that (almost) all the immobilised biomolecules are active.

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Transducer

Figure 2.17: Specific covalent bonds between biomolecules and the transducer surface.

2.6.8 Biospecific adsorption This procedure exploits the natural interactions occurring between biomacromolecules and some other biological molecules that can be easily bound to the transducer surface. A typical example is the use of the biotin–avidin interaction. For instance, biotin can be immobilised on the transducer surface, while the bioreceptor is functionalised with avidin (Figure 2.18). When avidin binds to biotin, the attached bioreceptor will consequently be immobilised. We can use also the inverse approach, which is immobilising avidin on the transducer that can interact with biotinylated proteins or polynucleotides. Avidin can also be substituted by streptavidin, which can interact with biotin in the same way.

= Avidin Transducer

= Biotin

Figure 2.18: Immobilisation of biomolecules tagged with avidin onto a biotin-modified transducer surface.

Advantages: – relatively easy procedure; – it can be adapted to a variety of biomacromolecules, as long as they can be functionalised with avidin, streptavidin or biotin. Many biotinylated or streptagged (modified with streptavidin) receptors are also commercially available. Disadvantages: – it can be more expensive than other methods.

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2.7 Immobilisation of biomolecules in practice Here below, we will provide more details concerning the preparation and application of some of the immobilisation procedures summarised earlier.

2.7.1 Entrapment within dialysis membranes This immobilisation procedure employs a preformed membrane to trap the bioreceptors, typically proteins. Dialysis membranes have been developed for ultra-filtration purposes, in which suspended solids, bacteria or high-molecular-weight solutes must be separated. These filtration processes require membranes with pore size from microto nanometres. As in any filtration process, the aim is to separate a dispersed phase from a fluid (liquid or gas) that forms a continuous phase. The suspension is passed through a filter (the membrane), and the fraction that permeates is collected downstream. The suspended solids, viruses, bacteria and macromolecules are totally or partially retained by the membrane surface. The most commonly used dialysis membranes are made of cellulose acetate (CA) or cellulose nitrate. CA membranes present a lower specific adsorption of proteins than cellulose nitrate, which makes CA the preferred choice for biological applications. Moreover, CA shows higher thermal resistance to solvents, particularly to low-molecular-weight alcohols, and can tolerate repeated steam sterilisations. The pores of a CA membrane are obtained by controlled hydrolysis of an initially continuous film of cellulose ester, by exposure to an alkaline medium (KOH or NaOH solution). The hydrolysis time determines the pore diameter and, consequently, the cut-off number of the

Enzyme solution

CA solution

Platinum electrode

Drop of enzyme solution CA membrane

Thin CA layer

O-ring Electrode body (I)

(II)

(III)

Figure 2.19: Steps to entrap an enzyme within a dialysis membrane onto the surface of an electrode: (I) a drop of cellulose acetate (CA) solution is deposited on the electrode surface; (II) a drop of enzyme solution is then casted on the CA thin layer; (III) a CA dialysis membrane is finally placed on the top of the electrode covering the enzyme drop, and stopped with an O-ring.

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membrane. For example, a membrane with cut-off number of 10 kDa will retain 90% of a macromolecule with MW = 10 kDa and, obviously, all molecules with MW > 10 kDa. Dialysis processes are used for the purification of biological molecules as well as for therapeutic purposes, for example, to “clean” the blood of patients with renal failure. In biosensing applications, dialysis membranes are used to retain the bioreceptor molecules in close contact with the transducer surface, typically in electrochemical or optical biosensors. Figure 2.19 summarises the steps for the preparation of an amperometric biosensor based on the entrapment of enzymes within a CA membrane.

2.7.2 Entrapment within polymeric matrices Polymers can be used to entrap biological receptors allowing the substrates and the products to pass through. This method is generally employed with enzymes and cells, and requires the synthesis of the polymeric network in the presence of the biological substrate. Normally, hydrophilic polymers are used that form soft gels when hydrated, the so-called hydrogels. Hydrogels are highly water absorbent and, therefore, ideal to entrap biological substrates like enzymes and cells. 2.7.2.1 Enzyme entrapment in the polymer structure during chemical polymerisation For this procedure, the enzyme is added to a solution containing a reactive monomer. The polymerisation is initiated by heating, UV radiation, light and/or by addition of suitable reactants (initiators). As the polymer builds up, its structure forms a sort of net that entraps the enzyme molecules. Several methodologies can be used, which have been developed to entrap enzymes as catalysts in bioreactors. We present here a typical example of this methodology: the entrapment of an enzyme in a polyacrylamide gel. The starting point is a solution of the enzyme and acrylamide monomer. In the presence of N,N′-methylenebis(acrylamide) as a crosslinker, using potassium persulphate as an initiator and β-dimethylaminopropionitrile as an accelerator, the polymeric structure grows around the enzyme molecules creating a lattice in which the enzyme is trapped (Figure 2.20). This method is often used for preparing enzymatic reactors, and less frequently for biosensors. The main disadvantages are that the polymerisation is performed in organic solvents and the reaction is exothermic, so that denaturation of the protein can occur. Therefore, the conditions in which the polymerisation takes place must be carefully evaluated. 2.7.2.2 Enzyme entrapment in electropolymerised films This immobilisation method involves the use of conductive polymers. Figure 2.21 shows the structure of some of the most widely used conducting polymers, and a conductivity scale of the polymers (from insulating to highly conducting) is reported in

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85

Figure 2.20: Entrapment of an enzyme during the polymerisation of acrylamide.

Figure 2.21: Structures of some of the main conducting polymers.

Figure 2.22. It is worth reminding that a conductor can be distinguished from a semiconductor by looking at the dependence of the ohmic resistance on the temperature: in fact, in a metal conductor, this parameter increases with increasing temperature, while in a semiconductor, it decreases. Note that conducting polymers are conjugated polymers in which the π-electrons are delocalised all over the structure. Defects in the electronic structure can be generated, for instance by oxidising the polymer, which eliminates one electron producing a “hole” in the π-electrons cloud. The so generated defect can propagate all along the

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Figure 2.22: Conductivity of organic polymers and other materials.

polymer structure under the influence of an electric field, making the oxidised polymer a conductor. Many conductive polymers present a polycationic state, with a positive ionic charge every four/five monomeric units, and incorporate counter-ions (anions) to keep electroneutrality conditions. The most widely used conductive polymers in biosensors construction are those easily prepared in aqueous solutions, such as polyaniline, poly(3,4-ethylenedioxythiophene) or polypyrrole (PPy) (see their structures in Figure 2.21). They can be directly prepared on the electrode surface by electrochemical oxidation of the respective monomer. For instance, the structural formula of oxidised PPy is shown in Figure 2.23.

Figure 2.23: Structural formula of oxidised polypyrrole (PPy), where X − is the counter ion (e.g. Cl−, ClO4– and BF4−) that neutralises the positive charge of the polymer (with n = 4–5).

The electrochemical polymerisation process is summarised in Figure 2.24. If the electropolymerisation is carried out in the presence of an enzyme, the enzyme remains trapped within the structure of the conducting polymer formed at the surface of the electrode.

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87

Figure 2.24: Electrochemical polymerisation of polypyrrole.

Electropolymerisation is generally carried out using a constant or scanning potential, with a fixed concentration of monomer. It is also possible to covalently bind a mediator or the enzyme itself to the monomer before its electrodeposition. This method is normally performed using mild conditions (at room temperature and in an aqueous solution) since the ability of the polymer to transfer electrons and ions is exploited. In addition, this procedure allows electrochemical control of the thickness of the deposited film. For these reasons, enzyme entrapment in electropolymerised films is widely used for the production of electrochemical biosensors. Other polymeric matrices employed for the entrapment of enzymes are polysaccharides, natural polymers of carbohydrates composed of monosaccharide units bound together by glycosidic linkages. This method consists in the formation of a hydrogel in the presence of the enzyme using polysaccharides such as alginic acid or κ-carrageenan (Figure 2.25). The gel is formed by dropping the polysaccharide in an aqueous solution with a salt (sodium or calcium chloride in the case of alginic acid, potassium chloride in the case of κ-carrageenan). In the presence of cations, these polysaccharides form salts (alginates or carrageenans) which aggregate in spheres of gelatinous consistency that can entrap the enzyme molecules.

Figure 2.25: Structure of the natural polymers alginic acid and κ-carrageenan.

2.7.2.3 Enzyme entrapment with sol–gel technique The term “sol” indicates a liquid colloidal system consisting of large molecules or small dispersed particles. The term “gel” instead indicates an intermediate material between the solid and liquid state. The so-called sol–gel technique is based on controlled hydrolysis and subsequent condensation, starting from a precursor that hydrolyses in an aqueous media.

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Figure 2.26: Steps of the sol–gel polymerisation.

Typical precursors are the tetraalkoxy- or chlorosilanes (compounds with the general formula Si(OR)4). The polymerisation occurs as summarised in Figure 2.26. Briefly: i) a tetraalkoxysilane is hydrolysed via acid catalysis; ii) hydrolysis is followed by condensation to form the “sol”, a mixture of partially hydrolysed and partially condensed silica particles; and iii) the condensation goes on until forming the gel, which is dried by evaporation. At the end of the process, a network of siloxane bonds (–Si–O–Si–) is formed accompanied by the production of water or alcohol (ROH). The sol–gel technique is used to prepare cross-linked inorganic polymers, operating at room temperature. If condensation is performed in the presence of an enzyme, this will be trapped in the formed gel. The encapsulated enzymes maintain a high biological activity. 2.7.2.4 Entrapment within polyelectrolyte polymers This technique consists in mixing a polyelectrolyte dispersion in an aqueous or hydroalcoholic medium containing the biomacromolecule of interest. The best conditions are those for which the polyelectrolyte and biomacromolecule have opposite ionic charges. A microvolume of the dispersion is deposited on the surface of the transducer by spin coating or drop casting, and let dry. The polyelectrolyte most commonly used for this aim is Nafion®, a perfluorinated ionomer that can be dispersed in hydroalcoholic solutions thanks to its pending sulphonic groups (see the general structure in Figure 2.27).

2.7 Immobilisation of biomolecules in practice

C F2

F2 C

CF

89

F2 C

n O F2 F2C F O O C C S C F2 O O CF3

Figure 2.27: Chemical structure of Nafion®.

During the recasting process, Nafion® tends to self-assemble forming a stable film. Indeed, the polymer tends to self-aggregate in hydrophilic domains bound together through intermolecular interactions between its hydrophilic functional groups (SO3−), and hydrophobic domains involving the fluorinated alkyl chains. For this reason, Nafion® can interact with biomacromolecules not only by ionic bonding, but also through hydrophobic interactions with the hydrophobic domains of proteins or other biomolecules. The limitations in using Nafion® are represented by the fact that it cannot be dispersed in pure water and is relatively expensive. An alternative polyelectrolyte that can be used in substitution of Nafion® is the polyestersulphonate Eastman AQTM 55 (see its general structure in Figure 2.28). This polymer is dispersible in water, relatively cheap, and fully biocompatible, being used also for cosmetic products.

Figure 2.28: Chemical structure of the polymer Eastman AQTM 55.

2.7.3 Encapsulation in bilayer lipid membranes Bilayer lipid membrane is the barrier between the interior and exterior of cells. In nature, its purpose is to retain inside the cell large biomolecules and facilitate the exchange of small molecules (ions, nutrients, etc.). Such characteristic can be exploited in biosensors for the same usage. Enzymes and other proteins can be entrapped inside lipid vesicles called liposomes, formed in aqueous solution upon the dispersion of certain amphiphilic molecules such as phospholipids. The lipid vesicles are spheres constituted of self-closed molecular bilayers, in which the hydrophilic heads of the phospholipids (the polar head groups) are in contact with the aqueous phase, and the hydrophobic parts (the aliphatic chains) forms the interior of the bilayer (Figure 2.29).

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Hydrophylic heads Hydrophobic chains

Figure 2.29: Schematic representation of a lipid vesicle formed of phospholipid molecules: the vesicle is a sphere where the hydrophilic heads of the phospholipids are in contact with the aqueous phase (inside and outside), and the hydrophobic chains form the interior of the lipid bilayer.

Lipid vesicles can be prepared in the presence of enzymes in order to encapsulate them. This can be used to retain the biocatalysts close to the transducer surface. Another approach to reproduce the properties of lipid membranes (to which some proteins are bound in their natural environment) can be achieved by modifying the transducer surface with lipid layers. This technique is usually employed with carbon transducers (like graphite electrodes) that can be modified by mixing lipids and enzymes with a carbon paste.

2.7.4 Cross-linking Immobilisation by cross-linking is achieved by forming intermolecular cross-linkages between proteins using bi- or multi-functional reagents, for example, glutaraldehyde (GA). GA (a dialdehyde) reacts with the amino groups of the lysine residues of polypeptide chains forming two imine bonds for each molecule. In this way, one molecule of GA can link two protein molecules together, as shown in Figure 2.30. Since lysine residues are rather abundant in proteins, the reaction with GA results in a network of protein molecules interconnected between each other. However, an excessive number of cross-linking bonds could alter the protein

2.7 Immobilisation of biomolecules in practice

2 Enzyme

NH2 N

+ O

91

O

–2H2O

N

Enzyme

Enzyme

Glutaraldehyde Figure 2.30: Cross-linking reaction of glutaraldehyde (GA) with two enzyme molecules.

structure. To avoid that, we should control the cross-linking level. To this aim, we can use the so-called cross-linking factor fCL , defined as fCL =

% GA % TP

(2:24)

where % GA is the percentage by weight of GA and % TP is the percentage by weight of the enzyme and other components of the film (TP = total protein content). The optimal GA percentage to maintain sufficient enzyme activity must be approximately ≤ 0.27%, and the optimal cross-linking value fCL = 0.008–0.014. If fCL is increased too much, the enzyme activity can decrease. For example, for a 5–10folded increase of fCL , there is a decrease in sensitivity of about 30 times. In order to decrease the effect of the cross-linking reaction on the enzyme, this can be diluted by addition of an inert protein, so that %TP becomes larger. Typically, dilution of the enzyme of interest with bovine serum albumin is used to this aim. In addition to protein cross-linking, the reactivity of GA can be exploited also to bind proteins to transducer surfaces functionalised with reactive amino groups. In this case, the reaction with GA will form bridges between the amino groups of the protein and those on the transducer surface. It is recommended to operate with excess of GA so that each GA molecule binds to the surface only with one aldehydic

Nylon matrix

C O NH

HCl/H2O Hydrolysis

COOH GA

COOH

NH2

N

O

E NH2 COOH N

N

Enzyme

Figure 2.31: Functionalisation of the nylon surface with amino groups through hydrolysis of its surface amide bonds, followed by reaction with glutaraldehyde (GA) and, finally, with the enzyme amino groups.

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group, while the other one remains free and ready to react with the amino groups of the protein. For instance, nylon (a polyamide) surfaces can be functionalised by hydrolysis of the amide bonds, followed by reaction with GA (Figure 2.31).

2.7.5 Covalent bonding Covalent bonds are formed through reaction between the functional groups of biomolecules and the functional groups on the transducer surface, which can be already present on the supporting material or introduced on its surface using different methods. Sometimes, the functional group used for the immobilisation reaction can be artificially introduced in the biomolecule, especially in the case of: – nucleic acids, which do not present a large variety of functional groups useful for immobilisation; – particular immobilisation reactions involving functional groups not naturally present in biological molecules (e.g. azide). The formation of covalent bonds is a very efficient immobilisation procedure resulting in stable linkages between the biomolecule and the supporting material that can resist also to extreme conditions. Table 2.3 summarises the most common reactions and functional groups employed for the covalent immobilisation of biomolecules, in particular proteins. Here, we will discuss them in more details. a) The exposed amino groups of lysine residues can react with supports bearing activated carboxylic groups. Common reagents used to activate carboxylic acids forming the corresponding esters are carbodiimides (such as 1-ethyl-3-(3-dimethylaminopropyl)carbodiimide, known as EDC) with an auxiliary nucleophile (e.g. N-hydroxysuccinimide, NHS). Then the esters will form stable amide bonds with the lysine residues of the protein. Care should be taken to avoid inactivation of the intermediate O-acylisourea via undesired rearrangement to form acylurea (Figure 2.32). b) The nucleophilicity of the amino groups on the protein surface allows also reaction with epoxide-functionalised materials at basic pH. Lysine residues are quite common and abundant in proteins, so that these methods can be employed very often for a variety of proteins. However, such immobilisation techniques result in random orientations of the biomolecules on the support, so that it is not appropriated in the case one needs to immobilise enzyme with a specific orientation. c) Other functional groups used for protein immobilisation are the carboxylic groups of aspartic and glutamic acid residues, which can react with aminebearing supports upon activation with the same EDC/NHS coupling reagents. The advantage of this combination of reagents is that both EDC and NHS are water soluble and may be used in aqueous media. This method presents the same advantages and disadvantages as the previous ones, since aspartic and glutamic acid residues are rather abundant in proteins.

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Table 2.3: Most common reactions and functional groups employed for the covalent immobilisation of biomolecules.

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Surface modified with carboxylic acid groups

O-Acylisourea (reactive intermediate)

Carbodiimide R

R

N

O C OH

HN

O

+ C

C O

C N

N R'

R' E

NH 2

Rearrangement

R NH O C

O

H C N

+

O Enzyme

R'

C N

NH

R C

R'

NH

O Desired product

Urea

Acylurea

The most commonly used carbodiimide: EDC =

CH3 H 2C H 3C

CH2 N

H2C C

CH2

N CH3

N

Figure 2.32: Activation of carboxylic acid groups at the transducer surface using carbodiimide reagents to form activated esters (O-acylisourea) that readily react with the amino groups of proteins. Below, the structure of the most commonly used carbodiimide (EDC) is shown.

d) For a specific orientation of proteins (especially enzymes) on the solid support, several selective immobilisation methods have been developed. Some of these methods rely on the labelling of proteins with an azide moiety, which can react with alkynes (Huisgen 1,3-dipolar cycloaddition or “click chemistry” catalysed by Cu(I), see Figure 2.33) or be activated with a phosphine to react with a variety of electrophiles, for example, carboxylic groups (Staudinger ligation). The Huisgen cycloaddition between an azide group and an alkyne catalysed by copper(I) (CuAAC), commonly known as “click chemistry”, is increasingly applied for the preparation of biosensors. This reaction, introduced by Sharpless et al. in 2001, is characterised by high yields, absence of by-products, high selectivity as well as the possibility to operate in aqueous medium and under physiological conditions. The biosensor preparation strategy is based on functionalisation of the transducer surface (typically electrodes) with diazonium salts bearing alkyne groups,

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2.7 Immobilisation of biomolecules in practice

Enzyme

+ N N N

Alkyne

Cu(I)

N N N

Enzyme

Azide

Figure 2.33: Huisgen cycloaddition (click chemistry) between an alkyne immobilised at the transducer surface and an azide group introduced on the protein.

which then react with an azido-derivatised protein, such as azido-HRP (horseradish peroxidase) like in the example shown in Figure 2.34. The Cu(I) catalyst can be introduced directly as a copper(I) salt (chloride, iodide, or acetate) or can be generated in situ by reduction of Cu(II) salts, such as sulphate or acetate, using hydroquinone or ascorbic acid as reducing agents. Interestingly, Cu(I) can be produced electrochemically to functionalise electrode surfaces. This methodology is known as “electro-click chemistry”.

HRP N N N

HRP

N N N

HRP

Gold surface

N2

N3

in ethanol or acetonitrile

Cu(I)

Figure 2.34: Functionalisation of a gold surface with diazonium salt bearing an alkyne group, which then reacts with azido-modified horseradish peroxidase (HRP) through Huisgen cycloaddition catalysed by copper(I).

e) It is possible to obtain site-specific immobilisation exploiting the chemistry of thiols, namely of cysteine. The thiol group of enzymatic cysteine residues can react with vinyl groups or maleimide groups introduced on the solid support. As proteins generally have very few surface-exposed cysteine residues, this method can be highly site-selective especially if combined with site-directed mutagenesis to engineer the removal of all but one surface cysteine, or to insert a single cysteine on the surface of the protein. f) Another method is given by the introduction of genetically encoded affinity tags in the protein structure. The most well-known tag is the polyhistidine tag (His-tag),

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usually consisting of six sequential histidine residues that can chelate metals such as Cu(II), Co(II), Zn(II) or Ni(II). However, the strength of the binding interaction is relatively weak (kd ≈ 1–10 μM), and the selectivity of this method is rather low since several proteins have been identified that are also able to bind metal ions, thus competing with the desired histidine tag. In addition, the His-tag can be introduced only at the N- or C-terminus of the polypeptide chain.

2.8 Functionalisation of transducer surfaces The functionalisation of transducer surfaces is fundamental in order to introduce reactive groups that can form covalent bonds with the biomacromolecules of interest. Therefore, reagents that can act as bridges between the transducer surface and the biomolecule must be used. For metal surfaces, in particular gold or silver, SAMs of thiols are usually employed to this aim. The functionalisation of silica or glass surfaces instead is based on silanisation reactions. Finally, aryl diazonium salts can be used to functionalise a variety of different materials, especially gold and carbonbased surfaces. In this section, we give some details on these methodologies.

2.8.1 Self-assembled monolayers This method is used to modify the surface of gold supports in order to make them suitable to accommodate biomolecules, both by simple adsorption (or ionic binding) or by formation of covalent bonds. Gold is a material often employed to build biosensor transducers, especially for electrochemical biosensors, thanks to its high conductivity and high biocompatibility. In addition to these interesting properties, gold is very easily modified by reaction with thiols even in mild conditions and without the need of additional reagents or catalysts. The spontaneous adsorption of thiols (–SH) or disulphides (–S–S–) results in the formation of Au–S bonds, as shown in Figure 2.35.

Figure 2.35: Spontaneous adsorption of thiols (A) or disulphides (B) via reaction with gold surfaces to produce selfassembled monolayers (SAMs).

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The result of the passivation of gold surfaces with thiols is a surface film named self-assembled monolayer (SAM). We can directly immobilise the biomolecules on the gold substrate by using the thiols already present in proteins (such as the one of cysteine residues) or –SH groups artificially introduced in thiolated polynucleotides. However, it is more common to form SAMs of smaller molecules presenting two functional groups: – one thiol group to be attached to the gold surface and – another functional group suitable to further react with the biomolecules. Some examples of this strategy are shown in Figure 2.36.

Figure 2.36: Two examples of functionalisation of gold surfaces with self-assembled monolayers (SAMs) bearing amino groups (A) or carboxylic acid groups (B), which can be reacted with functional groups of proteins as discussed in Section 2.7.5.

This method is commonly used to modify gold surfaces with amino groups or carboxylic acid groups that can be used for the covalent immobilisation of proteins as described in Section 2.7.5. In other cases, one or more thiol groups can be introduced in the biomolecule that we want to immobilise (e.g. at one end of a DNA strand), so that this can easily react in aqueous solution with the gold substrate. SAMs have many advantages including easy preparation, formation of denselypacked structures and the possibility of introducing a great number of functional groups at the monolayer surface. However, there are limitations concerning the thermal and mechanical stability, and the gold–thiol bonds can suffer from oxidative or reductive desorption. Very often, for the covalent immobilisation of biomolecules on gold substrates modified with SAMs or other types of supports, the bioreceptor is maintained quite far from the transducer surface thanks to “spacers”. A spacer is a linear molecule, generally constituted of four or six carbon atoms and two functional groups at the two extremities, which allow covalent binding to the solid support on one side and

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to the biomolecule on the other side. The spacer is introduced to lengthen the linkage making it more flexible and accessible to the biomolecules. In addition to the spacer, often also a “passivating group” is added (Figure 2.37). This is normally a small molecule co-grafted at the transducer surface with the linker forming a two-component monolayer. The passivating group has the purpose of diluting the reactive groups (the functional groups that will react with the biomolecules) on the transducer surface, providing also a film compatible with the surface of the biomolecule that we want to immobilise, thus to reduce denaturation. In fact, normally biomolecules are large, three-dimensional objects with non-uniform surfaces. Thus, dilution of the reactive group at the transducer surface and the choice of a suitable length of linkage allow the reactive group to react with the functional groups at the biomolecule surface without steric restrictions.

Reactive group Spacer Passivating group

Figure 2.37: Schematic representation of a transducer surface modified with a two-component SAM: a passivating group (green) and a spacer (blue), coupled with a reactive group (red) suitable to immobilise the biomolecule of choice.

2.8.2 Silanisation Silanisation reactions are exploited to introduce specific reactive groups on the surface of silica-based transducers, such as those made of ceramics, quartz or glass. Figure 2.38 summarises the most common strategies used for the silanisation of transducer surfaces. The silanol groups (Si–OH), present on the surface of hydrated silica materials, can react with activated alkoxysilanes. For instance, to introduce amino groups at the silica surface, hydrated silica can be reacted with 3-aminopropyl-triethoxy-silane. Eventually, the amino groups can subsequently be reacted with other molecules to introduce other functionalities at the transducer surface: for example, aldehydic groups by reaction with GA, or carboxylic acid groups by reaction with succinic anhydride. Epoxy groups can also be introduced at the silica

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99

(A) Silanization - general principle: O Si O Si

OR'

Si OH +

R'O Si

Si O Si

OR'

Si OH

(CH2)n R

O

(CH2)n R

+ n R'OH

(CH2)n R

O Alkoxy-silane

Hydrated silica

(B) Reaction with 3-aminopropyl-triethoxy-silane (APTS): NH2

NH2

O Si OH

+

O

O Si

Si O

O

+ CH3CH2OH

Si O

(C) Reaction with 3-glycidoxypropyl-trimethoxy-silane (GLYMO): O

O Si OH + O

Si

O

Si O

O

O

Si O

O

O

+ CH3OH

(D) Amino-functionalised silica can react with glutaraldehyde (GA):

NH2 + O

O

N

O

+ H2O

H (E) Amino-functionalised silica can react with succinic anhydride: O NH2 +

O

O N H

OH O

O

Figure 2.38: Silanisation of transducer surfaces: (A) General principle. (B) Introduction of amino groups by reaction with 3-aminopropyl-triethoxy-silane (APTS). (C) Introduction of epoxy groups by reaction with 3-glycidoxypropyl-trimethoxy-silane (GLYMO). The amino groups can subsequently react with glutaraldehyde (GA) to introduce aldehydic groups (D) or with succinic anhydride to introduce carboxylic acid functionalities (E).

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surface by using 3-glycidoxypropyl-trimethoxy-silane. All these functionalities can be exploited for the covalent immobilisation of proteins as described in Section 2.7.5.

2.8.3 Functionalisation of surfaces with diazonium salts Another methodology for the functionalisation of carbon or gold surfaces is represented by the grafting of aryl diazonium salts. The covalent attachment of aryl diazonium salts is normally performed in aqueous or acetonitrile solutions by one-electron reduction. This is a simple process that can be driven by electrochemical (applying a reductive potential, usually of –0.7/–0.8 V vs. SCE), chemical or photochemical reduction. Spontaneous degradation of the diazonium salt at high temperatures (normally 60 °C) is also used to modify some surfaces, such as CNTs. At the end, the transducer surface will be functionalised with the aryl group of the diazonium salt cation, bearing the R substituent (see Figure 2.39).

Figure 2.39: Synthesis of an aryl diazonium salt starting from an aromatic amine, and subsequent grafting onto a transducer surface via electrochemical reduction.

The functionalisation with diazonium salts can be performed on metals, metal oxides and carbon materials (including glassy carbon, CNTs, graphene and graphene oxide). The electrochemical grafting is obviously limited to functionalisation of conductive or semi-conductive materials. Aryl diazonium salts form robust covalent bonds with the transducer surfaces, producing very stable organic layers that can resist to high temperatures up to 400 °C. Diazonium salts are prepared by reacting aromatic amines with sodium nitrite (NaNO2) in acidic aqueous solutions (see Figure 2.39), or with tert-butyl nitrite or nitrosonium tetrafluoroborate (NOBF4) in acetonitrile. In general, diazonium salts are not very stable compounds and some of them, in particular chlorides, are explosive. The functional group of the R substituent in para position to the diazo-compound can be chosen in order to immobilise the biomolecule of interest. Normally, it can bear an amino or carboxylic acid group, so that the strategies for the formation of covalent bonds described in Section 2.7.5 can be employed. Also other functionalities can be introduced at the transducer surface through the diazonium salt grafting, such as azides that can readily react with alkyne-bearing biomolecules like in the example shown in Figure 2.40.

Further readings

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Figure 2.40: Functionalisation of a carbon nanotube (CNT) with an aryl diazonium salt bearing an azide, which can react with enzymes modified with alkyne groups via “click chemistry”.

Further readings Books and book chapters Antonacci A, Arduini F, Moscone D, Palleschi G and Scognamiglio V. Commercially available (bio) sensors in the agrifood sector. In: Scognamiglio V, Rea G, Arduini F, Palleschi G (Eds), Comprehensive analytical chemistry. Vol. 74, Elsevier Ltd. Amsterdam, The Netherlands, 2016, chapter 10. Bhatia S. Introduction to pharmaceutical biotechnology, 2. IOP Publishing Ltd., Bristol, UK, 2018. Banica F-G. Chemical sensors and biosensors: Fundamentals and applications. John Wiley & Sons Inc, NY, USA, 2012. Bartlett PN (Ed.). Bioelectrochemistry: Fundamentals, experimental techniques and applications. John Wiley & Sons, Ltd, NY, USA, 2008. Brena BM and Batista-Viera F. Immobilization of enzymes: A literature survey. In: Guisan JM. (Ed.), Methods in biotechnology: Immobilization of enzymes and cells. Humana Press Inc., Totowa, NJ, USA, 2013, 15–31. Cunningham AJ. Introduction to bioanalytical sensors. John Wiley & Sons Inc, NY, USA, 1998. Guisan JM (Ed.). Immobilization of enzymes and cells, 2nd edition. Humana press, Totowa, NJ, USA, 2006.

Review and research papers Bélanger D and Pinson J. Electrografting: A powerful method for surface modification, Chem Soc Rev 2011, 40, 3995–4048. Cosnier S. Biomolecule immunization on electrode surfaces by entrapment or attachment to electrochemically polymerized films. A review, Biosens Bioelectron 1999, 14, 443–456.

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Hetemi D, Noel V and Pinson J. Grafting of diazonium salts on surfaces: application to biosensors, Biosensors 2020, 10, 4. Homaei AA, Sariri R, Vianello F and Stevanato R. Enzyme immobilization: An update, J Chem Biol 2013, 6, 185–205. Yates NDJ, Fascione MA and Parkin A. Methodologies for “wiring” redox proteins/enzymes to electrode surfaces, Chem Eur J 2018, 24, 12164–12182. Samantha D and Sarkar A. Immobilization of bio-macromolecules on self-assembled monolayers: Methods and sensor applications, Chem Soc Rev 2011, 40, 2567–2592. Sheldon RA and Van Pelt S. Enzyme immobilisation in biocatalysis: Why, what and how, Chem Soc Rev 2013, 42, 6223–6235. Thevenot DR, Toth K, Durst RA and Wilson GS. Electrochemical biosensors: Recommended definitions and classification, IUPAC, Pure Appl Chem 1999, 71, 2333–2348. Turner APF. Biosensors: Sense and sensibility, Biosens Bioelectron 2013, 42, 3184–3196.

3 Enzymatic biosensors 3.1 Properties of immobilised enzymes Most often, enzymes immobilised on the transducer surface present different behaviour and properties compared to enzymes in their natural environment. Indeed, immobilisation on artificial substrates can alter the properties of the biocatalyst as a result of the perturbation of the environment around the enzyme active site. Therefore, the intrinsic kinetic characteristics of enzymes (like the Michaelis–Menten constant, KM ) may vary. Another consequence of the immobilisation is that the rate of the enzymatic reaction may be affected by the mass transport of the substrate (diffusion, migration and convection), in addition to the reaction kinetics. Therefore, it is important to facilitate the mass transport of the substrate towards the enzyme layer at the transducer surface, for instance, using ultrathin layers or supports with highly exposed surface area. In some cases, the immobilisation process may require a high thermic stability of the enzyme, in order to avoid a possible denaturation. Nevertheless, once immobilised, the enzyme may be more stable towards harsh environmental conditions such as relatively high temperatures, pH variations or organic solvents. In addition, immobilised enzymes are often less exposed to oxygen that, in some cases, may represent an inhibitor or inactivation agent for many biocatalysts. In fact, if the enzyme is immobilised on the surface of a highly polar support (such as metals, metal oxides and silica), the effective ionic strength near the surface would be higher than in the bulk solution. Given that oxygen has a very low solubility in polar fluids (being a non-polar molecule), its concentration near the polar surface of the transducer will be extremely low. Here below, we further describe the effects of the microenvironment at the transducer surface on the pH of the solution in contact with the transducer and on the properties of the immobilised enzyme. The pH, in fact, is an important parameter for the stability of enzymes that are, generally, charged macromolecules constituted of chains of amino acids, as explained in Chapter 1. Variations in the pH value may change the number of positively and negatively charged amino acids, therefore, changing the interactions between them and, as a consequence, the tertiary structure of the enzyme. After immobilisation, the system enzyme-support constitutes a new phase with physico-chemical properties different from the bulk solution and the natural environment of the enzyme. For instance, if the enzyme is immobilised on a negatively charged support, the concentration of H+ ions close to the surface (and the enzyme) would be higher than in the bulk solution. Consequently, the pH at the transducer

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surface will be lower. We would have the opposite situation in the case of a positively charged support, which would attract OH− ions, therefore, increasing the local pH (see Figure 3.1).

Figure 3.1: Effect of transducer surfaces negatively charged (A) or positively charged (B) on the pH of the solution layer close to the surface and the immobilised enzyme (in green).

An example of negatively charged material is Nafion, a synthetic polymer with sulfonic groups that are strongly acidic (Figure 3.2A). The pH close to the surface of Nafion is always much lower than in the bulk solution. However, this inconvenience can be avoided by adding Na+ or K+ ions in solution to replace H+ at the Nafion surface (Figure 3.2B/C).

Figure 3.2: (A) Chemical structure of Nafion; (B) effect of the Nafion surface on the local pH; and (C) addition of Na+ to avoid the pH gradient in the solution.

The nature and charge of the transducer surface where the enzyme is immobilised affect also the affinity of the biocatalyst for its substrate that, for many enzymes, can be quantified by the Michaelis–Menten constant (KM ). We remind that low KM values indicate high efficiency for the enzyme–substrate interaction. In immobilised enzymes, this parameter can differ from its normal value, becoming a so-called apparent

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Michaelis–Menten constant (KMAPP ). In general, if the substrate and the transducer surface are both charged, we can identify two cases: a) If the substrate and the transducer have the same charge, then KMAPP > KM : in fact, the substrate would be repulsed by the transducer surface making the interaction with the enzyme more difficult. b) If the substrate and the transducer have opposite charge, then KMAPP < KM : in this case, the substrate would be attracted by the transducer surface, facilitating the interaction with the enzyme. The affinity between the enzyme and its substrate would be “apparently” higher, decreasing the KM .

3.1.1 Effects of the thickness of the enzymatic layer Some considerations on the thickness of the enzymatic layer at the transducer surface are also necessary. The enzyme molecules, in fact, can form monolayers or multilayers of different thickness on the transducer surface. In a monolayer, the efficiency of all the enzyme molecules (as a function of the enzyme concentration) is higher compared to a multilayer. However, because a monolayer presents a lower number of catalyst molecules as compared to a multilayer, the amount of analyte reacted will be lower. On the other hand, a multilayer allows a greater number of enzyme molecules to be immobilised at the transducer surface, resulting in a higher conversion of analyte. Another effect is due to the mass transport of the substrate in solution: a thin enzymatic layer will facilitate mass transport of the substrate towards all the enzyme molecules, with fast transport rates. This is more problematic in the case of thicker multilayers, which would present slower mass transport rates of the substrate.

3.2 Electrochemical biosensors Electrochemical biosensors are devices that detect changes in the electrical properties (redox potential, current or conductivity) related to a biorecognition event. The transducer is constituted of an electrode, that is a conductive element (typically made of metals or semi-conductive materials such as carbon), dipped in an electrolyte solution. Generally, the bioreceptor is immobilised on the electrode. Electrochemical biosensors can be classified into three main classes depending on the parameter that is measured: – potentiometric biosensors, when the transducer measures changes in the electrical (or electrochemical) potential; – conductimetric biosensors that detect changes in the conductivity; – amperometric (or voltammetric) biosensors that detect changes in the faradaic current.

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In this chapter, we focus on potentiometric and amperometric biosensors, which constitute the most well-developed and commercially successful type of electrochemical biosensors. For each type of biosensors, we will first summarise the basic electrochemistry concepts necessary to understand the functioning principle of the biosensor. Readers interested in a deeper insight on fundamentals and applications of electroanalytical techniques are addressed to classic textbooks as well as handbooks presenting the most recent advances. The analytes detected by electrochemical methods are redox species, for example, molecules or ions that can release/acquire electrons to/from electron acceptor/ donor species. Such electron acceptors/donors can be molecules or metal surfaces characterised by oxidising/reducing properties. Generally, analytical electrochemical methods are classified in equilibrium methods (potentiometry) and dynamic ones (polarography, amperometry, voltammetry, coulometry and others). The analytes that undergo a real oxidation or reduction transformation during the analytical process are defined as “electroactive” species: this term is typically used in the literature concerning dynamic techniques.

3.2.1 Potentiometric biosensors Potentiometry consists of measuring the difference in the electrical potential, under conditions of zero current flow, between two electrodes, named “indicator” and “reference electrode”, both dipped in the same electrolyte solution containing the analyte. The analyte must be electrochemically active, meaning that, from a thermodynamic point of view, it can undergo a redox reaction like the following: Ox + ne − ! Red

(3:1)

where Ox and Red are the oxidised and reduced forms of the analyte, and n is the number of electrons exchanged in the process for each analyte molecule (n = 1, 2, etc.). In the simplest case illustrated in Figure 3.3, the indicator electrode is made of a metal wire (e.g. Ag, Pb or Au) and is connected to the positive input of the measurement instrument, usually a digital voltmeter. The negative input of the digital voltmeter is connected to the reference electrode. The digital voltmeter allows one to measure the difference in potential between the two electrodes at zero current, that is in open circuit conditions. The reference electrode is typically an electrode of the second kind (e.g. Ag/AgCl with saturated KCl, or Hg/Hg2Cl2 with saturated KCl) with fixed and known redox potential. In the potentiometric cell described earlier, the indicator electrode is forced to act as the cathode and the reference electrode as the anode. Therefore, the measured

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107

+ V – Ag

Salt bridge

AgCl M0

Mn+

X–

First kind indicator electrode

K+

Cl–

Reference electrode

Figure 3.3: Scheme of the simplest potentiometric cell: the indicator is an electrode of the first kind (a metal immersed in a solution of its salt); the reference is an electrode of the second kind (here Ag/AgCl in a solution of KCl). The two electrodes are in ionic contact between each other through a salt bridge and in electric contact through an external circuit connected to a digital voltmeter (V).

potential, also named “electromotive force” (EMF), is given by the difference between the potential of the cathode (Ec ) and the one of the anode (Ea ): EMF = Ec − Ea

(3:2)

We remind that the cathode is the electrode where electrochemical reduction occurs, while oxidation takes place at the anode. By applying the Nernst equation, the potential of the indicator electrode (in this case the cathode) is given by
 RT ½Ox 0′ (3:3) Ec = E + ln nF ½Red where R is the gas constant (8.314 J K–1 mol–1), T is the temperature in K, n is the number of electrons involved in the elementary process, F is the Faraday constant (96 485 A s mol–1), and ½Ox and ½Red are the concentrations of oxidised and reduced species. We assume here that the conditions of the diluted solutions are valid, for which the activities are equal to the concentrations. E0 ′ is the formal potential, evaluated in the experimental conditions used for the analysis by extrapolation at ½Ox=½Red = 1. E0 ′ differs from the “true” standard potential E0 in that the latter is measured in specific conditions of pH, ionic strength, complexing agents and others. If concentrations are used instead of activities, the ionic strength of the standard solutions and the sample must be adjusted to a high and constant value, in order to assure a constant activity coefficient. Measuring all solutions with a constant ionic strength reduces the margin of error between measurements.

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At fixed experimental conditions, E0 ′ is constant as well as the potential of the reference electrode, which is Ea . Therefore, by substituting Ec in eq. (3.2), the EMF is given by
 RT ½Ox (3:4) EMF = const + ln nF ½Red with const = E0 ′ − Ea . Operating at room temperature (25 °C = 298 K) and taking into account the conversion factor to pass from natural to base-ten logarithm (log(x) = 2.3 ln(x)), eq. (3.4) becomes
 0.059 ½Ox (3:5) log EMF = const + n ½Red In the simplest potentiometric cell, the indicator electrode is an electrode of the first kind, which is a metal in contact with a solution containing the cation derived by its oxidation (e.g. a Cd wire dipped in a Cd2+ solution). Given that the activity of a pure solid (in this case the metal) is unitary, then eq. (3.5) can be simplified as EMF = const +

0.059 log½Ox n

(3:6)

According to eq. (3.6), the EMF measured at an electrode of the first kind, using an experimental set-up like the one shown in Figure 3.3, is directly proportional to log½Ox with a slope of 0.059=n V. For instance, an indicator electrode constituted of a Cd wire can be used to measure the concentration of Cd2+ ions in a sample, after calibration with Cd2+ standard solutions, by applying the following equation: EMF = const +

 0.059 log Cd2 + 2

(3:7)

An electrode of the second kind is composed of a metal in contact with an unsoluble salt formed by the cation of the metal, dipped in a solution containing the precipitating anion of the unsoluble salt. A typical example is the Ag/AgCl electrode dipped in KCl solution. It can be easily demonstrated that the potential of a second kind electrode depends on −log of the activity (or concentration) of the precipitating anion. For the case of the Ag/AgCl/KCl electrode, the potential is given by the following equation: E = E0 ′ − 0.059 log½Cl − 

(3:8)

Equation (3.8) shows that: i) a second kind electrode can be used to measure the concentration of the precipitating anion, in this case chloride; ii) when [Cl–] is constant, the potential of a second kind electrode is set constantly at a known value.

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Point (ii) explains the reason why second kind electrodes are widely used as reference electrodes with known and stable potential. However, in some cases, the use of electrodes of the first and second kind as indicator electrodes presents some practical drawbacks due to low reproducibility, relatively high detection limits and, in complex samples like biological ones, sensitivity to surface poisoning, interferences and mixed potential effects. 3.2.1.1 Ion-selective electrodes The most common potentiometric sensors used to analyse biological samples are the ion-selective electrodes (ISEs), utilised for the determination of ions such as Ca2+, K+, F−, Cl− and NH4+. The most well-known and commonly used ISE is the glass pH electrode employed to measure the concentration of the ion H+, which gives indeed the pH. We remind that pH = –log[a(H+)], where a(H+) is the activity of H+. As indicated by their name, ISEs possess a high degree of selectivity towards some ions. This selectivity is due to particular semi-permeable membranes that, ideally, allow the passage of only one specific ion. However, since electroneutrality has to be maintained, the passage through the membrane of, for instance, only cations is not possible. As a consequence, when the membrane is interposed between two solutions containing different concentrations (or activities) of a given cation, a difference in electrical potential is generated between the two faces of the membrane, which opposes the passage of the cation from the more concentrated to the more diluted solution. This is the so-called membrane potential (Em ), which is given by the Donnan equation:
 0.059 ½ion1 (3:9) Em = const + log ½ion2 z where ½ion1 and ½ion2 are the concentrations (or activities) of a given ion on the two sides of the membrane, and z is the net ionic charge of the ion (analyte). In the event that ½ion2 is known and constant (because solution 2 is a standard solution with a known concentration of the analyte), then eq. (3.9) becomes Em = const ±

0.059 log½ion1 z

(3:10)

Note that the operative equations used in potentiometry, like eq. (3.10), contain the term z (charge of the ion) instead of n (number of exchanged electrons) used in the classical Nernst equation. This is because the membrane potential is not due to redox reactions (no electrons are exchanged with the electrodes) but to the capacity of a certain ion to pass through the ion-selective membrane. This is due to the interaction with the fixed ionic groups present in the membrane: ions of the same charge will be repelled, while ions of opposite charge (counter ions) will be attracted. Moreover, because of the steric constrains typical of the membrane structure, only the counter ions presenting a certain charge/radius ratio will be allowed to pass through the membrane.

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Equation (3.10) predicts a linear dependence of the sensor response (the potential difference, Em ) with the logarithm of the concentration (or, more rigorously, the activity) of the ion in solution. For example, in the case of the pH electrode, there is an increase in the potential of 59 mV for every decade increase in the concentration of H+, that is, for each pH unit decrease, given that pH = − log½H +  (see Figure 3.4).

12 11 10

8

9

7

pH 6 5

4

2

3

1

0

0.0 –0.1

E vs. SHE (V)

–0.2 –0.3 –0.4

Slope = 59 mV

–0.5 –0.6 –0.7 –0.8 10–12

10–10

10–8

10–6 [H+]

10–4

10–2

1

(M)

Figure 3.4: Dependence of the potential (measured versus the standard hydrogen electrode, SHE) as a function of the proton concentration or pH.

V

Cathode

Anode

[ion]1

[ion]2

Solution 1

Solution 2 Ion-selective membrane

Figure 3.5: Scheme illustrating the functioning principle of ion-selective electrodes (ISEs).

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111

One practical problem arises: how can we measure the difference in potential between two faces of a semi-permeable membrane? As schematised in Figure 3.5, this can be done using two reference electrodes, placed in the two sides of the ion-selective membrane. A reference electrode is dipped in solution 1 and connected as the cathode in the measurement circuit, while the other reference, connected as the anode, is dipped in solution 2 (e.g. the standard solution with known analyte concentration). In this way, a galvanic chain is built in which the overall difference in potential is the sum of all sources of potential difference in the circuit, which is EMF = Em + Ec − Ea

(3:11)

where Ec and Ea are the potentials of the reference electrodes in solutions 1 and 2, respectively, and Em is the membrane potential. If the two reference electrodes dipped in solutions 1 and 2 are of the same type, then their potentials are equivalent (Ec = Ea ), and eq. (3.11) becomes 0.059 log½ion1 z

EMF = Em = const ±

(3:12)

The general scheme illustrating the example of a potassium-selective ISE (taken as a typical example of ISE) is shown in Figure 3.6. V External Ag/AgCl reference electrode

Digital voltmeter ISE

Ag/AgCl wire Internal reference solution Membrane selective to K+

Porous septum Sample solution with unknown [K+]

Figure 3.6: Measurement assembly for a potassium-selective ISE. The ISE with the potassium-selective membrane (electrode on the right) presents an internal reference solution containing known and constant concentrations of K+ and Cl−. The external Ag/AgCl reference electrode (on the left) has the same Cl− concentration as the ISE. The voltmeter measures the potential difference (the EMF).

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The tip of the ISE is covered by the ion-selective membrane that separates the inner solution, containing a known and fixed concentration of the ionic analyte, from the sample solution, containing the unknown concentration of analyte. Two reference electrodes are dipped in the inner and in the outer compartments (e.g. two Ag/AgCl electrodes in contact with solutions containing the same Cl− concentration) and connected to the digital voltmeter as anode and cathode, respectively. Sometimes, in order to facilitate the use of such a set-up, the outer reference electrode is incorporated in the sensor body. In any case, this reference electrode is in ionic contact only with the outer solution via a small porous septum, having no physical connection with the inner electrolyte. This sensor architecture is often used for commercial glass electrodes for pH measurements, which are known as “combined electrodes” (see Figure 3.7).

Figure 3.7: Combined glass electrode for pH measurement. (A) Electrode body, usually made of non-conductive glass or plastic. (B) External reference electrode, usually Ag/AgCl or saturated calomel electrode (SCE). (C) Reference solution, usually 0.1 M or saturated KCl. (D) Porous septum, junction between the external reference electrode and the sample solution, usually made of ceramics or quartz fibre. (E) Internal reference electrode, usually of the same type as the external one (B). (F) Internal solution, usually a pH 7 buffer containing 0.1 M or saturated KCl. (G) Ion-selective membrane: bulb made of a specific glass selective to H+ ions.

ISEs use different types of membranes, specific for different types of ions: a) Glass membranes: they are constituted of ion exchange types of glass (normally silicates), which have a good selectivity for several single-charged cations, such as H+, Na+ and Ag+. The most famous glass electrode is the one used to measure the pH (selective for the ion H+). This is typically made of a thin membrane of Corning 015 glass whose composition (22% Na2O, 6% CaO, 72% SiO2 in wt%) makes it suitable to specifically exchange H+ cations. b) Crystalline/solid-state membranes: they are made of mono- or polycrystalline insoluble inorganic salts. They can be used to determine both the cation and the anion of the salt forming the membrane, and they present a good selectivity. An example is the fluoride-selective electrode based on LaF3 crystals.

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c) Organic polymer membranes: they are based on special organic polymers containing specific ion-exchange sites, or impregnated with ion exchangers or chelating resins. The most widely used are polyvinylchloride membranes or silicone rubber impregnated with ion-selective substances. This is the most widespread type of ISE for ions others than H+, since several types of membranes can be prepared to be specific for a variety of different ions. In biology, the potassium-selective electrode is widely used to determine K+ concentration by employing, as a selective element, a plastic membrane impregnated with the potassium-selective exchanger valinomycin (an antibiotic that specifically complexes K+, see Figure 3.8).

H N

O

HN

O O

O

O

O HN O

K+

O

O

NH

O

O O N H

O

O

O O

O NH

O

Figure 3.8: Structure of valinomycin, an antibiotic used as complexing agent specific for the cation K+ in potassium ISE. The cavity within the molecular crown presents a specific size in which only the cation K+ fits.

3.2.1.2 From ISE to potentiometric biosensors An ion-selective electrode fits perfectly with the definition of chemical sensor, since it is a self-contained analytical device, capable of providing specific chemical information using a chemical recognition layer (the ion-selective membrane) integrated with a potentiometric transducer. An ISE can become a potentiometric biosensor when it is coupled to a biological receptor. The bio-components usually incorporated in ISE-based biosensors are enzymes, typically combined with pH-sensitive glass electrodes. This is because many enzymatic reactions are associated with changes in pH, which can be monitored by the glass electrode. The immobilised enzyme reacts specifically with the analyte of interest producing or consuming H+ or OH− ions (or also other ions) that can be detected by the ISE. Nowadays, potentiometric biosensors are applied for the determination of many organic and inorganic species such as sugars, urea, antibiotics, neurotransmitters, pesticides, ammonia and carbon dioxide. The typical approach used for immobilising enzymes on the surface of ISEs is entrapment within dialysis membranes (see Section 2.7.1 in Chapter 2). Eventually, cross-linking with glutaraldehyde can be employed to avoid possible leaking of the bioreceptor through the dialysis membrane. Figure 3.9 schematises the functioning principle of a simple potentiometric biosensor with an H+ selective membrane.

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V External reference electrode

Digital voltmeter ISE –

Em

– H+

–

– H+

–

–

–

H+

H+

H+

–

–

H+

H+ H+

H+

–

Internal reference solution Ion-selective membrane Semi-permeable membrane

H+

Analyte

Sample solution Figure 3.9: A simple potentiometric biosensor: a semi-permeable membrane entraps the biocatalyst (green) next to the ion-selective membrane of the ISE. The biocatalyst reacts with the analyte in solution producing H+ ions that permeate the ion-selective membrane generating a membrane potential (Em ).

Table 3.1: Reactions involving the release or consumption of ions that may be used in potentiometric biosensors for different analytes (highlighted in pink). Some reactions can be detected by more than one ion-selective electrode given that more than one ion is involved.

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Table 3.1 (continued )

Examples of reactions and enzymes employed in potentiometric biosensors are reported in Table 3.1. Note that also the enzyme-catalysed release or consumption of ions other than H+ or OH− can be exploited to detect analytes of biological interest. Obviously, in these cases, ISEs specific for I−, CN− or other ions are employed.

3.2.2 Principles of dynamic electrochemical techniques In dynamic (or transient) electrochemical techniques, an electrical current i or a charge Q (with i = dQ=dt) is measured as a result of an external excitation, typically a difference in potential (or voltage) applied between the electrodes in an electrochemical cell containing an electroactive analyte. Since the obtained current signal is the consequence of redox state transformations that obey Faraday’s law, this current is named faradaic current.

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3.2.2.1 Reactions controlled by mass transport Mass transport can take place according to three different mechanisms (illustrated in Figure 3.10):

Figure 3.10: Schematic representation of the three different mechanisms of mass transport: diffusion, migration and convection. WE is the working electrode, CE is the counter electrode, and symbols in the equations are explained in the main text.

a) Diffusion: it is the spontaneous movement of a substance under the effect of a concentration gradient. The substance moves from the regions with higher concentration towards those with lower concentration, thus tending to minimise the concentration gradient. b) Migration: it is the movement of charged species (ions) due to a potential gradient. In all electrochemical cells, a potential gradient exists since an electrical potential difference is applied between two electrodes to drive a current. c) Convection: it is the transport caused by massive physical movements of the fluid where the analyte is dispersed or dissolved. This situation occurs when, for instance, the electrolyte solution is stirred or the electrode is rotated or vibrated (forced convection), or because of density gradients (natural convection). The flux J measures the mass transfer rate. It is defined as the number of molecules that cross a unit of surface in the unit of time and is expressed in mol/cm2 s. When

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operating under semi-infinite linear diffusion, that is, unrestricted diffusion to a large planar electrode, only one spatial coordinate (x = linear distance from the electrode surface) can be used to describe the motion of the molecules. The overall flux of an analyte from the bulk solution to the electrode surface is mathematically described by a differential equation known as the Nernst–Planck equation that, expressed in a single spatial dimension x, is given by

 ∂Cðx, tÞ zFDC ∂Φðx, tÞ + ½Cðx, tÞ ½V ðx, tÞ (3:13) − J ðx, tÞ = − D ∂x RT ∂x where D is the diffusion coefficient (cm2/s); ∂Cðx, tÞ=∂x is the concentration gradient at distance x and at time t; ∂Φðx, tÞ=∂x is the potential gradient; z and C are, respectively, the charge and the concentration of the electroactive species, and V ðx, tÞ is the linear hydrodynamic speed of the fluid in the x direction. F, R and T are the same constants that we have already defined for eq. (3.3). For analytes dissolved in aqueous solutions, D generally takes values between 10−5 and 10−6 cm2/s. By applying Faraday’s law, the resulting current intensity i is directly proportional to the flux according to the following equation: i = − nFAJ ðx, tÞ

(3:14)

where A is the area of the electrode (in cm2). As indicated by eq. (3.13), the situation is quite complex when the three modes of transport operate simultaneously. In this case, it is difficult to correlate the current to the analyte concentration. Note that diffusion is a much less efficient mass transport mechanism than convection or migration. However, from an analytical point of view, it can be convenient to eliminate migration and convection. The first one is inhibited by using an excess of supporting electrolyte (an inert electrolyte, such as NaCl or KCl). Convection is eliminated by performing measurements in un-stirred solutions and avoiding thermal gradients. Under these conditions, the only mass transport mechanism is diffusion, which is directly related to the analyte concentration (the most important analytical parameter) through the concentration gradient. According to Fick’s first law, the flux due to diffusion only is directly proportional to the slope of the concentration gradient: J ðx, tÞ = − D

∂Cðx, tÞ ∂x

(3:15)

Therefore, under conditions of diffusion control, the general expression for the current response becomes i = nFAD

∂Cðx, tÞ ∂x

(3:16)

Equation (3.16) points out that the current is directly proportional to the concentration gradient. As indicated in the earlier equations, the diffusion flux depends on

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the time. This dependence is described by Fick’s second law, which for semi-infinite linear diffusion results in ∂Cðx, tÞ ∂2 Cðx, tÞ =D ∂x ∂x2

(3:17)

This equation reflects the rate of change in concentration over the time through parallel planes located at a distance x and x + dx from the electrode. Fick’s second law is valid for planes parallel to each other and perpendicular to the direction of the flux: these are the conditions of semi-infinite linear diffusion observed when the dimension of the electrode is significantly larger than the thickness of the concentration gradient. This type of diffusion motion occurs at millimetre-sized electrodes. The solution of these partial differential equations (eqs. (3.15) and (3.17)) is relatively complex and details can be found in specialised textbooks. For the semiquantitative treatment adopted here, the understanding of the physical meaning of these equations is sufficient. 3.2.2.2 Fundamentals of cyclic voltammetry The term “voltammetry” is a contraction of “volt-amperometry” which means the measurement of an electrical current (amperometry) as a function of an applied voltage. Normally, experiments are performed under diffusion control conditions, so that the faradaic current is a function of the concentration gradient of the electroactive analyte (see eq. (3.16)). With planar electrodes of dimensions larger than the thickness of the diffusion layer, diffusion follows the earlier described semiinfinite planar diffusion model. The magnitude measured in voltammetry is the intensity of the electrical current flowing in the circuit composed of a working electrode (WE, the “sensing” electrode), the electrolyte solution and a counter electrode (CE), which is a large-area electrode with imposed polarity opposite to the one of the working electrode. The excitation waveform is an electrical potential applied to the working electrode with respect to a third electrode, which acts as an unpolarised reference electrode (RE, commonly, an electrode of the second kind such as Ag/AgCl in a Cl− solution). The voltammetric cell is therefore defined as a three-electrode cell, and the instrumental setup used for these measurements is composed by a potentiostatic circuit controlled by a function generator. In cyclic voltammetry (CV), the potential applied (Eappl ) to the working electrode, with respect to the RE, changes linearly with time according to the following equation: Eappl = E1 ± vt

(3:18)

where E1 is the starting potential and v (in V/s or mV/s) is the scan rate (constant within the frame of each CV measurement) at which the potential is swept. The sign

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“+” applies to the forward portion of the scan (for instance in anodic oxidative direction), and the sign “–” to the backward scan (cathodic reduction, in our example). As shown in Figure 3.11, the CV excitation waveform is indeed a triangular perturbation composed of two linear variations of the applied potential versus time, with slopes of opposite sign: the forward scan segment starts from E1 and reaches a vertex value E2 ; the backward scan starts from E2 and goes back to E1 . Note that the scan rate v corresponds to the absolute value of the slope of the two segments.

Potential (V)

E2

Slope = scan rate (V/s)

E1 Time (s) Figure 3.11: Shape of potential versus time in cyclic voltammetry (one cycle only). Scan rates are typically in the range of 0.02–1.00 V/s. The potential width (E2 − E1 ) can be 0.5–1.5 V, in some cases even larger, but not exceeding the limits imposed by the oxidation or reduction of the supporting electrolyte or of the solvent.

The starting potential E1 is chosen in order to be a non-perturbing potential, which means that it is lower in absolute value than the formal potential E0′ of the analyte. If the forward scan drives an oxidation reaction, then E1  E0′ , while it will be the opposite when the forward scan drives a reduction process. The typical peak-shaped or “duck-shaped” voltammetry pattern shown in Figure 3.12, recorded at a stationary electrode, is due to a combination of kinetic and mass transport factors. We discuss here the case of a typical oxidation process, starting with the substrate fully reduced in solution: Red ! Ox + ne −

(3:19)

When we scan the potential towards positive values, starting from a potential well below E0′ , we will detect a forward current vs. potential pattern characterised by: a) an initial current increase, due to the oxidation of the substrate molecules already present at the electrode/solution interface. The more positive the potential is, the higher will be the number of Red molecules that are oxidised to Ox

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b Red

Current (A)

ipa

Ox c

a Backward scan

Forward scan

0

ipc

Ox E1

Red

E 0'

Potential (V)

E2

Figure 3.12: Typical pattern of a peak-shaped or “duck-shaped” cyclic voltammogram (CV). In the forward scan, three regions are highlighted: a) kinetic control zone; b) mixed control zone; and c) diffusion control zone. E1 is the starting potential, E2 the vertex potential, E 0 ′ the formal potential, ipa and ipc are the anodic and cathodic peak currents, respectively.

per unit of time. Note that the concentration of substrate in the oxidised or reduced state at the electrode surface (½Oxx = 0 and ½Redx = 0 , respectively, where x represents the distance from the electrode surface) depends on the applied potential (Eappl ) according to the Nernst equation:
 0.059 ½Oxx = 0 (3:20) Eappl = E0 ′ + log ½Redx = 0 n where 0.059 (expressed in V) is a constant that groups R, T (for T = 298 K) and F, as well as the conversion factor from natural to base-ten logarithm (as already explained passing from eqs. (3.4) to (3.5)). Briefly, the Nernst equation means that, by increasing the applied potential Eappl, the ratio ½Oxx = 0 =½Redx = 0 must increase, resulting in more and more Red molecules being oxidised. This implies more electrons being transferred to the electrode (at a faster rate), so that the current also increases. In the very first part of the voltammogram, the current intensity is dominated by the electron transfer kinetics. However, when Eappl becomes closer to E0 ′, the progressive oxidation of Red at the electrode surface generates a concentration gradient that, consequently, draws the diffusion of new Red molecules from the bulk solution towards the electrode surface. This effect becomes more and more important while scanning the potential towards more positive values, up to prevail in part b of the voltammetric pattern.

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b) Increasing the applied potential in the vicinity of E0 ′ results in the progressive increase of the concentration gradient of Red between the bulk solution and the electrode surface. When Eappl is equal to a certain value, named the peak potential (Ep), all the Red molecules that reach the electrode surface are istantaneously oxidised, so that [Red]x=0 = 0. At this point, the concentration gradient reaches its maximum which results in a maximum current, named peak current (ip). c) After reaching a maximum value, the current starts to decresase because of mass transport limitations. According to the Nernst equation, by increasing the potential, the ratio ½Oxx = 0 =½Redx = 0 should increase infinitely, and so also the current. This does not happen because, after the peak current, the new Red molecules that diffuse towards the electrode (attracted by the concentration gradient) must travel across an increasingly longer distance. This distance is the thickness of the diffusion layer, which is the layer of solution in contact with the electrode surface where the concentration gradient is formed. Diffusion is slow, so that the replacement of oxidised Red with newly diffusing Red molecules takes place at a much slower rate than that of the oxidation process. For this reason, the thickness of the concentration gradient increases with time. This means that less and less substrate molecules reach the electrode surface, resulting in the progressive decrease of the oxidation current while the potential (and the measurement time) increases. Since in this part of the voltammogram the current is fully limited by the diffusion of the substrate towards the electrode, we talk of “diffusion limited” zone. When the direction of the potential scan is inverted, we record a backward scan that resembles the forward one, but with a current of opposite sign (negative). In this case, the substrate molecules in the form Ox, continuously produced at the electrode until Eappl > E0 ′ and still present in the solution layer close to the electrode surface, are reduced while the potential is scanned in the negative direction. The negative current (cathodic) first increases, reaches a peak and then decreases for the same reasons already explained for the forward scan case. The study of a cyclic voltammogram provides plenty of analytical information, both qualitative and quantitative. First, it allows the identification of the analyte by its E0 ′ value, strictly related to its standard potential. E0 ′ can be determined by the socalled half-wave potential (E1=2 ). For reversible redox couples, it is demonstrated that E1=2 =

Epa + Epc ffi E0 ′ 2

(3:21)

where Epa and Epc are the potentials of the anodic (or forward) and cathodic (or backward) peaks, respectively. In voltammetry, an electrochemical process is defined reversible when the electron transfer process between the analyte and the electrode is faster than mass

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transport by diffusion. The separation between the peak potentials (ΔEp ) for a reversible couple is given by ΔEp = Epa − Epc =

59 mV n

(3:22)

where n is the number of electrons exchanged in the elementary process. This equation allows the determination of n: the peak-to-peak separation is 59–60 mV (at room temperature) for a one-electron process, becoming ≈ 30 mV for a two-electron process. As presented earlier, the faradaic peak current recorded during a CV depends on the rate of diffusion of the analyte from the bulk solution to the electrode surface, this being inversely proportional to the thickness of the diffusion layer. This points out that voltammetric responses are time dependent (or scan rate dependent). The thickness of the diffusion layer (δ) depends on the time of the experiment according to pffiffiffiffiffiffiffiffi δ = πDt (3:23) In voltammetry, t depends on the scan rate (v) according to t=

RT Fv

(3:24)

Scan rates normally used in voltammetry are in the range of 0.02–1.00 V/s, and diffusion coefficients for solution species are of the order of 10−5–10−6 cm2/s, so that it is easily estimated that the thicknesses of diffusion layers are in the micrometre range. From eqs. (3.23) and (3.24), it is possible to calculate that the peak current (ip ) depends on the scan rate (v), on the value of the analyte diffusion coefficient (D), on the number of exchanged electron (n) and on the bulk concentration of the analyte (C). Indeed, the current of a voltammetric peak for a reversible process obeys the so-called Randles–Sevcik equation: ip = 2.69 × 105 A n3=2 D1=2 v1=2 C

(3:25)

where A is the electrode area in cm2, D is expressed in cm2/s, v in V/s, and C is given in mol/cm3. From an analytical point of view, this equation means that, when the scan rate is held constant during the measurement, the peak current is directly proportional to the analyte concentration through a proportionality factor which depends on A, n and D (that are usually constant during the measurement). Equation (3.25) can be applied for drawing a calibration plot in order to find the concentration of electroactive analytes. It is worth reminding that, sometimes, because of kinetics complications, the electrochemical process may not be perfectly reversible. However, in many (although not all) of these cases, the peak current is still directly proportional to C, even if with a proportionality factor that can differ from the one provided by eq. (3.25). Finally, note that the voltammetric signal (the peak current) is directly proportional to the analyte concentration, while the EMF measured in potentiometry is

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proportional to the logarithm of the concentration. This suggests that voltammetry is intrinsically a more sensitive technique than potentiometry. 3.2.2.3 Steady-state voltammetric techniques As discussed earlier, the peak shape of cyclic voltammetric patterns is related to the progressive depletion of the analyte at the electrode/solution interface, during the time of the measurement. However, there are cases in which such a depletion is somehow contrasted and the rate of diffusion of the analyte towards the electrode surface reaches a steady state, independent of time. Consequently, also the current would be constant over the time, without decreasing after achieving the maximum value. This is observed with mechanisms suitable to continuously provide new analyte to the diffusion layer. For some analytical applications, steady-state current responses can be advantageous. Here below, the principles of some electroanalytical techniques that can provide steady-state current responses will be presented. a) Polarography Polarography is based on the periodic renewal of the diffusion layer. This technique is historically important having been invented approximately one century ago by Jaroslav Heyrovs, Nobel laureate in chemistry in 1959. Polarography employs a dropping mercury electrode. The analyte, typically a metal ion, is electrochemically reduced at the surface of the mercury drop electrode with a rate depending on the diffusion of the analyte towards the electrode and on the growth rate of the mercury drop. When the mass of the drop exceeds its surface tension, the drop falls down mixing the solution and renewing the initial conditions. As a consequence, when Eappl  E0 ′, a stationary pattern of the current versus time is obtained, allowing the identification of a mean stationary limiting current directly proportional to the analyte concentration. Except for its great historical relevancy, polarography presents several drawbacks that limit its utilisation: – It can be applied only to reduction processes, because Hg is easily oxidised. – A waved (or jagged) current pattern is obtained, characterised by low signal/ noise ratios. – It employs large amounts of Hg, which needs to be purified by complex distillation procedures, making polarography a not very “green” analytical technique. b) Hydrodynamic techniques These techniques operate in stirred or flowing solutions, producing a mixed convective/diffusive mass transport regime. Note that, even when convection is operative, a thin layer of stagnant solution is always present near the electrode surface. Transport of the analyte through this stagnant layer can occur only by diffusion, so that a mixed convective/diffusive regime is established, in which the diffusion through the stagnant layer is the rate determining step (see Figure 3.13).

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Electrode Insulating material Metal disc

𝛿

Laminar flow Convective flow

Figure 3.13: Representation of a mixed convective/diffusive mass transport regime caused by a rotating disc electrode (RDE). A cross section of the electrode, which rotates around its own axis, is represented. Near the electrode surface, a thin layer of stagnant solution is present in which mass transport occurs only via diffusion (laminar flow). In the rest of the solution, convection is the predominant mass transport mechanism.

Figure 3.14: (A) Concentration profile obtained at a rotating disc electrode (RDE): the mass transport of analyte is diffusion controlled near the electrode surface (until δ), where the flow is laminar. In the rest of the solution, controlled convection due to the electrode rotation is the predominant mass transport mechanism, ensuring a constant concentration of analyte. (B) Typical voltammogram recorded with a RDE for an oxidation case. The potential is swept in one direction, from lower (left) to higher (right) potential values.

Since the thickness of the stagnant layer is determined by the hydrodynamic conditions ruling the convective flux, the concentration gradient is constant over the time.     Therefore, when Eappl   E0 ′, a stationary current proportional to C is recorded, like

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the one shown in Figure 3.14B. From a practical point of view, controlled hydrodynamic conditions are achieved using either i) A rotating disc electrode (RDE), which rotates around its vertical axis at a highly controlled rate (typically at 1 000–2 000 rpm), avoiding vibrations and any other source of mechanical perturbation of the hydrodynamic conditions. ii) A flow cell, in which the electrolyte is pumped under controlled flow conditions to the surface of a stationary working electrode. Flow cells are used as electrochemical detectors in high-performance liquid chromatography or in flow injection analyses. In a mixed convective/diffusive mass transport regime, the diffusion rate can be controlled by carefully choosing the hydrodynamic conditions. This can be both an advantage and a drawback, since even small uncontrolled changes in the hydrodynamic conditions may be a source of error. c) Membrane amperometric electrodes A typical example of membrane amperometric electrode is the Clark electrode for the analysis of dissolved oxygen. This sensor is made of a metal disc electrode (typically Au, Ag or Pt) very close or in contact with a polymeric membrane (e.g. Teflon), which is few micrometres thick and permeable to O2 (Figure 3.15A). (B)

1.5 V – +

1 7

6 5

Without O2

0

2

3 4

Cathodic current (A)

(A)

1) O2 + 2H+ + 2e– → H2O2

2) H2O2 + 2H+ + 2e– → 2H2O With O2 –2.0

–1.5 –1.0 –0.5 Potential vs. Ag/AgCl (V)

–0.0

Figure 3.15: (A) Schematic representation of the Clark oxygen electrode: (1) insulating cylinder of the Pt electrode; (2) KCl buffer solution; (3) platinum disc electrode (cathode); (4) membrane permeable to O2; (5) approximately 10 µm thick solution layer between the cathode and the membrane (not to scale in the figure); (6) silver ring electrode (anode); (7) plastic body. (B) Typical current versus potential response of the Clark oxygen electrode in the presence (red line) and absence (blue line) of O2.

When a negative potential is applied (typically of −0.4 or −0.5 V vs. Ag/AgCl), the molecular oxygen present (or reaching the metal electrode) is reduced to hydrogen peroxide. Local depletion of oxygen by its electrochemical reduction generates two diffusion layers: one located at the solution/membrane interface, and the other at

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the membrane/electrode interface. Since diffusion in solution is significantly faster than diffusion through the membrane, the latter step will determine the overall diffusion rate of oxygen towards the electrode. Under these conditions, the thickness of the diffusion layer is equal to the thickness of the membrane so that a steady-state current signal is typically detected (Figure 3.15B). The limiting current is directly proportional to the concentration of oxygen in the sample. Going at even more negative potentials (around –1.2 or –1.3 V vs. Ag/AgCl), the hydrogen peroxide produced during the first step is reduced to water, generating a second steady-state current signal. One limit of membrane electrodes is that they are characterised by a slow response time due to the slow diffusion rate through the membrane. d) Ultramicroelectrodes Ultramicroelectrodes are very small electrodes having at least one dimension of the same magnitude order as the diffusion layer (typically around 20 µm or smaller). In such a case, the diffusion flux becomes radial. The electrode is so small that is seen as a point by the molecules diffusing towards it, which assume a convergent diffusion pathway (see Figure 3.16). Radial diffusion is a transport process much more efficient than linear     diffusion, so that when Eappl   E0 ′, the concentration gradient reaches a maximum thickness that is maintained constant over time. The voltammogram, therefore, assumes a sigmoidal shape like the one already shown in the case of RDE (Figure 3.14B).

Figure 3.16: Radial diffusion at an ultramicroelectrode.

It is important to stress that, for an ultramicroelectrode of fixed radius r, pure radial diffusion condition holds at slow scan rates, that is for measurement time scales satisfying the following condition: pffiffiffiffiffiffiffiffi r  πDt (3:26) Ultramicroelectrodes provide enhanced mass transport conditions. Moreover, because of their very small surface area and small current signals (in the order of

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picoampere, pA), the voltammetric patterns are less distorted by ohmic and capacitive effects. For the same reason, ultramicroelectrodes can be employed in simple two-electrode cells (with working and reference/counter electrode only). One limit is that, because of the very small current signals provided, they require a Faraday cage to shield the electrochemical cell from outer electric fields. e) Electrocatalytic processes A steady-state current can be observed even with a regular millimetre-sized electrode, dipped in a quiet solution, when a chemical process is coupled to the electron transfer process so that the initial redox species is continuously regenerated. Such a process can be schematised as follows: Red ! Ox + ne −

(3:27)

Ox + Mred ! Red + Mox

(3:28)

where Mred and Mox are the reduced and oxidised form of a redox molecule that acts as a co-catalyst, chemically regenerating the species Red. Mred can be a molecule dissolved in the electrolyte solution or deposited on the electrode surface, able to reduce Ox to Red. This occurs when E0 ′ðMred =Mox Þ < E0 ′ðRed=OxÞ. Electrocatalytic processes are often indicated as EC’ processes, where E stays for the heterogeneous electron transfer step (reaction (3.27) in our example), and C’ is the catalytic regeneration step (reaction (3.28)). Note that the molecular mechanism of electrocatalytic processes is different from the mechanism of conventional electrocatalysis, in which the intrinsic catalytic properties of special electrode materials (noble metals, oxides and alloys) are exploited to accelerate the rate of the heterogeneous electron transfer at the atomic or structural level.

Figure 3.17: Cyclic voltammograms recorded for Red in the absence and presence of Mred . In the absence of Mred , we have a regular reversible CV (blue line), while in the presence of Mred , the CV assumes the typical pattern of an electrocatalytic process (pink line).

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Figure 3.17 shows two cyclic voltammograms recorded for Red in the absence and in the presence of Mred . In the absence of Mred , a regular reversible CV pattern is detected (blue line). In the presence of Mred , the return peak due to the electrochemical reduction of Ox disappears and the oxidation current assumes a sigmoidal shape (pink line). This is because Ox is quickly converted into Red by the fast chemical reaction (3.28). Note that the regeneration process is so efficient that the electrode senses a higher local concentration of Red than in the absence of Mred , generating a much higher oxidation current. It can be said that reaction (3.28) operates a sort of chemical amplification of the anodic current, whose extent depends on the concentration of Mred (if Red is constant). A similar consideration, with proper adjustments, applies to the case of an electrocatalytic process where reaction (3.27) is a reduction and reaction (3.28) is an oxidation. Electrocatalytic processes are important in the study and bioanalytical application of enzyme-catalysed electrochemical processes.

3.2.3 Electron transfer between enzymes and electrodes Some considerations should be made on the electron transfer between electrodes and enzymes, which are normally large macromolecules with molecular weights in the order of kDa (typically between 10 and 100 kDa, or more). The enzymes that catalyse redox reactions (usually called “redox enzymes”) often contain metal centres or metal clusters (generally constituted of transition metals such as Fe, Cu, Ni, Co, Mo and Mn), or other cofactors (such as FAD, haem and quinones.). Such metals/cofactors can undergo redox reactions and are the enzymatic components responsible for the electron transfer between substrates and redox partners (in the natural environment), or between substrates and electrodes. The metal centres or cofactors generally constitute the active site of redox enzymes, where the conversion of the substrates into products occurs. However, some enzymes may present more than one metal cluster or cofactor (of the same or different kind), giving rise to different situations: a) Some enzymes can be constituted of more than one identical subunit (generally two), all containing the same type of metal cluster/cofactor acting as active site (Figure 3.18A). In this case, each enzyme molecule presents two (or more) active sites and can simultaneously react with two (or more) substrate molecules. This is the case, for example, of glucose oxidase. b) Some other enzymes present only one metal cluster/cofactor acting as the active site, while the other metal clusters/cofactors (generally different from the active site) can be electron relay sites (Figure 3.18B). These sites can also undergo redox reactions and, in general, help the transfer of electrons from the active site to the enzyme redox partner (or the electrode surface), and vice versa. The chemistry (such as steric factors and hydrophobicity) and the electronic properties of the metal centres and cofactors are controlled by the amino acid structure surrounding

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Figure 3.18: Two possible configurations of redox enzymes containing more than one metal centre or cofactor: (A) enzyme constituted of two identical subunits, each one containing an identical active site; (B) enzyme containing three metal centres/cofactors, of which one is the active site and the other two are electron relay sites. The latter configuration presents an electron transfer chain going from the active site to the enzyme redox partner, or vice versa. S and P are the substrate and product of the enzymatic reaction, respectively.

the redox centres. In general, the active sites are well away from the surface of the enzymes, in order to be well protected against environmental changes. Therefore, the electron transfer to/from enzymatic redox centres is more complicated than that occurring with small redox molecules, ions or metal atoms. The orientation of the macromolecule on the electrode surface will determine the distance that the electrons must cover, hence influencing the rate constant of electron transfer. The standard electron transfer rate constant (kET ) is estimated using the following equation: kET = kET exp − βx

(3:29)

where x is the distance between the redox centre and the electrode, β is a constant determined by the height of the energy barrier and kET is a proportionality constant. Figure 3.19 reports some examples of electron transfer rate constants estimated for different orientations of a simple enzyme (containing only one active site) immobilised

Figure 3.19: Influence of the enzyme orientation at the electrode surface on the electron transfer distance (x) and, consequently, on the standard electron transfer rate constant (kET ).

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onto an electrode. We can see that small increases of few nanometres in the electron transfer distance enormously decrease the electron transfer rate constant. 3.2.3.1 Direct and mediated electron transfer Not surprisingly, direct electrochemical oxidation/reduction of enzymes can be a very slow process. One approach to overcome this problem is to use redox mediators. Redox mediators are typically small molecules or ions that undergo fast electron transfer with the electrode surface (preferably at a potential close to the equilibrium potential of the enzyme) and can then diffuse to the enzymatic redox centre shuttling electrons between it and the electrode surface. A good redox mediator should present the following characteristics: – it should easily access the enzyme active site; – it should present a very fast and reversible redox behaviour, and react rapidly with the enzyme (faster than the substrate); – it should have a redox potential suitable to regenerate the enzyme active site in its active state. Some examples of efficient redox mediators are water-soluble ferrocene derivatives (like ferrocenecarboxylic acid or ferrocenemethanol), ferricyanide, conducting salts, quinones, organic dyes (like viologen, Prussian blue, methylene blue), osmium and ruthenium complexes. It follows that two different types of electron transfer are possible (see Figure 3.20):

Figure 3.20: Schematic representation of the direct and mediated electron transfer mechanisms, for the case of a catalytic oxidation reaction.

a) Direct electron transfer (DET), which is the ideal mechanism since the biosensor construction would be simpler, minimising the influence of possible interfering components. However, an efficient DET is not always easy to achieve since it is influenced by the characteristics of the enzyme, the nature of the electrode and the immobilisation procedure. For some enzymes, DET is

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impossible since the redox centre is buried inside the protein structure. The reaction mechanism of a DET process can be summarised as follows: Sred + Enzox ! Sox + Enzred

(3:30)

Enzred ! Enzox + ne −

(3:31)

Reaction (3.30) is the reaction between the enzyme and the substrate, while reaction (3.31) is the electrodic process by which the enzyme is regenerated to its active state. The combination of the two reactions produces an electrocatalytic cycle. b) Mediated electron transfer (MET): this is, generally, a three-step mechanism in which the substrate reacts with the enzyme active site, which in turn reacts with a redox mediator that exchanges electrons with the electrode surface. The MET mechanism is schematised as follows: Sred + Enzox ! Sox + Enzred

(3:32)

Enzred + Mox ! Enzox + Mred

(3:33)

Mred ! Mox + ne −

(3:34)

where Mox and Mred are the oxidised and reduced form of the mediator. Since the active form of the enzyme is continuously regenerated by the combination of reactions (3.33) and (3.34), the overall process is defined as a mediated electrocatalytic cycle. The mediator is chosen so that the kinetics of reactions (3.33) and (3.34) is faster than that of reaction (3.32). In this case, reaction (3.32) is the rate determing step. 3.2.3.2 Deviations from the ideal catalytic CV The signal generated by the electrocatalytic process of an enzyme adsorbed or immobilised at an electrode surface can, sometimes, deviate from the ideal catalytic voltammogram already shown in Figure 3.17. Depending on which reaction is the rate determining step, we can distinguish different cases that can apply both for DET and MET mechanisms: a) Substrate diffusion faster than catalysis rate When the diffusion of the substrate and the re-oxidation of the enzyme are faster than the rate of the reaction between the enzyme and the substrate, the voltammogram will present a sigmoidal pattern shown in Figure 3.21 (pink curve), character    ised by a plateau current. In fact, when Eappl   E0 ′, all the enzyme molecules are continuously re-oxidised at the electrode via DET or MET (by reaction (3.31) or (3.33) +(3.34)). Since there are no mass transport limitations for the substrate, the current is limited only by the rate of the catalytic reaction between the enzyme and the

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Figure 3.21: Typical shape of a conventional reversible CV (blue), ideal catalytic CV (pink) and diffusion-controlled catalytic CV (violet).

substrate (reaction (3.30) or (3.32)). The result is a constant (plateau) current, called “catalytic current”. Note that, in MET, the rates of the reaction between the mediator and enzyme (3.33) and the electrochemical regeneration of the mediator (3.34) can be eventually accelerated by operating at high concentrations of mediator. When the direction of the potential scan is reversed, the backward scan overlaps the forward scan. This is because the catalytic reaction keeps on occurring exactly like during the forward scan, and the current follows the same trend, being limited only by the catalytic reaction. b) Substrate diffusion slower than catalysis rate This situation corresponds to a deviation from the ideal case described above. A slow mass transport of the substrate gives rise to voltammograms like the one shown in Figure 3.21 (violet curve), in which the catalytic current, after reaching a maximum, decreases while the potential is increased. The backward scan does not overlap the forward scan, but the oxidation current keeps on decreasing. Nevertheless, a reduction current is not observed as the enzyme is always reduced by the chemical reaction with the substrate (reaction (3.30) or (3.32)). This voltammetric pattern is observed when the overall process is limited by the diffusion rate of the substrate towards the electrode surface. The slow mass transport causes a progressive depletion of the substrate concentration in the vicinity of the electrode, where the catalytic reaction takes place. From Fick’s first law (eq. (3.14)), we know that the rate of diffusion (known as substrate flux, J) is

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proportional to the concentration gradient (dC=dx). Therefore, different factors can contribute to such behaviour: – very slow diffusion of the substrate in solution due to small diffusion coefficient D, for example when the substrate is a macromolecule; – very low concentration of the substrate, which slows down the flux; – very fast catalysis rate, which quickly consumes the substrate molecules reaching the electrode surface. This is the case of enzymes with a very high catalytic constant (kcat ) or a very high affinity for the substrate (low KM ). Limitations due to poor substrate diffusion can be overcome by accelerating the mass transport of the substrate, for instance, by using a RDE. c) Deviations due to the scan rate Other deviations from the pure, or ideal, catalytic CV can be due to the potential scan rate chosen, which determines the timescale of the experiment. Different situations can be observed depending on the relative values of the experiment timescale and the half-life of the species Enzox , which is produced by the electrodic reaction (3.31) and consumed by the chemical reaction (3.30). The half-life of the species Enzox is usually determined by the catalysis rate. By changing the scan rate, different situations can be observed: i) When the potential scan rate is sufficiently low so that the timescale of the experiment is long compared to the half-life of Enzox , all the Enzox molecules are converted to Enzred by the chemical reaction to be reoxidized by the electrodic process. The voltammogram will display the typical sigmoidal pattern shown in Figure 3.22A. v = 0.1 V/s

v = 0.01 V/s

(B)

(C)

v = 1 V/s

(D) v = 10 V/s

Current (A)

Current (A)

(A)

Potential (V)

Potential (V)

Figure 3.22: Effect of the potential scan rate on the shape of the catalytic voltammogram. Note that the values of the scan rate v in the figure are indicative, since they depend on the specific process under study.

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ii) At the other extreme, when the timescale of the experiment is short compared to the half-life of Enzox (using a very fast scan rate), the response is not significantly influenced by the chemical reaction (3.30) and the voltammogram will display the pattern typical for a reversible CV, see Figure 3.22D. Under these conditions, the chemical reaction is slower than the electrodic process, therefore the electrogenerated reduced form of the enzyme has no time to react with the substrate and is deteced in the return scan, producing a reduction peak. iii) At intermediate scan rates, when the timescale of the experiment and the half-life of Enzox are comparable, the CV will display patterns similar to those shown in Figure 3.22B and C. The detection of a hysteresis between the forward and backward scans (Figure 3.22B) or, more significantly, the appearance of a small anodic and cathodic peak superimposed on the sigmoidal trend (Figure 3.22C) is indicative of the operativity of mixed kinetic conditions. Therefore, in order to record a voltammogram under pure, or ideal, catalitic control (case A in Figure 3.32), it is important to carefully choose the most appropriate potential scan rate. Typical scan rates employed in electrocatalysis with enzymes are in the range of 10–50 mV/s. 3.2.3.3 Catalytic current Earlier we have described an electrocatalytic process, that is, a catalytic process in which the catalyst (the enzyme in this case) is regenerated through an electrodic reaction via direct or mediated electron transfer. In the simplest case, when the biocatalyst is in close proximity to the electrode surface and there are no mass transport limitations for the substrate, the faradaic current is exclusively due to the flow of electrons produced or consumed by the enzymatic reaction occurring at the electrode surface. The rate of such electron flow gives the current intensity (i), which is directly proportional to the rate of the biocatalytic reaction (v) for the equation: i = nFAv

(3:35)

where n is the number of electrons produced or consumed per each substrate molecule, F the Faraday constant and A the electrode area. If we substitute v with its expression given by the Michaelis–Menten equation already described in Chapter 1 (eq. (1.29)), the current intensity will be given by i=

nFAk2 ½ET ½S KM + ½S

(3:36)

where k2 is the rate constant of the enzyme–substrate complex breakdown (often defined also as catalytic constant, kcat ), ½ET is the total enzyme concentration, ½S the

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135

substrate concentration and KM the Michaelis–Menten constant. When [S] >> KM, eq. (3.36) becomes: imax = nFAk2 ½ET

(3:37)

which defines the maximum current reachable with a given electrode (or biosensor) at high substrate concentrations. Note that imax is independent of the substrate concentration. By substituting eq. (3.37) in eq. (3.36), we obtain: i=

imax ½S KM + ½S

(3:38)

Equation (3.38) is the potential-dependent Michaelis–Menten equation because the current intensity depends on the applied potential. However, it becomes potentialindependent when i = icat. If we perform voltammetric measurements at an electrode modified with an enzyme (such as glucose oxidase) in solutions containing different concentrations of the substrate (glucose in this case), we will obtain a series of voltammograms like the ones shown in Figure 3.23A. Note that the intensity of the catalytic plateau current increases by increasing the substrate concentration. The plot of the catalytic current versus [S] (Figure 3.23B) presents the same trend as the Michaelis–Menten plot already shown in Chapter 1 (Figure 1.44) and can be fitted using eq. (3.38) to find the parameters KM and imax .

Figure 3.23: (A) Catalytic cyclic voltammograms recorded at an enzyme-modified electrode with different concentrations of substrate in solution. (B) Plot of the catalytic current versus the substrate concentration: the data were fitted with the Michaelis–Menten equation (3.38) (pink line).

The catalytic current can be used to quantify the rate of an enzymatic reaction, and therefore the substrate concentration, exactly like other enzymatic methods. An accurate quantification of the substrate concentration is easily performed operating in

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the linear portion of the plot in Figure 3.23B, that is for concentrations much lower than KM , where eq. (3.38) becomes ½S  KM ) i =

imax ½S imax ½S = KM + ½S KM

(3:39)

In this condition, the reaction rate is pseudo first-order with respect to the analyte, and the current intensity is directly proportional to the substrate concentration. Such linearity between the current intensity and the analyte concentration is lost at higher concentrations. 3.2.4 Amperometric biosensors Enzyme-modified electrodes operating under ideal catalytic control provide sigmoidal voltammograms characterised by a limiting current, whose intensity is determined by the rate of the reaction between the enzyme and substrate. This situation is ideal for performing the amperometric detection of the catalytic current by applying a constant potential. This potential value is chosen by looking at the catalytic voltammogram, selecting a potential value within the range where the plateau current is detected (e.g. between 0.0 and 0.1 V in the example in Figure 3.23A). The catalytic current obtained by amperometric measurements obeys eq. (3.38). Indeed, the application of a constant potential is equivalent to recording a CV at infinitely slow scan rate, so that the conditions are ideal and a pure catalytic control is achieved. Amperometry is a very convenient technique to study catalytic reactions, since it provides a very fast response for each perturbation occurring in the electrochemical cell, such as changes in the analyte concentration. For instance, one can apply a constant potential to the enzyme-modified electrode (chosen by looking at the cyclic voltammogram) and make several additions of the substrate in solution. The current observed after each addition is the catalytic current corresponding to the substrate concentration present in solution at that moment. In this way, it is possible to draw a Michaelis–Menten plot in a very short time (Figure 3.24B). An alternative to amperometry is offered by coulometry. Coulometric measurements are based on the same principle as amperometry, that is, applying a constant potential to the sample, but measuring the electrical charge (that is the current integrated over a certain time) instead of the current intensity. The amount of analyte reacted in this preset time is directly obtained by applying Faraday’s laws. If a microcell with known volume (a thin layer cell) is used, coulometry allows one to perform an absolute determination of the analyte concentration without the need of a calibration plot. 3.2.4.1 Electrochemical glucose biosensors: a successful story Among all metabolites, glucose has been the subject of most studies in the field of biosensors. This is because the determination of glucose is one of the most common

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Figure 3.24: (A) Amperometric response of an enzyme-modified electrode to successive additions of substrate in solution. The electrode potential was held constant at 0.1 V in order to detect the oxidation catalytic current. (B) Plot of the catalytic current versus the substrate concentration: the data were fitted with the Michaelis–Menten equation (3.38) (pink line).

routine analyses in clinical chemistry, in particular for the diagnosis and home-care of one of the most widespread disease in modern time, diabetes, which affects approximately 500 million people all over the world. Diabetes causes sharp increase in the blood concentration of glucose, which needs to be frequently monitored by the patient to eventually proceed with the controlled administration of insulin. In addition, glucose is a very common substrate in biotechnology, and it can have interesting applications also in the development of biofuel cells. The first enzyme that has been extensively studied and employed in glucose biosensors is the native glucose oxidase (GOx) from Aspergillus niger, which contains a FAD active site. Its role in nature is to catalyse the oxidation of glucose to gluconolactone using oxygen as an electron acceptor, with the overall reaction shown in Figure 3.25. CH2OH O OH OH +O

2

OH

OH

Glucose

GOx

CH2OH O OH

O + H2O2

OH

OH Gluconolactone

Figure 3.25: Oxidation of glucose catalysed by glucose oxidase (GOx).

For glucose detection, an amperometric biosensor can be constructed based on the oxidation or reduction of any electrochemically active species involved in the reaction illustrated in Figure 3.26.

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Glucose

FAD

H2O2

Gluconolactone

FADH2

O2 Figure 3.26: Oxidation of glucose catalysed by glucose oxidase (GOx) from Aspergillus niger.

GOx

Starting from the first amperometric biosensor invented by Leyland Clark in 1962, three different generations of glucose biosensors have been developed until now, whose functioning principles are summarised in Figure 3.27. Briefly: i) First-generation glucose biosensor: the electrochemical signal is produced by molecular oxygen or hydrogen peroxide that is consumed or produced, respectively, by the re-oxidation of GOx in physiological conditions. ii) Second-generation glucose biosensor: an electroactive species with fast and reversible redox behaviour is used as an artificial mediator (M) to shuttle electrons between the enzyme and the electrode surface. iii) Third-generation glucose biosensor: direct electron transfer between the enzyme active site and the electrode is exploited, often facilitated by specially designed electrode surfaces.

Glucose

Enzox

H2O2

Gluconolactone

Enzred

O2

Glucose

Enzox

Mred

Gluconolactone

Enzred

Mox

Glucose

Enzox

Gluconolactone

Enzred

e–

e–

First generation

e– Second generation

Third generation

Figure 3.27: Classification of amperometric glucose biosensors.

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Here, we provide some details on the different types of amperometric glucose biosensors. First-generation glucose biosensor The first glucose biosensor developed by Clark was an oxygen electrode constituted of three layers: an inner oxygen semi-permeable membrane, on the top of which glucose oxidase was immobilised onto polyacrylamide gel and covered with a dialysis membrane permeable to glucose. Detection of glucose concentration was achieved by monitoring the oxygen consumed by the biocatalytic reaction (Figure 3.28A). The functioning principle of the membrane oxygen electrode was described in Section 3.2.2.3. (A) Glucose

FAD

H2O2

Gluconolactone

FADH2

O2

O2

GOx

e–

H2O

(B) Glucose

FAD

H2O2

H2O2

Gluconolactone

FADH2

O2

O2

GOx

e–

H+

Figure 3.28: Two types of first-generation glucose biosensors with glucose oxidase (GOx) entrapped in a glucose-permeable membrane: (A) the electrode (in reduction mode) detects the oxygen consumed, and (B) the electrode (in oxidation mode) detects the hydrogen peroxide produced by the biocatalytic reaction with glucose.

Another type of first-generation biosensor was constructed with the same enzyme by detecting the amount of hydrogen peroxide (H2O2) produced during the reaction with glucose, since one molecule of H2O2 is produced for each molecule of glucose that reacts with the enzyme (Figure 3.28B). The hydrogen peroxide is measured by applying a constant potential of +0.68 V (vs. Ag/AgCl) at a platinum electrode, so that the following reaction occurs: H2 O2 ! O2 + 2H + + 2e −

(3:40)

However, first-generation biosensors using the natural electron acceptor of glucose oxidase (oxygen) presented some drawbacks. In the case of biosensors detecting the amount of oxygen consumed, the concentration of O2 dissolved in the solution should be equal or higher than the concentration of glucose. This is not always the case, so that the current is often limited by oxygen concentration giving a wrong

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response. On the other hand, the response of the biosensor detecting the hydrogen peroxide may be affected by the presence of other species in the sample (such as ascorbate, urate or cysteine) that can be oxidised at the same potential. Second-generation glucose biosensors To overcome the limits of glucose biosensors based on the detection of O2 or H2O2, researchers opted for the use of artificial electron acceptors having lower oxidation potentials compared to H2O2. Such substances are called redox mediators and, as we have explained earlier, they are molecules with a very fast and reversible redox behaviour that help transferring electrons between the enzyme active site and the electrode. Devices employing redox mediators are called second-generation biosensors. Ferrocene derivatives have long been considered the best redox mediators due to very high stability in both oxidised and reduced form. Moreover, ferrocene does not react with oxygen, is not affected by pH changes and has a very fast electron transfer kinetics. The biocatalyst can be always glucose oxidase, or other glucose-oxidising enzymes. The use of glucose oxidase as a biocatalyst presents some disadvantages, in particular linked to the fact that oxygen is the natural and very efficient electron acceptor of this enzyme. If present, O2 can compete with any artificial mediator producing H2O2 that, in turn, could deteriorate the enzyme or disturb the measurement. Therefore, other enzymes have been studied and selected as biocatalysts for glucose biosensors. An alternative glucose-oxidising enzyme is, for example, glucose dehydrogenase (GDH), which does not react with oxygen and has a high catalytic efficiency. GDHs with a pyrrolo-quinoline quinone cofactor have been found to be quite thermally instable and not very specific towards glucose. However, GDHs with a FAD active site are more stable and selective enzymes, regarded as the most promising alternative to glucose oxidase in glucose biosensors. Third-generation glucose biosensors Third-generation biosensors are based on the direct electron transfer between the glucose-oxidising enzyme and the electrode. Some researchers classify in this group biosensors constituted of conducting organic salts (such as tetrathiafulvalene-tetracyanoquinodimethane) or conducting polymers, that are used not only to entrap the enzyme close to the electrode surface but also to mediate electron transfer. However, since conducting salts and polymers act as redox mediators shuttling electrons between the biocatalyst and the electrode, it is more appropriate to classify them as another type of second-generation biosensors. In this book, we consider as “third-generation biosensors” only the devices based on the pure DET between the biocatalyst and the electrode surface. In fact, some glucose-oxidising enzymes have been claimed to be capable of exchanging electrons directly with the surface of electrodes. Among them, we can find some types of FAD-GDH, as well as pyranose oxidase, pyranose dehydrogenase and cellobiose dehydrogenase. For the moment, these enzymes have been mainly investigated as possible biocatalysts in biofuel

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cells as they are not highly selective towards glucose. This topic is still somehow controversial and, unlike first- and second-generation biosensors, any prototype of commercial third-generation glucose biosensors has not been developed yet. 3.2.4.2 Commercial glucose biosensors Nowadays, more than 30% of all world-wide commercialised biosensors are glucose sensors. The most used configuration for glucose biosensors entails glucose oxidase as the biocatalyst and a functionalised electrode in a miniaturised amperometric or coulometric cell as the transducer. Glucose biosensors are employed also in the agri-food sector to check food quality and composition, but the field that more than any other has boosted the development of glucose biosensors is the medical and healthcare sector. In this field, simple and non-expensive devices for the detection of glucose are particularly required for the homecare of diabetic patients. Diabetes mellitus (often called just “diabetes”) is a chronic metabolic disorder caused by inadequate production or use of insulin, a hormone produced by the pancreas that allows the body to use and store glucose. Untreated diabetes can, over time, lead to hyperglycaemia (elevated blood sugar) that can cause serious damage to nerves, organs and blood vessels. In type 1 diabetes, usually diagnosed in children, teenagers and young adults, the pancreas produces very little or no insulin. Therefore, these patients need insulin injections to regulate their blood sugar level. In order to determine when insulin should be administered, patients are required to monitor their glucose level. Typically, patients intermittently collect a small blood sample (usually by pin pricking a fingertip) and place it on a test strip, which is then inserted into a handheld blood glucose meter (Figure 3.29A). The result assists the patient in determining if they require insulin injection. Alternatively, a more recent innovation called “continuous glucose monitor” (CGM, Figure 3.29B) has been approved for use by diabetes patients to track their glucose levels. This device is worn continuously by patients, usually on their arm or stomach, and needs to be replaced

Figure 3.29: (A) Handheld blood glucose meter (Kwangmoozaa/iStock/Getty Images Plus). (B) Continuous glucose monitor (dzika_mrowka/iStock/Getty Images Plus).

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Transmitter Glucose sensor Epidermis Glucose Interstitial fluid Skin cell

Blood vessel

Figure 3.30: Illustration of a continuous glucose monitor (CGM) in contact with the skin.

every few days. Instead of measuring glucose levels in the blood, it monitors the glucose level in the interstitial fluid of the skin. As shown in Figure 3.30, a small probe is placed just below the surface of the skin to make this measurement, which is then transmitted wirelessly to a handheld reader or a smartphone. Thanks to CGM devices, patients can continuously monitor their glucose levels without interruption and be alerted by an alarm whenever they require insulin. Very recently, some companies integrated an insulin pump with a CGM device, which automatically administers insulin from a portable reservoir worn by the patient whenever the CGM reads a high glucose concentration. All the new glucose monitoring devices approved in the past 4 years by the American Food and Drug Administration are CGM, often integrated with insulin pumps. Among the four big companies producing this kind of devices, three developed CGM based on amperometric or coulometric biosensors: Medtronic MiniMed, Abbott Diabetes Care and Dexcom. All these sensors continuously monitor the glucose concentration in the interstitial fluid, night and day, collecting data every 5 min, and need to be replaced every 7 days (14 days for the newest device from Abbott Diabetes Care). They are all second-generation biosensors using glucose oxidase as biocatalyst. In some of them, GOx is “wired” to the electrode surface through redox polymers. For instance, the device commercialised by Abbott Diabetes Care (called FreeStyle Navigator) is constructed by wiring glucose oxidase to a polymeric matrix bearing osmium complexes. The principles for this approach were set by Adam Heller and co-workers in 1994. The polymer physically and electronically wires the enzyme to the electrode surface, ensuring efficient electron transfer between the enzymatic active site and the electrode, as schematised in Figure 3.31. A series of chain redox reactions within and between polymeric chains shuttle the electrons to the electrode surface. The osmium-decorated poly(vinylpyridine)-based polymer (see its structure in Figure 3.32A) forms an hydrogel on the electrode surface upon

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Glu Glu Glu

GOx

Os

Os

Os

GOx Os

Os

Glu

GOx

Glu

Os

Os

GOx

Os

Os GOx

Os Os

Os

Os

Os

Os Os

Working electrode Figure 3.31: Scheme of the sensing layer on the surface of the working electrode in the amperometric glucose biosensor which employs an osmium wired polymer. The biocatalyst (glucose oxidase, GOx) is covalently attached to the redox polymer bearing osmium complexes. Efficient electron transfer (represented by red arrows) occurs from glucose molecules to the FAD active site of GOx, and from there to the Os complexes that shuttle electrons to the electrode surface.

(A) 2

17

1

(B) N

N

N

O O

O

O NH

O O O

4Cl–

n

O 3+

N

O N N H3C

N

H3C N

N N

Os N

CH3 N

N

CH3 N CH 3

Figure 3.32: Chemical structure of (A) the redox polymer used in the glucose biosensor commercialised by Abbott Diabetes Care and (B) the cross-linking reagent polyethylene glycol diglycidyl ether (PEGDGE).

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cross-linking the enzyme via a bi-functional epoxide (PEGDGE, Figure 3.32B), which reacts with the free amino groups of the polymer and of the enzyme. Moreover, the device developed by Abbott Diabetes Care overcomes another issue connected to the amperometric response of the biosensor. In fact, the performance of this type of devices may be complicated at high rates of analyte flux, as the relationship between the glucose concentration in the sample fluid and the biosensor response becomes non-linear. This kinetic problem was solved by interposing a glucoselimiting membrane between the sensing layer and the sample fluid. This consists of a bio-compatible polymer used to coat the outer surface of the working electrode to limit the flux of glucose towards the biorecognition layer. In 2014, the company Novartis teamed up with Google announcing the plan to develop smart contact lenses for diabetic patients, which would continuously measure their glucose levels as well as assist those with eye problems. The lenses, in fact, would contain a biosensor capable of detecting glucose concentration directly in the patient’s tears, being non-invasive, and sending alerts to their smartphone when insulin should be taken. Unfortunately, tears have not proved to be a reliable matrix for measuring glucose levels in human, and the project has been dismissed.

3.3 Optical biosensors Optical detection is performed by exploiting the interaction of UV–visible or infrared (IR) radiations with biorecognition elements such as enzymes, antibodies or nucleic acids. In this section, we will focus in particular on enzymatic optical biosensors, even though some general considerations can be valid also for other types of optical devices. The optical transducer normally detects changes in the absorbance, luminescence, polarisation or refractive index, which should be proportional to the concentration of the analyte. Optical biosensing can be broadly divided into two general modes: – Label free: the detected signal is generated directly by the interaction of the analysed material with the transducer (direct detection). – Label based: it involves the use of a label that produces an optical signal (indirect detection). While optical bioassays can employ many different methods, instrumentations and set-ups, optical biosensors mainly exploit the properties of optical fibres. The main components of an optical biosensor are a) a light source; b) an optical fibre, which both transmits the light and acts as a supporting material; c) the biorecognition element (enzymes, nucleic acids, antibodies or cells), usually immobilised on the surface of the end face of the fibre; d) a detector to measure the output light signal.

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145

3.3.1 Optical fibres Optical fibres are small and flexible wires that can transmit light between their two ends, with minimal loss and over long distances. In the last few decades, due to the rapidly growing use of optical fibres in the telecommunication field, new technologies have been developed so that high-quality and inexpensive optical fibres became available. Optical fibres are wires constituted of two different materials; they present – a core, with refractive index n1 , surrounded by – a cladding, with refractive index n2 , with n2 < n1 . The difference in the refractive indices between the core and the cladding enables their interface to act as a mirror so that a series of internal reflections transmits the light from one end of the fibre to the other end. Light undergoes total internal reflection (without losing energy) at the core/cladding interface when two conditions are fulfilled as shown in Figure 3.33: – the light radiation enters the fibre with an angle within the acceptance cone; – the light radiation hits the cladding with an angle greater than the “critical angle” (φc ), which is defined by the ratio between the cladding and the core refractive indices: sin φc =

n2 n1

(3:41)

Figure 3.33: Structure of an optical fibre. The light propagation through the optical fibre occurs when the total internal reflection condition exists at the interface between the core (n1 ) and the cladding (n2 ), such as n1 > n2 . The entering angle should be within the acceptance cone angle range and greater than the critical angle (φc ).

Optical fibres are usually made of plastic or glass and can present many different configurations and shapes. For most fibre-optic biosensors, fibres with diameters

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ranging from 50 to 500 µm are employed. Glass fibres are the most commonly used as they can transmit light in the visible and near-IR regions of the light spectrum (between 400 and 700 nm) and, therefore, they are suitable for fluorescence signals. For applications in the UV region of the spectrum, quartz (pure silica) is used as core material and doped silica (with a lower refractive index) is used as the cladding material. The use of optical fibres presents many advantages such as – they provide remarkably strong, flexible and durable supporting materials that can be used in harsh and hazardous environments; – fibre-optic biosensors can be easily miniaturised, as the fibres can be made with diameters of a few micrometres (50–100 µm), allowing also in vivo measurements by introducing one extremity of the optical fibre in the living organism (or cell) and keeping the detector outside; – they are non-conductive and do not require any connection to electrical power source, making them highly suitable for applications where the presence of electrical fields is detrimental (e.g. for in vivo measurements inside a patient body); – they can be used for multi-analyte biosensing. The main limitations are due to – ambient light interference: in this case, the detector should be kept inside a completely dark container; – very often the reagents are photoactive and/or photosensitive substances, so that they should be kept in a completely dark container; – optical detection does not suit very coloured or cloudy samples; – the linear range of calibration curves is generally narrow. Note that optical fibres can be used to send as well as to collect light from the sample depending on the application. Optical biosensors can be constituted of individual optical fibres, dual optical fibres or bundles or arrays comprising thousands of identical fibres each with a diameter of a few micrometres (Figure 3.34). Fibre-optic bundles present a higher sensitivity since the final signal is given by the sum of the signals produced by each single fibre. They are also suitable for multi-analyte biosensing since each individual fibre can carry a different bioreceptor and transmit its own light signal from one end of the array to the detector. As defined in Chapter 2, in a biosensor, the biorecognition layer and the transducer are strictly integrated, typically with the biomolecules deposited on the transducer surface. In optical biosensors, the transducer is the optical fibre. The simplest optical biosensor is the one reported in Figure 3.35A: the incident light, emitted by a suitable radiation source, travels through an input optical fibre and is totally reflected by the cladding material at the bottom. The reflected light is transmitted by a second output fibre, connected with an optical photon detector, typically a photomultiplier or

3.3 Optical biosensors

Individual optical fibres

147

Fibre-optic bundle

Figure 3.34: From individual optical fibres to fibre-optic bundle.

a photodiode. At the bottom, the light radiation interacts with the immobilised recognition element, which undergoes physico-chemical transformations changing its optical properties upon interaction with the analyte. Therefore, the output light signal can be correlated to the analyte concentration. Two different designs can be used for fibre-optic biosensors: a) a single fibre is used to transmit the light from the light source to the bioreceptor and back to the detector; b) multiple fibres are used, in which one fibre transmits the light to the bioreceptor and the other one(s) serves to transmit the light back to the detector (Figure 3.35B).

Figure 3.35: (A) Scheme of the simplest optical biosensor. (B) Different designs of biosensors with multiple fibres: (1) two fibres, one carrying light to the sensing layer (in green) and one carrying the signal to the detector; (2) bifurcated fibre, with the sensing layer placed on the fused end of the fibre; (3) multiple fibres with the sensing layer placed on the central fibre and the surrounding fibres used to collect the light signals.

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An optical sensor constituted of optical fibres, molecular recognition layer and specific instrumentation (light source, detector and other electronics) is named “optode” (or “optrode” from the combination of the words “optical” and “electrode”). When a biorecognition element is integrated with an optical fibre transduction system, we can talk of “bio-optode”. 3.3.2 Light source and detector Different light sources and detectors (photon detection devices that absorb photons and convert them into electrical signals) employed in optical biosensors are summarised in Table 3.2. Table 3.2: Light sources and detectors used in optical biosensors. Light sources Type

Wavelength (nm)

Tungsten lamp

IR-NIR, visible

Characteristics

High-power output, bulky, expensive, used together with wavelength selection devices

Deuterium lamp

–

Xenon lamp

– 

LEDs

– 

Low-power output, high stability, long life, robust, compact size, inexpensive

Laser (N, Ar+, He–Ne)

, –, 

Very high-power output, monochromatic, directional, bulky, expensive

Laser diodes

–

High-power output, long life, narrow spectral band, compact size, inexpensive

Type

Advantages

Limitations

Photomultipliers

Sensitive, fast, low noise, internal amplification, compact

Need high-power voltage supply, destruction by overexposure

Photodiodes (PDs)

Fast, robust, compact, inexpensive

High noise, no internal amplifier

Charge-coupled devices (CCD)

Very sensitive, can be used for imaging

Slow, expensive, need a cooling system to provide high sensitivity

Avalanche PDs

Lower noise than conventional PDs, fast, sensitive, can tolerate intense illumination

More expensive than conventional PDs

Light detectors

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3.3.3 Optical phenomena employed in biosensors 3.3.3.1 Absorption Absorption occurs when a species (atom or molecule) absorbs a light radiation passing from the ground energy state to a higher energy excited state. The absorbed energy is dissipated non-radiatively (e.g. thermally) to the medium when the excited species relaxes back to the ground energy state. The change in absorbance (A) is related to the concentration (C) of the species via the Beer–Lambert law: A = log

I0 =ε C l I

(3:42)

where I0 and I are the intensities of transmitted light in the absence and presence of the absorbing species, respectively, l is the length of the light path in the sample and ε is the molar absorption coefficient (characteristic of each species at a specific wavelength, when C is not too high, typically < 10−2 M). Optical fibres are used to measure absorbance by transmitting light through the fibre to the sensing layer and measuring changes in the scattered light. 3.3.3.2 Fluorescence Fluorescence is a phenomenon occurring at certain molecules known as fluorophores or fluorescent dyes, when they are excited at a specific wavelength and re-emit radiation at lower energy (longer wavelength). The fluorescence process is illustrated using the Jablonski diagram (Figure 3.36) and can be divided into three main steps: i) Excitation: a photon of energy hνEX is supplied by a light source and absorbed by the fluorophore, promoting it from the ground state (S0 ) to an excited state S1 ′. ii) Excited-state lifetime: the fluorophore in the excited state S1 ′ undergoes conformational and vibrational changes and is subjected to possible interactions with its molecular environment. These processes have two important consequences: (i) the energy of S1 ′ is partially dissipated yielding a relaxed excited state S1 , from which fluorescence emission originates; (ii) not all the molecules initially excited in the first step return to the ground state by fluorescence emission, but other processes may depopulate S1 (such as heat emission). iii) Fluorescence emission: a photon of energy hνEM (lower than hνEX ) is emitted while the fluorophore returns to its ground state S0 . Fluorescent dyes absorb light at short wavelengths (e.g. in the UV spectrum) and emit it at longer wavelength (e.g. in the visible or IR regions of the electromagnetic spectrum). Each fluorescent molecule has its unique fluorescence spectrum since the excitation and emission occur only at distinct energy levels corresponding to particular wavelengths. This allows multiple fluorescent dyes to be used simultaneously in a single analytical assay. In fluorescence-based biosensors, the excitation light is

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S1′ Energy

S1

hvEX

hvEM

S0

S0

(i)

(ii)

(iii)

Figure 3.36: Jablonski diagram for fluorescence emission: (i) the fluorophore is excited passing from the ground state S0 to the excited state S1ʹ; (ii) relaxation to the excited state S1 (of lower energy than S1ʹ); and (iii) the fluorophore returns to the ground state S0 emitting a photon of energy hν EM < hν EX (fluorescence emission).

transmitted through an optical fibre, and the emission light is measured, using a detector. Usually, the increase or decrease in the fluorescence intensity is measured, and then correlated to the analyte concentration. The decrease in fluorescence intensity is normally due to quenching of the fluorescent dye caused by the biorecognition event. For example, a dye that undergoes fluorescence quenching when the pH decreases can be coupled to an enzymatic reaction that converts a substrate into an acidic product, resulting in a pH drop. Thus, the decrease in fluorescence can be correlated to the appearance of the acidic product and, therefore, to the substrate concentration. The most commonly used fluorescent molecules in biosensors are organic dyes containing polycyclic aromatic units, such as fluorescein and rhodamine (Figure 3.37) and their derivatives. In the 1960s and 1970s, fluorescent proteins have been isolated from marine organisms and applied for biosensing purposes. In particular, the green fluorescent protein (GFP), originally extracted from the jellyfish Aequorea victoria, resulted particularly interesting as it is highly fluorescent and very stable to heat and pH

HO

O

O

H2N

O

NH2 Cl–

COOH

Fluorescein

Rhodamine

Figure 3.37: Chemical structure of fluorescein and rhodamine.

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151

changes. The scientists Roger Y. Tsien, Osamu Shimomura and Martin Chalfie were awarded the 2008 Nobel Prize in Chemistry for their studies on GFP. 3.3.3.3 Chemiluminescence Chemiluminescence occurs when a chemical reaction yields a product in an electronically excited state, which emits light when it returns to its ground state. Many biosensors exploit the chemiluminescence of luminol. In fact, the reaction of luminol with hydrogen peroxide, catalysed by proteins containing metal atoms, produces a luminescence signal that can be detected and correlated to the H2O2 or protein concentration (Figure 3.38). This reaction can be used, for example, in enzyme-based biosensors in which the enzymatic reaction generates H2O2. The concentration of H2O2, quantified by chemiluminescence, can be correlated to the initial concentration of the enzymatic substrate.

NH2 O

NH2 O

O

NH2 O

O– + 2H+ + N2 O–

NH CAT. + H2O2 NH

Luminol

*

O 3-Aminophtalate excited state

O– + light O– O 3-Aminophtalate

Figure 3.38: Reaction of luminol with hydrogen peroxide producing chemiluminescence.

Enzymes like horseradish peroxidase (HRP) and other heme-contaning proteins catalyse luminol luminescence. This is the basis for the detection of blood traces with luminol in forensic analyses. HRP catalyses also the oxidation of some chromogenic substrates such as 2,2ʹ-azino-bis(3-ethylbenzothiazoline-6-sulfonic acid) and 3,3ʹdiaminobenzidine, using H2O2 as an oxidising agent and producing a chemiluminescence signal useful for biosensing purposes. 3.3.3.4 Bioluminescence Bioluminescence is another phenomenon that can be exploited in optical biosensors. It is produced by biological chemiluminescent reactions. Many organisms produce bioluminescence for signalling, self-protection, mating, attracting prey and finding food. A typical example of bioluminescence is the light emission by fireflies. The process is catalysed by the enzyme luciferase, which transforms a molecule named luciferin into oxyluciferin via a catalytic cycle (Figure 3.39) in which adenosine triphosphate (ATP, the energy source of living cells) is consumed. Other bioluminescent organisms exploit different types of molecules, for example, aldehydes and flavins are used by bacteria and imidazolopyrazines by some fishes and squids.

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HO

HO

S

N

S

S

COOH

ATP Luciferin

Regeneration reaction

N

N

PPi N

O

O

S

Oxyluciferin

HO

N

N

S

S

O O P O Ribose OH Adenine

Luciferyl adenylate AMP + CO2

O2

+ light Figure 3.39: Catalytic cycle producing bioluminescence in fireflies. The entire set of reactions is catalysed by the enzyme luciferase that converts the firefly luciferin to oxyluciferin consuming adenosine triphosphate (ATP) and oxygen. PPi is the pyrophosphate anion ([PO3–O–PO3]4−) formed by the hydrolysis of ATP, which becomes adenosine monophosphate (AMP).

3.3.4 Principles of the most common optical sensors Enzymes are used as recognition elements in bio-optodes, thanks to their ability to bind specific substrates and, eventually, catalyse their conversion into optically detectable products. The optical signal obtained (such as absorbance or fluorescence) is proportional to the product concentration and, consequently, to the analyte concentration. Products with intrinsic optical properties can be measured directly. However, the most common enzymatic products (such as H+, ammonia, O2, CO2 and H2O2) do not possess optical properties and are measured indirectly by employing different methods, as presented below. 3.3.4.1 Optical sensors for H+ (or pH) Optical biosensors to detect enzymatic reactions that produce or consume H+ ions are scarcely used, since the pH electrode (pH-meter) is more sensitive and more commonly employed. However, for some particular applications, optical detection is preferred. These optical biosensors use pH indicators, molecules that change their optical properties upon interaction with H+. For example, fluorescein is used as pH indicator as its fluorescence intensity can be correlated to changes in H+ concentration. Depending on the solution pH, fluorescein exists in different forms (see Figure 3.40) that show different fluorescence intensities. In particular, the emission intensity is dramatically reduced at acidic pH, resulting in a gradient of fluorescence

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153

Figure 3.40: Ionisation equilibria of fluorescein at different pH values.

Fluorescence emission

intensity close to the physiological pH (around pH 6.5, see Figure 3.41). Other fluorescein derivatives, such as carboxyfluorescein, present the fluorescence intensity gradient at slightly different pH values, so that we can choose the most appropriate indicator to detect the pH change associated with the enzymatic reaction of interest.

3

4

5

6

7

8

9

10

pH Figure 3.41: pH dependence of fluorescence emission intensity of fluorescein measured at 520 nm.

To construct an optical biosensor, the pH indicator is immobilised at one end of the optical fibre together with the enzyme. Some limitations are due to the narrow pH range in which the optical signal is linear, and the photosensitivity of some indicators. 3.3.4.2 Optical sensors for ammonia and carbonate Also these sensors are based on pH indicators, integrated with ammonia or carbonate permeable membranes. When ammonia or carbonate (or CO2) produced by the enzymatic reaction permeate the membrane, the pH of the solution inside the membrane changes. Such pH variation is detected by the indicator that changes its optical properties, which can be correlated to the analyte concentration. Figure 3.42

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3 Enzymatic biosensors

Light source

Detector

In− + H+ ⇄ HIn Indicator solution

NH3 + H+ ⇄ NH4+

NH3 permeable membrane NH3 Figure 3.42: Functioning principle of an optical sensor to detect ammonia.

shows the functioning principle of an optical sensor suitable to detect ammonia. When NH3 permeates the membrane, it sequestrates H+ ions becoming ammonium (NH4+) and increasing the pH of the solution where the indicator is dissolved. An indicator commonly used in these biosensors is a water-soluble pyrene derivative (hydroxypyrene-trisulfonic acid, Figure 3.43), whose deprotonated phenolate form emits fluorescence, while the protonated form does not.

HO O O S

OH

O S HO O

S O O OH

HO O O S

O

H+

Phenolic form

O S HO O

S O O OH

Phenolate form

Figure 3.43: Ionisation equilibrium of 8-hydroxypyrene-1,3,6-trisulfonic acid (HPTS, also known as pyranine), with a pKa of 7.3.

Like ammonia, also carbonate and CO2 produce pH changes, due to the following equilibrium reactions: H2 CO3 ! HCO3− + H + ! CO23 − + 2H +

(3:43)

H2 CO3 ! CO2 + H2 O

(3:44)

The addition of carbonate (CO32−) in aqueous solutions results in a pH increase since carbonate tends to sequestrate H+ ions to become bicarbonate (HCO3−). On the other hand, the addition of carbon dioxide results in a pH decrease as CO2 (in equilibrium with its hydrated form H2CO3) tends to lose H+ ions to become bicarbonate too.

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155

3.3.4.3 Optical sensors for oxygen Optical sensors to detect oxygen are very common and compete with the Clark electrode (described in Section 3.2.2.3). They are based on the fact that O2 is a strong fluorescence quencher, meaning that the fluorescence intensity of a fluorophore species will decrease with increasing O2 concentration. Common fluorophores used in these sensors are pyrene and its derivatives or ruthenium complexes, whose fluorescence emission is inhibited by oxygen. The fluorescence quenching is an example of intermolecular deactivation, in which the presence of a chemical species called “quencher” (Q) accelerate the decay rate of a species in its excited state (the fluorophore, F*) according to the reaction: F* + Q ! F + Q

(3:45)

The kinetics of this process follows the Stern–Volmer equation: I0 = 1 + kQ τ0 ½Q I

(3:46)

where I0 and I are the fluorescence intensities in the absence and presence of the quencher, respectively, kQ is the quencher rate coefficient and τ0 is the lifetime of the excited state of F (F*) in the absence of the quencher Q. Using this equation, calibration curves of I0 =I versus the concentration of quencher (e.g. oxygen) can be drawn and used to calculate the quencher concentration in unknown samples (see Figure 3.44).

Figure 3.44: Calibration curve of I0 =I versus oxygen concentration. The concentration of oxygen in the sample can be calculated using the Stern–Volmer equation (3.46).

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3 Enzymatic biosensors

3.3.5 Enzymatic optodes 3.3.5.1 Bio-optodes for glucose Optical detection methods can overcome some of the challenges of electrochemical biosensors. For instance, optical methods often present inherently higher sensitivity and less in vivo interferences than electrochemical ones. However, the system design is critical to avoid detection of tissue autofluorescence. Like amperometric glucose biosensors, most optical biosensors for glucose are based on the enzyme glucose oxidase, which catalyses the oxidation of glucose to gluconolactone using O2 as co-substrate and producing H2O2 (see Figure 3.25). And like the firstgeneration amperometric glucose biosensors, bio-optodes for glucose can employ two approaches: measuring the amount of oxygen consumed or H2O2 produced by the enzymatic reaction. In the first case, the bio-optode is based on the optical sensor for oxygen (described in Section 3.3.4.3). Figure 3.45 shows the typical architecture of such a biosensor: glucose oxidase is trapped between a dialysis membrane permeable to glucose and oxygen, and a membrane permeable only to O2. In this layer, glucose is oxidised by GOx consuming O2, so that a lower concentration of oxygen will pass through the gas-permeable membrane. This results in a lower fluorescence quenching of the fluorophore present in this layer (usually a ruthenium complex). Excitation radiation

Emission radiation

Optical fibre

O2 + F*

O2 + F

Fluorophore solution O2 permeable membrane GOx

Glucose + O2

Gluconolactone + H2O2 Dialysis membrane

Glucose

O2

Sample solution

Figure 3.45: Architecture of an optical glucose biosensor based on the detection of the oxygen consumed by the enzymatic reaction, catalysed by glucose oxidase (GOx), via fluorescence quenching.

In the second case, the H2O2 produced by the GOx-catalysed glucose oxidation is detected using a chemiluminescence indicator, such as luminol (see Figure 3.38).

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157

H 3C O

O

N

CH3

N

Figure 3.46: Structure of Nile red fluorescent dye.

Some optical glucose biosensors are based on equilibrium reactions rather than analyte consumption. The equilibrium binding mechanism does not consume analyte that may deplete the analyte concentration in the fluid immediately surrounding the sensor. This method is used by the company Becton Dickinson (BD) that is developing an implantable glucose biosensor based on a mutated galactose glucose binding protein (GGBP). The mutated GGBP is modified with derivatives of Nile red fluorescent dye (Figure 3.46). The protein conformation changes upon binding glucose, producing a fluorescence/Förster resonance energy transfer (FRET) effect between the fluorophores attached on the two domains of the protein (see Figure 3.47). FRET is a process related to energy transfer between two chromophores. A donor chromophore, initially in its electronic excited state, can transfer energy to an acceptor chromophore via non-radiative coupling between dipoles. The efficiency of the process is inversely proportional to the sixth power of the distance between donor and acceptor. Therefore, FRET is highly sensitive to small changes in the distance between the chromophores. The FRET intensity is proportional to the analyte binding and is used to determine the glucose concentration.

Glucose FRET

Open GGBP

Close GGBP

Figure 3.47: Binding of glucose to the mutated galactose glucose binding protein (GGBP). GGBP is a small protein constituted of two domains connected by a hinge region. Upon glucose binding, a large conformational change occurs, which brings the two domains close to each other entrapping glucose in the middle. The Nile red derivative dyes attached on the two domains of the protein are brought closer to each other, producing a FRET effect.

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3.3.5.2 Bio-optodes detecting NADH Some optical biosensors exploit the fluorescence of a common enzymatic co-substrate, nicotinamide adenine dinucleotide (NADH, see structure in Figure 1.36 in Chapter 1). In Chapter 2, we have seen that its reduced and oxidised forms (NADH and NAD+, respectively) possess different absorption spectra, with NADH having a peak at about 340 nm that is not present in the spectrum of NAD+ (see Figure 2.5). Therefore, when excited at 340 nm, NADH is capable of fluorescence emission at about 460 nm while the oxidised form NAD+ does not. This allows to detect NADH produced by an enzymatic reaction using an optical fibre, correlating the fluorescence emission intensity to the analyte concentration. The working principle of such biosensors is schematised in Figure 3.48. Enzymes that normally use NADH as co-substrate are the dehydrogenases. Excitation radiation

Emission radiation

Optical fibre

Dehydrogenase Substrate + NAD+

Product + NADH

Dialysis membrane Substrate

Sample solution

Figure 3.48: Architecture of an optical biosensor for the detection of different substrates, whose oxidation reaction produces NADH as co-product. The dialysis membrane entraps the enzyme (typically a dehydrogenase) and the co-substrate NAD+, while it is permeable to the substrate.

This type of biosensor is employed, for instance, for the detection of glutamate, an important amino acid that is also the most abundant neurotransmitter in the vertebrate nervous system. This optical biosensor utilises the enzyme glutamate dehydrogenase (GLDH) as biocatalyst, which oxidises glutamate to α-ketoglutarate reducing, at the same time, NAD+ to NADH (Figure 3.49). O

O

O O +

O NH3 Glutamate

NAD+ +

H2 O

GLDH

O + + O + NADH + NH4 + H

O O α-Ketoglutarate

Figure 3.49: Oxidation of glutamate catalysed by the enzyme glutamate dehydrogenase (GLDH) using as co-substrate NAD+.

3.3 Optical biosensors

159

The same principle is employed for the detection of lactate using lactate dehydrogenase (LDH) as biocatalyst. As already described in Section 2.1.1, LDH oxidises lactate to pyruvate reducing NAD+ to NADH. In other cases, a sequence of enzymatic reactions is used to detect a specific analyte. In bio-optodes of this type, two or three enzymes are immobilised together on the optical fibre in such a way that sequential reactions can occur. The first enzyme catalyses the conversion of the analyte to a product that serves as substrate for a second enzyme and so on. An example of such biosensors is the one used for the detection of glutamate employing bioluminescence and three different enzymes: GLDH, NADH-FMN oxidoreductase and luciferase. The whole detection mechanism is given by the following three reactions (Figure 3.50):

Figure 3.50: The three sequential reactions exploited in the bio-optode for the bioluminescent detection of glutamate.

In the first reaction (A) GLDH oxidises the substrate and, in turn, reduces NAD+ to NADH. NADH serves as substrate for the second enzyme, NADH-FMN oxidoreductase, which oxidises NADH and reduces FMN to FMNH2. This last product will be the substrate of the third reaction (C) producing bioluminescence thanks to the enzyme luciferase. The luciferase used in this biosensor is a bacterial luciferase (obtained by microorganisms such as Photobacterium phosphoreum or Vibrio harveyi) that, unlike firefly luciferase, does not require luciferin. The bioluminescence, in fact, is produced by a complex formed from luciferase + FMNH2 + a fatty acid aldehyde (R–CHO). The measurement of emitted luminescence enables the detection of as little as 0.1 fmol (femtomoles = 10−15 mol) of NADH, meaning that traces of glutamate can be detected. Another analyte that can be detected with this method is ethanol using the enzyme alcohol dehydrogenase, whose reaction produces NADH (Figure 3.51).

+ OH + NAD

Ethanol

ADH

H + NADH + H+ O Acetaldehyde

Figure 3.51: Oxidation of ethanol catalysed by the enzyme alcohol dehydrogenase (ADH).

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3 Enzymatic biosensors

3.3.5.3 Bio-optodes based on inhibition reactions Inhibition of enzymatic reactions can also be used as sensing mechanism in biooptodes. In this approach, exploited also in electrochemical biosensors, the inhibitor is the analyte and the measured signal is the decrease in enzymatic activity. An example is the detection of organophosphate and carbamate pesticides using the enzyme acetylcholinesterase (AChE). The enzyme is immobilised on the optical fibre together with a pH indicator: the substrate acetylcholine is hydrolysed by AChE causing a change in the local pH (Figure 3.52) and, consequently, a fluorescence signal from the dye. When the pesticide is present in the sample, the enzyme is inhibited: this results in little or null change of pH and, thereby, little or null fluorescence signal. The decrease in the fluorescence signal can be correlated to the pesticide concentration. The working principle of this type of biosensors is schematised in Figure 3.53.

O O

O

+ H2O AChE

N

Acetylcholine

O

+

N

HO

Acetate

+ H+

Choline

Figure 3.52: Hydrolysis of the neurotransmitter acetylcholine catalysed by the enzyme acetylcholinesterase (AChE). The reaction produces H+ ions decreasing the local pH.

Excitation radiation

Emission radiation

Excitation radiation

Emission radiation

ACh P ACh

P

ACh

AChE

ACh

H+ H

AChE

Without analyte

F

PEST.

+

AChE

AChE

F

With analyte

Figure 3.53: Working principle of a bio-optode based on inhibition reaction. The enzyme acetylcholinesterase (AChE) and the fluorophore (F) are co-immobilised on the optical fibre. In the absence of analyte (pesticide), the enzyme catalyses the hydrolysis of the substrate acetylcholine (ACh) producing H+ ions and decreasing the local pH. At low pH values, F is fluorescent and detected by the device. In the presence of the pesticide, AChE is inhibited, the local pH does not change and F does not emit fluorescence radiation.

Further readings

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Further readings Books and book chapters Bard AJ, Faulkner LR. Electrochemical methods, 2nd ed. Wiley, NY, USA, 2000. Bartlett PN (Ed.). Bioelectrochemistry: Fundamentals, experimental techniques and applications, NY, USA, John Wiley & Sons, Ltd, 2008. Biran I, Walt DR. Optrode-based fiber optic biosensors (bio-optrode). In: Ligler F and Rowe Taitt C (Eds.), Optical biosensors: Present and future, Elsevier Science BV, 2002, 3–82. Christian GD, Dasgupta PK, Schug KA. Analytical chemistry, 7th ed. John Wiley & Sons, NY, USA, 2013. Moretto LM, Kalcher K (Eds). Environmental analysis by electrochemical sensors and biosensors: Fundamentals (2 vol. set), Springer Science + Business Media, NY, USA, 2014. Rubinson KA, Rubinson JF. Contemporary instrumental analysis, Upper Saddle River, NJ, USA, Prentice–Hall Inc., 2000. Setford SJ, Newman JD. Enzyme biosensors. In: Barredo J (Ed.), Methods in biotechnology, Vol. 17: Microbial enzymes and biotransformations, Totowa, NJ, USA, Humana Press Inc., 2005, 28–36. Ugo P. Polymer based voltammetric sensors. In: Grimes CA, Dickey EC and Pishko MV (Eds.), Encyclopedia of sensors, Stevenson Ranch, CA, American Scientific Publishers, 2006, 67–85. Wang J. Analytical electrochemistry, 3rd ed. John Wiley & Sons, Inc., NY, USA, 2006.

Reviews and research papers Benito-Pena E, Valdes MG, Glahn-Martinez B, Moreno-Bondi RC. Fluorescence based fiber optic and planar waveguide biosensors. A review, Anal Chim Acta 2016, 943, 17–40. Bollella P, Gorton L. Enzyme based amperometric biosensors. Curr Opin Electrochem 2018, 10, 157–173. Bollella P, Katz E. Enzyme-based biosensors: Tackling electron transfer issues. Sensors 2020, 20, 3517, 1–32. Chen C, Wang JS. Optical biosensors: An exhaustive and comprehensive review. Analyst 2020, 145, 1605–1628. Clark LC, Lyons, C. Electrode systems for continuous monitoring in cardiovascular surgery. Ann NY Acad Sci 1962, 102, 29–45. Leung A, Shankar PM, Mutharasan R. A review of fiber optic biosensors. Sens Actuat B Chem 2007, 125, 688–703. Gifford R. Continuous glucose monitoring: 40 years, what we’ve learned and what’s next. ChemPhysChem 2013, 14, 2032–2044. Monosik R, Stredansky M, Tkac J, Sturdik E. Application of enzyme biosensors in analysis of food and beverages. Food Anal Methods 2012, 5, 40–53. Ohara JT, Rajagopalan R, Heller A. Wired Enzyme electrodes for amperometric determination of glucose or lactate in the presence of interfering substances. Anal Chem 1994, 66, 2451–2457. Ronkainen RJ, Halsall HB, Heineman WR. Electrochemical biosensors. Chem Soc Rev 2010, 39, 1747–1763.

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Thevenot DR, Toth K, Durst RA, Wilson GS. Electrochemical biosensors: Recommended definitions and classification, IUPAC. Pure Appl Chem 1999, 71, 2333–2348. Wang J. Electrochemical glucose biosensors. Chem Rev 2008, 108, 814–825. Yeh H-S, Ai H-W. Development and applications of bioluminescent and chemiluminescent reporters and biosensors. Ann Rev Anal Chem 2019, 12, 129–150.

4 Immunochemical assays and immunosensors 4.1 Introduction The specificity of the antigen–antibody interactions is the basis of important bioanalytical methods and devices developed starting from the second half of the last century, beginning with immune electrophoresis and radioimmunoassay (RIA) and going until the most recent and highly efficient microbeads-based methods. Modern analytical methodologies exploiting the principles of immunochemical reactions can be classified into two main groups: a) Immunoassays, where the biorecognition event, which is the formation of the Ag–Ab complex, and its detection are performed in two separate steps. The interaction between the antigen and the antibody can be performed with all components initially in solution (homogeneous immunoassay), or with one of the components immobilised on a surface (heterogeneous immunoassay). In both cases, the detection of the biorecognition event is performed in a separate step, using instrumentation physically separated from the vessel or platform where the biorecognition event takes place. b) Immunosensors, in which the antigen–antibody interaction occurs directly at the surface of a transducer that generates the detection signal. As for biosensors in general (see Chapter 2), also in immunosensors, the biorecognition layer (constituted of an antibody suitable for recognising the antigen of interest, or vice versa) is immobilised on the transducer surface. Immunosensors are, therefore, self-contained devices able to directly provide specific analytical information in a quantitative or semi-quantitative way by exploiting immunochemical recognition events. Historically, immunoassays were developed at the beginning of the 1960s (Sussman and Berson described the first RIA), followed by further developments including the pioneering studies on electrochemical immunosensors in the 1980s (see W. Heineman, 1980s). The advantages of immunoanalytical systems are: – high selectivity of the antigen–antibody interaction; – high accuracy and sensitivity; – applicability in complex samples, avoiding or reducing to a minimum the pretreatment steps. The main limits are represented by – low sampling rate; – the impossibility of reusing many times the molecular recognition layer;

https://doi.org/10.1515/9783110589160-004

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– requirement to perform numerous washing/blocking steps to minimise false positives; – sensitivity to unspecific interactions; – high cost and limited stability of immunoreagents. For both immunoassays and immunosensors, particular strategies are required in order to detect and quantify the formation of the antigen–antibody complex. A widely used approach exploits chemical labels that are bound to the antigen or antibody in order to track and detect the formation of the Ag–Ab complex by means of suitable instrumentation (radiochemical, spectroscopic or electrochemical). Such labels can be: – radioactive labels used for RIA; – enzyme labels used for enzyme immunoassays (EIA) and enzyme-linked immunosorbent assays (ELISA), as well as in immunosensors; – fluorescent, chemiluminescent and electrochemiluminescent labels, used in optical assays and sensors. Note that, recently, label-free methods and devices have been developed and their application is expected to quickly progress in the next future. They are based on techniques able to detect the formation of the Ag–Ab complex by monitoring the changes in a quantity directly associated with the formation of the complex (see Section 4.8 for details).

4.2 Immunoprecipitation and radioimmunoassay Immunoprecipitation methods are based on the precipitation of the antigen (which is the analyte) from a solution in which the specific antibody is added. This allows to separate, and then concentrate, the target antigen from complex biological samples containing many other, potentially interfering, proteins. Typically, immunoprecipitation requires the following steps (summarised in Figure 4.1): i) formation, in homogeneous solution, of the antigen–antibody complex by addition of a suitable primary Ab to the sample containing the target antigen; ii) addition of a precipitating agent (for instance, a protein such as a secondary antibody), which causes the precipitation of the Ag–Ab complex; iii) separation of the precipitate from the supernatant by centrifugation; iv) acquisition of analytical information by measuring the amount of antigen in the precipitate and supernatant.

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165

Figure 4.1: Steps of a general immunoprecipitation assay (A) and radioimmunoassay (B).

The secondary agents commonly used to favour the precipitation of the Ag–Ab complex can be: – a secondary antibody (sec-Ab) against the primary (capture) Ab. For instance, if rabbit IgG is used as primary agent to complex the antigen, then the addition of anti-rabbit IgG from goat will cause the complex to precipitate; – protein A or protein G bound to agarose beads (Sephadex); – Staphylococcus aureus with protein A present on the cell surface. The final step, that is, the quantification of the concentration of the target antigen, is usually performed by adding known amount of a labelled antigen. In RIA, this is achieved by introducing the radioactive isotopes 125I or 131I into the tyrosine residues of the antigen protein (Figure 4.2). The method was developed by Rosalyn Sussman Yalow, Nobel laureate for Medicine in 1977. The biorecognition event exploits the formation of a complex between the radiolabelled antigen Ag* and the antibody:  (4:1) Ag* + Ab ! Ag* –Ab The method is based on the competition between the radioactive and the unlabelled (or “cold”) antigen for the antibody binding sites. In fact, when the unlabelled Ag is added, the competing reaction to form [Ag–Ab] occurs. By increasing the amount of added “cold” Ag, the equilibrium is progressively shifted towards the formation of the “cold” [Ag–Ab] complex, so that the radioactivity of

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4 Immunochemical assays and immunosensors

Antigen

Antigen

+ 125ICl

Oxidising agent

+ HCl

OH Tyrosine residue

OH 125I

Figure 4.2: Introduction of a radioactive isotope of iodine into the tyrosine residue of an antigen protein.

the precipitate decreases while the radioactivity of the supernatant increases. As a consequence, the B/F ratio between the radioactivity of the precipitate (indicated with “B” for bound radioactive antigen) over the radioactivity of the supernatant (indicated with “F” for free radioactive antigen) will decrease by increasing the amount of “cold” Ag. By plotting the B/F ratio as a function of added “cold” Ag, calibration curves like the one shown in Figure 4.3 are obtained. Therefore, if we add the same amount of labelled Ag* to the sample and measure its B/F ratio, by simple interpolation on the calibration plot, we can determine the concentration of the target “cold” antigen in the sample (see Figure 4.3).

Figure 4.3: Calibration plot of B/F ratio (ratio between bound and free radiolabelled antigen, Ag*) versus concentration of added unlabelled antigen (Ag). By adding the same amount of Ag* in the unknown sample solution, from the value of B/F we can extrapolate the concentration of Ag in the sample.

Historically, RIA has long been one of the most important clinical and biochemical techniques for the quantitative analysis of hormones, steroids and drugs, thanks to the favourable combination between the specificity of immunoreactions and the sensitivity of radioisotopic techniques. However, nowadays radioimmunochemical methods are rarely employed because of the many problems related to the use of radioactive substances, such as high cost of reagents and instrumentation, waste disposal and requirement of performing continuous radiometric monitoring of the laboratory staff. Moreover, because of the relatively short half-life of 125I (60 days) and 131I (8 days), it is necessary to frequently renew the stock of the radiolabelled antigens by using the above-described labelling procedures with radioactive reagents.

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4.3 Enzyme immunoassays (EIA and ELISA) An alternative to radiolabelled immunoreagents was found introducing the use of fluorescent or enzyme labels. Note that, in immunoassays based on the use of antigens or antibodies marked with fluorescent or radioactive labels, the amount of bound analyte is directly related to the amount of label. On the contrary, in the case of enzyme labels, each label unit (i.e. the enzyme) acts as a biocatalyst able to convert many substrate molecules into detectable products. This results in a biochemical amplification of the signal, with the advantage of improving the sensitivity of the analysis. The bioanalytical strategy based on the use of enzyme labels was initially named enzyme immunoassay (EIA). The development of EIAs allowed to achieve sensitivities comparable to RIAs, avoiding the risks and costs related to the use of radioactive isotopes. However, the use of EIA requires the introduction of additional steps in the analytical procedure, such as the binding of the enzyme label to the Ag (or Ab) as well as the addition of the enzymatic substrate in order to perform the detection. In order to facilitate the separation of the Ag–Ab complex and, at the same time, decrease the amount of reagents required for the assay, a new methodology has been developed. According to this methodology, the capture agent (Ag or Ab, depending on the strategy) is immobilised on a solid surface, typically a plastic platform (polycarbonate, polyamide, polyester or others). This method, and the immobilisation of the capture agent on a surface based on enzyme labels, is named enzyme-linked immunosorbent assay (ELISA). The enzyme labels typically used are dehydrogenases, horseradish peroxidase (HRP) or alkaline phosphatase (ALP). The substrates and principles of signal generation and detection (usually UV–visible spectrophotometry or fluorimetry) are summarised in Table 4.1. Table 4.1: Typical enzyme labels and detection methods commonly used in EIA and ELISA. Enzyme label

Substrate for the enzymatic reaction

Detection principle

Dehydrogenase Glucose or similar substrates suitable (e.g. glucose for the specific dehydrogenase, which dehydrogenase) uses NAD+ as H acceptor.

Spectrophotometric monitoring of the increase in NADH absorption at  nm.

HRP

Colorimetric or fluorometric detection of the generated dye.

Chromogenic or fluorogenic compounds, such as ,′,,′-tetramethylbenzidine (TMB); ,′-diaminobenzidine (DAB); ,′-azino-bis(-ethylbenzothiazoline-sulfonic acid (ABTS) or others, which are turned into a dye by HRP in the presence of HO.

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Table 4.1 (continued ) Enzyme label

Substrate for the enzymatic reaction

Detection principle

HRP

Luminol and HO: HRP catalyses the generation of products in excited electronic state, which produce chemiluminescence.

Qualitative or semi-quantitative detection by impressing photosensitive materials (photographic paper or plates). Quantitative detection by using photomultiplier tubes or photodiodes.

Alkaline phosphatase

Generation of dyes from chromogenic or Colorimetric or fluorometric detection fluorogenic compounds thanks to the of the generated dye. breaking of the phosphate ester bond catalysed by alkaline phosphatase.

HRP, horseradish peroxidase.

The two most widely used strategies for performing ELISA are based on (Figure 4.4): a) competitive assay; b) sandwich or non-competitive assay.

Figure 4.4: Example of colorimetric detection of ELISA tests performed using chromogenic substrates (jarun011/iStock/Getty Images Plus).

The principles of the two main strategies employed in ELISA immunoassays are summarised in Figures 4.5 and 4.6. In the competitive assay, the capture agent is an antibody immobilised on the surface of the biorecognition platform, which can capture the antigen in the sample. If a known amount of labelled antigen (Ag*) is added in the sample, competition occurs between the native Ag and Ag* for the binding sites of the capture antibody (see Figure 4.5A). This results in a lower signal when the unlabelled Ag (that is the target analyte) is in excess in the sample, while higher signals are

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produced when smaller concentrations of unlabelled Ag are present in the sample. The typical response curve is shown in Figure 4.5B.

Figure 4.5: Principle of competitive immunoassays. (A) The antigen molecules of the sample compete with a fixed amount of labelled antigen for the binding sites of the immobilised antibody. (B) Typical response curve: the smaller the target Ag concentration in the sample, the larger the signal generated by the competing labelled antigen. ΔS and ΔC indicate the signal and concentration range suitable for quantitative analyses.

Note that the molecule generating the signal is always the substrate of the enzyme used to label the antigen. Depending on the procedure, the enzyme-labelled antigen can be: – first captured on the biorecognition platform, and then released because of the competition with the unlabelled Ag in the sample; – added to the sample to compete “on site” with the unlabelled Ag; – added in increasing amounts to the detection platform, after preliminary incubation of the capture antibody with the unlabelled Ag in the sample. The choice of one of these methodologies depends on many factors including amount and nature of the sample, cost of labelled antigen and possible interferences. In any case, it is important to remember that the signal (generated by the enzyme label) decreases with increasing the concentration of the target (unlabelled) antigen in the sample. In the sandwich assay, the target antigen in the sample is captured by a primary antibody, bound onto the surface of the biorecognition platform. After accurate washing in order to eliminate unspecifically bound molecules, the platform is incubated with a solution containing the labelled secondary antibody (see Figure 4.6A).

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Figure 4.6: Principles of non-competitive sandwich ELISA. (A) The antigen sample is added to an excess of capture antibody immobilised on the solid support, leading to fractional occupancy of antibody binding sites. A secondary antibody with a label is then added, and a sandwich complex is formed allowing detection. (B) Typical response curve: the larger the analyte concentration, the larger the signal generated. ΔS and ΔC indicate the signal and concentration range suitable for quantitative analyses.

Note that, in order to form the “sandwich”, the primary and secondary antibodies must recognise different epitopes in the target antigen. For this reason, in general the primary capture Ab and the secondary Ab (sec-Ab) are produced from different host species, for example, mouse and donkey, or rat and goat. Firstly, the antigen is captured by the primary Ab immobilised on the platform, and then the sec-Ab with the enzyme label binds to the Ag. Once the sandwich is formed, detection is performed by adding the enzymatic substrate. Therefore, the amount of enzyme label is directly proportional to the amount of captured Ag, and the signal will increase with increasing concentrations of the target analyte in the sample. Repeated accurate washing must be performed between each step. The typical response curve for non-competitive assays is shown in Figure 4.6B. In some cases, sandwich assays are performed using three different antibodies, as shown in Figure 4.7: i) a primary capture antibody immobilised on the platform, which captures the target antigen; ii) a secondary antibody (sec-Ab) added in solution, which binds to the captured Ag; iii) a labelled antibody, specific for the sec-Ab, which is used for the detection. The advantage of such assay strategy is that the third labelled antibody is specific for some epitopes of the sec-Ab, not of the target antigen. In this way, the same

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Labelled anti-sec-Ab Sec-Ab Ag Capture Ab

Figure 4.7: Detection scheme for a sandwich ELISA with three different antibodies: primary capture Ab bound onto the solid substrate, sec-Ab, and anti-sec-Ab antibody with label.

labelled Ab can be employed in ELISA analyses for many different antigens, as long as these antigens react with secondary antibodies of the same class. In the above-mentioned description, we have focused on the case in which the analyte is an antigen and the capture agent is an antibody. However, in some assays, the roles can be inverted, so that the capture agent can be an antigen while the analyte can be an antibody. The principles of the assays remain the same, as schematised in Figure 4.8 for competitive and sandwich ELISA assays.

Competitive assay

Sandwich assay

Figure 4.8: Competitive and sandwich ELISA assays for antibody analytes. The capture agent is an antigen (in yellow) immobilised on the platform, which specifically binds the target Ab (in blue). In the competitive assay, labelled and unlabelled antibodies compete for binding to the Ag. In the sandwich assay, a labelled sec-Ab (in green) is added and used for detection.

The immobilisation of antibodies (or antigens) onto plastic surfaces as well as their functionalisation with enzyme labels are typically performed using the covalent immobilisation techniques described in Chapter 2. However, it is important to avoid negative effects on the antigen–antibody binding capability. Indeed, we should take into account that the biorecognition sites of antibodies (the paratopes) are located at the end of the arms of the Y structure of the Ab, close to the N-terminus of the polypeptide chains, while their C-termini are located at the stem of the antibody Y structure. Therefore, immobilisation methods that exploit the reactivity of the Ab carboxylic groups should be preferred. In some cases, however, labelling techniques that exploit the reactivity of the amino groups of lysine residues (which are also widely distributed

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on the Ab structure, not only at the paratopes) are required. In such a case, the amount of added label should be limited to the minimum, and the functionality of the antibody after the functionalisation should be verified. Alternative functionalisation procedures are also available which exploit the reactivity of the sulphhydryl groups (under reducing conditions), or of the carbohydrate moiety of the antibody (after suitable oxidation to create reactive aldehydic sites). After immobilisation of the bioreagents of interest, the residual reactive sites eventually present on the surface must be neutralised by suitable “blocking” procedures, that is, by reaction with inert proteins such as bovine serum albumin (BSA) or casein, or with reconstituted non-fat dried milk. In heterogeneous immunoassays, the eventuality of false positive responses caused by unspecific binding can be minimised by performing careful and repetitive washing steps of the biorecognition platform between each step of the analytical process. This can make immunoassays time-consuming processes; however, this caution is compulsory to guarantee the specificity of the analysis.

4.4 Lateral flow immunoassays Recently, ELISA evolved in user-friendly devices suitable for performing the so-called lateral flow immunoassays (LFIA) or, more simply, lateral flow tests. These tests, generally providing a yes/no qualitative response, are suitable for the decentralised (home, bedside or on-the-field) detection of highly requested target analytes such as hormones, some disease biomarkers, human and farm animal pathogens or for water and food control. LFIAs are designed to provide quick and low-cost information about the presence or absence of the analyte, by applying very simple and intuitive protocols. The most popular LFIA is the pregnancy test, but also other applications are becoming increasingly widespread. In general, LFIA are used as alarm tests to be confirmed by quantitative laboratory analysis. An LFIA device is essentially a strip composed of a plastic support coated with a porous material through which the sample moves by capillary action. During its flow, the sample fluid encounters suitable reagents and capture elements which allow detection of the analyte by providing a visual positive or negative result. The porous material can be constituted of a series of capillary beds, filter paper or microstructured polymers. The pad is covered by a plastic mask with small windows which constitute the sample application zone and the test and control lines. Figure 4.9 illustrates the inner architecture and the function principle of an LFIA strip. The sample pad acts as a sponge, holding the excess of sample, which can be a drop of blood, urine, saliva or other fluids. Once the sample pad is soaked, the fluid flows to a second pad, named “conjugate pad”, which is impregnated with a freezedried soluble bioactive reagent. When wetted by the fluid flow, the bioactive reagent reacts specifically with the analyte generating an easily detectable conjugate.

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Sample drop Analyte Capillary flow

(1)

A

B

C

D

E

(2)

(3)

Figure 4.9: Architecture of a lateral flow immunoassay (LFIA) test strip: (A) sample pad; (B) conjugate pad impregnated with the labelled bioactive reagent (in blue); (C) test line with immobilised capture agent (in green); (D) control line with affinity ligand (in purple) specific for the bioactive reagent and (E) wicking pad. The functioning principle is the following: (1) the sample drop is placed on the sample pad; (2) the bioactive reagent binds the analyte (in yellow) forming a conjugate, which flows by capillarity towards the test and control lines; (3) if the analyte is present, the conjugate is captured on the test line (positive test); if the bioactive reagent is present in the strip, it is captured on the control line (valid test). Unreacted molecules are collected in the wicking pad.

The conjugate then moves by capillarity through a transport pad, where the reaction between the antigen and bioactive reagent has the time to complete, until reaching the pads where the test and control lines are located. The test line contains an immobilised capture agent which recognises the conjugate eventually formed, developing the signal, most commonly a coloured line. The control line contains an affinity ligand able to detect the soluble bioactive reagent, so allowing to check the efficacy of the reagents and the correctness of the procedure. Finally, a wicking pad collects the waste. As for classical ELISA, LFIA can be performed either using a competitive or a sandwich format. The response of a sandwich LFIA is positive when a colouration appears on the test line, while a competitive assay is positive when the test line remains uncoloured. The fact that, for untrained users, the appearance of a colour is more reassuring than its absence explains way sandwich LFIA tests are more popular than competitive ones. The bioactive reagent is typically an antibody or an antigen functionalised with a label, which is usually a chromophore or a fluorophore, but also magnetic beads and

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gold nanoparticles can be employed. For this application, gold nanoparticles are used as colouring agents, since they can assume a typical purple-burgundy colouration because of surface plasmon resonance (SPR) phenomena (see Sections 4.8.1 for further details). Attempts to develop instrumentation for performing quantitative LFIAs have also been reported. However, the most widespread application areas of LFIA tests seem those requiring a qualitative detection of the presence or absence of the analyte, once a pre-set threshold has been defined.

4.4.1 Pregnancy test Immediate tests for verifying pregnancy are based on the detection of the presence of the human chorionic gonadotropin (hCG) hormone, which appears in the urine with increasing concentration in the first weeks of pregnancy. The chorionic gonadotropin is a glycoprotein produced by the placenta and composed of two parts, called alpha and beta. The beta part of hCG is detected in pregnancy tests. The pregnancy test consists of a lateral flow strip containing suitable antibodies, and is based on a sandwich format. The functioning principle, similar to the one of a typical LFIA shown in Figure 4.9, is summarised in Figure 4.10.

Input window

Test window

Flow

hCG

Mobile tracer antibody

hCG−Tracer complex

Immobilised Sandwich complex capture antibody

Figure 4.10: Pregnancy test: functioning of a test strip device for human chorionic gonadotropin (hCG). The hCG, if present, forms a complex with the mobile tracer antibody labelled with a blue dye. Arriving to the test window, the hCG-tracer complex is captured by an immobilised Ab forming a sandwich and developing a blue colour. Note that the two antibodies (the tracer and the capture) must be different and recognise different epitopes on the antigen hCG.

A capture antibody is immobilised on the strip, in correspondence of the test window, while a second antibody (named “tracer antibody”) is usually labelled with a dye. Both antibodies bind hCG, but recognising different epitopes of the protein, eventually forming a sandwich. To perform the test, a drop of urine is applied on the input window and, after a few minutes, in the positive case, lines or other symbols appear on the visible part of the test strip.

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In order to check the reliability of the test, which could be impeded by failure of the reagents or improper execution, after the test window a second window appears, named control window. Here, an antibody that specifically recognises the mobile tracer antibody is immobilised. If the test is performed correctly and reagents are fine, this window must always develop the blue colour. In conclusion, the correct reading of the test strip is summarised in Figure 4.11. In case of pregnancy, both the test and control windows will develop the characteristic blue colour.

Test window

Control window

Positive test Flow

Negative test Flow

Failed test Flow

Figure 4.11: Positive and negative results of a pregnancy test. The control window always shows the colour of the label (in this case a blue dye): in the opposite case, the test is failed (unreliable). The test window only shows the blue colour if the pregnancy marker hCG is present in the sample.

4.4.2 Strip tests for antibodies and antigens related to SARS-CoV-2 Very recently, LFIA tests have been developed for the quick detection of antibodies developed by the human immune system as a consequence to infection by the severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) (the virus responsible for the COVID-19 pandemic in 2020). Two classes of antibodies are detected by the test, namely IgG and IgM immunoglobulins. IgM is the first type of antibody produced by the organism under an infection attack, developed at an early stage of the infection. IgG are more specialised antibodies, which bind specifically to the antigen they are fighting, and their presence indicates a later stage of the infection. The test strip displays the classical architecture for LFIA being composed of a sample pad, a porous membrane, two pads for the detection of IgM and IgG, respectively, and a control line. The conjugate bioactive reagents, which bind to the target antibodies in the blood sample, are SARS-CoV-2 recombinant antigen and rabbit (or mouse) IgG, both labelled with red-purple colloidal gold nanoparticles. The two test

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lines, one for IgG (G line) and the other for IgM antibody (M line) are made of nitrocellulose pads with immobilised anti-human IgG and anti-human IgM, respectively. The control line (C line) is functionalised with goat anti-rabbit (or anti-mouse) IgG. The IgG/IgM LFIA allows the qualitative detection and differentiation of IgM and IgG antibodies for the SARS-CoV-2 virus in serum, plasma or whole blood samples. The correct reading of the response is clearly illustrated in Figure 4.12. Note that this test is an aid in the diagnosis of patients with suspected SARS-CoV-2 infection, in conjunction with clinical evidences and other laboratory tests, and should not be used as the sole basis for diagnosis.

Figure 4.12: Picture and result interpretation of the lateral flow immunoassay test for SARS-CoV-2 antibodies. The appearance of a red-purple line in the C (control) line indicates that reagents and execution of the test are valid. Red-purple colouration of the G, M or both lines indicates the presence of IgG, IgM or both immunoglobulins, respectively. Lack of the red-purple colour in the C line is always indicative of an error (Photograph on the top is from Ilze Kalve/iStock/Getty Images Plus).

The above-described LFIA tests detect the antibodies produced as a consequence of infection by SARS-CoV-2, but do not directly detect the presence of the coronavirus, therefore not providing information on the first stages of the infection, which is

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fundamental for early diagnosis. Further progress towards more reliable tests for an early diagnosis of COVID-19 has come with the so-called “antigen tests”. Although the golden standard for the direct viral detection is based on the analysis of the virus RNA via real-time PCR (see Chapter 5), these antigen tests introduced a new and faster way to detect the SARS-CoV-2 in body fluids. The antigen tests detect the SARS-CoV-2 nucleocapsid protein, that is the most abundant protein in coronavirus, which is recognised via a sandwich assay by two specific antibodies. The test is typically performed on nasopharyngeal or nasal swab specimens introduced on a lateral flow strip, exploiting a fluorescent label for generating the detection signal (see Section 4.5.5). A positive test indicates that the antigen from SARS-CoV-2 is detected and the patient is infected. By the time of this writing (summer 2020) the medical use of the antigen test developed by Quidel Corporation has been made available by the US Food and Drug Administration under an emergency access mechanism called emergency use authorisation. Similar tests are being developed by other companies. Note that nucleocapsid immunodetection methods have been previously proposed also for other SARS-CoV viruses (see review by Zhu and Wong, 2020).

4.5 Western blotting Western blotting is a bioanalytical procedure that combines the separation capability of electrophoresis with the specificity of the identification of a target protein by reaction with an antibody. Western blotting is performed in different steps: i) separation of the proteins in the sample by electrophoresis; ii) transfer and fixing of the separated proteins onto a solid membrane; iii) blocking unspecific binding; iv) reaction with primary and secondary antibodies; v) detection. 4.5.1 Electrophoretic separation The separation of the proteins is achieved by sodium dodecyl sulphate–polyacrylamide gel electrophoresis (SDS-PAGE): in this technique, proteins treated with SDS (an anionic surfactant) and dissolved in a buffer are electrically forced to migrate through the polyacrylamide gel. SDS binds strongly to proteins with a fixed stoichiometry, that is, each SDS molecule binds two amino acid residues (or 1.2 g of SDS binds 1 g of protein). The interaction with SDS turns the tangled structure of the native protein into that of a linear polyanion. In order to break eventual disulphide bridges present in the biomolecule, a reducing agent, typically dithiothreitol or β-mercaptoethanol, is also added. The

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linear structure of the protein is functional to the efficiency of the electrophoretic separation. We remind here that the electrophoretic mobility μep is given by μep =

q 6πηr

(4:2)

where q is the ionic charge, η is the viscosity of the separation medium and r is the radius of the ion. Because of the fixed stoichiometry of the SDS–protein interaction, the charge/mass ratio of different proteins is the same so that their electrophoretic mobilities are theoretically equal. Nevertheless, an efficient separation of the proteins can be achieved by SDS-PAGE thanks to the molecular sieving capabilities of the polyacrylamide gel. Indeed, the gel contains nanometric pores whose size is determined by the percentage of the cross-linking agent bis-acrylamide used during the controlled preparation of the gel. Therefore, with this technique, proteins are separated on the basis of differences in molar masses, the smaller one migrating more quickly because their migration is slowed less than for larger proteins.

4.5.2 Transfer to solid membrane After electrophoresis, the separated proteins are transferred and fixed onto a solid membrane, typically made of nitrocellulose or polyvinylidene fluoride (PVDF). The transfer can be performed exploiting capillary forces or by electroblotting. In capillary blotting, the transfer membrane is placed in contact with the gel and several sheets of filter paper are stacked on top, applying some mechanical pressure to improve the contact between the layers. The buffer used for the separation moves by capillary forces into the pores of the filter paper dragging the proteins from the gel to the membrane, where they stick mainly because of hydrophobic interactions. The process takes approximately 12 h. For electroblotting, an electric field is applied perpendicularly to the sandwich composed of the gel, the membrane and the filter paper wetted with buffer. To this aim, two electrodes are placed in parallel to the sheets. Figure 4.13 shows a typical electroblotting assembly, from top to bottom: (A) cathode; (B) sponge; (C) two or more sheets of filter paper soaked with transfer buffer; (D) electrophoretic gel with separated proteins (polyacrylamide gel); (E) transfer membrane (nitrocellulose or PVDF) and (F) anode. An electrical current is imposed between the anode and cathode, which accelerates the transfer of the proteins from the gel to the membrane. In this case, the transfer is more efficient and takes approximately 1 h. Note that the SDS used for the electrophoresis can hinder the binding of the proteins with the membrane itself, in particular when the membrane is made of PVDF. On the other hand, proteins can bind so strongly to nitrocellulose, sometimes hindering the interaction with the antibody used for the following identification of the target protein.

4.5 Western blotting

−

− −

−

−

− −

179

−

−

A Transfer direction

B C D E C B F

+

+

+

+

+

Figure 4.13: Typical electroblotting assembly. From top to bottom: (A) cathode; (B) sponge; (C) two or more sheets of filter paper soaked with transfer buffer; (D) electrophoretic gel with separated proteins (polyacrylamide gel); (E) transfer membrane (nitrocellulose or PVDF) and (F) anode.

For electroblotting, a constant current is applied between the cathode and anode, typically at 1 A for 1 h, using a 25 mM Tris, 190 mM glycine and 20% methanol (optional) buffer.

4.5.3 Blocking unspecific binding Before recognition with the specific antibodies, it is necessary to block the nonreacted binding sites of the membrane by incubation with a suitable blocking solution. This will reduce background signals, preventing the direct binding of the primary antibody to the membrane. Typical blocking solutions are Tris or phosphate buffers containing 10% (w/v) BSA or 5% non-fat dried milk (30 min at 37 °C, or 1 h at room temperature). Protein-free blocking solutions are also commercially available, which may further improve signal-to-noise ratios by minimising the possible interaction between the primary antibody and the blocking agent.

4.5.4 Incubation with antibodies After accurate washing with suitable buffers, the blocked membrane is incubated with a solution of primary antibody (typically in Tris or phosphate buffer saline, for 30 min at 37 °C, or 1 h at room temperature, or overnight at 4 °C, with gentle agitation). At the end, the incubated membrane is washed several times with buffer containing 0.1% Tween surfactant. Finally, incubation with a labelled secondary antibody is performed in conditions similar to the above ones. The membrane is now ready to develop the detection signal.

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4.5.5 Detection The detection technique depends on the type of label bound to the secondary antibody (Figure 4.14). Typical labels used in Western blotting are enzyme labels, such as ALP or HRP, which catalyse either photon-producing reactions by a chemiluminescent substrate or colour-producing reactions using a chromogenic substrate.

(A) Colorimetry

(B) Chemiluminescence

Substrate

(C) Fluorescence

Substrate

Enz

Fluorophore

Enz

+ Light Product

Coloured product

Figure 4.14: Mechanisms of detection chemistry. In each method of western blot detection, a detectable signal is generated following binding of an antibody specific for the protein of interest. In colorimetric detection (A), the signal is a coloured precipitate. In chemiluminescence (B), the reaction itself emits light. In fluorescence detection (C), the antibody is labelled with a fluorophore.

Chemiluminescent signals are detected by using photographic films, which need to be developed by usual processes for argentic photo plates, or by photomultipliers or photodiodes. Colorimetric signals are detected qualitatively by eyes, or quantitatively by measuring the optical density of the blot. Chemiluminescence detection is more sensitive than colorimetry, because the former is based on the measurement of emitted photons, while the latter on absorbed photons. Fluorescence detection is also often applied, with some advantages: – fluorescent signals are stable, and blots can be archived and re-imaged; – a variety of fluorophores are available allowing to perform the multiplexed detection of multiple targets. Fluorescence detection can be achieved using either: – a fluorophore conjugated directly to the sec-antibody; – a fluorogenic substrates that emits fluorescence undergoing a reaction catalysed by an enzyme-label bound to the sec-antibody (chemifluorescence).

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4.6 Enzyme label immunosensors As described in Chapter 2, the first efforts in the biosensors field were directed to the development of biocatalytic electrochemical sensors, which exploit the specificity of the enzyme–substrate reaction to detect, typically, the substrate as the analyte. In the 1980s–1990s, the first examples of electrochemical detection of immunochemical reactions for analytical sensing purposes were proposed by Heineman and Halsall, to be followed by the first proposal of electrochemical immunosensors where antibodies were immobilised on the electrode surface. These biosensors exploit the specificity of the antigen–antibody interaction in order to detect one of the two partners as the analyte. For instance, Ag can be immobilised on the electrode to capture Ab, or vice versa. In order to achieve electrochemical detection by this approach, it is necessary to use a label which is electroactive itself or able to generate or consume an electroactive molecule. By exploiting the long-lasting know-how developed for ELISA assays, the most commonly used labels in electrochemical immunosensors are enzyme labels. Although also other kinds of techniques and transducers can be employed for developing immunosensors, however, the majority of the proposed immunosensors are based on the use of electrodes as functionalised transducers. Therefore, in the following section, we will focus in particular on this type of immunosensors. Enzyme-labelled electrochemical (ELEC) immunosensors represent indeed an evolution of the ELISA approach, where the biorecognition platform plays an active role, not only as an immobilisation site for the capture agent, but also as the place where the analytical signal is generated and detected. Following this approach, many sensors have been developed to detect a number of disease markers. Figure 4.15 schematises the two basic architectures used in ELEC immunosensors, which can follow alternatively a direct or a sandwich approach. The biorecognition schemes of ELEC immunosensors recall those of classical ELISA. However, in this case, in order to develop a signal, it is necessary to add a suitable substrate for the enzyme label and a redox mediator that shuttles electrons between the label and the electrode surface. The enzymes used in labelled immunosensors are typically HRP, glucose oxidase (GOx) and ALP. Obviously, different enzyme labels require different substrates and different mediators (see Table 4.2). Indeed, for HRP or GOx, the mediator is an electroactive compound which undergoes an electrocatalytic cycle in combination with the enzyme–substrate cycle (see Chapters 2 and 3). In the case of ALP, no mediator is required, since the enzyme substrate (chosen on purpose) is usually the phosphoric ester of an organic compound, which acts as an “electrogenic” reagent. The initial ester is chosen to be non-electroactive, or electroactive in a potential window different from that of the product. As shown in Figure 4.16A, the hydrolysis of the phosphoric ester catalysed by ALP releases the electroactive compound ROH, which is detected at the electrode by its oxidation. ROH can be p-aminophenol, ascorbic acid

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Figure 4.15: Two different architectures used in enzyme-labelled electrochemical (ELEC) immunosensors: (A) the target antigen (Ag, in blue) is directly captured at the electrode surface; (B) the target antigen is captured by a primary antibody immobilised on the electrode surface (in yellow). EL is the enzyme label bound to a secondary antibody (in red); Sub. and Prod. are the substrate and product of the enzyme EL; MED. is a redox mediator that shuttles electrons between the enzyme and the electrode surface.

A O R O P O + H 2O OH

ALP

O R OH + HO P O OH ox. R O + nH+ + ne-

B

OH O H 2N

O OH

OH HO

Para-amino phenol (PAP)

OH

Ascorbic acid (AA)

Figure 4.16: (A) Hydrolysis of a phosphoric ester catalysed by alkaline phosphatase (ALP), releasing an electroactive molecule (ROH) which is oxidised at the electrode surface. (B) Some of the electroactive molecules (ROH) used to form the phosphoric ester.

or other compounds (see the structures in Figure 4.16B). Table 4.2 lists some enzymes, substrates and mediators typically employed in ELEC immunosensors. ELEC immunosensors find wide application in the biomedical field, their use including the detection of tumour markers, cardiac disease markers, autoimmune

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Table 4.2: Examples of enzyme labels and reagents used in enzyme-labelled electrochemical (ELEC) immunosensors. Enzyme labels

Substrates Redox mediators

HRP

HO

GOx

ALP

Average electroactivity range (vs. Ag/AgCl)

Hydroquinone

−./. V

Methylene blue

−./–. V

,ʹ,,ʹ-Tetramethylbenzidine

Around +. V

Ferrocenemethanol

Approx. +. V

-(Methacryloyloxy)ethyl ferrocenecarboxylate

Approx. +. V

Ferrocenecarboxylic acid

Approx. +. V

Potassium ferri/ferrocyanide

Approx. +. V

PAPP

PAPP is hydrolysed to electroactive p-aminophenol

+./+. V

AAP

AAP is hydrolysed to electroactive ascorbic acid

+./+. V

Glucose

HRP, horseradish peroxidase; PAPP, p-aminophenyl phosphate monosodium; AAP, 2-phospho-Lascorbic acid; GOx, glucose oxidase.

disease markers, viruses and bacteria. Moreover, these biosensors are employed for food and biotechnological processes control.

4.7 Microbead-based immunoassays Microbeads are spherical particles with sizes approximately approaching that of living cells, that is, in the µm range. They can be made of different materials from plastics to glass, from metals to oxides and semiconductors. In the following sections, we will mainly focus on immunoassays based on polystyrene/latex and magnetic microbeads. The basic idea is that a microbead can be used as the well of a multi-well ELISA plate to perform a biorecognition event, usually detected by optical techniques. The principle of biorecognition with microbeads is schematised in Figure 4.17. The generally applied approach is based on the sandwich assay. The capture antibody is bound onto the surface of the microbead using different methods such as: a) passive adsorption; b) covalent bonding between the antibody amino groups and carboxylic groups introduced at the microbead surface; c) binding of biotinylated antibodies to avidin- or streptavidin-modified microbeads.

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Label sec-Ab Antigen (analyte) Capture Ab

Microbead Figure 4.17: Basic principle of the microbead sandwich immunoassay.

The target antigen (analyte) is captured by the Ab-coated microbead, to form later a sandwich complex with a secondary antibody conjugated with a suitable label, typically a fluorophore or a luminophore. The amount of label (also named reporter) bound onto the microbeads will be proportional to the analyte concentration in the sample. After the formation of the sandwich complex on the microbead surface, the challenge is to separate one or a group of microbeads in order to read the signal that they eventually generate. Two strategies are mainly employed for this purpose, widely applied also in commercial instrumentations: a) flow cytometry; b) magnetic capture. Below, we present the functioning principles of these methodologies and some applications.

4.7.1 Multiplexed microbead immunoassays based on flow cytometry Flow cytometry is a technique originally developed to measure optical signals from single cells in a flowing fluid. In immunoassays, it can be applied for collecting optical signals from cell-sized microbeads. As shown in Figure 4.18B, in flow cytometry, the fluid in which the microbeads are suspended is made to flow in a tube with a bottleneck slightly larger than the size of the particles. Because of the Bernoulli effect, the particles pass one by one through the bottleneck. A laser excitation system stimulates the luminescence of the reporter molecules (usually fluorophores) that are bound to the microbeads as a result of the molecular recognition event. The emitted fluorescence is measured by a suitable detector. As shown in Figure 4.18, an interesting feature of microbead flow cytometry is the possibility of performing multiplexed analysis. To this aim, microbeads coloured with two dyes, mixed in different ratios, are used so that each microbead can be identified by optical interrogation. Associating a certain capture agent to the

4.7 Microbead-based immunoassays

185

Figure 4.18: Simplified scheme illustrating a multiplex immunoassay based on flow cytometry with coloured microbeads. (A) A series of four microbeads coloured with two different fluorophores mixed in different ratios (here, red to orange fluorescence). (B) Laser interrogation of the microbeads by conventional flow cytometry. (C) Set of four different microbeads conjugated with different antibodies specific for distinct antigens (A, B, C and D). After the capture antibodies bind the antigens, sandwich complexes are formed by adding a secondary antibody labelled with a fluorophore (F). Therefore, the microbeads are sequentially analysed by the flow cytometer.

microbeads of a certain colour, it is possible to simultaneously identify the microbead colour and the response of the biorecognition event related to a certain capture agent by using two different radiation sources. The microbead-bound analyte is detected by measuring the fluorescence of the reporter antibody, which is typically a biotinylated Ab conjugated with a streptavidinated fluorochrome. The fluorochrome may be streptavidin–phycoerythrin (a red protein pigment present in red algae) or, alternatively, a green fluorescent dye. The detected reporter fluorescence is proportional to the amount of target antigen captured by each microbead. The microbeads are coloured with mixtures of red and infrared, or red and orange dyes. The microbeads are interrogated one by one when they flow through the bottleneck of the cytometer. A red laser excites the dye molecules bound to the microbeads, classifying the different types of particles, while a green laser quantifies the response of the biorecognition events. Depending on the reporter used, the laser excitation

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source can emit at 488 nm for Alexa Fluor 488 dye or fluorescein isothiocyanate, or at 532 nm (yttrium aluminium garnet laser) for phycoerythrin. Three independent detectors measure the green, orange (or infrared) and red fluorescence of each microbead. The reporter signal (green) is averaged and expressed as the median fluorescence intensity for each microbead set. Finally, the concentrations of the different target antigens in the sample are quantified by extrapolation from an internal standard. Multiplexed microbead immunoassays based on flow cytometry are widely employed in commercial instrumentations, produced by several companies all over the world such as Luminex (TX, USA), Becton Dickinson Biosciences (CA, USA) and DiaSorin (Italy).

4.7.2 Magnetic beads electrochemiluminescence assays Highly automatised instrumentation for immunochemical and other affinity assays is based on the functionalisation of magnetic microbeads with capture agents, performing the detection by electrochemically induced luminescence (called electrochemiluminescence, ECL). These immunoassays exploit the ECL phenomenon, deeply studied in the 1980s, which will be described in more detail in the following section. Commercial instruments for ECL immunoassays have first been developed by Igen, later acquired by Roche (Switzerland) that nowadays commercialises them worldwide with the Elecsys® trademark. Paramagnetic beads functionalised with streptavidin constitute the basis of the assay. As summarised in Figure 4.19, streptavidinated beads bind very strongly biotinylated capture agents, which can be constituted of: a) an antibody to detect an antigen; b) an antigen-antibody complex to detect another antibody with different paratopes; c) a single-stranded polynucleotide chain to detect the complementary polynucleotide sequence.

Antibody

Antibody + antigen

Nucleic acid Paramagnetic Streptavidin microbead

Biotin

Figure 4.19: Basic principle of magnetic beads electrochemiluminescence (ECL) immunoassay.

4.7 Microbead-based immunoassays

187

In the following, we will focus on immunochemical reactions, therefore, we will not go into further details for case (c). In the real practice of this approach, the biorecognition event is performed in homogeneous phase, and the detection in heterogeneous conditions. Focusing on case (a), as shown in Figure 4.20A, the target antigen binds to a biotinconjugated primary antibody and to a secondary antibody functionalised with an electrochemiluminescent label, namely a Ru(II) bipyridyl derivative (Ru(bpy)32+). The two antibodies are added to the sample solution, and form a sandwich complex with the antigen. In the following step (Figure 4.20B), the streptavidinated magnetic beads are introduced in the reaction mixture and bind the sandwich complexes, thanks to the high affinity between biotin and streptavidin. A properly oriented magnetic field is then applied to drive the magnetic beads onto the surface of a platinum working electrode, inside an electrochemical cell that operates as detector (Figure 4.20C). The other components in the sample are washed away and a co-reagent, namely tripropylamine (TPA), is introduced into the cell (Figure 4.20D).

Figure 4.20: Electrochemiluminescence (ECL) immunoassay with magnetic beads. (A) Two antibodies specific for different binding sites of the antigen (analyte) are added in the sample solution, forming a sandwich complex with the antigen. The primary Ab is labelled with biotin, and the secondary Ab with Ru(bpy)32+. (B) Paramagnetic microbeads functionalised with streptavidin are introduced in the solution, binding to the biotinylated Ab of the sandwich complex. (C) The solution is transferred into the measuring cell, where a magnetic field is applied so that the paramagnetic beads bind to the surface of the electrode. (D) The cell is washed to remove unbound molecules, and tripropylamine (TPA) is introduced to activate the Ru2+ complex for the ECL process.

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4 Immunochemical assays and immunosensors

Finally, an oxidising potential, typically +1.2 V versus Ag/AgCl, is applied. This initiates a sequence of electrochemical and chemical reactions involving both the ruthenium complex and TPA, whose final product is Ru(II) complex in excited electronic state which returns to the ground state by emitting photons. The intensity of the electrochemically induced luminescence, measured by a photodetector (photomultiplier or CCD), is directly proportional to the amount of ruthenium label bound to the magnetic beads and, consequently, to the concentration of target antigen in the sample. Note that, in commercial instrumentation, all these steps are executed automatically. This methodology presents several advantages that can be summarised as follows: i) the biorecognition event is performed in homogeneous phase, that is, in better conditions to guarantee high specificity and high yields; ii) streptavidinated microbeads can be used to bind different biotinylated antibodies, so that a large number of different analytes can be determined sequentially with the same instrument; iii) like all chemiluminescence processes, ECL is extremely sensitive. Moreover, being activated electrically, ECL ensures a precisely controlled and timed reaction; iv) ECL responses are linear over a wide linear range, extended over six magnitude orders; v) as we will explain later, ECL continuously regenerates the ruthenium label, providing stable and reproducible photoemission signals. 4.7.3 Mechanism of electrochemiluminescence As introduced earlier, ECL is a form of chemiluminescence initiated by electrochemical generation of reactive species that can undergo high-energy electron transfer reactions producing excited-state luminophores and emitting light. The overall procedure involves electrochemical, chemical and luminescent processes, with the electrochemical process being the initiator of the cascade reaction that produces photoemission. Historically, emission of light during electrochemical reactions was first observed in the nineteenth century. However, it was in the 1970s–1980s that the extended studies by Prof. Allen J. Bard and other researchers pioneered the modern applicative development of ECL. The mechanism of ECL in aqueous media is based on the use of co-reactants, which are sacrificial reagents that, after electrochemical oxidation, undergo chemical transformations to generate a strongly reducing intermediate. Such intermediate can either react with the ruthenium complex directly or after its electrochemical oxidation. In 1991 Igen researchers introduced TPA (see structure in Figure 4.21B) as co-reactant for ECL in immuno- and genoassays. The simplified reaction mechanism is illustrated in Figure 4.21A.

4.8 Label-free immunosensors: SPR and QCM

189

Figure 4.21: (A) Reaction scheme for the electrochemiluminescence (ECL) of Ru(bpy)32+ using tripropylamine (TPA) as a co-reactant. [Ru(bpy)]32+]* is the complex in excited state. (B) Chemical structure of the two reagents.

At a potential of 1.2 V versus Ag/AgCl, Ru(bpy)32+ and TPA are oxidised via the following electrodic reactions: RuðbpyÞ3 2 + ! RuðbpyÞ3 3 + + e − +

TPA ! TPA

+e

−

(4:3) (4:4)

The radical TPA•+ produced by reaction (4.4) is deprotonated: TPA + ! TPA + H +

(4:5)

The deprotonation product (TPA•) is a strongly reducing agent, which reduces Ru (bpy)33+ produced by the electrodic reaction (4.3): h i* RuðbpyÞ3 3 + + TPA ! RuðbpyÞ3 2 + + products

(4:6)

The product of reaction (4.6) is a reduced ruthenium complex in excited state, which returns to the ground state by emitting orange light at λ = 620 nm: h i* RuðbpyÞ3 2 + ! RuðbpyÞ3 2 + + hν

(4:7)

Note that the overall process is electrocatalytic, continuously regenerating Ru(bpy)32+.

4.8 Label-free immunosensors: SPR and QCM Label-free immunosensors have been studied to lead to the recent development of commercially available instrumentation for research and routine applications, their

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4 Immunochemical assays and immunosensors

use being expected to quickly progress in the next future. These immunosensors exploit techniques able to detect the formation of the Ag–Ab complex by monitoring the change in a quantity directly associated with the formation of the immunocomplex. The label-free immunosensors employed nowadays on a large scale are those based on SPR spectroscopy and quartz crystal microbalance (QCM). Numerous studies also proposed the use of electrical and electronic measurements so that conductometric bridges, electrochemical impedance spectroscopy and field-effect transistors have been tested to develop label-free immunosensors. Although promising, these devices have not yet found large-scale application outside the research field so that, in the following sections, we will focus on the presently more successful SPR and QCM immunosensors.

4.8.1 Surface plasmon resonance immunosensors SPR is an optical phenomenon that takes place at the interface between two media with dielectric constants of opposite sign, such as, for example, a metal film in contact with an insulator (typically glass). SPR occurs when p-polarised light (such as a laser) at a certain wavelength hits with a certain incident angle a thin metal film and is reflected under total internal reflection condition. The internal reflection of the light causes an evanescent field that excites surface plasmons (electromagnetic surface waves) at the metal/medium interface. Such excitation is achieved at a specific incidence angle (called SPR angle). At the SPR angle, the plasmons are set to resonate with light, resulting in absorption of light at that angle. This creates a dark line (or minimum of intensity) in the reflected beam. The SPR angle depends on the optical properties of the metal and the medium in contact with the metal (usually a liquid), as well as on the thickness and mass density of the layer at the medium/ metal interface. This is because the SPR angle is highly sensitive to changes in the refractive index (RI) of a thin layer adjacent to the metal surface (~200 nm). Therefore, shifts of the SPR angle can be correlated to molecular binding events taking place on or near the metal film, or even conformational changes of the molecules bound to the metal film. For instance, the binding of an analyte to ligands immobilised on the chip surface increases the mass density at the metal surface, and the SPR angle shits towards higher values. Consequently, tracking the changes in the SPR angle allows to monitor biomolecular interactions in real-time, without the need of any label. The SPR angle shift is measured in degrees (°) or millidegrees (m°), or also in response unit (RU). A change of 1/106 in RI unit corresponds to 1 RU, which is roughly equivalent to a shift in surface density of protein of approximately 1 pg/mm2. An SPR biosensor consists of a hemi-cylindrical prism (normally made of glass) with a thin metal layer (~30 nm) deposited on its surface, a p-polarised light source (e.g. a laser), and a photodetector that measures the intensity of the totally reflected light (see Figure 4.22). Macromolecules specific for the target analyte are

Evanescent field

4.8 Label-free immunosensors: SPR and QCM

191

Analyte Ligand Gold layer Glass disc φSPR

Laser beam

Hemi-cylindrical prism

Detector

Figure 4.22: Configuration of a typical SPR immunosensor. When the analyte (antigen) binds to ligand molecules (antibody) immobilised on the sensor surface, the intensity minimum (a dark line) produced by the SPR effect undergoes an angular shift measured by the detector.

immobilised on the metal surface. When the sample solution is added, the ligand recognises the analyte forming a complex, which modifies the RI of the medium near the metal surface. The optical reader of the biosensor measures the shift of the SPR angle, which depends on the RI of the solution layer near the metal film. Gold is the metal typically used to produce the thin metal film on the glass prism of SPR sensors. For the immobilisation of the capture agent on the gold layer, the most widely used technique is based on the chemistry of self-assembled monolayers (SAM) (see Section 2.8.1) of functionalised thiols. For instance, thiols bearing carboxylic groups are used, which can bind the amino groups of immunoglobulins via EDC/NHS activation. Without additional labels, SPR spectroscopy allows to study the binding between pairs of biomolecules, such as antigen–antibody, but also lipid–protein, carbohydrate–protein, DNA–protein, DNA–DNA and RNA–DNA. Moreover, in addition to the detection of target analytes, SPR immunosensors can be used to gain fundamental information on the kinetics and other parameters that characterise the receptor–target interaction. Specialised instrumentation, such as the ProteOnTM system by Bio-Rad Laboratories, utilises SPR to detect the interaction between two unlabelled biomolecules in real time. A typical experiment consists in the immobilisation of a capture agent onto a functionalised gold SPR sensor chip, followed by the addition of the target analyte to investigate the binding affinity and kinetics between the analyte and the ligand. The analyte binding to the ligand is monitored by following the change in the SPR signal over time, obtaining a dynamic plot named sensorgram (Figure 4.23).

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4 Immunochemical assays and immunosensors

Figure 4.23: Typical SPR sensorgram of an antibody–antigen interaction. Different steps can be identified: (i) initial baseline (only the Ab is immobilised on the sensor chip); (ii) increase in response during association when the Ag solution is added; (iii) constant response when equilibrium is reached; (iv) decrease in response due to dissociation when flowing analyte-free buffer; (v) final equilibrium with only the Ag molecules strongly bound to the antibody. The red dashed line is the fit to study the binding kinetics between Ag–Ab; the blue arrow represents the angle shift when Ag binds stably to Ab.

Figure 4.23 shows a typical sensorgram for the antibody–antigen interaction. The binding response initially increases as the analyte is flown over the sensor chip and associates with the immobilised capture agent, until reaching a constant plateau which corresponds to the binding equilibrium. Afterwards, the analyte solution is replaced with an analyte-free buffer and the Ag–Ab complex dissociates so that the signal decreases. Fitting the sensorgram data with a binding model allows the calculation of the association (ka ) and dissociation (kd ) constants to determine the binding affinity (see Section 1.4.4 in Chapter 1). For example, the association constant ka can be determined by fitting the sensorgram (only the part relative to the association reaction) with the following equation:   (4:8) R = R0 + ðRmax − R0 Þ 1 − e − ka t where R is the sensorgram response, R0 is the starting response and Rmax is the maximum response at the plateau. The real-time SPR data obtained are analysed by specialised software and several different binding models can be applied. Automated systems are based on the use of 6 × 6 interaction arrays, making possible the interrogation of six different ligands each one interacting with a series of up to six different concentrations of the analyte in a single injection. Figure 4.24 shows typical sensorgrams.

4.8 Label-free immunosensors: SPR and QCM

193

Response (RU)

200 160 120 80 40 0 0

2

4

6 Time (s)

8

10

12

Figure 4.24: Interaction analysis sensorgram between an antibody Fab fragment and biotinylated glycoprotein MHC (major histocompatibility complex) class I/Tyr achieved using a ProteOnTM sensor chip from Bio-Rad Laboratories, Inc. (Reproduced with permission from Bio-Rad Laboratories).

4.8.2 Quartz crystal microbalance: immunosensors based on piezoelectric effect The piezoelectric effect is a physical phenomenon which refers to the ability of a material to generate a voltage when mechanically stressed. The piezoelectric effect can be reversed in the so-called reverse piezoelectric effect, in which the application of an alternate voltage causes the shear oscillation of the material. These effects are typical of anisotropic crystals, which are crystals without symmetry centre such as SiO2 (quartz), aluminium phosphate (berlinite), aluminium nitride, zinc oxide and others. Figure 4.25 schematised the reasons behind the piezoelectric effect for the case of a SiO2 crystal, the blue spheres being the positively charged Si4+ cations and the red spheres the O2− anions. The ions are arranged in a distorted tetrahedral lattice and, in normal conditions, positive and negative charges are perfectly balanced, even if they are not symmetrically arranged. The compression of

(A)

(B)

Distortion Node Shear

Figure 4.25: Deformation of a piezoelectric SiO2 crystal during oscillation at a certain frequency, illustrated at (A) microscopic and (B) macroscopic scale. The blue spheres represent the positively charged Si4+ cations, and the red spheres the O2− anions.

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4 Immunochemical assays and immunosensors

the crystal results in a geometry shift that causes the asymmetrical movement of the oppositely charged ions so that the dipole moments no longer cancel out. This produces a net displacement of electric charges, which determines a voltage difference across the opposite faces of the crystal. In general, in order to induce oscillations based on the reverse piezoelectric effect, two electrodes are attached to the opposite faces of a disc-shaped piezoelectric crystal (Figure 4.26). When an alternating electric field is applied, the crystal responds by vibrating at a certain frequency.

Quartz wafer Bottom electrode pad

Electrode Quartz wafer Electrode

Top electrode pad Figure 4.26: Reverse piezoelectric effect: disc of quartz crystal with the two metal electrodes on the opposite faces, which are used to apply an alternating potential causing the shear oscillation of the crystal.

The fundamental vibration frequency (f0 ) of the crystal obeys the following equation: f0 =

vtr 2tq

(4:9)

where vtr is the transversal wave velocity in the crystal (3.34 × 104 m/s) and tq is the resonator thickness. Equation (4.9) points out that the thinner the crystal, the higher its fundamental frequency. When a mass is deposited on one face of the piezoelectric crystal, a change in the vibration frequency (Δf ) is observed. For rigid deposits, this change is directly proportional to the deposited mass (Δm), according to the Sauerbrey equation: f0 2 Δm − Δf = 2  1=2 A ρq μq

(4:10)

where A is the piezoelectrically active crystal area (cm2), ρq is the density of the quartz crystal (2.648 g/cm3), and μq is the crystal shear modulus (2.947 × 1011 g/cm s2). It is important to note that a polymer film behaves as a rigid film when its thickness is very

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4.8 Label-free immunosensors: SPR and QCM

thin, typically in the micrometre or, better, submicrometric range. However, this limit depends on the hydration level of the film as well as on the f0 value of the crystal. On the basis of the constancy of A, f0, pq and mq, equation (4.10) can be simplified as follows: − Δf = Cf Δm

(4:11)

where Cf is a proportionality coefficient. This equation points out that, by monitoring changes in the frequency of the crystal as a function of the deposited mass, we can obtain linear calibration plots suitable to measure microscopic mass changes (see Figure 4.27).

Figure 4.27: Calibration plot for quartz crystal microbalance (QCM), which can be fitted by eq. (4.11).

From an analytical point of view, the slope Cf of the straight line shown in Figure 4.27 represents the sensitivity of the method. By carefully looking at eqs. (4.10) and (4.11), one realises that the sensitivity of the QCM scales with the square of f0 , meaning that thinner crystals provide higher sensitivities. QCMs typically detect mass changes in the µg/cm2 range. However, using specialised instrumentation and measurement methods, it is possible to go down to the ng/cm2 range (quartz crystal nanobalance). QCM platforms are quite popular for the construction of immunosensors, also thanks to the fact that piezoelectricity is already widely used in many electronic devices such as quartz watches, amplification pickups for electric guitars, cigarette lighters, ultrasonic nozzles and motors, piezoelectric inkjet printing, radio transmission and others. A piezoelectric device is ideal for the construction of biosensors, since it can detect all interactions which result in a mass change. As schematised in Figure 4.28, in a QCM immunosensor, a capture antibody is immobilised on one of the electrodes deposited on the faces of the quartz crystal. When the specific antigen is present in the sample, it is captured by the antibody causing a change of mass on the surface of the piezoelectric crystal, which is detected as a decrease in oscillation frequency. As presented above, QCM allows the detection of the biorecognition event (the formation of the Ag–Ab complex) without using any label or reagent, since mass is an intrinsic property that all molecules possess. The lack of specificity of a mass

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Buffer

Target Ag Sample

Capture Ab Electrode Quartz crystal

+ Δm – Δf

Figure 4.28: Functioning principle of a quartz crystal microbalance (QCM): a capture antibody is immobilised on one electrode deposited on the quartz crystal. When the target antigen is present in the solution deposited on the QCM, it is captured by the antibody causing a change of mass, which is proportional to the change of frequency.

measurement is indeed overcome in QCM immunosensors thanks to the immobilisation of specific antibodies on the piezoelectric crystal, which make the response selective for the target antigen only. QCM immunosensors combine together the advantages of ELISA and LFIA. In fact, a piezoelectric immunosensor can provide quantitative analytical information like ELISA tests, although avoiding the labelling procedure. In addition, QCM presents an ease of use quite comparable to LFIAs, while overcoming the limit of LFIA of providing mainly qualitative information. These reasons make QCM immunosensors of particular interest for on-field analysis, such as for the quick determination of pathogens or protection against biological warfare agents, to prevent military misuse or terrorist attacks. An interesting implementation of the QCM is the so-called electrochemical quartz crystal microbalance (EQCM). It is based on the fact that one of the metal electrodes used to stimulate the oscillation of the piezoelectric crystal can function also as working electrode in a voltammetric cell. Figure 4.29 schematised the instrumentation employed for this kind of measurements. The EQCM has the advantage of combining the information derived from the simultaneous monitoring of changes in mass and faradaic current as a function of the applied potential.

Further readings

197

PC

Potentiostat C

R

W Oscillating unit

Oscillating circuit QCM

Main unit

Frequency counter Electrochemical cell

CPU Analogue to digital converter

Monitor

Keyboard

Figure 4.29: Scheme illustrating the instrumentation used in electrochemical quartz crystal microbalance (EQCM). C, R and W refer to the counter, reference and working electrodes, respectively.

Further readings Books Cristea C, Florea A, Tertis M, Sandulescu R. Immunosensors. In: Rinken T. (Ed.), Biosensors-micro and nanoscale applications. In Tech, 2015. Manz A, Dittrich PS, Pamme N, Iossifidis D. Bioanalytical chemistry, 2nd ed. London, UK, Imperial College Press, 2015. Price PC, Newman DJ. Principles and practice of immunoassays, 2nd ed. New York, NY, USA, Stockton Press, 1997. Sojic N (Ed). Analytical electrogenerated chemiluminescence: From fundamentals to bioassays. Royal Society of Chemistry, London, UK, 2019. Ugo P, Moretto LM (Eds.). Electrochemical immunosensors and aptasensors, Basel, Switzerland, Mdpi AG, 2017. Voet D, Voet JG. Biochemistry. 4th ed. Pratt CW. Fundamentals of biochemistry: Life at the molecular level. Hoboken, NJ, USA, John Wiley & Sons, 2006.

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Review and research papers Altintas Z, Uludag Y, Gurbuz Y, Tothill IE. Surface plasmon resonance based immunosensor for the detection of the cancer biomarker carcinoembryonic antigen. Talanta 2011, 86, 377–383. Felix F, Angnes L. Electrochemical immunosensors – A powerful tool for analytical applications, Biosens Bioelectron 2018, 102, 470–478. Fung YS, Wong YY. Self-assembled monolayers as the coating ia quartz piezoelectric crystal immunosensor to detect salmonella in aqueous solution. Anal Chem 2001, 73, 5302–5309. Heineman WR, Halsall HB. Strategies for electrochemical immunoassy. Anal Chem 1985, 57, 1321A–1332A. Huang L, Tian SL, Zhao WH, Liu K, Ma X, Guo JH. Multiplexed detection of biomarkers in lateral-flow immunoassays. Analyst 2020, 145, 2828–2840. Knoll W, Zizlsperger M, Liebermann T, Arnold S, Badia A, Liley M, Piscevic D, Schmitt FJ, Spinke J. Streptavidin arrays as supramolecular architectures in surface-plasmon optical sensor formats, Colloid Surf A-Physicochem Eng Asp 2000, 161, 115–137. Krishnan VV, Khan IH, Luciw PA. Multiplexed microbead immunoassays by flow cytometry for molecular profiling: basic concepts and proteomics applications. Crit Rev Biotech 2009, 29, 29–43. Landry JP, Fei Y, Zhu X. Simultaneous measurement of 10,000 protein-ligand affinity constants using microarray-based kinetic constant assays. Assay Drug Dev Technol 2012, 10, 250–259. Liu Z, Oi W, Xu G. Recent advances in electrochemiluminescence. Chem Soc Rev 2015, 10, 3117–3142. Miao WJ. Electrogenerated chemiluminescence and its biorelated applications. Chem Rev 2008, 108, 2506–2553. Miao WJ, Choi JP, Bard AJ. The tris(2,2’-bipyridine)ruthenium(II), (Ru(bpy)32+)/tri-N-propylamine (TPrA) system revisited – A new route involving TPrA(center dot+) cation radicals. J Am Chem Soc 2002, 124, 14478–14485. Muzyka K. Current trends in the development of the electrochemiluminescent immunosensors, Biosens Bioelectron 2014, 54, 393–407. Pei XM, Zhang B, Tang J, Liu BQ, Lai WQ, Tang DP. Sandwich-type immunosensors and immunoassays exploiting nanostructure labels: a review. Anal Chim Acta 2013, 758, 1–18. Pohanka M. Overview of piezoelectric biosensors, immunosensors and DNA sensors and their applications. Materials 2018, 11, 448. Ricci F, Adornetto G, Palleschi G. A review of experimental aspects of electrochemical immunosensors. Electrochim Acta 2012, 8474–8483 Schuck P. Reliable determination of bonding affinity and kinetics using surface plasmon resonance biosensors. Curr Opin Biotech 1997, 8, 498–502. Shankar DR, Gobi KVA, Miura N. Recent advancements in surface plasmon resonance immunosesnors for detection of small molecules of biomedical, food and environmental interest. Sens Actuat B Chem 2007, 121, 158–177. Stanley MD, Goldsmith J. Radioimmunoassay: review of basic principles. Semin Nucl Med 1975, 5, 125–152. Wen W, Yan X, Zhu CZ, Du D, Lin YH. Recent advances in electrochemical immunosensors. Anal Chem 2017, 89, 138–156. Xiao Q, Xu C. Research progress on chemiluminescence immunoassays combined with novel technologies. TrAC 2020, 124, 115780. Zhu N, Wong PK. Advances in viral diagnostic technologies for combating COVID-19 and future pandemics. SLAS Tech 2020, 1–9, DOI: 10.1177/2472630320953798.

5 Analysis of nucleic acids 5.1 DNA extraction The first step in the analysis of nucleic acids is their extraction from relevant biological samples and numerous methodologies exist depending on the target biomolecule, the sample type and the desired analytical application. In general, the main three steps of DNA extraction methods are the lysis of cells, the separation of DNA from the other molecules presents in a cell and finally the isolation of DNA. Lysing a cell means that the phospholipid bilayer forming the cell membrane needs to be disrupted and multiple methods can be used to achieve this. For example, the bilayer can be destabilised by using heat, freeze–thaw cycles, mechanical stress, detergents (like sodium dodecyl sulphate or SDS), osmotic shock (by placing the cells in a hypotonic environment), alkaline environments, organic solvents and chaotropic agents (disrupting hydrogen bonds). The choice of the method or, for example, the choice of a specific detergent will affect the speed at which the lysis process occurs and the effect it has on other biomolecules on the top of the lipid bilayer. While the use of a detergent has a strong effect on lipids, the addition of a protease like proteinase K can ensure that the proteins present in the cell like histones are digested and using an RNase can achieve the same with RNA. The addition of a saline solution facilitates the removal of the cellular debris by centrifugation, as digested proteins, lipids and RNA clump together. The DNA that remains in the supernatant solution can be then further purified by an organic extraction method like ethanol precipitation, where ice-cold ethanol (or isopropanol) is used to precipitate DNA via centrifugation. After the removal of the supernatant, the precipitated DNA can be dissolved in a buffer or pure water before moving to the desired analysis technique. Other methods of DNA purification can be used, for example solid phase extraction methods exploit the fact that DNA adsorbs on silica under specific salt and pH conditions, therefore, spin columns with a silica resin can be used to facilitate trapping DNA and washing away impurities. Another way of simplifying the removal of impurities can be the use of magnetic beads that DNA can reversibly bind to, as the beads with the DNA can be trapped in the vial by a magnet while the supernatant containing the impurities can be easily separated.

5.2 Southern blotting After DNA is extracted from a biological sample, it is often fundamental to understand whether specific sequences are present in the genomic material, as they could, for example, indicate whether particular mutations linked with genetic diseases are present. Southern blotting is a method developed and published by Edwin Southern https://doi.org/10.1515/9783110589160-005

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in 1975 that allows the detection of a specific DNA sequence via the fragmentation of the genomic material, its separation by electrophoresis and a detection step with a radiolabelled hybridisation probe complementary to the target sequence. A probe is a short oligonucleotide containing a reporting moiety (e.g. a fluorophore or a radiolabel) that has a sequence complementary to a target sequence present in the analyte (e.g. a mutation site in a gene of interest). The first step in the Southern blotting procedure (Figure 5.1) is the fragmentation of high-molecular-weight DNA into smaller pieces using restriction endonucleases. Restriction endonucleases are enzymes that cleave dsDNA at a specific internal site, as they recognise particular sequences usually 4–8 bases long. Numerous restriction endonucleases are available and Figure 5.2 reports two different examples that show how the fragmentation can leave either sticky ends (i.e. the fragmented dsDNA has a short ssDNA sequence at the ends) or blunt ends (where the entire fragment is made of dsDNA).

–

Restriction endonuclease

Agarose gel electrophoresis

+

Genomic dsDNA Denaturation and southern transfer

Weight Filter paper Remove and bake the Filter paper wick Buffer membrane

Paper towels Nylon/nitrocellulose membrane

Gel

Nylon/nitrocellulose membrane

Hybridisation with P-labelled probes

Labelled probe

Wash unbound probes

Probe hybridised to the complementary sequence Expose X-ray film to the membrane

Autoradiogram

Figure 5.1: Southern blotting. Genomic DNA gets fragmented and separated using electrophoresis. The denatured DNA is then transferred to a nitrocellulose membrane that is exposed to radiolabelled probes. An X-ray film is exposed to the membrane revealing the target sequences.

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Figure 5.2: Restriction enzymes cut DNA at specific sites. EcoRI produces fragments with sticky ends, while EcoRV produces fragments with blunt ends.

The dsDNA fragments are separated by agarose gel electrophoresis, in which the negative charges of DNA are exploited to induce the fragments to migrate to the positively charged anode and, since the distance they move depends on their size, a size separation of the fragments can be achieved. The gel is then soaked in an alkaline solution (0.5 M NaOH) to denature the dsDNA fragments, obtaining the singlestranded counterparts needed for hybridisation with radiolabelled probes in a later step. A nylon/nitrocellulose membrane is overlaid on the gel and they are placed on a filter saturated with buffer. Dry filter paper, paper towels and a weight are placed on the top of the nylon/nitrocellulose membrane and therefore the ssDNA fragments are transferred from the gel to the membrane by capillary action. The membrane is then baked at ~80 °C for a couple of hours to fix the DNA on it before exposing it to a solution containing labelled probes (usually radiolabelled with 32P or labelled with fluorophores) complementary to the sequences of interest. The probes are left to anneal at a suitable temperature for several hours and then the labelled nylon/nitrocellulose membrane is washed to remove all the unbound probes. Finally, a phosphorimager or X-ray films can be used to visualise the labelled DNA sequences that have been detected with this methodology.

5.3 Amplification and detection of specific DNA sequences: PCR 5.3.1 The polymerase chain reaction A technique which gave a fundamental contribution to the development of the analysis of DNA and its sequencing is the polymerase chain reaction (PCR), conceived by Kary B. Mullis in 1983. In 1993, Mullis was awarded the Nobel Prize in Chemistry along with Michael Smith for his work on PCR. PCR allows the amplification of a few molecules of DNA to produce many millions of copies of a DNA consisting of the specific region of interest (called amplicon). PCR can be used to amplify a DNA region from 50 to over 25,000 base pairs in length. In order to use PCR, it is necessary to know at least the sequence of the flanking parts of the DNA segment to be amplified. For initiating the amplification reaction it is indeed necessary to start the

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process from two short (often from 18 to 24 bases long) oligonucleotides called PCR primers, of which one is complementary to the 3′ end of the sense strand of the DNA target, while the other is complementary to the 3′ end of the antisense one. The first step of PCR (Figure 5.3) is the denaturation of DNA by heating to ~95 °C (①), then the temperature is lowered to ~55 °C to allow the primers to anneal to the DNA target sequences (②). Finally, the temperature is increased to ~72 °C and the DNA synthesis is carried out by a DNA polymerase extending the primers in the 5′ to 3′ direction (③). The temperatures for the three PCR steps can vary and they depend on various factors like the polymerase used, the buffer conditions, the sequence of the primers and their concentration. Originally the polymerase was extracted from the bacterium Escherichia coli, but this enzyme is irreversibly inactivated by the high temperature required for the denaturation step, so fresh enzymes had to be added at the start of each cycle. Nowadays, one of the commonly used polymerases is the Taq polymerase as originally shown by Saiki in 1988. This polymerase is derived from the bacterium Thermophilus aquaticus which survives at high temperatures, therefore the Taq polymerase is not deactivated during the denaturation step. Steps ① to ③ are repeated for n cycles (typically, n = 25 or 30), thus obtaining in theory 2n PCR products. Region of interest to be amplified ① Heat denaturation ② Annealing of the primers 5′ 3′

5′ 5′

5′

5′

3′

5′ 5′

Repeat steps ① and ② 5′ 5′ 5′ 5′

5′ 5′

Repeat steps ①, ② and ③

5′

5′

5′

5′

③ 5′→3′ DNA synthesis with thermostable polymerase 5′

5′ 5′

5′

5′

Cycle 2: 22 molecules

3′ 5′

5′

Cycle 3: 23 molecules

Cycle 1: 21 molecules

5′ 3′

5′ 5′ 5′ 5′

5′ 5′

5′ 5′

③ 5′→3′ DNA synthesis

5′

5′

5′ 5′

5′

5′

5′ 5′ 5′

Continue for more cycles

Figure 5.3: The polymerase chain reaction (PCR). ① Denaturation, ② annealing, ③ polymerisation.

PCR is a widely used technique; it is useful in medicine to amplify low quantities of viral DNA, and it is also useful for forensic analysis to amplify very small amount of DNA. As already said, one of the limits of PCR is the fact that the sequence must be

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known in order to be able to synthesise the primers. Another limitation is the fact that the polymerase amplifies reliably only DNA segments that are less than 2 kb (kilobases) long. Moreover, the procedure is time consuming and requires relatively expensive apparatus.

5.3.2 Analytical PCR: quantitative PCR or real-time PCR A disadvantage of tests based on nucleic acid analysis to diagnose genetic alterations or infections is the low sensitivity due to the low amounts of genetic material often present in samples. Amplification of such genetic material through PCR can increase the sensitivity while maintaining the specificity. In conventional PCR, the product is detected by an end-point measurement, for example thanks to gel electrophoresis. In order to reduce the opportunities for contamination and to increase throughput while also decreasing the time necessary to complete an experiment, it is possible to combine the amplification step with the detection step of the PCR product. This process is called quantitative or real-time PCR (qPCR), and with this technique, the detection of the amplicon occurs in real time usually by exploiting a fluorescent signal. Real-time PCR can be used both in a semi-quantitative manner, allowing the detection of the presence or absence of a sequence, and in a quantitative manner (qPCR) that allows the measurement of the initial amount of the target sequence. As it is possible to see in Figure 5.4A, with increasing cycles of PCR the fluorescent signal increases and it is possible to divide the curve in roughly four sections. During the first PCR cycles the fluorescence remains at background level, while during the exponential phase there is a significant increase in fluorescence as the amount of PCR product doubles after every cycle, with an ideal 100% efficiency (2n PCR products). At this point, the DNA polymerase is still highly efficient and all the reagents are still in excess. Moreover, the amounts of amplicon produced are still low, limiting the competition with the annealing of the primers. However, the efficiency does not stay at 100%, as PCR reagents are depleted cycle after cycle and the PCR products can self-anneal. This is visible in the curve, first in the linear phase showing a reduction in efficiency and finally in the plateau phase where amplification does not occur anymore. For quantification purposes, it is important to establish a threshold for the realtime PCR reaction, which is a level where there is a statistically significant increase in fluorescent signal compared to the baseline. This threshold is often equal to 10 times the standard deviation of the baseline fluorescence; however, it can also be set at another value within the exponential phase. The threshold cycle (CT), also called quantification cycle (Cq), is the cycle number at which the amplification curve crosses the threshold line. This CT value inversely correlates with the initial amount of the target sequence: the lower the value, the higher the starting concentration of the target. Therefore, it is possible to measure the CT values of a dilution

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(A) Fluorescence

Plateau phase

Log-linear phase

Baseline phase

Threshold line Exponential phase CT Cycle

Fluorescence

(B)

Threshold line CT1 CT2 CT3 CT4 CT5 CT6 Cycle

CT

(C)

log (concentration)

Figure 5.4: Quantitative PCR (qPCR). (A) Phases of a real-time amplification plot. (B) Amplification curves for a 6-point 10-fold dilution series of standards of known concentration. (C) CT vs. log (concentration) calibration curve. The concentration of the unknown sample (in green) can be calculated from its CT value thanks to the calibration curve.

series made with a standard target of known concentration (Figure 5.4B) and when these CT values are plotted against the corresponding starting concentration (log(concentration)), a calibration curve can be obtained (Figure 5.4C). This curve can then be used to find the concentration of an unknown sample just by measuring its CT value. A good calibration curve needs to be highly linear (R2 > 0.980) and it is possible to use it to obtain useful information about the PCR reaction. While the intercept can be used to estimate the theoretical limit of detection (i.e. the CT measurable

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with the smallest amount of target giving a statistically significant amplification), the slope is a measure of the reaction efficiency. In fact, the amplification efficiency (E) is calculated from the slope as follows: E = 10 − 1=slope and the percentage efficiency (%E) is therefore   %E = 100ðE − 1Þ = 100 10 − 1=slope − 1

(5:1)

(5:2)

This means that a PCR reaction with 100% efficiency has a slope of −3.32, thereby producing a 10-fold increase in PCR amplicon every 3.32 cycles. Ideally, in order to have a template doubling in every cycle during the exponential phase, the amplification efficiency should be 100% but, on a practical level, values between 90% and 110% are acceptable. The efficiency can be influenced by multiple factors like the GC content of the amplicon, its length (which for qPCR should be approximately between 50 and 150 bp) and the eventual presence of secondary structures. Low efficiencies can be due to non-optimal concentrations of the various reagents or the quality of the enzymes. Efficiencies above 100% can be due to the presence of PCR inhibitors, the amplification of non-specific products (e.g. primer dimers) or pipetting errors during the serial dilution. At the end of a qPCR experiment, once the target has been fully amplified, it is possible to add a final dissociation of the dsDNA to measure the melting temperature (Tm) of the amplicon produced. In this procedure, the fluorescence decreases with the temperature (Figure 5.5 left) and it is possible to determine the various melting temperatures by finding the peaks of the negative first derivative of the melting curve (Figure 5.5 right). The Tm depends on the sequence of the amplicon and therefore its value can be used to assess the specificity of the PCR reaction, because if the incorrect target was amplified it would likely have a different Tm. The amplification of a single target leading to a pure PCR product would result in a single symmetrical peak, while the presence of anomalies like multiple peaks or shoulders would indicate the presence of several products, suggesting the lack of specificity of the PCR process. Also the presence of mismatches affects the melting temperature, as it would destabilise the dsDNA leading to a lower Tm that can, for example, be used to distinguish a fully complementary wild type sequence from a mutant one containing a mismatch (Figure 5.5).

–d (Fluorescence)/d(T)

5 Analysis of nucleic acids

Fluorescence

206

Temperature Wild type (WT)

TmMT

Tm

WT

Temperature Mutant (MT)

Figure 5.5: Fluorescence melting curves (left) and their first derivatives (right) of samples with either the fully complementary wild type or a mismatch containing mutant.

5.3.3 Signal generation in PCR As mentioned in the previous section, it is usual to follow the qPCR process through the generation of a fluorescent signal that increases with the amplicon concentration. This signal can be produced by fluorescent dyes binding to dsDNA in a nonspecific way or by a fluorogenic DNA probe, which is a short synthetic oligonucleotide complementary to a specific sequence in the PCR amplicon. In this case, the fluorescence is only present when the probe binds to the target amplicon, so there is a positive signal only if the target sequence has been correctly amplified. Numerous designs can be adopted and this section describes some of them as examples. Dyes that bind dsDNA in a non-specific manner are commonly used to follow PCR amplification and SYBR Green I is one of the most used. When free in solution, SYBR Green exhibits little fluorescence but, when it binds to dsDNA, its fluorescence increases up to 1 000-fold (Figure 5.6). The intensity of the fluorescence increases with the amount of SYBR Green binding dsDNA and therefore it increases with the amount of amplicon produced. However, since the binding occurs with all dsDNA, the fluorescent signal will be altered by the presence of impurities and nonspecific products (such as primer dimers), that will in turn affect the accuracy of quantification. Moreover, the lack of specificity also implies that no multiplexing to detect multiple targets with a single reaction can be performed, as the fluorescence due to different amplicons cannot be distinguished. Although the lack of specificity is an important drawback of dyes like SYBR Green, melting curve analysis can be performed and, as explained before, it can be used to help discriminate between different amplicons and detect the presence of non-specific products, allowing to check the quality of the PCR reaction and its eventual lack of amplification specificity. Other advantages of using dsDNA binding

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207

Hybridisation

N N N N+ S

SYBR Green I Figure 5.6: Detection of PCR amplicons through fluorophores binding in a non-specific way to dsDNA. The dye (e.g. SYBR Green) possesses an extremely low fluorescence when free in solution (red molecules), while the signal produced increases significantly when the dye binds to dsDNA (green molecules).

fluorophores are the low initial cost (the dyes are cheaper than sequence-specific probes) and the simplicity of the assay (no probe is required and so only two primers need to be designed). Although SYBR Green is widely used, it presents a number of limitations. For example, SYBR Green binds preferentially to GC-rich regions and it inhibits PCR at high concentrations. These issues can be solved by using a different dye like EvaGreen, that does not appear to have a GC bias and that can be used at higher concentrations than SYBR Green before showing any PCR inhibition. Moreover, when using SYBR Green the melting temperature depends both on the dye concentration and the DNA concentration. Alternative dyes like SYTO-13 and SYTO-82 show no PCR inhibition, no GC preferential binding and no influence on melting temperature even at high concentrations, solving the SYBR Green shortcomings described earlier. TaqMan hydrolysis probes are single-stranded oligonucleotides containing a fluorophore (often fluorescein/FAM) at the 5′-end and a quencher (often Black Hole Quencher 1/BHQ-1) at the 3′-end (Figure 5.7). When the probe is hybridised to its target, the fluorophore is quenched via FRET (Förster/fluorescence resonance energy transfer) by the quencher, which is a process during which a donor chromophore in its excited electronic state transfers energy to an acceptor chromophore via a nonradiative dipole–dipole coupling that strongly depends on the distance between the two moieties. During the PCR reaction, the TaqMan probe hybridises to its target sequence, while a polymerase like Taq extends the primer. When the polymerase encounters the probe, its dsDNA 5′→3′ exonuclease activity is used to digest the TaqMan oligonucleotide, thereby releasing the fluorophore that is now no longer quenched.

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Quenching

Primer

Polymerase

Fluorophore

Quencher

Figure 5.7: TaqMan hydrolysis probes. When hybridised to the target sequence, the TaqMan probe is non-fluorescent, as the fluorophore is quenched. When a polymerase with 5′ exonuclease activity amplifies the target, the TaqMan probe is digested and so fluorophore and quencher are separated, leading to the generation of a fluorescent signal.

Compared to dyes like SYBR Green, TaqMan probes are more expensive but, at the same time, they possess a high signal to noise ratio and high specificity. Moreover, since different fluorophore/quencher pairs can be used, TaqMan probes are also amenable to multiplexing. The conjugation of a minor groove binder (MGB) to a TaqMan probe can further improve its properties. Thanks to that modification, the Tm increases and so shorter probes can be used. This boosts the probe specificity while also improving the quenching, thus reducing the background fluorescence. HyBeacon probes (Figure 5.8) present fluorophore moieties attached to internal nucleotides that exhibit low fluorescence in the unstructured ssDNA state, but that are considerably more fluorescent when hybridised to the target DNA sequence.

Hybridisation

Fluorophore Figure 5.8: HyBeacon probes. The probe shows a significant increase in fluorescence when it is hybridised to the target sequence.

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209

HyBeacons can be used for melting curve analysis, allowing the discrimination of sequences that differ by only a single nucleobase thanks to the change in Tm with different probe/target duplexes. This is also due to the lack of any secondary structure in the ssDNA form of the probe, thus giving clean and reproducible melting transitions. Since HyBeacon probes can be obtained with various dyes attached, multiplexing is possible and a system with six different colours has been reported. Molecular beacons (Figure 5.9) are single-stranded probes with a fluorophore bound to one end and a quencher bound to the other end. The loop region contains a sequence (typically 15–35 nucleotides long) that is complementary to part of the target amplicon, while the two ends of the molecular beacon are complementary with each other and form the stem region that is usually 5–6 bp long.

Quenching

Fluorophore

Hybridisation

Quencher

Figure 5.9: Molecular beacon probes. In its ssDNA form, the probe adopts a stem–loop conformation that keeps the fluorophore quenched. Upon hybridisation, fluorophore and quencher are distanced and therefore a fluorescent signal is present.

The stem–loop structure is adopted by the molecular beacon only when there is no target to bind to. In the stem–loop form, the fluorophore and the quencher are near, so fluorescence quenching occurs, and no or little fluorescence is observed. When the probe is hybridised to the target, the fluorophore and the quencher are kept far from each other and a strong fluorescence is detected. It is important to measure the fluorescence at a temperature at which the stem–loop structure is stable (usually around 50 °C), as at high temperatures the probes not bound to the target and not in the stem–loop form do indeed produce an unwanted background fluorescence. Molecular beacons can be synthesised with different fluorophore/quencher pairs, allowing their use for multiplexing. They also exhibit high specificity for the target sequence and, instead of being digested during each amplification step, they are just displaced; for this reason, a polymerase lacking 5′ exonuclease activity is used. A drawback of molecular beacons is that they are difficult to design. This is because the stem region needs to be stable enough to facilitate the formation of the correct hairpin over any other possible structure that would keep fluorophore and quencher separate, which would produce undesired fluorescence. At the same time, the stem region should not be too stable, as that would interfere with a proper hybridisation to the target amplicon.

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Stem–loop Scorpion primers (Figure 5.10) are PCR primers with a molecular beacon attached to the 5′ terminus of the PCR primer itself, via a linker which acts as a PCR stopper. The fluorophore is linked to the 5′-end of the molecular beacon, while the quencher is attached to the 3′-end. The probing mechanism is similar to the one described earlier for molecular beacons. Scorpion primers essentially produce self-probing amplicons and thus the probing mechanism is unimolecular/intramolecular. Compared to the bimolecular/intermolecular probing mechanism of molecular beacons, the process with Scorpion primers is much faster because of its intramolecular nature.

Quenching

Quenching

PCR extension

PCR primer

Denature and anneal

PCR blocker Fluorophore Quencher Figure 5.10: Scorpion primers. A molecular beacon is attached to the 5′-end of a PCR primer, yielding fluorescent self-probing amplicons.

There is another type of Scorpion primer called the duplex Scorpion primer. In these primers, the stem–loop structure is replaced with a duplex probe, where the strand attached to the primer has a fluorophore linked to it and the complementary strand contains a quencher. Duplex Scorpion primers give more intense fluorescence signals than stem–loop Scorpion primers, because the achievable separation between the fluorophore and the quencher is larger.

5.3.4 PCR in action: the coronavirus disease (COVID-19) Thanks to its specificity, sensitivity and relative simplicity, qPCR is a commonly used technique both for research and as a diagnostic tool. An example of the latter is the use of a variation of qPCR called reverse transcription real-time PCR (qRT-PCR) as a diagnostic methodology during the coronavirus disease 2019 (COVID-19) pandemic in 2019–2020. This infectious disease is caused by the severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2), which is a positive-sense single-stranded RNA (+ssRNA)

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211

virus with a genome roughly 30 000 bases long. After its viral RNA was sequenced, multiple laboratories across the world started designing and developing tests based on the amplification of different genes specific to SARS-CoV-2 and/or common with other SARS-related coronaviruses (e.g. SARS-CoV-1). Table 5.1 reports some of the sequences and primers recommended by different organisations (March 2020) for the detection of SARS-CoV-2.

Table 5.1: Sequences of primers and probes for the detection of SARS-CoV-2. W = A or T, R = A or G, M = A or C, S = G or C; FAM = 6-carboxyfluorescein; BHQ1 = Black Hole Quencher 1. Origin

Primer ID

Description

Sequence (ʹ → ʹ)

US CDC

-nCoV_N-F

-nCoV_N forward primer

GACCCCAAAATCAGCGAAAT

-nCoV_N-R

-nCoV_N reverse primer

TCTGGTTACTGCCAGTTGAATCTG

-nCoV_N-P

-nCoV_N probe

FAM-ACCCCGCATTACGTTTGGTGGACCBHQ

-nCoV_N-F

-nCoV_N forward primer

TTACAAACATTGGCCGCAAA

-nCoV_N-R

-nCoV_N reverse primer

GCGCGACATTCCGAAGAA

-nCoV_N-P

-nCoV_N probe

FAM-ACAATTTGCCCCCAGCGCTTCAGBHQ

-nCoV_N-F

-nCoV_N forward primer

GGGAGCCTTGAATACACCAAAA

-nCoV_N-R

-nCoV_N reverse primer

TGTAGCACGATTGCAGCATTG

-nCoV_N-P

-nCoV_N probe

FAM-AYCACATTGGCACCCGCAATCCTGBHQ

RP-F

RNAse P forward primer

AGATTTGGACCTGCGAGCG

RP-R

RNAse P reverse primer

GAGCGGCTGTCTCCACAAGT

RP-P

RNAse P probe

FAM-TTCTGACCTGAAGGCTCTGCGCGBHQ

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Table 5.1 (continued ) Origin

Primer ID

Description

Sequence (ʹ → ʹ)

WHO

RdRP_SARSr-F

RdRP gene

GTGARATGGTCATGTGTGGCGG

RdRP_SARSr-R

CARATGTTAAASACACTATTAGCATA

RdRP_SARSr-P

FAM-CAGGTGGAACCTCATCAGGAGATGCBHQ

RdRP_SARSr-P

FAM-CCAGGTGGWACRTCATCMGGTGATGC-BHQ

E_Sarbeco_F

China CDC

E gene

ACAGGTACGTTAATAGTTAATAGCGT

E_Sarbeco_R

ATATTGCAGCAGTACGCACACA

E_Sarbeco_P

FAM-ACACTAGCCATCCTTACTGCGCTTCGBHQ

-nCoV-OFP

ORFab

CCCTGTGGGTTTTACACTTAA

-nCoV-ORP

ACGATTGTGCATCAGCTGA

-nCoV-OP

FAM-CCGTCTGCGGTATGTGGAAAGGTTATGG-BHQ

-nCoV-NFP

Nucleoprotein-protein N

GGGGAACTTCTCCTGCTAGAAT

-nCoV-NRP

CAGACATTTTGCTCTCAAGCTG

-nCoV-NP

FAM-TTGCTGCTGCTTGACAGATT-BHQ

Figure 5.11 shows a simplified workflow for the qRT-PCR assays used for SARSCoV-2. The US CDC recommends collecting an upper respiratory specimen for initial diagnostic testing, and this should be done preferentially via a nasopharyngeal swab, although also oropharyngeal swabs and sputum can be used. The viral RNA is then extracted from the sample and a reverse transcriptase generates the complementary DNA strand (cDNA) by extending primers specific to the genes of interest. This cDNA can then be further amplified using qPCR, and the presence of the target sequence, and hence also the presence of the virus in the patient, can be detected thanks to fluorescent probes like TaqMan.

5.3 Amplification and detection of specific DNA sequences: PCR

RNA extraction

213

Primer Viral RNA

Reverse transcription cDNA

qPCR

CT Figure 5.11: Detection of SARS-CoV-2 via qRT-PCR. A sample is collected from a patient via a nasopharyngeal swab and viral RNA is extracted from it. A reverse transcriptase generates the complementary DNA (cDNA) which is then amplified by PCR and detected with a fluorescent probe.

5.3.5 Digital PCR As explained in the previous sections, qPCR can be used to quantify the amount of a target sequence by using fluorescent probes/dsDNA-binding dyes and a calibration curve made with standards of known concentration. This indirect method of quantification relies on the availability of suitable standards and therefore a technique that could allow a direct measurement would be preferable. A solution to this problem is a technique called digital PCR (dPCR) that allows the absolute quantification of a desired target in a sample. dPCR (Figure 5.12) relies on the partitioning of the PCR reaction mixture in numerous sub-reactions that are completely independent from each other and that should contain ideally one target molecule only, but that in reality contain between zero and just a few molecules. These partitions can be achieved by having an emulsion of water droplets in oil or by having arrays of chambers/wells physically isolated from each other. Regardless of how the bulk reaction mixture is split, the various partitions undergo PCR amplification and detection independently from one another. Detection occurs with the same methodologies used for qPCR-like TaqMan probes and SYBR Green, while the measurement is just an end point one, so no real-time fluorescence is followed nor any CT is obtained. The number of partitions having a fluorescent signal is counted, and this value is used to estimate the amount of target in the initial sample. It is important to notice that dPCR does not distinguish whether preamplification there was one target molecule or more in a partition, as it counts 1 or 0 just depending on the presence or absence of the target, thereby giving a “digital” 1-0 response. Since some fluorescent/positive partitions have multiple target molecules, counting them would lead to underestimate the number of molecules initially present

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Partitioning

Amplification

Detection

Sample preparation

Fluorescence

Results

Threshold line

Event number Figure 5.12: Digital PCR. The bulk sample reaction mixture is partitioned in many independent PCR sub-reactions in such a way that each partition contains either no target molecule or just a few. Amplification occurs independently in every partition, and the presence of the desired sequence is detected at the end of the process with a fluorescent probe. The various partitions are divided based on the presence or the absence of a fluorescent signal, allowing to count the number of partitions that contained at least one molecule of the desired target.

in the bulk sample. A correction is necessary and partitioning statistics in the form of the Poisson distribution is used to obtain a better estimate of the initial amount of target. In dPCR, the number of target molecules (m) is usually smaller than the number of partitions (n), and since the distribution is random, there is a range in the number of targets per partition. The average number of molecules per partition (λ) can be then calculated as follows: λ=

m = Cs V p n

(5:3)

where Cs is the concentration of the sample and Vp is the partition volume. In the case of a random partitioning process where there are n partitions, the probability of having a molecule in a specific partition is 1/n; this is then repeated m times for each target in the initial sample (there are m chances that a molecule will

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215

land in a partition). The binomial distribution can be used to determine the probability (p) that a partition will have k copies of a target molecule as follows: !

!

  m m 1 k 1 m−k λ k λ m−k pðkÞ = 1− = 1− (5:4) n n m m k k and since the number of partitions is usually large in dPCR, the Poisson distribution can be used to approximate the binomial distribution, giving a probability equal to pðkÞ =

λk e − λ k!

(5:5)

with mean and variance of the Poisson distribution identical to each other (µ = σ2 = λ). It is then possible to use eq. (5.5) to determine the probability of having an empty partition (E): pð0Þ =

λ0 e − λ = e−λ = E 0!

(5:6)

and the value of E can in turn be estimated from the ratio of partitions containing a molecule (F) as follows: E=1−F=1−

f n

(5:7)

where f is the number of positive partitions that contain the target molecule and that are therefore fluorescent. By rearranging eq. (5.6) and substituting eq. (5.7), it is possible to correlate the average number of molecules per partition λ with the number of positive partitions f:
 f (5:8) λ = − lnðEÞ = − ln 1 − n Finally, by combining eq. (5.8) with eq. (5.3) it is possible to determine the number of target molecules m and the concentration of the sample Cs as follows:
 f (5:9) m = n λ = − n lnðEÞ = − n ln 1 − n   f λ − lnðEÞ − ln 1 − n Cs = = = (5:10) Vp Vp Vp One of the advantages of dPCR is its capacity of detecting rare events like low amounts of a specific sequence in the presence of others. In fact, since qPCR measures bulk fluorescence, it can struggle to detect the presence of a rare mutant target (e.g. < 0.1%, that would therefore have a very high CT) in the presence of a background of wild type sequences (Figure 5.13). On the other hand, the partitioning

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Partition

Bulk reaction (qPCR)

Partitioned reaction (dPCR)

Figure 5.13: The power of partitioning in detecting rare events. When trying to detect the presence of a very rare sequence (red DNA) in a bulk reaction (qPCR), the signal deriving from it might be hidden by the presence of a large excess of other targets (blue DNA). By partitioning the bulk reaction aiming to have only a very small number of molecules per partition, it is possible to detect the presence of the desired target even when mixed with other targets, as its signal remains detectable (dPCR).

used during dPCR helps in overcoming qPCR limitations thanks to a number of factors. As the partitions have a very small volume (often in the range of nL) compared to the starting reaction mixture, there is a de facto increase in the concentration of the target in the partitions (e.g. 1 molecule in 1 nL rather than 1 molecule in 10 µL), which leads to an improved limit of detection. Moreover, the desired target is enriched as it is not surrounded by as many other molecules increasing the background, thereby leading to a clearer signal that is easier to detect. dPCR instruments usually use thousands of partitions (20 000 is a common value) and, the more they are, the better the precision and the dynamic range get. However, while an improved precision is a benefit of dPCR over qPCR, the latter can use bigger sample volumes (e.g. 200 µL instead of 20 µL) and it maintains a better dynamic range, as concentrated samples have to be diluted before being used in dPCR. qPCR instruments and consumables are also usually less expensive and allow for higher throughput, having therefore also a lower cost per sample.

5.4 DNA microarrays As mentioned earlier, determining whether a sample presents specific sequences is of the utmost importance and parallelising this kind of analysis would allow the simultaneous screening of thousands of genes. A way of achieving this is through the use of DNA microarrays, which consists of a high number of different DNA probes localised in spots (having the same sequence) on a surface. While multiple types of microarray exist, the overall working principle is always the same. A sample containing a number of different target DNA molecules is exposed to the surface

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of a microarray covered with multiple probes. Therefore, the target DNA hybridises to the complementary probes, while the DNA that cannot find its complement is washed away. The hybridisation events are visualised via fluorescence that, for example, can be achieved through the labelling of the target DNA prior the hybridisation step (Figure 5.14). Since multiple probes having the same sequence are present in each spot, the intensity of the fluorescent signal can be correlated to the amount of DNA that is hybridised to that spot. Multiplexing is also possible, as using dyes that absorb and emit at different wavelengths allows testing multiple samples on the same microarray chip. For example, this feature can be used to compare the gene expression of different tissues at the same time.

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Figure 5.14: DNA microarrays. Spots of oligonucleotides probes are organised on a surface and the presence of a target sequence (in yellow) in the sample is measured via a fluorescent signal. The DNA that is not complementary to the probes (in red) does not hybridise, and it is washed away. Since multiple probes for the sequence are present in each spot, the intensity of fluorescence correlates with the amount of DNA hybridised.

There are multiple ways of fabricating DNA microarrays, and it is worth mentioning three main families. Oligonucleotides can be easily synthesised using the phosphoramidite chemistry and conjugating them to surfaces can be achieved through different methodologies. Spotted arrays take advantage of that, as the probes are spotted on the surface of a chip using a robot to obtain an ordered array. Conversely, with in situ synthesised arrays the oligonucleotide probes are directly synthesised on the surface of the microarray. A way to do this is combining photolabile protecting groups with photolithography to perform a modified version of the chemical synthesis of

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oligonucleotides directly on the surface of a chip. Finally, self-assembled arrays employ DNA probes synthesised on polystyrene beads that are then placed on a substrate with microwells where the beads can deposit. Each bead carries probes with a single sequence and the ensemble of the different beads/probes is randomly distributed on the chip. Therefore, a decoding step is necessary to determine the location of each bead/probe pair before the microarray can be used. The Illumina microarrays use the self-assembled bead-based technology described earlier, where 3 µm beads are placed on silica slides with microwells etched on them. Various assays can be performed with this technology and the Infinium® wholegenome genotyping assay is an example (Figure 5.15). Genotyping allows to assess the naturally occurring variation at specific loci in the genomes of different organisms of a biological species. Very often this process involves testing for single-nucleotide polymorphisms or SNPs (often pronounced “snips”), which are single-nucleotide variations occurring at specific places in the genome. With the Infinium® assay, the target genetic material is captured by beads having probe sequences that stop one base short of an SNP locus. This means that by performing a single-nucleotide extension, it is possible to incorporate a modified nucleotide opposite to the SNP of interest. This modified nucleotide is used to generate a fluorescent signal that depends on the

Figure 5.15: Illumina Infinium® assay. Beads containing the probe oligonucleotides are deposited on a surface with microwells. After the target DNA (in blue) is hybridised to the probes, a single-base extension is performed with modified nucleotides. Fluorescence is then used to visualise the beads with target DNA hybridised to them, and the wavelength of emission is used to determine the single-nucleotide polymorphisms (SNPs) present in the sample. When a person is homozygous at that locus, the fluorescent signal will be monochromatic (i.e. red or green, left and right of the picture), while when heterozygous, the signal will be mixed (i.e. red and green, central part of the picture).

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nucleobase of the SNP and thus it allows the determination of the sequence of the genetic material analysed. With this Illumina dual channel system, when the organism tested is homozygous at a locus of interest, the bead will have only one type of dye and, therefore, it will have only one colour of fluorescence. On the other hand, a heterozygote sample will produce a bead with two different types of dyes that will be excited and emit in both fluorescence channels (i.e. red and green).

5.5 DNA sequencing 5.5.1 Sanger sequencing In order to be functional, the methods described earlier that rely on probes require knowledge about the sequences being tested. Over the years, different methods to read the sequence of nucleobases in a DNA or RNA strand have been developed, and they ended up having a pivotal role in the growth of biological and medical research. One of the earliest methods that had such a big impact was the so-called Sanger sequencing (also known as the chain terminator or the dideoxy method), which was developed by the Nobel Laureate Frederick Sanger and his colleagues in 1977. Sanger sequencing (Figure 5.16) relies on a DNA polymerase to generate a copy of the ssDNA to be sequenced following the 5′ to 3′ directionality starting from a primer. When the DNA to be synthesised is a product of fragmentation achieved using a restriction enzyme, that information can be used to select primers annealing to the same restriction site. Techniques like PCR can also be used to obtain sufficient amounts of template DNA to be sequenced. Normally, polymerases extend the primers using deoxynucleoside triphosphates (dNTPs), and the novelty of the Sanger methodology is the use of chemically synthesised 2′,3′-dideoxynucleoside triphosphates (ddNTPs) that lack the usual 3′-OH group. Therefore, when a ddNTP is incorporated, no further extension can occur and so the DNA synthesis is terminated. This dideoxy method requires to perform the DNA copying in four different reaction mixtures, each containing all four natural dNTPs and only one ddNTP (e.g. ddATP only). This means that the ddNTP will be added at a random site having a specific nucleobase and in doing so it will terminate the DNA synthesis. The 32P-labelled DNA fragments produced are size separated using a denaturing polyacrylamide–urea gel, and since the four reactions with the different chain terminators run in parallel, it is possible to reconstruct the sequence of the original DNA just by reading the gel results. A more modern version of Sanger sequencing that is amenable to automation uses ddNTPs that have dyes attached to them (Figure 5.17). This means that each nucleobase is associated with a different fluorophore and the sequence can be determined based on the emission wavelength. Thanks to this, it is not necessary to

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Figure 5.16: Sanger sequencing. A DNA polymerase copies the DNA to be sequenced using a mixture of dNTPs and ddNTPs. Only one ddNTP is present in each reaction mixture, which means that the polymerase extension will be terminated at a random site having a specific nucleobase. Gel electrophoresis allows to visualise the production of DNA copies terminated at different positions and, therefore, it allows the reconstruction of the original DNA sequence.

separate the sequencing reactions into four different ones, and four natural dNTPs and four fluorescent ddNTPs are used at the same time. As for the original Sanger method, the polymerase extension is terminated at a random location when a ddNTP is incorporated, and depending on the colour of the fluorescence, the nature of the ddNTP can be determined. To increase the throughput, the polyacrylamide gel is replaced by capillary electrophoresis, where arrays of capillary tubes are used allowing the automation of sample preparation/loading and data collection/analysis. Since using 96 capillaries at the same time is not uncommon and since the automation requires little hands-on time, this system allows reading ∼1.6 million bases per day, compared to the old Sanger method that allowed only ∼25 000 bases per year due to the highly manual process.

5.5.2 Illumina sequencing: an example of short-read next-generation sequencing Although the dye terminator Sanger sequencing was pivotal for the success of the Human Genome Project and it was a transformative technology, its throughput was still too low to make whole human genome sequencing common and affordable. The whole Human Genome Project took ∼13 years to complete for a cost of about

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Figure 5.17: Dye terminator Sanger sequencing. Using ddNTPs labelled with a different dye depending on the nucleobase being probed allows to perform the dideoxy method with a single reaction. Capillary electrophoresis is then used to separate the various fragment, and the fluorescent signal generated by the dyes is used to read the sequence of the DNA under study.

$2.7 billion, with the first draft taking ∼15 months and costing about $300 million. Therefore, new technologies were needed to democratise DNA sequencing and the advent of next-generation sequencing (NGS) techniques allowed to greatly reduce the cost and the time required for sequencing, making it a commonly used technique with a cost to sequence a human genome now below $1 000 (Figure 5.18). Multiple sequencing techniques have been developed and commercialised over the years with new ones constantly being researched, therefore, listing them all is beyond the scope of this book. Rather than giving a full chronological overview of the techniques, only a couple of examples will be described and the division will be between short read second-generation sequencing, where short fragments of DNA (up to ∼500 bases) are read mostly using ensembles of copies of the same DNA sequence, and long read third-generation sequencing, where long DNA fragments (even 30 000 bases or more) are sequenced at a single-molecule level. A prominent example of short read sequencing is the technology used by Illumina (San Diego, USA), which was initially developed by Solexa (based in Cambridge, UK, and

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bought by Illumina in 2007). This amplifies fragments of genomic DNA in clusters on the surface of a flow cell, where reagents are flushed to perform a sequencing by synthesis (SBS) process that, in a similar way to the dye terminator Sanger method, uses fluorescently labelled dNTPs to decipher the DNA sequence. The first stage for every sequencing technology is the preparation of the DNA to be sequenced, a step commonly called library preparation. It is important to highlight that the library preparation methodology depends on the sequencing technology that will be used and on the sequencing application of interest (e.g. whole genome vs. whole exome vs. targeted sequencing). This means that there is a really high number of methods to prepare DNA samples for sequencing, and as an example Figure 5.19 shows the TruSeq™ DNA PCR-Free method for whole genome sequencing on Illumina instruments. In this case, the genomic DNA is fragmented using a mechanical methodology (usually sonication), which yields fragments of different sizes that have overhangs (sticky ends). After this, a repair step is performed where a polymerase fills the overhangs, thus leading to blunt-end dsDNA fragments. A kinase then performs the phosphorylation (i.e. the addition of a phosphate group) of the 5′-ends of the duplexes and after a size selection step, used to obtain a more uniform length of fragments, the 3′-ends have a single A added in a process commonly called A-tailing. After this, sequencing adapters (having a 3′-T overhang and a 5′-phosphate) are added and ligated to both ends of the fragments and, after a final size selection step (which removes byproducts like adapter dimers), the sequencing library is ready for use. The sequencing adapters are what makes this TrueSeq™ process specific for Illumina sequencers, as

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Sequencing Figure 5.19: TruSeq™ DNA PCR-Free library preparation. Genomic DNA is fragmented by sonication and the sticky ends of the fragment are repaired forming blunt-end fragments. The 5′-ends are phosphorylated and, after a size selection step, a single A is added to the 3′-end. The sequencing adapters are then ligated to the fragments and, after a final size selection step, the library is ready for sequencing. The sequencing adapters contain a P5 and a P7 sequence to allow hybridisation to the P5 and P7 oligonucleotides present on the surface of the flow cell. Two sequencing primers (SP) are also included: one for the first read (R1) and one for the second (R2). Finally, two indexes can be part of the adapter to allow multiplexing different samples at the same time.

the P5 and P7 sequences are the ones used to hybridise DNA to the oligonucleotides present on the surface of the flow cells used in this technology. Moreover, the adapters carry the sequences specific for Illumina sequencing primers for read 1 and 2 (R1 SP and R2 SP) and optional indexing sequences, which allow to run multiple samples within the same sequencing run that can be then divided bioinformatically. After the dsDNA sequencing library is prepared, it needs to be denatured (often using diluted NaOH) to its ssDNA form in order to be able to hybridise to the P5/P7 primers present on the surface of the flow cell. The idea is to produce copies of the hybridised DNA fragments yielding to clusters of DNA having the same sequence. This means that the signal generated would be an ensemble one, which improves the

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signal-to-noise ratio and so the accuracy. The first process developed by Illumina to cluster DNA on the flow cell is called Bridge Amplification (BridgeAmp) and Figure 5.20 shows a schematic of how it works. After the hybridisation of the sequencing library, a polymerase performs a first extension generating the complimentary copy of the initial DNA molecule. The original library is then dehybridised and

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removed from the flow cell. This means that the first extension DNA strand is able to form a bridge with the P5/P7 primers on the surface, which in turn can be extended by a polymerase to form a copy of the initial library molecule. This process is repeated cyclically until clusters of an adequate size are produced. The flow cells used with this methodology do not have any form of nano-structuring and, therefore, the distribution of the clusters is random. More recent version of Illumina instruments use patterned surfaces, where the surface has regularly spaced nano-wells that should contain one cluster each. This change required the development of a new clustering method called Exclusion Amplification (ExAmp), where the seeding (hybridisation) and amplification step are performed at the same time to avoid having more than one sequence/fragment forming a cluster within a well (hence one sequence excludes the others). Once the clusters are formed, the sequencing process can start (Figure 5.21) and since they contain both the sense and the antisense strands, the first step is a linearisation that cleaves the antisense strand allowing to read the library DNA in only one directionality. This is the beginning of read 1, where a sequencing primer is added and a polymerase extends it using fluorescent dNTPs that are used to decipher the sequence. Usually Illumina sequencers use this process to read up to 150 bases in a row with some instruments going up to 300. If a paired-end workflow is used, then the sequencer can read double the amounts mentioned earlier. This is because after the read 1 synthesis product is finished, it can be dehybridised and removed from the flow cell, thus allowing the re-amplification of the DNA attached to the surface in a similar fashion to what described for the clustering process. This means that a second linearisation can be performed, where this time the sense strand is cleaved and removed so that the antisense one can be read instead after the addition of a read 2 sequencing primer. This means that during a, for example, 2 × 150 process the sequencer reads the first 150 bases of a fragment from the sense strand, while it reads the last 150 bases from the antisense one, thereby yielding a fragment where the sequence of the ends is known, while the sequence of the middle part is unknown (Figure 5.22). These fragments can be then aligned with a reference genome, and this process is helped by the fact that portions of the sequences overlap with each other. This means that the same area of the genome is covered multiple times and it is not uncommon to achieve 30× or higher coverage, thereby allowing to improve the accuracy by reducing errors through these consensus sequences. Illumina sequencing works through a SBS process (Figure 5.23), during which fluorescent dNTPs are used to extend a sequencing primer, thus generating a copy of the genomic DNA analysed. The system has some similarities with the dye terminator Sanger method, as also in this case the colour of the fluorophore used correlates to the nucleobase present. However, Illumina SBS uses a reversible terminator that allows the insertion of only a single nucleobase by a polymerase, but that can also be unblocked to allow the addition of another dNTP later on. A 3′-O-azidomethyl blocking group is used for this, as it prevents polymerase extension by protecting the

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Figure 5.21: Illumina paired-end sequencing workflow. At the end of clustering, both the sense and antisense strand are present, therefore, a linearisation step removes the antisense so that the cluster can be read in one directionality only. A sequencing primer (R1 SP) is hybridised and it is extended to read the sequence of the genomic fragment under study (read 1). After the extension terminates, the DNA strand produced is dehybridised and the DNA on the surface is re-amplified. A second linearisation step cleaves the sense strand, and now the antisense one can be copied and deciphered by extending a second sequencing primer (R2 SP) during the so called read 2.

3′-OH and it can be subsequently removed using a phosphine like TCEP. Also the dye is cleavable thanks to the presence of the same azido group in the linker, which means that when the 3′-OH is deprotected, the dye is also removed. The process then works in a cyclical fashion, where a polymerase adds one fluorescent dNTP during the incorporation step, which is then excited by a light source (like a laser), and the fluorescence wavelength and intensity are measured. The system uses a microscope to work only on a portion (tile) of the flow cell at a time, and by

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Figure 5.22: Paired-end reads and the alignment to the reference genome. During the paired-end workflow, the two ends of a fragment are read (top) while the middle part remains unknown. This means that when all the sequences of the fragments are aligned to the reference genome (bottom), some of them will partially overlap covering the same area, while the unknown portions of each fragment are deciphered by the read 1 or 2 of other fragments.

moving the stage, the entire flow cell can be imaged tile by tile. After the imaging is complete, the 3′-blocking group and the dye are cleaved and so the cycle can start again by incorporating the next dNTP. The process continues until the desired read length is achieved or until the quality of the sequencing deteriorates. In fact, this deterioration due to the specific chemistry used is the reason why the maximum length for a single read remains in the region of 150–300 bases. Figure 5.24 shows a schematic representation of a portion of a flow cell during the various imaging cycles. As it is possible to see, the colour of the various spots (clusters) changes cycles after cycle, thereby allowing to decipher the original DNA sequence. The figure also highlights the differences between the two types of flow cells used by Illumina. While random flow cells have clusters randomly distributed on the surface that leave variable amounts of unused space, patterned flow cells that have nanostructured surfaces with nano-wells can achieve a better control of clusters size and spacing by packing them in an ordered fashion, thus improving the amount of data/ clusters contained per image. Another advantage of the patterned approach is that the clusters will always be in the same spots and so there is no need to map the position of the various clusters at the beginning of the run. All of this translates into higher sequencing outputs, while having lower sequencing times and costs.

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Over the years, Illumina developed different SBS chemistries (Figure 5.25) with the original one being the four-channel one. For this, as described so far, four fluorescent dyes (one for each nucleobase) are used, which means that four different pictures of the entire flow cell have to be taken during the scanning step of every cycle. This is of course a time-consuming step and therefore a two-channel chemistry was developed, as it meant having to take only two pictures per cycle rather than four. Two-channel SBS has a green fluorophore on the T and a red one on the C. The A can be both red and green, while G does not have a dye at all and it is therefore “dark”. When plotting the green fluorescence intensity versus the red one on a scatterplot, it is possible to see that four clouds of clusters form at the edges of a rough square. This scatterplot can then be used to decode the nature of the dNTP added to each cluster. Interestingly, the new NextSeq 2000 and 1000 use blue and green fluorophores rather than green and red, this is in order to be able to image smaller features. Finally, the one-channel chemistry was developed for the iSeq 100, which uses complementary metal-oxide semiconductor (CMOS) chips. On this instrument, the nano-wells are fabricated over the CMOS chip, and they are aligned over each photodiode (pixel). One-channel SBS keeps a dark G and a green T during both

Figure 5.25: Types of SBS chemistries. Four-channel chemistry uses four different dyes, one for each nucleobase and so four images need to be taken. Two-channel chemistry uses only red and green dyes, where T is always green, C is always red, A is green and red, while G does not have any dye attached to it and so it is dark. If the intensities in the green and the red channel are plotted on a scatterplot, it is possible to see how the clouds of the fluorescence intensities for each cluster in a cycle distribute roughly on the edges of a square (bottom plot). One-channel chemistry uses only green dyes. G remains dark, T is always green, A has a fluorophore attached for the first picture (which is then removed) and C has a dye that is added just before the second picture is taken.

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images, while C and A are either dark or green in each of the images. During the first picture, C is dark while A has a green fluorophore attached to it. After a chemistry step, the A dye is removed and a dye is added to the C and, therefore, C is green and A is dark during the second picture. Compared to the Sanger sequencing used during the Human Genome Project, the latest Illumina technology can achieve higher throughputs and therefore lower costs. For example, the NovaSeq 6 000 with two S4 flow cells can analyse ∼6 000 Gb (gigabases) in about 2 days, which is the equivalent of ∼48 whole human genomes having a 30× coverage, and all of this can be achieved for less than $1 000 per genome. Of course, it is important to remember that Illumina is not the only player in the short read NGS field and it also not the only one using an SBS approach. It is, for example, worth remembering Ion Torrent (commercialised by Thermo Fisher Scientific, Waltham, USA) with its technology licenced from DNA electronics (London, UK) and MGI (Shenzhen, China) with the technology developed by Complete Genomics (San Jose, USA). Ion Torrent works by detecting the hydrogen ions released during the polymerisation of DNA thanks to an ion-sensitive field-effect transistor (ISFET) sensor. This means that no modified dNTP is needed, but the four nucleotides need to be added during separate cycles. MGI uses a system closer to the one used by Illumina, as fluorescently labelled dNTPs with a reversible blocker are employed as well. However, the “clustering” technology is different as DNA nanoballs (DNBs) are used instead of forming clusters directly on the surface of the flow cell. DNBs are produced by rolling circle amplification (RCA), which is used to replicate circular ssDNA by forming a long molecule containing multiple copies of the original cyclic DNA.

5.5.3 Pacific Biosciences and Oxford Nanopore Technologies sequencing: examples of long-read third-generation sequencing The short-read technologies mentioned in the previous section allow to have highthroughput and low-sequencing costs, making them ideal to re-sequence genomes (e.g. to sequence multiple different human genomes). However, this kind of technology is less suitable for de novo sequencing of a new genome, as having longer reads facilitate the generation of the scaffold of the genome as the areas of overlap between different fragment are longer. Two main technologies, both working at a singlemolecule level, should be mentioned in this domain: Pacific Biosciences (PacBio, Menlo Park, USA) with its single-molecule real time (SMRT) sequencing and Oxford Nanopore Technologies (ONT, Oxford, UK) with its biological nanopores. PacBio SMRT sequencing (Figure 5.26) starts with fragmented genomic dsDNA that can have a length from 1 kb to more than 100 kb. To this, stem loop adapters called SMRTbell adapters are ligated forming a dumbbell shaped circular DNA. A polymerase and a primer are added to the dumbbell DNA and this complex is

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Figure 5.26: PacBio sequencing. The sequencing library (top) used in PacBio sequencing is dumbbell shaped as the SMRTbell adapters are stem loops that cyclise the genomic dsDNA fragments. A primer and polymerase are added to the dumbbell library which is then loaded on a SMRT cell where one polymerase/library conjugate is loaded in each ZMW, with the polymerases sticking to the bottom of the well. Each well is illuminated from the bottom and a continuous “movie” is recorded. As the polymerase incorporates a fluorescence-labelled, phospholinked nucleotide (bottom right) the dye stays in the detection zone of the ZMW long enough to be detected and so a positive peak is recorded in the fluorescence movie (plot in the right) with the colour being linked to the nucleobase being incorporated. Once the incorporation is completed, the phospholinked dye is released and diffuses away causing a drop in the fluorescence signal.

loaded on a SMRT Cell, which is a special chip with millions of zeptolitres (∼10−21 L) wells called zero-mode waveguides (ZMW). These wells are ∼70 nm large in diameter and they are fabricated in a ∼100 nm aluminium film deposited on a transparent glass substrate. Only one polymerase + DNA complex should be loaded in each ZMW (following a Poisson loading) with the polymerase binding to the bottom of

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the well. ZMWs are illuminated from below, and given the extremely small diameter of the well and the wavelength of light, only the lowest part of the ZMW is illuminated giving a detection volume (where fluorophores can be excited) of only ∼20 zeptolitres. The sequencing reaction follows a SBS methodology, as fluorescently labelled phospholinked nucleotides are used to extend a primer, thereby generating a copy of the original DNA. Instead of normal fluorescent dNTPs, deoxynucleoside hexaphosphates (dNHPs) are used, which have a dye attached to the last phosphate group via a linker. When one of these fluorescent dNHPs is being incorporated by the polymerase, the dye is kept within the detection zone of the ZMW for long enough to give rise to a measurable fluorescent signal, and the colour of the fluorescence is linked to the nucleobase being incorporated. Once the incorporation is terminated, the cleaved polyphosphate with the dye is released and diffuses away, causing a drop of the signal back to the background fluorescence. With SMRT sequencing, the incorporations events occur non-stop, as no reversible terminator is used. This means that the fluorescence needs to be followed continuously and so a “movie” rather than a photo of the fluorescence generated by all the ZMWs is recorded. Since the DNA loaded on the SMRT cell is circular, the same molecule can be read multiple times, and since sequencing errors are stochastic, it is possible to achieve high accuracy levels with this circular consensus sequencing (however, this can be done only with inserts between 10 and 30 kb). Finally, it is interesting to know that since a movie of the incorporation events is recorded, the PacBio technology tracks the rate of DNA polymerisation. This can be exploited to detect the presence of epigenetic modifications (like methylation), as they cause small changes in polymerisation kinetics and, therefore, they have an effect on the timing of when the fluorescent signals are measurable. ONT sequencing (Figure 5.27) is a substantially different technique compared to the others presented before, as it does not use a SBS approach nor it reads the nature of the nucleobase being sequenced indirectly (e.g. via a fluorescent signal). In fact, ONT sequencing uses protein nanopores to read single molecules of DNA and RNA directly without any modification needed and, because of this, ONT sequencing has also the potential of detecting the presence of epigenetic modifications (like methylation), which is something ONT has been working on. The protein nanopore permeates a high electrical resistance membrane made with a synthetic polymer that separates two reservoirs filled with a saline solution. When a voltage is applied, a measurable ionic current is generated through the nanopore, and this open pore current is disrupted whenever a molecule translocates through the pore causing a temporary blockade. The drop in the open pore current depends on the extent of the blockade and so this signal can be used to decipher which molecule is passing through. An array of singly addressable pores can be prepared to allow parallel analysis of multiple sequences, which is a necessary step to increase the throughput.

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For example, the flow cell chip used on the MinION has 512 nanopores, while the PromethION can analyse 48 chips having 3 000 nanopores each.

1 kb to >2 Mb fragment Motor protein Adapter

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A Time Figure 5.27: ONT sequencing. Long DNA fragments are ligated with adapters that have a motor protein pre-attached. A protein nanopore permeates an electrically resistant membrane, and when a voltage is applied, an ionic current is generated. The motor protein unwinds the dsDNA and feeds it through the nanopore at a constant rate. Since the DNA passing through the nanopore disrupts the ionic current, this change can be used to identify the sequence of the DNA under study. The plot at the bottom shows a schematic and simplified version of the decoding where the amount of current disruption is correlated to a specific nucleobase passing through. In reality, ONT sequencing reads k-mers (usually of 5–6 bases long) passing through the pore and not single bases (which means there are more than 1 000 possible signals rather than just 4).

ONT libraries are made from long DNA or RNA fragments (typically one to hundreds of kilobases, but several megabases long fragments were successfully sequenced as well), which have adapters ligated to their ends. The adapters carry a motor protein, which has the role of unwinding duplexes and feeding single-stranded oligonucleotides through the pore at a constant rate (usually 450 bases/s for DNA and 70 bases/s for RNA), which is necessary to avoid a translocation too fast to generate a clear signal. The nanopore presents a narrow section within the barrel part that acts as a reader and, as a ssDNA or ssRNA molecule passes through it, the ionic current is disrupted, and the intensity of this event correlates to a specific nucleobase or better to a specific nucleobases sequence. In fact, the approach used by ONT is k-mer based as, when translocating through the pore, multiple bases affect the signal. Normally five to six bases are part of the k-mer, which means that rather than having just four levels

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of current disruption (one for each nucleobase) there are more than 1 000 signal levels and that means that algorithms to interpret the current signal and perform the base calling are essential.

Further readings Books and book chapters Elkins KM. Chapter 4 – DNA Extraction. In: Elkins KM, ed. Forensic DNA Biology. San Diego, Academic Press, 2013, 39–52. Griffiths AJF, Wessler SR, Lewontin RC, Gelbart WM, Suzuki DT, Miller JH. Introduction to genetic analysis, 8th ed. New York, W.H. Freeman and Co., 2005. Johnson AD, Alberts B, Morgan D, Lewis J, Roberts K, Raff M, Walter P. Molecular biology of the cell, 6th ed. New York, WW Norton and Co., 2014. Korlach J, Bjornson KP, Chaudhuri BP, Cicero RL, Flusberg BA, Gray JJ et al. Chapter 20 – Real-Time DNA Sequencing from single polymerase molecules. In: Walter NG, ed. Methods in enzymology. Academic Press, 2010, 431–455. Kulkarni S, Pfeifer J. Chapter 5 – Emerging DNA sequencing technologies. In: Kulkarni S, Pfeifer J, eds. Clinical Genomics. Boston, Academic Press, 2015, 69–76. Manz A, Dittrich PS, Pamme N, Iossifidis D. Bioanalytical chemistry, 2nd ed. London, UK, Imperial College Press, 2015. Nelson DL, Cox MM. Lehninger principles of biochemistry, 7th ed. New York, USA, W.H. Freeman and Co., 2017. Voet D, Voet JG. Biochemistry, 4th ed., Hoboken, NJ, USA, John Wiley & Sons, 2011.

Reviews and research papers Bartlett JMS, Stirling D, Short A. History of the polymerase chain reaction. Methods Mol Biol 2003, 226, 3–6. Basu AS. Digital assays part I: Partitioning statistics and digital PCR, SLAS TECHNOLOGY: Translating Life Sciences Innovation. 2017, 22, 369–386. Bumgarner R. Overview of DNA microarrays: Types, applications, and their future, Curr Protoc Mol Biol. 2013, 101, 22.1.1–22.1.11. Chen F, Dong M, Ge M, Zhu L, Ren L, Liu G et al. The history and advances of reversible terminators used in new generations of sequencing technology. Genomics Proteomics Bioinformatics 2013, 11, 34–40. Feng Y, Zhang Y, Ying C, Wang D, Du C. Nanopore-based fourth-generation DNA sequencing technology. Genomics Proteomics Bioinformatics 2015, 13, 4–16. Goodwin S, McPherson JD, McCombie WR. Coming of age: Ten years of next-generation sequencing technologies. Nat. Rev Genet 2016, 17, 333–351. Heid CA, Stevens J, Livak KJ, Williams PM. Real time quantitative PCR. Genome Res 1996, 6, 986–994. Logsdon GA, Vollger MR, Eichler EE. Long-read human genome sequencing and its applications. Nat Rev Genet 2020, 21, 597–614.

Further readings

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Metzker M. Sequencing technologies – the next generation. Nat Rev Genet 2010, 11, 31–46. Quan P-L, Sauzade M, Brouzes E. dPCR: A Technology review. Sensors 2018, 18, 1271. Sanger F, Nicklen S, Coulson AR. DNA sequencing with chain-terminating inhibitors. Proc Natl Acad Sci 1977, 74, 5463. Steinbock LJ, Radenovic A. The emergence of nanopores in next-generation sequencing. Nanotechnology 2015, 26, 074003. Tan SC, Yiap BC. DNA, RNA, and protein extraction: The past and the present. Biomed Biotechnol 2009, 574398 (10 pp.).

6 Nanotechnologies for bioanalysis 6.1 Introduction Researches concerning the application of nanotechnologies in the field of bioanalytical chemistry and biosensors witnessed an impressive growth in the last two decades, thanks to the development of new concepts and knowledge in the nanomaterial field as well as to the capability of building complex nanostructures that can act as miniaturised lab-on-chip units. The terms “nanomaterial” and “nanostructure” will be used here to describe engineered materials that are intentionally constructed or synthesised for specific purposes, excluding naturally occurring nano-sized bio-objects such as biological cells, organelles and biomacromolecules. Further important progresses towards real-world applicability and commercialisation of nanostructured biosensors and devices are expected in the very next future, in particular for point-of-care diagnostics and decentralised bioanalysis. Indeed, nanostructures and nanomaterials are attracting considerable interest both as novel diagnostics tools and as therapeutic agents. While for the latter applications, there is still a need for more information and detailed studies for understanding the pharmacokinetics, distribution in the human body, side effects and safety profiles of nanomaterials, the former application (namely the use as advanced diagnostic tools for body fluid samples) seems less problematic and more feasible in the immediate future. In fact, several companies already market bioanalytical instrumentation that exploits nanomaterials and nanodevices for routine diagnostic purposes.

6.2 Classification of nanomaterials A nanomaterial is defined as a material presenting at least one critical dimension in the nanometre (10−9 m) range. A major advantage of nanomaterials is the high surfaceto-volume ratio, which can maximise the analytical sensitivity. Moreover, lowering at least one dimension of the material to the nanometre range introduces special optical, electrical or magnetic properties together with the possibility to perform extremely localised bio-functionalisations.

6.2.1 Zero-dimensional nanomaterials Zero-dimensional (0D) nanomaterials are all the objects with all dimensions in the nanometre range, typically in the 2–50 nm range. All nanoparticles (NPs) with different symmetrical shape (nanospheres, nanocubes, nanopyramids, etc.) belong to https://doi.org/10.1515/9783110589160-006

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this group. They find extensive analytical application as signal tracers, transducers, immobilisation supports, labels, carriers, emitters or catalysts. Noble-metal NPs (mainly Au and Ag, but also Pt, Pd and Ru NPs) find wide bioanalytical application thanks to their easy preparation and functionalisation, excellent electrical conductivity and special optical properties related to localised surface plasmon resonance (LSPR) phenomena. Semiconductor NPs made of elements of the II–IV, III–V or IV–VI groups of the periodic table (such as CdS, CdTe, CdSe, ZnS, PbS, Cu2O, TiO2 and ZnO) are exploited especially for their luminescence or photochemical properties. For example, quantum dots are semiconductor NPs that display optoelectronic properties which can be tuned by varying the particle size. In particular, the wavelength of emitted fluorescence increases by increasing the particle size (see Figure 6.1). Because of quantum confinement, in quantum dots the difference in energy between the conduction and valence band (responsible for the fluorescence emission) is discrete, and decreases as the size of the quantum dot increases.

Figure 6.1: Red shift of the emitted fluorescence in quantum dots, for example of CdTe, of increasing size, from approximately 1–2 nm (blue-green) to 5–8 nm (orange-red) (The picture on the right is from dmelnikau/iStock/Getty Images Plus).

Other NPs possess magnetic properties and are used as nanocarriers in many electrochemical and optical biosensors. Magnetite (Fe2O3) can present different magnetic properties depending on the particle size displaying ferromagnetic behaviour for particles in the micrometre scale and superparamagnetic behaviour in the nanoscale.

6.2.2 One-dimensional nanomaterials Nanomaterials such as nanotubes, nanorods or nanowires (NWs) which present one dimension larger than the nano-range are classified as one-dimensional (1D)

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nanomaterials. Electron confinement occurs along the nano-sized dimension (typically radius or diameter), while electrons are delocalised along the long axis. For this reason, 1D nanomaterials are highly sensitive to changes in the surface state. Therefore, they are used to detect analytes that can bind to their surface, which can be functionalised with suitable capture agents, for example, in electrochemical nanosensors. Moreover, thanks to their high surface area/volume ratio, they are used as high surface area materials for biorecognition after functionalisation with enzymes or other biomacromolecules. Nanotubes and nanorods are also frequently employed as nanocarriers. 6.2.2.1 Carbon nanotubes Probably, the most widely studied and employed 1D nanomaterials are carbon nanotubes (CNTs). Carbon nanomaterials such as graphene and CNTs have been extensively studied and applied for biosensing purposes. Graphene and CNTs present similar carbon structures being composed by planar sheet of sp2-bonded carbon atoms organised in hexagonal lattices which allow the delocalisation of π electrons (Figure 6.2). This feature makes graphene and CNTs excellent electrical conductors to be successfully employed, in particular, in electrochemical biosensors. The structure of graphene and CNTs allows the formation of strong π–π interactions with aromatic molecules, which can strongly bind to the nanomaterial surface. Depending on the number of “enrolled” graphene sheets, CNTs are classified as single-wall carbon nanotubes (SWCNTs, with typical diameters between 0.4 and 2 nm) and multi-wall carbon nanotubes (MWCNTs, with diameters going from 2 to 100 nm, and interlayer distance around 0.3–0.4 nm, see Figure 6.2). The CNT length ranges from tens of nanometres to several micrometres. CNTs can be synthesised via electric arc discharge, laser ablation, high-pressure carbon monoxide synthesis or catalytic chemical vapor deposition (cCVD). cCVD produces high-quality CNTs with high yield, while the other methods generate also a variety of by-products requiring a following cleanup to purify the product. The ends of the CNTs can be open or close. The open ends of CNTs present properties comparable to edge plane highly oriented pyrolytic graphite (HOPG), while their walls are comparable to basal plane HOPG.

HOPG

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Figure 6.2: sp2 carbon structures of highly oriented pyrolytic graphite (HOPG) with exposed basal planes, graphene, single-wall and multi-wall carbon nanotubes (SWCNT and MWCNT, respectively). HOPG is a macromaterial, graphene is a 2D nanomaterial and SWCNTs and MWCNTs are 1D nanomaterials.

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CNTs can be used as electrodes in electrochemical biosensors as well as modifying agents to promote electron transfer between biorecognition molecules and electrodes. SWCNTs have been employed as molecular wires to establish electrical communication between electrodes and the prosthetic group of redox proteins, in order to prepare reagent-less third generation electrochemical biosensors. Carbon nanomaterials can present surface defects such as oxygen-containing groups. The presence of defects lowers the conductivity of the CNT because each sp3 carbon atom interrupts the electron delocalisation. On the other hand, these defects are useful for the CNT functionalisation and biosensing applications. For instance, carboxylic groups can be introduced on CNTs by acid treatment (see Figure 6.3) and employed to covalently bind primary amine-containing molecules. Otherwise, the functionalisation with carboxylic groups can be advantageous to electrostatically adsorb CNTs onto positively charged surfaces. Highly carboxylated CNTs can bind enzymes, antibodies and DNA, or can be functionalised with redox or fluorescent labels. Alternatively, CNTs can be functionalised by introducing reactive aryl derivatives via reduction of aryl diazonium salts (see Chapter 2).

HOOC

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Figure 6.3: Single-wall carbon nanotube before and after functionalisation by acid treatment.

Alternative non-covalent functionalisation routes are offered by π–π stacking and hydrophobic interactions between the hydrophobic sidewalls of CNTs and hydrophobic domains of proteins. Thanks to their dimensions, CNTs can have high loading capacities, with an excess of 9 000 enzyme molecules per CNT, significantly improving the analytical sensitivity of biosensors. 6.2.2.2 Metal nanorods Nanorods are rod-like-shaped nanoparticles, the shorter form of nanowires. The aspect ratio (AR), which is the ratio between length and diameter, differentiates nanorods from NWs. Nanorods have typical diameters in the range of a few tens of

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nanometres, with lengths going from several tens to a hundred of nanometres, giving AR values between 3 and 10. Nanorods are obtained by template-assisted growth, with seed-mediated synthesis being the most popular method (see Section 6.3.2). The trick is in the choice of suitable ligands which act as shape control agents binding to different facets of the crystal lattice with different strengths. This allows different faces of the nanorod to grow at different rates, producing an elongated object. Nanorods can be synthesised from metals or semiconducting materials. The precursor can be reduced by using chemical or electrochemical reduction. Nanorods are studied for application, for instance, in display technologies since it is possible to tune their reflectivity by changing their orientation with an applied electric field. Nanorods are also under investigation for applications in cancer therapeutics since they absorb in the near-infrared generating heat. Such a heat can destroy the tumour cells to which functionalised nanorods have bound. In bioanalysis and biosensors, gold nanorods are used as label carriers and electrode modifiers for electrochemical biosensors, similarly to gold nanoparticles (AuNPs). 6.2.2.3 Nanowires and nanofibres Nanowires and nanofibres are high-AR (AR > 100) nanomaterials with cylindrical geometry. They can be made of metals, oxides and semiconductors (carbon or silicon), conducting or insulating polymers. NWs with diameters approaching a few nanometres (named quantum wires) display important quantum mechanical effects since they present discrete values of electrical conductance. Such discrete values are due to quantum mechanical restraint on the number of electrons that can travel through the wire at the nanometre scale. Quantum wires are important for advanced electronic applications while, in the field of molecular diagnostics and biosensors, slightly larger NWs (with diameters > 30–40 nm) are preferred, presenting electrical conductivity comparable to that of the bulk material. NWs can be easily functionalised with biomolecules acting as nano-sized supporting material or nanocarriers, with metallic and conducting polymer NWs finding application in electrochemical biosensors. The most widely used methods to prepare NWs for biosensing aims are hard template synthesis and hydrothermal growth (see Section 6.3.2). By using the electrospinning technology, it is even possible to obtain nanofibres with lengths of the order of metres.

6.2.3 Two-dimensional nanomaterials Two-dimensional (2D) nanomaterials are materials with only one nano-sized dimension, while the other two dimensions are much larger. They resemble a large, but very thin sheet (like a paper sheet). Often, the thickness of the material can be decreased

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down to a single atom. This is the case for the most well known 2D nanomaterial, that is graphene. Graphene (already shown in Figure 6.2) was the first “modern” 2D nanomaterial to be isolated in 2004 by Geim and Novoselov (Nobel laureates in 2010 for this discovery). Graphene is a covalently bonded hexagonal lattice of carbon atoms, just one atom thick (~0.14 nm). It is the basic structure of other carbon allotropes, including graphite, charcoal, CNTs and fullerenes. Thanks to its unique band structure, electrons move through it in the two-dimensional plane at very high speeds. Indeed, graphene has the highest known thermal and electrical conductivity, with current densities 106 times greater than copper. It is optically transparent and possesses the highest tensile strength of any known material (at the same weight, graphene is hundreds of times stronger than most steels). Due to all these exceptional properties, as well as to its large surface-to-volume ratio and excellent biocompatibility, graphene has drawn increasing interest for use in biosensing applications. Its surface, with specific defects and chemical functionalities, is an excellent reaction site for catalysis and electrochemical biosensing. The successful application of graphene in various biosensing technologies has sparked exploration of other 2D nanomaterials for bioanalytical applications. Here, we briefly describe some of the most successful 2D nanomaterials employed or under investigation for bioanalytical and biotechnological applications. Hexagonal boron nitride (hBN) is another sp2 compound isomorph with graphene, as it presents the same crystallographic appearance, but with boron and nitrogen atoms instead of carbon (Figure 6.4). Despite having the same hexagonal lattice structure, graphene and hBN present very different physical properties: graphene is an

Figure 6.4: Structure of graphene (A) and hexagonal boron nitride (B). The spheres represent the atoms of carbon (in white), boron (in blue) and nitrogen (in pink).

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excellent conductor, while hBN is a wide-bandgap insulator. Because of that, boron nitride nanosheets are often used as dielectrics in electrical devices. Transition metal dichalcogenides (TMDs): they are 2D nanomaterials with chemical formula MX2, where M is a transition metal (such as Mo or W) and X is a chalcogen (such as S, Se or Te). TMDs are constituted by three atomic layers, with the metal layer in between two chalcogenide layers. TMDs can present various crystal structures. The most common is the 2H phase with trigonal prismatic geometry, which results in semiconducting characteristics (this is typical of MoS2, WS2 and MoSe2). Such semiconducting TMDs are very attractive for optoelectronics. Another possible structure is the metallic 1T phase with octahedral or trigonal antiprismatic geometry, typical of WTe2. Xenes and MXenes: monolayers of silicene (Si), germanene (Ge) and stanene (Sn) are collectively known as Xenes (following the naming convention of graphene). They have a honeycomb hexagonal structure similar to graphene, but are buckled to varying degrees. Recently, 2D analogues of other elemental materials have also been created, such as antimonene (from Sb) and bismuthene (from Bi). Following the same nomenclature, another class of 2D materials created in the last years has been named MXenes, which gathers transition metal carbides and carbonitrides with general formula Mn +1XnTx (where M is an early transition metal, X is carbon and/or nitrogen and T is a surface termination, such as =O, –OH or –F). MXenes have high electric conductivities and show promising results in energy storage applications and gas sensing.

6.2.4 Three-dimensional nanomaterials Given that the nanomaterials described until now presented three (for 0D), two (for 1D) or one (for 2D) nano-sized dimensions, it is logical to think that a material with zero nano-sized dimensions cannot be classified as nanomaterial. In fact, 3D nanomaterials, called also bulk nanomaterials, are not confined to the nanoscale in any dimension, having three arbitrary dimensions above 100 nm. However, a bulk material can be called nanomaterial if it presents a nanocrystalline structure or features at the nanoscale. For example, a 3D nanomaterial can be composed of a multiple arrangement of nano-sized crystals, most often in different orientations, or of bundles of nanotubes or NWs. Other 3D nanomaterials can be bulk powders, nanolayers or nanoporous materials (materials with nano-sized pores). It is difficult to define a proper 3D nanomaterial and give some examples of that since, in principle, any kind of material can be or become a bulk nanomaterial, including metals, silica, crystals and polymers. The only common property among all 3D nanomaterials is the presence of nanostructures which enormously increase the surface area per volume of material. In the course of this chapter, different bulk

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nanomaterials will be described, such as sphere segment void (SSV) substrates, porous metal electrodes and nanowire arrays.

6.3 Synthesis of nanomaterials Usually, the methodolgies used to prepare nanomaterials are classified according to two approaches, named top-down and bottom-up (see Figure 6.5). A top-down approach reduces a large piece of material into small pieces by means of various techniques such as lithography, milling or thermal oxidation, and radiation-induced methods. In bottom-up approaches, the nanomaterial is synthesised by combining constituent atoms or molecules using several common laboratory techniques, including chemical reactions in homogeneous and heterogeneous phase, electrochemical deposition and etching, vapour deposition and self-assembly. The majority of the most recent nanofabrication procedures combines bottom-up and top-down methods, taking the best from both approaches. Indeed, the distinction between top-down and bottom-up methods can, sometimes, look questionable. For example, dip-pen lithography (see below) can be considered a top-down method when used for etching, or a bottom-up approach when used to “write”, i.e. to deposit a nanostructure. In the following, we prefer to use a classification based on the distinction between

Figure 6.5: Top-down and bottom-up approaches for the preparation of nanomaterials.

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methodologies based on physical changes and on chemical transformations. Note that physical methods mainly overlap with top-down approaches and chemical methods with bottom-up approaches.

6.3.1 Approaches based on physical changes A variety of physical methodologies for the fabrication of nanomaterials exists, going from classical photolithography to spin coating, vapour deposition, pyrolysis, laser ablation, plasma deposition/etching, chemical and electrochemical deposition/etching and many others. Most of these methods have been developed and refined for microsystems and microelectronics and, nowadays, they find application also in the nanotechnology field. Here, we will focus only on those techniques that are more specific for the fabrication of nanomaterials and nanostructures, which find application in bioanalytical devices and biosensors. The reader interested in general aspects of microengineering and nanofabrication can find more information in specialised textbooks. 6.3.1.1 Nanolithography The evolution of photolithography to the nanoscale has led to the development of nanolithographic techniques suitable for etching, writing and printing at the nanometre scale. In principle, all these techniques use either light, electrons, focused electron beams or electrostatic forces to selectively create nanostructures and nanopatterns leading to the development of ordered arrays of nanomaterials. The most widely used nanolithographic techniques are briefly described here below. Photolithography In order to apply photolithography to the nanoscale, it is necessary to overcome the diffraction limit by resorting to short wavelength radiations like in the case of X-ray lithography or extreme ultraviolet lithography. Optical lithography is based on the possibility of changing the solubility of a precursor, named photoresist, by the action of an electromagnetic radiation. In the case of positive photoresist, the exposure to high-energy photons results in an increased solubility of the photoresist, which is typically a polymer film. The opposite happens with negative photoresists: the exposure to photons results in a decreased solubility, typically due to photopolymerisation of the precursor. After exposure to the radiation, in order to obtain the desired pattern, it is necessary to wash away the soluble part of the photoresist by using a suitable solvent that acts as developer.

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Electron-beam lithography Electron-beam lithography (EBL) exploits the action of a focused beam of electrons (e.g. generated by an adapted scanning electron microscope, SEM) which is scanned onto a surface coated with an electron-sensitive film such as a thin film of poly(methyl methacrylate) (PMMA), hydrogen silsesquioxane (HSQ) or polycarbonate. This allows to engrave the desired pattern without the need of using a mask. Exposure to the ebeam changes the solubility of the resist so that, by subsequent selective removal of material with a suitable solvent, sub-10 nm resolution can be achieved. Figure 6.6 illustrates, as an example, the steps and the nanopatterns obtained by applying EBL to a metal-coated surface using polycarbonate as high-resolution e-beam resist. (A)

(B) 1 mM

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Figure 6.6: (A) The different steps used in electron-beam nanolithography for the case of a polycarbonate (PC) resist. (B) Electrochemiluminescence imaging of arrays of nanoelectrodes patterned by e-beam nanolithography, obtained using Ru(bpy)32+ luminophore and dipropylamine as co-reactant at the six different concentrations indicated in the figure (M. Sentic et al., Anal. Bioanal. Chem. 2016. Reproduced with permission from Springer-Verlag).

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In particular, the images in Figure 6.6B were obtained by ECL using the patterned nanodots or nanobands as electrodes which electrochemically stimulate the emission of photons at the interface with a solution containing Ru(bpy)32+ (acting as electroactive photoemitter) and tripropylamine as co-reactant (for details on ECL, see Chapter 4). Scanning probe lithography Scanning probe lithography (SPL) allows the patterning at the nanometre scale by etching away unwanted material, or by directly writing a nanopattern of an added material onto a substrate by action of a nanometre-sized scanning tip or probe. The tip used to this aim can be an atomic force microscope (AFM) tip. Dip-pen nanolithography (DPN) is a SPL technique widely used for the aim of creating nanopatterns of biological molecules. For instance, it is possible to use DPN to write patterns of thiolated compounds onto a gold surface. The thiol acts as a molecular ink which is delivered from the scanning DPN tip onto the gold surface through a water meniscus (see Figure 6.7). Note that the deposited thiol can be an alkanethiol, with functional groups (typically amines, carboxylic groups or aldehydes) that can successively react with the desired biomacromolecule (as described in Chapter 2), or a thiolated biomolecule (e.g. thiolated oligonucleotides).

SH SH SH

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Nanoimprint lithography Nanoimprint lithography (NIL) allows to replicate nanopatterns by using nanomoulds that produce mechanical deformation onto an imprint resist (Figure 6.8). The resist is typically a monomer precursor or a polymer that is cured by heat or UV light during imprinting. NIL is capable of producing patterns at sub-10 nm levels with high repetitiveness and production output. Mould

Pressing + UV radiation

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Figure 6.8: Scheme illustrating the basis of nanoimprint lithography (NIL).

6.3.1.2 Milling Ball milling can be used to produce nanoparticles from macro-sized materials. The kinetic energy transfer from balls to powder is behind the reduction in grain size. The size of the obtained NPs depends on the type of mill, milling atmosphere, milling media, intensity, time and temperature. To this aim, shaker mills, tumbler mills, vibratory mills, attrition mills or planetary mills are employed, with the attrition process being widely used for the bulk production of nanomaterials. On the other hand, one limit of the milling process is the imperfect surface and significant crystallographic damage of the obtained materials. 6.3.1.3 Electrospinning Electrospinning is a fibre production method in which a liquid droplet of polymer solution or polymer melt is electrified generating a jet that is stretched and elongates to produce a fibre with diameter in the hundred nanometre range. The apparatus is simple and includes a high-voltage power supply, a syringe pump, a hypodermic needle with blunt tip that acts as spinneret and a conductive collector (see Figure 6.9B). When a potential difference is applied between the collector (grounded) and the spinneret, charges with the same sign as the polarity applied to the spinneret migrate towards the surface of the droplet, producing an excess surface charge that increases with the applied potential. As a consequence, the initial spherical shape of the droplet (in the absence of applied field) is progressively stretched up to turning, at a certain applied potential, into a stream of liquid (named Taylor cone) erupting from the spinneret (see Figure 6.9A). The jet extends initially in a straight line to later undergo

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whipping motion because of electrostatic repulsion. While progressing in its motion, the stream becomes thinner and solidifies, finally producing a solid fibre of constant size gathered on the collector. The collector can be a rotating cylinder acting as a fibre collection coil. The parameters ruling the electrospinning process are the following: a) molecular weight, molecular-weight distribution and architecture (branched, linear, etc.) of the polymer; b) viscosity, conductivity and surface tension of the fluid (solution or melt); c) applied potential, flow rate and concentration; d) distance between the capillary and collection screen; e) temperature and humidity; f) motion and size of the collector; g) characteristics of the needle. Electrospinning is particularly suitable to produce long nanofibres composed of large and complex molecules.

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6.3.2 Approaches based on chemical trasformations 6.3.2.1 Template synthesis A widely applied methodology for the easy synthesis of nanomaterials, in particular metals, oxides, salts and conducting polymers, is the so-called template synthesis. It is based on the use of templating agents that interact with the forming nanomaterial, so directing its growth to finally obtain a nanomaterial with a defined structure or geometry. Two template modes have been developed, depending on the nature of the templating agent. In soft template synthesis, the structure-directing

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agent is a molecular component, while in hard template synthesis the directing agent is a rigid material which can be a nanoporous membrane, mesoporous particles or other nanostructured assembly of rigid materials. Soft template synthesis Soft template synthesis is based on the chemical reduction of metal ions in solution operating in the presence of one or more molecular agents acting as structuredirecting agents. Because of its molecular nature, a soft template does not present a fixed rigid structure but directs the formation of the nanomaterial by means of intra- and inter-molecular interactions such as hydrogen bonding, hydrophobic or hydrophilic interactions, acid–base interactions and complexation, adsorption and surface reactions. The most widely used soft templates are ligands and chelating agents, surfactants, polyelectrolytes and biopolymers. The advantages of this methodology are constituted by its good repeatability, easy execution, low cost and wide accessibility. For the typical case of soft template preparation of metal NPs, a metal ion in solution is reduced by a suitable reducing agent in the presence of a stabilising agent. In the most common method, that is the production of gold nanoparticles (AuNPs) with citrate, the latter acts both as reducing and capping agent. Turkevich et al. developed a synthetic method for preparing AuNPs in 1951: they treated tetrachloroauric acid (HAuCl4) with citric acid in boiling water until observing the appearance of a red/purple colour in the reaction vessel, due to plasmonic interaction between the formed colloidal AuNPs and light. The size of the particles can be controlled by varying the ratio of reducing/stabilising agents as well as the pH of the system. As schematised in Figure 6.10, the capping agent forms a sort of electrostatic shield around the particle which prevents excessive growth as well as coagulation and precipitation of the NPs. Like AuNPs, also silver nanoparticles (AgNPs) can be prepared using citrate as reducing and capping agent, as proposed by Lee and Maisel in 1982, by reaction of AgNO3 solution with trisodium citrate under reflux at 100 °C for 1 h. Other synthetic methods to prepare AgNPs are based on the use of two separate agents, one for the reduction and one for stabilising the NPs. The reducing agent can be sodium borohydride (NaBH4) or hydrazine. Sodium borohydride acts according to the following reaction: Ag + + BH4 − + 3H2 O ! Ag0 + BðOHÞ3 + 3.5H2

(6:1)

The benefit of using a strong reducing agent like sodium borohydride is given by the possibility to achieve high monodispersity. This is because NaBH4 reacts quickly with Ag+ so that silver nitrate is rapidly reduced generating plenty of new nuclei which have no time to grow. Contrarily, a weaker reducing agent such as citrate reacts more

6.3 Synthesis of nanomaterials

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O

OH

O

O

OH O

OH

Citrate

O

HO O

OH

Figure 6.10: Citrate forming an electrostatic shield around a gold nanoparticle (AuNP). Citric acid is commonly employed as both reducing and capping agent in the preparation of AuNPs.

slowly with the metal ions, leading to the formation of a smaller number of larger particles. When borohydride or hydrazine is used as reducing agent, a capping agent such as citrate, poly(vinylpyrrolidone), sodium dodecylsulphate or dodecanethiol must be added to the reaction mixture to prevent coagulation of the formed AgNPs. Moreover, the pH of the medium plays a crucial role in modulating the size and shape of the particles. At high pH, the reduction is faster so that both rod and spherical NPs are produced. On the other hand, at relatively lower pH (5–6), triangular and other polyhedral silver structures are obtained because of the slower reaction rate. Nanoparticles can be prepared by operating in microemulsion. This methodology is used for the synthesis of palladium, rhodium and platinum NPs. To this aim, two separate microemulsions containing salts and reducing agents are mixed together in the presence of an amphiphilic surfactant, which promotes the mixing of the reactants and the formation of the NPs. By using two or more different capping agents, it is possible to obtain anisotropically shaped nanomaterials, such as metal nanostars (Figure 6.11). In this case, in fact, one agent promotes the formation of the central nucleus of the structure, while the other one directs the growth of the tips.

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Figure 6.11: Silver nanostar prepared by one-pot template growth via reduction of AgNO3 with a solution containing hydroxylamine, trisodium citrate and NaOH (M. S. Zalaffi et al., Nano Express 2020. Reproduced under Creative Commons License DEE).

Hard template synthesis The hard template synthesis is employed to produce high-AR nanomaterials, such as nanowires. The technique is based on the use of a template as a mould for the oriented growth of metal structures, which traces the shape of the pores or other nanoscopic defects in a solid surface. This method was first developed using nanoporous mica as template by G.E. Possin in 1970, and then optimised and adapted to other substrates. Nanoporous membranes of different materials are employed to this aim, mainly anodised alumina (with regular pores but rigid and fragile) and tracketched polycarbonate (flexible but with random pore distribution). Alumina membranes, prepared through controlled anodisation of aluminium, are characterised by dense and ordered pores, hexagonally organised in a matrix (Figure 6.12A). For tracketched polycarbonate membranes, the pores are obtained by tracking the polymeric film with high-energy ions (produced in a nuclear reactor or in an ion accelerator) and then a chemical etching in alkaline solution. They are extremely flexible, but present an irregular distribution of the pores (Figure 6.12B).

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Figure 6.12: SEM images of commercially available (A) alumina and (B) polycarbonate nanoporous membranes (P. Ugo et al., Encyclopedia of Electrochemical Power Sources 2009. Reproduced with permission from Elsevier).

Electroless deposition The electroless template deposition of metal ions is based on the use of a chemical acting as reducing agent. According to the methodology introduced by Menon and Martin in 1995, the kinetics of this reaction, namely the electron transfer from the reducing agent to the metal ions, must be slow. If the reducing agent is adsorbed on the surface and on the pores of the template, the deposition starts from the pore walls and then continues inwards. For this reason, if the deposition time is stopped in the early steps, it is possible to obtain hollow nanomaterials, such as metal nanotubes. Instead, when the process is let to proceed until completion, typically after 24 h, the pore structures are completely filled giving rise to nanowires. This is what happens, for instance, during the electroless growth of gold nanowires (AuNWs) using track-etched polycarbonate membranes as templates. As shown in Figure 6.13A, the membrane is first sensitised with Sn2+, then it is dipped in a Ag+ solution and, finally, in an Au+ salt solution. The following step is the addition of formaldehyde which catalytically reduces Au+ to Au° (see reaction in Figure 6.13B) on the first Au nuclei generated by galvanic substitution of the first Ag° nanoparticles. Metallic gold progressively fills the pores, producing the nanowires and, at the same time, covering the outer faces of the membrane. After the deposition, the gold in excess is removed from one face of the membrane by peeling. Afterwards, the template can be removed, partially or totally, to expose the NWs. The templated electroless deposition of AuNWs in the shape of nanoelectrode ensemble (NEE) is schematised in Figure 6.14. Once the template membrane is removed, electroless deposition gives structures like the one shown in Figure 6.15 for the case of an array of copper NWs. This kind of nanostructures is suitable for further functionalisation procedures.

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Figure 6.13: Steps of electroless deposition of gold onto a polycarbonate membrane. (A) (i) Sensitisation of the membrane with Sn(II); (ii) activation by formation of Ag° nanoparticles; (iii) galvanic displacement of Ag° with Au° to form gold nuclei, which act as catalysts for the following reduction of additional Au(I) to metallic Au by reduction with formaldehyde. (B) Stoichiometry of the reaction between Au(I) and formaldehyde.

30 nm Polycarbonate filtration membrane

6 μm

Gold deposition

Gold removing from one face Gold nanodisc

Total etching

Gold nanowire (AuNW)

Figure 6.14: Steps for the preparation of gold nanowires (AuNWs) via electroless reduction of gold on track-etched polycarbonate membrane, which is thereafter etched in order to create the 3D gold structure.

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Figure 6.15: Array of copper nanowires obtained by membrane templated electroless deposition (A. M. Stortini et al., Sens. Actuators B Chem 2015. Reproduced with permission from Elsevier).

Electrochemical deposition In order to perform the electrochemical deposition of a metal inside a nanoporous nonconductive membrane, it is necessary to make one face of the membrane conductive. This can be achieved by sputtering a metal layer on one face of the membrane or by creating an intimate contact between the membrane and a solid metal surface, or by combining both methods. The prepared membrane is placed into an electrochemical cell where it acts as cathode (or working electrode, WE), in the presence of an anode (or counter electrode, CE) and, eventually, a reference electrode (RE) (see Figure 6.16). The metal ions in solution are reduced to metal at the interface between the electrolyte in the pores and the underlying metal electrode, by the application of a suitable reducing potential. Therefore, the pores are filled from the bottom to the top. The final result is an ensemble of solid wires whose matrix can be etched in order to expose them partially or completely. This kind of deposition is called potentiostatic when it involves the application of a constant reducing potential. Figure 6.17 shows a typical chronoamperogram (current vs. time) illustrating the trend of the current over the time for the electrodeposition of a metal within a porous substrate, with pores ideally of the same diameter and length. Typically, three different steps during the deposition are observed: i) the nanowires start growing into the pores with a low constant current, since the exposed metal surface is small (cross-section of the pores) and constant; ii) the pores are completely filled and the current increases steadily because the exposed surface increases; iii) further metal is deposited on the outer surface and the current reaches a new plateau because the exposed surface is again constant, but large, corresponding to the outer surface of the membrane.

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Working electrode

Counter electrode

Reference electrode

Copper wire

Insulator

Metal thin film

Nanoporous membrane

Electrolyte solution

Figure 6.16: Scheme of the electrochemical cell for the templated deposition of metal nanowires. The pre-sputtered membrane is attached to the surface of the working electrode. The electrodeposition is carried out in the presence of a counter and a reference electrode, in an electrolyte solution containing the metal precursor.

Figure 6.17: Chronoamperogram showing the typical trend of the current versus time during the electrodeposition of NWs: (i) the deposition of the metal starts and proceeds inside each pore, from the bottom to the top; (ii) after complete filling, the deposit starts to expand outside the pores; (iii) once all the outer surface of the membrane is coated with metal, the deposit can only increase in thickness.

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Microsphere templated electrochemical synthesis The synthesis of nanostructured surfaces presents the advantage of obtaining ordered and homogeneous substrates, characterised by the repetition of the same structures in the space. One of the procedures used for obtaining such nanostructured surfaces is based on electrodeposition methods which employ self-assembly of polymeric microspheres as hard templates. This is the electrochemical equivalent of the inverse opals chemical synthesis of photonic crystals. An example of this approach is represented by the preparation of the so-called sphere segment void (SSV) substrates and macroporous gold electrodes. These nanostructured materials consist of thin metallic films with a regular array of spherical cavities prepared by monolayers (in the case of SSV substrates) or multilayers (in the case of macroporous electrodes) of closelypacked polystyrene nanospheres. SSV substrates and macroporous electrodes are mostly prepared from gold, but also other metals can be employed such as silver, copper, platinum and palladium. The metal film on which the polystyrene spheres lie is used as working electrode for the following electrochemical deposition of the same or a different metal, which progressively fills the gap between the spheres. Figure 6.18 shows a typical chronoamperogram for the electrodeposition of gold onto overlapping monolayers of polystyrene spheres. The shape of the deposition current is dominated by the geometry of the template, oscillating with the time as a consequence of the periodic increase and decrease in the electrode area during the filling process. At the beginning, the cathodic current (which is negative as the 0.0 3/2 layers 7/2 layers

Current (A)

–1×10

–3

1 1/2 layers

–2×10–3

–3×10–3 0

100

200

300

400

500

600

Time (s) Figure 6.18: Typical chronoamperogram showing the current vs. time pattern for the gold electrodeposition during the preparation of a macroporous gold electrode. The gold deposition took place onto overlapping layers of polystyrene spheres (diameter = 240 nm), until reaching a height corresponding to 3, 7 and 11 half layers of spheres (S. Dal Borgo, P. Ugo, A. Kuhn, University Ca’ Foscari Venezia & University of Bordeaux/ENSCBP, unpublished results. Reproduced with permission from the authors).

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electrodeposition process is a reduction) increases rapidly while the gold nucleation begins. As the gold deposition is filling the gaps between the first layer of polystyrene spheres, the current decreases following the decrease in the exposed surface until reaching a minimum (less negative value) when the deposition height = r (where r is the radius of the spheres). Afterwards, the exposed surface and, as a result, the current increase again until reaching a maximum (most negative current) when the deposition height = 2 r, meaning that the first layer of spheres is completely coated. The process goes on in the same way with one minimum and one maximum for each layer of spheres which is coated, and can be stopped when the desired deposition level is reached. The entire process of preparation of SSV substrates is illustrated in Figure 6.19 for one single layer of polystyrene spheres.

1) Nanosphere template

2) Electrodeposition 3) Dissolution of spheres

4) SSV surface

Figure 6.19: Steps for the preparation of a SSV substrate: (1) a close-packed monolayer of polystyrene spheres is used as template; (2) electrodeposition of gold until the desired height (controlled by the current in the chronoamperogram); (3) once the gold deposition is completed, the polystyrene spheres are dissolved in DMF; and (4) sphere segment void (SSV) surface is obtained (Reproduced with permission from R. P. Johnson, Nucleic acid denaturation at an electrode surface, PhD Thesis, University of Southampton, UK, 2011).

After completing the metal deposition at the desired level, the polystyrene spheres are removed by chemical dissolution in a suitable solvent (such as dimethylformamide, DMF), leaving a gold nanostructure composed of ordered arrangements of spherical cavities (Figure 6.20). The prepared nanostructured substrates can be used for preparing miniaturised biosensors with high specific surface, suitable for accommodating large biomolecules inside their cavities and being used as working electrodes in electrochemical biosensors. Moreover, SSV substrates can be used as efficient supports for surface-enhanced Raman spectroscopy (SERS), since they are metal anisotropic nanomaterials with alternate ridges and nanocavities. This technique will be discussed more in detail in Section 6.6. 6.3.2.2 Seed-mediated growth Seed-mediated growth is a two steps synthetic method in which small, stable nuclei are generated at first, followed by their controlled growth in a separate chemical environment.

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Figure 6.20: Scanning electron microscopy (SEM) images at different magnification (see dimensional bar at 10, 5 and 1 µm) of a gold SSV substrate prepared by deposition of gold onto a monolayer of polystyrene spheres of 600 nm diameter. The ordered array of spherical cavities (dark) is clearly visible on the gold surface (bright) (M. Meneghello, unpublished results. Reproduced with permission from the author).

The nucleation of the seeds is obtained by performing, for a short time, the reduction of the metal ions to metal in the presence of suitable ligands, which bind to the surface of the metal particles, hindering further growth and preventing agglomeration. Ligand binding affinity and selectivity are exploited to control shape and growth. For seed synthesis, a ligand with medium-to-low binding affinity should be chosen, also to allow the exchange with a different ligand during the growth phase. For growing, the nanoseeds are placed in a growth solution which contains a low concentration of the metal precursor and a ligand (different from the one used for seed nucleation) that will be readily exchanged with the first seed ligand. A low concentration of a weak reducing agent is added, which must not be strong enough to reduce the metal precursor in the growth solution in the absence of the seeds, in order to avoid the formation of new nucleation sites. Growth is the result of the competition between surface energy (which increases unfavourably with growth) and bulk energy (which decreases favourably with growth). The balance between the energies of growth and dissolution is the reason behind the uniform growth of the preformed seeds. Range and direction of growth can be controlled by nanoseed, concentration of metal precursor, ligand and reaction conditions (heat, pressure, pH, etc.). Capping agents can be added which bind to specific crystal facets, so allowing to control the direction of growth and, therefore, the shape of the nanomaterial. This produces anisotropic particles with non-spherical shapes such as prisms, cubes, rods and stars (see also Figure 6.11 and related text). 6.3.2.3 Hydrothermal synthesis The term hydrothermal is of geological origin and defines the action of water at elevated temperature and pressure, explaining the formation conditions of various rocks and minerals, in particular under the form of single crystals. Hydrothermal

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reactions are heterogeneous processes occurring in aqueous solutions under high pressure and temperature conditions so that a material relatively insoluble in ordinary conditions is forced to dissolve and recrystallise. In the case in which water is substituted by a non-aqueous solvent, the process is named solvothermal. Although initial efforts in studying hydrothermal methods were devoted to synthesise bulk single crystals, since the 1990s, research efforts have been directed to obtain nano-sized particles with highly controlled size and morphology, in particular, but not only, ceramic oxides. Hydrothermal techniques for NP synthesis require the use of special instrumentation, called hydrothermal autoclave reactors. An autoclave reactor consists of steel cylindrical vessels with hermetic sealing, able to withstand high temperature and pressure. Since many hydrothermal processes require the use of corrosive solutions, at the interior of the autoclave, a corrosion resistant liner or beaker is used, commonly made of Teflon, platinum, gold or silver. A temperature gradient is maintained between the opposite ends of the growth chamber. At the hotter end the nutrient solute dissolves, while at the cooler end it is deposited on a seed crystal, growing the desired crystal. The so-called temperature difference method is the most widely used hydrothermal method. Supersaturation is achieved by reducing the temperature in the crystal growth zone. The nutrient is placed in the lower part of the autoclave filled with a specific amount of solvent. The autoclave is heated in order to create two zones at different temperature. The nutrient dissolves in the hotter zone, and the saturated aqueous solution in the lower part is transported to the upper part by convective motion of the solution. The cooler and denser solution in the upper part of the autoclave descends, while the counterflow of solution ascends. The solution becomes supersaturated in the upper part as the result of the decrease in temperature, and crystallisation occurs. The hydrothermal solutions (either acidic or alkaline) can be highly hazardous to human beings, in the case the reactor explodes. Therefore, the vessels should have rupture discs calibrated to burst above a given pressure. Such rupture discs are commercially available for various ranges of bursting pressure. Proper shielding and venting of the reactor should be provided to eventually divert the corrosive volatiles away from the personnel.

6.4 Functionalisation of nanomaterials In order to exploit nanomaterials for sensing and biorecognition purposes, it is necessary to stabilise and functionalise them. This can be achieved by introducing suitable functional groups to their surface which can be used to bind biorecognition elements by applying the immobilisation techniques presented in Chapter 2.

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Covalent conjugation Drug

Peptide

DNA

Protein O

S

S

C N

H

S N S

S

N N

AuNP S

S

S S

S S

S

Non-covalent conjugation Figure 6.21: Functionalisation of gold nanoparticles (AuNPs) with different biomolecules (DNA, proteins and small peptides) or drug organic molecules. Covalent conjugation can be achieved (i) by using biomolecules bearing a thiol linker, (ii) by reaction of a thiol linker bearing a carboxylic group with a (bio)molecule bearing an amino group (or vice versa), (iii) by reaction of a thiol linker bearing an azide group with a (bio)molecule functionalised with an alkyne (or vice versa). Non-covalent conjugation can be achieved by using thiol linkers bearing: (i) suitable antibodies or antigens which will specifically recognise their partner molecules (Ag or Ab), (ii) avidin or streptavidin which recognise biotinylated biomolecules (or vice versa), (iii) a metal ion (such as Cu(II), Co(II), Zn(II) or Ni(II)) which reacts with His-tagged proteins.

This approach is illustrated in Figure 6.21 for the case of gold or other metal (e.g. Ag, Cu and Fe) nanoparticles, using thiols as functionalisation agents. Organic thiols with suitable pendant functionalities (e.g. carboxylic or amino groups) bind to the surface of the metal NPs via metal–thiolate bond. Afterwards, using solidphase reactions or non-covalent interactions, the biomolecules of choice can be immobilised on the NP surface. Alternatively, the biomolecule may already bear a thiol linker which directly binds to the metal surface. Carbon-based nanomaterial, such as CNTs or graphene, can be functionalised using aryl diazonium reductive chemistry or by direct functionalisation of the carbon backbone via oxidation or amination. Silanisation reactions can also be applied to similar aims, in particular for silica-based nanomaterials, but not only restricted to them.

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6.5 Analytical techniques for nanomaterial characterisation The unique thermal, electrical, chemical and optical properties of nanomaterials rely on their size, morphology, electronic properties and surface charge. Among different types of microscopies, scanning electron microscopy (SEM), field-emission scanning electron microscopy (FE-SEM), transmission electron microscopy (TEM), high-resolution TEM (HR-TEM) and atomic force microscopy (AFM) are widely employed to characterise size, shape and morphology, or even crystal habit (with HR-TEM and AFM) of nanomaterials. Energy-dispersive X-ray spectroscopy (EDX) analysis is used in combination with electron microscopies to characterise the elemental composition of nanomaterials. Dynamic light scattering (DLS) allows to evaluate the average size of nanoparticles as well as their size distributions and surface charge. UV–visible (UV–vis) spectroscopy can provide useful information on the average size, distribution and concentration in suspension of metal NP colloids. Small-angle X-ray scattering (SAXS), extended X-ray absorption fine structure (EXAFS), X-ray absorption near-edge structure (XANES) analysis and electron spin resonance (ESR) are techniques that can provide information on the local structure and electronic properties of nanomaterials. Moreover, X-ray photoelectron spectroscopy (XPS), Fourier-transform infrared (FTIR) and Raman spectroscopies, and solid-state nuclear magnetic resonance spectroscopy (SSNMR) can provide information on their surface properties and composition. Matrix-assisted laser-desorption ionisation time-of-flight (MALDI-TOF) mass spectrometry, inductively coupled plasma mass spectrometry (ICP-MS), size-exclusion chromatography with UV–vis detection (SEC-UV-vis) can be used to study the surface and bulk composition of various nanomaterials. Finally, gel electrophoresis can be exploited to evaluate the effects of surface charges in bare and functionalised nanomaterials by measuring their electrophoretic migration behaviour.

6.6 Bioanalytical applications of nanomaterials 6.6.1 Optical properties of metal nanoparticles The most widely used nanomaterials in bioanalytical applications, evolving in commercial instrumentation, are metal nanoparticles, in particular AuNPs. This is mainly due to their easy preparation and functionalisation, high reproducibility and chemical stability as well as special optical and catalytic properties. Besides, a solid scientific knowledge has been gained in the last decades on the properties of AuNPs-based colloids. The unique optical properties of metal NPs represent their most exploited feature. The modern scientific approach to the study of the optical properties of colloidal gold was pioneered already in the 1850s by Michael Faraday, who recognised that the colour observed in suspensions of AuNPs (which can range from pink to

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red, to violet or pale blue) depends on the particle size. In more recent years, this effect has been rationalised in a quantitative way by application of the Maxwell equations and Mie and Rayleigh theories. Suitable theoretical models have been developed which correlate these properties with the nature and characteristics of the metal NPs. The fundamental phenomenon that determines the absorption of light by metal nanoparticles is the so-called localised surface plasmon resonance (LSPR). In physics, the plasmon is a collective excitation associated with the oscillations of the plasma of electrons contained in a system. Plasma oscillations are rapid oscillations of the electron density in a conducting medium. A plasmon is a quantum of plasma oscillations, or a quasi-particle (like photons for light) resulting from the quantisation of plasma oscillations. Surface plasmon excitation by light is described as surface plasmon resonance (SPR) for planar surfaces or localised surface plasmon resonance (LSPR) for metallic structures of nanometre size. In particular, LSPR is the optical phenomenon generated when light interacts with metal NPs with size smaller than the incident wavelength. The energy of the incident light is absorbed and collectively excites the electrons in the metal conduction band (see Figure 6.22). This results in coherent localised plasmon oscillations with a resonant frequency that depends on the composition, size, geometry, dielectric environment and separation distance between the NPs. Such an interaction of nanoparticles with light results in the absorption of some photons and scattering of others. LSPR absorption typically presents high molar extinction coefficients (ε), up to approximately 1011 M–1 cm–1, cm and Rayleigh scattering effects of magnitude orders larger than in the absence of the LSPR effect.

Electric field

Metal sphere Electron cloud Figure 6.22: Schematic representation of the localised surface plasmon resonance (LSPR) effect on metal nanoparticles. Incident electromagnetic radiation drives the coherent oscillation of conduction band electrons, which are physically displaced from the metal framework and oscillate.

In the case of particles with size < 100 nm, boundary and surface contributions become dominant, producing typical absorption bands. For instance, AuNPs with size in the 4–40 nm range present a maximum of absorbance between 510 and 530 nm, and their colloidal dispersions are red-coloured. Aggregation of the NPs results in a shift and

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broadening of the LSPR band so that the colour of the colloidal dispersion changes from red to purple or blue, depending on the aggregation state of the particles. The LSPR effect constitutes the basis for the wide application of metal nanoparticles for highly sensitive colorimetric bioassays as well as for other important NP-based bioanalytical techniques, such as surface-enhanced Raman spectroscopy (SERS) and fluorescence resonance energy transfer (FRET). SERS is a technique based on the enhancement of Raman spectra due to the surface plasmons present on particular nanostructured metallic surfaces. The phenomenon behind such enhancement was first observed in 1973 by the Fleischmann group at the University of Southampton (UK), where later Bartlett et al. developed the SSV substrates (already described in Section 6.3.2.1) which can generate surface plasmons with high reproducibility. Using SSV substrates, the SERS enhancement can be tuned by changing the morphology of the spherical cavities, and the Raman signals can be enhanced up to 1010–1011 times, so that even single molecules can be detected. Stimuli that cause the aggregation or dispersion of metal NPs can be detected and quantified by measuring variations in the UV–vis, SERS or FRET spectra (see also Sections 3.3.5 and 5.3.3), allowing to measure the presence and concentration of the analyte which can be responsible for the said changes. In the following, we will focus on the biosensing applications of metal nanoparticles.

6.6.2 DNA–AuNP sensors Functionalisation of AuNPs with oligonucleotides has gained a great interest because of the possibility to use programmed DNA base pairing to produce organised nanostructures whose formation can be detected optically or by other techniques. After two decades of research and development in this area, nowadays oligopeptide–AuNP conjugates find application in the fields of disease diagnosis and gene expression. The bases for this strategy were posed at the end of the 1990s by pioneering researches carried out by the Chad Mirkin group at the Northwestern University (IL, USA) and Paul Alivisatos at Berkeley (CA, USA). The approach of Mirkin and co-workers was based on the use of DNA as a linker to form macroscopic assemblies of AuNPs functionalised with ssDNA probes. It was demonstrated that the DNA attached to the AuNPs retains its ability to hybridise with complementary chains. Mirkin and co-workers in 1996 studied a three-component system in which two ssDNA probes (numbered 1 and 2 in Figure 6.23A) were used to detect one target sequence (numbered 3). Each NP was initially functionalised with a 28-mer oligonucleotide bound to a thiol tether. The first 13 nucleotides constituted a flexible spacer, while the last 15 acted as a recognition element for the target. When the target was present in the sample, it hybridised with the two probes acting as a crosslinker between the NPs, which therefore aggregated. In the absence of the target, the colloidal suspension of the oligo-functionalised AuNPs absorbs light at 520 nm

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Figure 6.23: Scheme illustrating the operational principle of the DNA–AuNPs sensors. (A) In the presence of the complementary target DNA (3), gold nanoparticles functionalised with ssDNA oligonucleotides 1 and 2 will aggregate, resulting in a change of the suspension colour from red to blue. (B) The aggregation process can be monitored using UV–vis spectroscopy or simply by spotting the solution on a silica support (R. Elghanian et al., Science 1997. Adapted with permission from the American Association for the Advancement of Science. N.L. Rosi and C.A. Mirkin, Chem Rev 2005. Adapted with permission from the American Chemical Society).

producing a red colour, which turns to blue after the NP aggregation driven by the target DNA. The process is thermally reversible (the NPs aggregate at T < Tm and separate at T > Tm ) and non-destructive, and well-defined melting curves can be obtained (schematised in Figure 6.23B). The effect of the length of the DNA strands on the inter-particle distance was also studied, finding that the shift in the absorption frequency is inversely dependent on the length of the oligonucleotide probes. As represented in Figure 6.23B, the melting curve observed with the DNA-AuNP aggregates is extremely sharp (orange curve), with the double helix denaturation

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occurring over a temperature range much narrower than that observed for the unmodified DNA (green curve). This suggests that NP-modified DNA presents a higher selectivity than conventional methods based on molecular fluorophores, probably due to the very dense loading of oligonucleotides on the AuNP surface. This feature allows high degrees of discrimination between perfectly matching target oligonucleotides and oligonucleotides with single base-pair mismatch (already described in Chapter 1). Moreover, the method is advantageously quick and easy, and does not require expensive instrumentation. However, it is limited by a not very high sensitivity, typically in the nanomolar range, while fluorophore assays have sensitivities in the picomolar range. For this reason, colorimetric detection with AuNPs can be applied mainly to PCR-amplified DNA targets. Higher sensitivities combined with multiplexing capabilities can be achieved with an AuNP-chip-based methodology, always developed by the Chad Mirkin group around 2000. This method allows to amplify the detection of the AuNP-labelled DNA by exploiting the catalytic properties of AuNPs, which catalyse the reduction of Ag+ to Ag° with hydroquinone. In this approach, schematised in Figure 6.24, a first DNA probe immobilised on a glass slide hybridises with one half of the target oligonucleotide. The second half of the target is complementary to a second probe bound to

Figure 6.24: Principle of scanometric DNA assay. (A) A surface-bound capture oligonucleotide binds to one half of the target sequence; the second half binds to a second probe labelled with a gold nanoparticle. Catalytic reduction of silver by hydroquinone onto the capture-strand/target/ nanoparticle sandwich results in a signal that can be detected scanometrically. (B) Chips can be modified with different capture oligonucleotides specific for different targets (T.A. Taton et al., Science 2000. Adapted with permission from the American Association for the Advancement of Science. D. Kim et al., Anal Chem 2009. Adapted with permission from the American Chemical Society).

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AuNPs. This bridging interaction causes the binding of the AuNPs onto the chip spot functionalised with the suitable probe. A thermal stringency wash is performed to remove unspecifically bound DNA strands. Afterwards, catalytic reduction of silver by hydroquinone onto the AuNPs is used to amplify the target signal so that a silver spot is formed in correspondence of the captured AuNPs. The capture-strand/target/NP sandwich can be detected by microscopy or visualised with a flatbed scanner: for that reason, the assay is named “scanometric”. NP-based scanometric assays allow to detect DNA target concentrations as low as 50 fM (50 × 10−15 M), with identification of single base-pair mismatches without need of PCR amplification. These achievements constitute the basis for the development of a workstation for nucleic acid and protein diagnostics named Verigene®, initially commercialised by Nanosphere and now by Luminex. The system has been implemented to detect a variety of nucleic acid signatures associated with genetic, bacterial and viral diseases, as well as for prostate cancer markers (based on proteins and miRNA) and for duplex and triplex DNA binders.

6.6.3 Colorimetric detection of proteins with nanoparticles The application of immunoblotting techniques with AuNPs for imaging cells and biomolecules began in the 1980s–1990s; however, this area is in continuous evolution. In this approach, the recognition of proteins is based on the interaction between AuNP-conjugated antibodies and their antigens. High detection sensitivity can be achieved, thanks to the LSPR properties of AuNPs. In practice, classical immunoassay formats have been adapted, simply substituting other kind of labels with the AuNPs. This approach is suitable to develop immunoassays for performing analysis directly in whole blood or human serum (see, for instance, Section 4.4.2 for the recent application in a quick test for Covid-19 antibodies). More recently, an innovative approach named plasmonic enzyme-linked immunoassay (ELISA) has been introduced by Molly Stevens and co-workers at the Imperial College London (UK). This approach is based on the use of enzyme labels that control the synthesis of AuNPs towards two possible alternative routes, namely i) formation of red-coloured AuNPs colloids; ii) formation of blue-coloured AuNPs aggregates. The method is based on the production of AuNPs using H2O2 as a reducing agent, so that the outcome of the nanoparticle generation dramatically depends on the concentration of hydrogen peroxide in the analysed solution. The concentration of hydrogen peroxide can be increased or lowered by using suitable enzyme labels. For instance, using glucose oxidase (GOx), the enzymatic oxidation of glucose will produce H2O2 in concentrations directly proportional to that of the enzyme captured at the surface of

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the detection platform. In another case, the enzyme label can be catalase, which instead consumes H2O2 catalysing its dismutation according to the following reaction: 2 H2 O2 ! 2 H2 O + O2

(6:2)

The detection scheme is summarised in Figure 6.25. In the absence of target analyte and, therefore, catalase label, the reduction of Au3+ cation proceeds up to the formation of a red-coloured colloidal suspension of AuNPs (Figure 6.25B). On the contrary, when the catalase label is bound at the surface of the detection platform (thanks to the formation of a sandwich complex between the target analyte and three different antibodies), the enzyme lowers the local concentration of H2O2 so that Au aggregates are formed giving the dispersion a blue colour (Figure 6.25A). This methodology can be applied to detect various disease markers such as prostate-specific antigen (PSA) or the HIV-1 antigens p24 and gp120. Spectrophotometry allows to perform sensitive quantitative analyses, while the qualitative detection can be performed with the naked eye, and the response can be sent remotely to the medical doctor via cellular phone.

(B) Without target analyte

(A) With target analyte 5 Cat. 4

H2O2 H 2 O + O2

3

Au3+

H 2O 2

2 1

Blue dispersion

Red AuNPs dispersion

Figure 6.25: Principle of the plasmonic enzyme-linked immunoassay. (A) When the target analyte (2) is present in the sample, it is captured by the capture antibody (1) immobilised on the platform. After washing and blocking, a sandwich complex is formed with a different primary antibody (3) and a sec-antibody (4). The sec-Ab is biotinylated and coupled to a streptavidin-modified catalase (5). Finally, H2O2 in MES buffer and AuCl3 solutions are added. The catalase label (Cat) consumes H2O2 and the final product is a blue-coloured colloidal dispersion of AuNP aggregates. (B) In the absence of the target, no catalase is bound onto the platform so that, after accurate washing, upon addition of H2O2 and AuCl3, a red-coloured dispersion of non-aggregated AuNPs is formed.

6.6.4 SERS assay for DNA denaturation The Bartlett group at the University of Southampton (UK) recently showed that it is possible to detect and discriminate DNA mutations using surface-enhanced Raman

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269

Figure 6.26: Scheme illustrating the principle of the SERS assay for DNA denaturation. A DNA probe (blue) is immobilised on the gold SSV surface via S–Au bonds, together with a small thiol (green) to passivate the substrate surface. The probe hybridises with the target DNA strand (light blue), which can be labelled with a Raman-active dye (A); otherwise, a DNA binding agent is added in solution in the label-free approach (B). The binding agent can be a Raman-active intercalator (yellow), which is typically a flat aromatic molecule interacting by intercalation between two DNA base pairs. Finally, a negative potential is applied to the gold surface, acting as working electrode, so that the DNA duplex denatures and the Raman signal disappears.

spectroscopy (SERS) on SSV substrates (see Section 6.3.2.1 for an introduction on SSV substrates, and Section 6.6.1 for a brief description of SERS). Although it is not yet a commercial application, however this technique is promising for the label-free detection of single DNA mismatches. In this type of assay, illustrated in Figure 6.26, a DNA probe is immobilised onto a gold SSV surface via a series of dithiol linkers. After immobilisation of the probe, the SSV surface is normally passivated with a small thiol (such as mercaptohexanol) to prevent unspecific adsorption and to force the DNA probe into an upright conformation. Afterwards, the target DNA strand is added so that it hybridises with the bound probe. The target oligonucleotide can be labelled with a Ramanactive molecule (Figure 6.26A). Alternatively, in the label-free approach illustrated in Figure 6.26B, a DNA binding agent (typically a Raman-active DNA intercalator) is

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added in solution and allowed to bind to the DNA double helix. Finally, the DNA double helix is denatured by applying an increasing temperature or an increasing negative potential, and the process is monitored by following the changes in the SERS spectra. In fact, as the two DNA strands are separated, the Raman-active molecule (covalently bound to the target strand or intercalated in the double helix) moves away from the SSV surface so that its SERS signal decreases. The Ramanactive molecules in the bulk solution do not contribute to the observed SERS signal because of the strong surface selectivity of the SERS enhancement. Figure 6.27 shows a typical denaturation (or melting) curve obtained by electrochemical denaturation of a DNA duplex at an SSV surface. Like in the case of thermal denaturation (already described in Chapter 1), the melting curve presents a typical sigmoidal shape. The point at which the Raman signal is 50% of its maximum, corresponding to the half of the dsDNA been denatured, is the melting point (or melting potential). The melting potential can be used to distinguish between different DNA sequences, usually the wild type from sequences presenting mutations. In fact, a double helix formed with a mutated target strand will be less stable and more readily denatured (at lower temperatures or lower negative potentials) than the wild-type sequence.

Figure 6.27: Typical melting curve for the electrochemical denaturation of a DNA double strand at an SSV surface, monitored by Raman spectroscopy. The experimental data (blue points) are fitted with a sigmoidal function (pink line) to find the inflection point which corresponds to the melting potential (Em).

In this technique, the gold SSV surface is used as both working electrode and nanostructured surface to enhance the Raman signal. Concerning the first use, it was found that a negative potential can act as driving force for DNA denaturation. The mechanism of this DNA denaturation is not yet totally explained. It has been proposed that electrostatic field effects could be responsible for the destabilisation of

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271

the dsDNA through repulsion of the DNA sugar–phosphate backbone away from the electrode surface. When a negative potential is applied, electrons are transported through the DNA strand which acts as a molecular wire. The more cathodic the potential, the more electrons are confined within the double helix. At some point, electrostatic repulsion between the increasing negative charges denatures the DNA duplex. The point at which this occurs depends on the thermodynamic stability of the dsDNA strand.

6.7 Electrochemical nano-biosensors As presented in Chapter 3, electrochemical biosensors present several advantages such as low cost, easy miniaturisation, easy use, no interference from coloured or turbid samples, direct signal transduction, applicability for in situ and point-of-care analyses. However, they present some limitations, especially due to the decrease in sensitivity caused by unspecific adsorption and fouling. In principle, the nanostructuration of the sensor surface could contribute to solve some of these problems. In some cases, nanostructured surfaces can be exploited to increase the specific area of the sensor so that higher amounts of biorecognition molecules can be immobilised, while keeping the overall size of the sensor extremely small. A further interesting feature is the possibility to separate, within the nanoscale range, biorecognition from transduction. For instance, the surface of gold nanoelectrodes can be protected from unspecific adsorption by functionalisation with self-assembled monolayers of thiols, while confining the biorecognition layer on the insulator in the proximity of the nanoelectrode. Moreover, the use of arrays of nanostructured electrodes allows an extreme miniaturisation of the

Figure 6.28: Different geometries for arrays of nanoelectrodes: (A) random array (ensemble), (B) square array, (C) hexagonal array, (D) linear array, (E) interdigitated nanoband array and (F) three-dimensional nanowire array.

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sensor, keeping its overall size one or two magnitude orders lower than with micrometre-sized electrodes. Nanoelectrodes can be produced as individual electrodes or in arrays. Currently, the first approach is of interest mainly for fundamental research purposes, while the latter is expanding its use also to real-world biosensing applications. The advantages of electrode arrays, with respect to individual nanoelectrodes, are the following: a) capability to provide higher current densities (easier to measure); b) easier construction since, in an array of a huge number of nanoelectrodes, the eventual failure of one or few nanoelectrodes is statistically non-relevant; c) higher active area suitable for functionalisation with the desired biorecognition element. Nanoelectrode arrays (NEAs) can be produced with different geometries as summarised in Figure 6.28. As proposed by Menon and Martin in 1995, random arrays of nanoelectrodes are often named nanoelectrode ensembles (NEEs). Bottom-up membrane template methods or top-down nanolithography are typically used for preparing arrays with controlled geometries. In principle, disordered arrays can be obtained also by dispersing metal NPs or CNTs in a thin layer of a dielectric binder or an ink. Another method for preparing modified electrode surfaces that behave like nanoelectrode ensembles is by coating the surface of a microelectrode with a nanoporous insulating layer, such as a defective self-assembled monolayer or nanoporous polymer like molecularly imprinted polymers. Note that, in all these arrays, the nanoelectrodes are electrically shortened so that they all experience the same applied potential. For multiplexing analyses, in principle, it would be necessary to individually address each nanoelectrode or, alternatively, to develop arrays of arrays, where different groups of nanoelectrodes are addressed using distinct current collectors. However, such multiplexing potentialities are still under investigation. Here, we present some fundamentals on the electrochemical behaviour of NEAs, as well as some examples of biosensing application.

6.7.1 Voltammetry with nanoelectrode arrays The fundamental characteristics that distinguish NEAs from conventional macro (millimetre-sized) or even micro (micrometre-sized) electrodes are: a) dramatic increase of signal-to-noise (S/N) ratios due to low capacitive currents, which act as noise in voltammetry; b) extreme sensitivity to the kinetics of charge transfer processes. When experimentally evaluating the success in the preparation of NEAs, the lack of one of these characteristics (like the persistence of high capacitive currents) should be taken as a diagnostic indication of a failure in the preparation procedure.

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As illustrated in Figure 6.29, NEAs made of nanodisc electrodes can exhibit three distinct types of voltammetric responses, depending on the scan rate or on the reciprocal distance between the nanoelectrode elements. Remember that the scan rate determines the time window of a voltammetric experiment, since for long times (low scan rates, of the order of few mV/s) the thickness of the diffusion layer is larger than for short times (high scan rates, typically > 500 mV/s).

Figure 6.29: Schematic drawing illustrating the different diffusion regimes at nanoelectrode arrays (NEAs) made of shortened nanodisc electrodes (yellow), embedded in an insulator (grey): (A) total overlap; (B) pure radial; (C) linear active. The time scale decreases going from (A) to (C), while the scan rate increases. The relevant equations for peak currents (A) and (C) and plateau current (B) refer to reversible redox systems. Aact is the active area (nanodisc surface), Ageom is the total geometric area of the ensemble (nanodiscs and insulator), q is the density of nanodiscs (disc/cm2), x is the distance from the array surface, and all other symbols have their usual meaning (P. Ugo et al., ChemPhysChem 2002. Adapted with permission from John Wiley & Sons).

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When radial diffusion boundary layers totally overlap (meaning that the radius of the diffusion hemisphere is larger than the average hemi-distance between electrodes, Figure 6.29A), NEAs behave like planar macroelectrodes with respect to faradaic currents. When the diffusion hemispheres become shorter (for higher scan rates, typically > 50 mV/s, Figure 6.29B), the current response is dominated by radial diffusion at each single element. At very high scan rates (several V/s), a linear active state is reached in which the current response is controlled by linear diffusion to each individual nanodisc (Figure 6.29C). Transition from one regime to the other as a function of the distance between nanoelements was experimentally demonstrated. Figure 6.29 reports also the equations of the peak/limiting current recorded for each of these situations, for a reversible redox couple. For the cases (A) and (C), the Randles–Sevcik equation applies (see eq. 3.25 in Chapter 3). However, for the total overlap case (A), the sensitivity depends on the geometric area (area of the nanodiscs plus the insulator, Ageom ), while for the linear active regime (C) it depends on the active area (overall surface area of the nanoelectrodes only, Aact ). For the pure radial regime (B), the overall current is the sum of the currents recorded at each nanoelectrode in the array, that is, Ageom q (where q is the number of nanodiscs per cm2, or nanodisc density). For all these three cases, the noise determined by the capacitive current is always proportional to Aact. It is evident that the two regimes that provide the highest currents and S/N ratios are the pure radial and the total overlap. Note that an inter-electrode distance of 10r (where r is the radius of the nanoelectrodes) is high enough to avoid crosstalking between the nanoelectrodes. An experimental comparison between these two regimes (pure radial and total overlap) has been performed by evaluating the current densities recorded at arrays of gold nanodisc electrodes (with nominal radius between 15 and 75 nm). The average inter-electrode distances were: i) 200 nm, for the total overlap regime providing peak-shaped voltammograms; ii) 3 000 nm, for the pure radial regime providing sigmoidal voltammograms. Figure 6.30 compares the current densities obtained with NEAs operating under pure radial (full line) or total overlap (dashed line) diffusion conditions. In Figure 6.30A and 6.30B, the current densities (in A/cm2) are calculated dividing the measured current by the geometric area and the active area, respectively. These data show that the pure radial regime provides higher current density with respect to the exposed surface of the nanoelectrodes in the array (Aact), while the total overlap regime provides higher current densities per unit area of the overall array (Ageom). If the first situation is useful for maximising the sensitivity of the sensor, the second is particularly attractive in cases requiring an extreme miniaturisation of the array.

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275

A fundamental parameter to define the properties of NEAs is the fractional electrode area (f ), calculated as the ratio between the active and geometric area: f=

Aact Ageom

(6:3)

For total overlap and pure radial regimes, the faradaic-to-capacitive current ratio depends on 1=f . Therefore, NEAs characterised by f values larger than 10−2 show high S/N ratios providing very low detection limits (two magnitude orders lower than electrodes with f = 1). (A) 6 × 10–7

I/Ageom (A/cm2)

4 × 10–7 2× 10–7 0 –2 × 10–7 –4 × 10–7 –0.4

–0.2

0.0

0.2

0.4

0.6

0.8

0.6

0.8

Potential (V vs. Ag/AgCl) –4 (B) 2 × 10

I/Aact (A/cm–2)

1 × 10–4 5× 10–5

0

–5 × 10–5 –1 × 10–4 –0.4

–0.2

0.0 0.2 0.4 Potential (V vs. Ag/AgCl)

Figure 6.30: Cyclic voltammograms recorded with arrays of nanoelectrodes operating under pure radial diffusion conditions (red full line) or total overlap conditions (black dashed line). The currents are plotted as current densities calculated with respect to (A) the geometric area and (B) the active area (L. M. Moretto et al., Nanotechnology 2011. Reproduced with permission from IOP Publishing).

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Another important characteristic of NEAs is their high sensitivity to the electron transfer kinetics. According to pioneer models proposed by Amatore et al. (at the Ecole Normale Supérieure in Paris) and more recent theoretical models, an NEA in total overlap diffusion conditions, behaves like a macroscopic electrode with partially blocked surface. For such electrodes, the current response is identical to that of a naked electrode with the same overall geometric area, but for which the electron transfer kinetics is ruled by a smaller apparent electron transfer rate constant (kapp ), which decreases as the surface coverage increases, according to the following equation: kapp = k ð1 − ϑÞ = k f

(6:4)

  where ϑ = Ageom − Aact =Ageom , and f is the fractional electrode area given by eq. (6.3). From an analytical point of view, eq. (6.4) means that high faradaic peak currents are observed at NEAs only for redox couples with fast electron transfer kinetics, such as the majority of the redox mediators used in second-generation electrochemical biosensors and electrochemical ELISA (see Chapters 3 and 4).

6.7.2 Bioelectroanalysis with nanoelectrode arrays Nanoelectrode arrays can be used for interesting bioanalytical applications. However, we should mention that NEAs with reliable electroanalytical characteristics have been described only very recently. Consequently, the number of examples of practical applications is very limited, even though the potentialities are huge. By looking at the microscopic structure of NEAs, one realises that they are composite materials made of arrays of metallic conductors, typically metal nanodiscs, nanowires or CNTs, with an insulating part, typically a synthetic polymer. Such a complex structure suggests that three different methods can be employed to modify or functionalise their surface. As schematised in Figure 6.31, these methods include: a) modification of the overall surface of the ensemble, constituted of the nanoelectrode heads and the insulating template membrane; b) functionalisation of the nanoelectrodes only; c) functionalisation of the insulating membrane only. The first method illustrated in Figure 6.31A is the simplest one. Interesting results are achieved through deposition of polymeric films by recasting polymer solutions or by resorting to Langmuir–Blodgett or layer-by-layer deposition techniques. The polymer film coats all the ensemble and can be used, for instance, to incorporate electroactive redox proteins exploitable as biocatalysts. The second approach (B) requires the very small surface area of the nanoelectrodes to be enlarged in order to immobilise high amounts of mediators and biorecognition

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(A) Coating all the NEA ensemble

(B) Coating only the nanoelectrodes

(C) Functionalise only the insulator S

Polymer

S

P

S

P

M

P

M

Nanoelectrode array ensemble Figure 6.31: The three different methods employed for the functionalisation of NEAs: (A) all the surface of the NEA is functionalised with a polymer film which incorporates the biorecognition elements and the redox mediators; (B) the nanoelectrodes (in yellow) are exposed by etching and, then, functionalised with the biorecognition elements, for instance enzymes which convert the substrate S into the product P; (C) only the insulator (in grey) is functionalised, for instance, with an enzyme, while a soluble redox mediator (M) shuttles electrons between the enzyme and the nanoelectrodes.

elements. To this aim, in the case of membrane templated NEAs, the membrane polymer layer can be partially or totally removed by chemical or oxygen plasma etching. This causes the structure of the final ensemble to change into a 3D “forest” of protruding metal nanowires. Different methods have been developed, for instance, by the James Rusling groups at the Pennsylvania State University (USA), to obtain “forests” of CNTs. The surface of the metal nanowires or CNTs can be functionalised with the biorecognition elements and (when required) redox mediators, using the methodologies presented in Chapter 2. Finally, the functionalisation of the membrane alone (C) allows to exploit the large area of the insulator to immobilise high amounts of biorecognition elements, such as enzymes or antibodies. The advantages of the latter method are given by: i) the possibility to exploit the large surface of the insulator which, in an array, is typically in excess with respect to the nanoelectrodes area; ii) the biomolecules are kept in an environment less stressing than the electrically polarised metal surface of the nanoelectrodes; iii) the S/N ratio is kept at the best values, typical of NEAs. A limit of this strategy is represented by the necessity of using soluble redox mediators to shuttle electrons between the bare nanoelectrodes and the biorecognition elements immobilised on the surrounding insulator. Summing up, 2D NEAs preserve the advantage of providing very high S/N ratios since the capacitive current (proportional to Aact ) is very small. On the other hand, 3D NEAs present higher capacitive currents (low S/N ratios), however, with the advantage of providing a high 3D surface suitable for the immobilisation of biorecognition elements.

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Applications in bioanalytical chemistry range from the detection of small molecules such as metabolites, drugs, neurotransmitters or enzyme substrates, to biomacromolecules such as oligonucleotides and DNA sequences, proteins and antibodies employed as disease markers.

6.8 Final remarks The important applicative results obtained by means of modern bioanalytical chemistry techniques have changed the way of conducting analysis in biological matrices. Despite the significant progresses and successfull achievements reached so far, further and exciting developments are expected in the next future, which can be achieved through the smart combination of the most recent progresses in the nanotechnology, biotechnology, analytical and biological chemistry fields. It is not difficult to predict that the many researches currently carried out in numerous laboratories all over the world will soon produce new techniques and devices, suitable to perform in a facilitated, but efficient way complex chemical/bioanalytical operations. This will answer to the growing demand for more reliable analytical tools, suitable to monitor in a diffuse way human health, foods, biotechnological productions and the environment.

Further readings Books and book chapters Altintas Z. Biosensors and nanotechnology: Applications in health care diagnostics, NY, USA, John Wiley & Sons, 2017. Bard AJ. Integrated chemical systems: A chemical approach to nanotechnology, NY, USA, Wiley Interscience, 1994. Cao G, Wang Y. Nanostructures and nanomaterials: synthesis, properties and applications, 2nd edition, Singapore, World Scientific Publishing Co. 2010. Feldheim DL, Foss JCA. Metal nanoparticles – Synthesis, characterization and applications New York, Marcel Dekker, 2002. Lin Y, Nalwa HS. Handbook of electrochemical nanotechnology (2 volume set), Stevenson Ranch, CA, USA, American Scientific Publishers, 2009. Madou MJ. Fundamentals of microfabrication: the science of miniaturization, 2nd ed. Boca Raton, FLO, USA, CRC Press, 2002. Ozin AG, Arsenault A, Cademartiri L. Nanochemistry: A chemical approach to nanomaterials, 2nd ed. Royal Society of Chemistry, London, UK, 2008. Ugo P, Moretto LM, Scrosati B. Nanoelectrodes. In: Garche J., Dyer C., Moseley P., Zempachi O., Rand D. and Scrosati B. (Eds), Encyclopedia of electrochemical power sources, 2. Amsterdam, Elsevier, 2009, 92–102. Zoski C (Ed.). Handbook of electrochemistry, Amsterdam, Elsevier, 2006.

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Review and research papers Aldewachi H, Chalati T, Woodroofe MN, Bricklebank N, Sharrack B, Gardiner P. Gold nanoparticlebased colorimetric biosensors. Nanoscale 2018, 10, 18–33. Alivisatos AP, Johnsson KP, Peng XG, Wilson TE, Loweth CG, Bruchez MP, Schultz PG. Organization of nanocrystal molecules using DNA, Nature 1996, 382, 609–611. Amatore C, Saveant J, Tessier D. Charge transfer at partially blocked surfaces. A model for the case of microscopic active and inactive sites. Electroanal Chem 1983, 147, 39–51. Azharuddin M, Zhu GH, Das D, Ozgur E, Uzun L, Turner APF, Patra HK. A repertoire of biomedical applications of noble metal nanoparticles. Chem Comm 2019, 55, 6964–6996. Bartlett PN, Baumberg JJ, Coyle S, Abdelsalam ME. Optical properties of nanostructured metal films. Faraday Discuss., 2004, 125, 117–132. Boisselier E, Astruc D. Gold nanoparticles in nanomedicine: Preparations, imaging, diagnostics, therapies and toxicity. Chem Soc Rev 2009, 38, 1759–1782. Cintra S, Abdelsalam ME, Bartlett PN, Baumberg JJ, Kelf TA, Sugawara Y, Russell AE. Sculpted substrates for SERS. Faraday Discuss 2006, 132, 191–199. De la Rica R, Stevens MM. Plasmonic ELISA for the ultrasensitive detection of disease biomarkers with the naked eye. Nat Nanotechnol 2012, 7, 821–824. Elghanian R, Storhoff JJ, Mucic RC, Letsinger RL, Mirkin CA. Selective colorimetric detection of polynucleotides based on the distance-dependent optical properties of gold nanoparticles. Science 1997, 277, 1078–1081. Faraday M. The Bakerian lecture: experimental relations of gold (and other metals) to light, Phil Trans Royal Soc 1857, 147, 145–181. Habtamu HB, Not T, De Leo L, Longo S, Moretto LM, Ugo P. Electrochemical immunosensor based on nanoelectrode ensembles for the serological analysis of IgG-type tissue transglutaminase. Sensors 2019, 19, 1233–1248. Habtamu HB, Sentic M, Silvestrini M, De Leo L, Not T, Arbault S, Manojlovic D, Sojic N, Ugo P. A sensitive electrochemiluminescence immunosensor for celiac disease diagnosis based on nanoelectrode ensembles. Anal Chem 2015, 87, 12080–12087. Karajic A, Reculusa S, Heim M, Garrigue P, Ravaine S, Mano N, Kuhn A. Bottom-up generation of miniaturized coaxial double electrodes with tunable porosity. Adv Mater Interfaces 2015, 2, 1–5. Karimian N, Ugo P. Recent advances in sensing and biosensing with arrays of nanoelectrodes. Curr Op Electrochem 2019, 16, 106–116. Kim D, Daniel WL, Mirkin CA. Microarray-based muliplexed scanometric immunoassay for protein cancer biomarkers using gold nanoparticles probes. Anal Chem 2009, 81, 9183–9187. Mahajan S, Richardson J, Brown T, Bartlett PN. SERS-melting: A new method for discriminating mutations in DNA sequences. Am Chem Soc 2008, 130, 15589–15601. Malhotra R, Patel V, Vaque JP, Gutkind JS, Rusling JF. Ultrasensitive electrochemical immunosensors for oral cancer biomarker IL-6 using carbon nanotube forest electrodes and multilabel amplification. Anal Chem 2010, 82, 3118–3123. Martin CR. Nanomaterials: a membrane-based synthetic approach. Science 1994, 266, 1961–1966. Menon VP, Martin CR. Fabrication and evaluation of nanoelectrode ensembles. Anal Chem 1995, 67, 1920–1928. Mirkin CA, Letsinger RL, Mucic RC, Storhoff JJ. A DNA-based method for rationally assembling nanoparticles into macroscopic materials. Nature 1996, 382, 607–609. Moretto LM, Tormen M, De Leo M, Carpentiero A, Ugo P. Polycarbonate-based ordered arrays of electrochemical nanoelectrodes obtained by e-beam lithography, Nanotechnology 2011, 22, 185305, 7.

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Ongaro M, Ugo P. Bioelectroanalysis with nanoelectrode ensembles and arrays, Anal Bioanal Chem 2013, 405, 3715–3729. Piner RD, Zhu J, Xu F, Hong S, Mirkin CA. Dip-pen nanolithography. Science 1999, 283, 661–663. Possin GE. A method for forming very small diameter wires. Rev Sci Instrum 1970, 41, 772–774. Rosi NL, Mirkin CA. Nanostructures in biodiagnostics. Chem Rev 2005, 105, 1547–1562. Sentic M, Virgilio F, Zanut A, Manojlovic D, Arbault S, Tormen M, Sojic N, Ugo P. Microscopic imaging and tuning of electrogenerated chemiluminescence with boron-doped diamond nanoelectrode arrays. Anal Bioanal Chem 2016, 408, 7085–7094. Stortini A, Moretto LM, Mardegan A, Ongaro M, Ugo P. Arrays of copper nanowire electrodes: Preparation, characterization and application as nitrate sensor. Sens Actuators B Chem 2015, 207, 186–192. Suvarnaphaet P, Pechprasarn S. Graphene-based materials for biosensors: a review, Sensors 2017, 17, 2161, 24. Taton TA, Mirkin CA, Letsinger RL. Scanometric DNA array detection with nanoparticle probes, Science 2000, 289, 1757–1760. Turkevich J, Stevenson PC, Hillier J. A study of the nucleation and growth processes in the synthesis of colloidal gold. Discuss Farady Soc 1951, 11, 55–75. Ugo P, Moretto LM, Vezzà F. Ionomer-coated electrodes and nanoelectrode ensembles as electrochemical environmental sensors: Recent advances and prospects. ChemPhysChem 2002, 3, 917–925. Wongkaew N, Simsek M, Griesche C, Baeumner AJ. Functional nanomaterials and nanostructures enhancing electrochemical biosensors and lab-on-a-chip performances: Recent progress, applications and future prospects. Chem Rev 2019, 119, 120–194. Xue J, Wu T, Dai Y, Xia Y. Electrospinning and electrospun nanofibers: Methods, materials, and applications. Chem Rev 2019, 119, 5298–5415. Zalaffi MS, Litti L, Canton P, Meneghetti M, Moretto L, Ugo P. Preparation and characterization of Ag-nanostars@Au-nanowires hierarchical nanostructures for highly sensitive surface enhanced Raman spectroscopy, Nano Express 2020, 1, 020006. Zoski C, Yang N, He P, Berdondini L and Koudelka-Hep M. Addressable nanoelectrode membrane arrays: fabrication and steady-state behavior, Anal Chem 2007, 79, 474–484.

Index absorbance 64, 149 absorption 149 acetylcholinesterase 160 active site 37, 128 adenine 1, 4 affinity 54, 56, 58, 192 affinity biosensor 73 agarose gel electrophoresis 201 alcohol dehydrogenase 43 alkaline phosphatase (ALP) 167, 181 alumina membrane 252 amide bond 92 amino acid 7, 21 ammonia 153 amperometric biosensor 136 amperometry 136 amplicon 201 anisotropic crystal 193 anisotropic nanomaterial 251, 258 antibody 47, 49, 74, 163 antigen 47, 163 antigen–antibody complex 47, 55, 163, 192 antisense oligonucleotide 18, 225 aspartic acid 23, 92 aspect ratio (AR) 240 atomic force microscope (AFM) 247, 261 avidin 74, 82 avidity 54 azide 94 B/F ratio 58, 166 ball milling 248 base-calling 234 Beer–Lambert law 149 bilayer lipid membrane 89 binomial distribution 215 bioaffinity reaction 73 bioanalytical assay 61 bioluminescence 151, 159 bio-optode 148, 156 bioreceptor 61, 105 biorecognition 61, 71, 105, 144, 163 biosensor 61, 71, 163 biospecific adsorption 82 biotin 74, 82, 187

https://doi.org/10.1515/9783110589160-007

Black Hole Quencher 1 (BHQ-1) 207 bottom-up nanomaterial fabrication 244, 249 Bridge Amplification (BridgeAmp) 224 calorimetric biosensor 75 capillary blotting 178 capillary electrophoresis 220 capture antibody 168, 170, 174, 183 carbodiimide 92 carbon nanotube (CNT) 239, 277 carbonate 153 catalase 268 catalytic biosensor 72 catalytic constant 46 catalytic current 132, 134, 136 cell lysis 199 cellulose acetate 83 central dogma of molecular biology 5 chain terminator 219 chemiluminescence 151, 156, 180 chirality 36 chromogen 65 Clark oxygen electrode 125, 139 click chemistry 94 codon 7 coenzyme 37 cofactor 39, 128 colorimetry 180 competitive ELISA 168 complementary metal-oxide semiconductor (CMOS) chips 229 conducting polymer 84, 140, 142 continuous glucose monitor (CGM) 141 convection 116, 123 convective/diffusive mass transport 125 coronavirus disease 2019 (COVID-19) 175, 210 coulometry 136 counter electrode 118 covalent interaction 30, 81, 92 cross-linking 80, 90, 113, 144 cross-reactivity 55 C-terminus 26 current density 274 cyclic voltammetry (CV) 118, 128, 131, 272 cysteine 95 cytosine 1, 4

282

Index

de novo sequencing 230 dehydrogenase 43, 158, 167 deoxynucleoside hexaphosphate (dNHP) 232 deoxynucleoside triphosphate (dNTP) 219 deoxyribonucleic acid (DNA) 1, 5, 199 derivative method 70 diabetes 141 dialysis membrane 77, 83, 113, 139, 156 diazonium salt 94, 100 dideoxynucleoside triphosphate (ddNTP) 219 diffusion 116, 118, 121, 123, 131, 274 diffusion layer 122, 126 digital PCR (dPCR) 213 dip-pen nanolithography (DPN) 247 direct electron transfer (DET) 130, 140 disulphide bridge 27, 30 DNA clustering 223 DNA denaturation 202, 270 DNA extraction 199 DNA intercalator 74, 269 DNA microarray 216 DNA mutation 8, 268 DNA nanoball (DNB) 230 DNA polymerase extension 202 DNA probe 200, 264 DNA purification 199 DNA sequencing 219 DNA–AuNP sensor 264 Donnan equation 109 double helix 3, 10 dynamic electrochemical technique 115 dynamic light scattering 262 electroblotting 178 electrocatalytic process 127, 134 electrochemical biosensor 105, 271 electrochemical denaturation 18, 270 electrochemical deposition of metal 255 electrochemical grafting 100 electrochemical immunosensor 181 electrochemical quartz crystal microbalance (EQCM) 196 electrochemiluminescence (ECL) 186, 188, 247 electrode of the first kind 108 electroless template deposition 253 electromotive force (EMF) 107 electron acceptor 41, 139 electron donor 41

electron relay site 128 electron transfer kinetics 120, 128, 276 electron transfer rate constant 129 electron transfer reaction 40, 119, 127–128 electron-beam lithography (EBL) 246 electropolymerisation 86 electrospinning 248 electrostatic adsorption 79 enzymatic kinetics 45, 66 enzymatic optical biosensor 144 enzyme 34, 66, 72, 103, 113, 128 Enzyme Commission number 35 enzyme immunoassay (EIA) 167 enzyme label 167, 180–181, 267 enzyme label immunosensor 181 enzyme-labelled electrochemical (ELEC) immunosensor 181 enzyme-linked immunosorbent assay (ELISA) 167, 172 enzyme–substrate complex 43, 67 epigenetic modification 232 epitope 47, 53, 170 epoxide 92 equilibrium dialysis 56 ethanol precipitation 199 EvaGreen 207 Exclusion Amplification (ExAmp) 225 exon 6 F(ab)2 fragment 52 Fab fragment 51 faradaic current 115, 118 Fc fragment 51 ferrocene 140 fibre-optic biosensor 146 Fick’s first law 117, 132 Fick’s second law 118 first-generation glucose biosensor 139 flavin adenine dinucleotide (FAD) 38 flavin mononucleotide (FMN) 38 flow 116 flow cell 125, 225 flow cytometry 184 fluorescein 150, 152, 207 fluorescence 149, 154, 158, 160, 180, 184 fluorescence quenching 150, 155–156 fluorescent ddNTP 220 fluorescent dNTP 225

Index

fluorescently labelled phospholinked nucleotide 232 fluorogenic DNA probe 206 fluorophore 149, 155, 180 formal potential 107, 121 Förster/fluorescence resonance energy transfer (FRET) 157, 207 Fourier-transform infrared spectroscopy 262 fractional electrode area 274 galactose glucose binding protein (GGBP) 157 genetic code 7 glass pH electrode 109, 113 globular protein 33 glucose 136, 156 glucose biosensor 137, 141 glucose dehydrogenase 140 glucose oxidase 42, 137, 139, 141, 156, 181, 267 glutamate 158 glutamate dehydrogenase 158 glutamic acid 23, 92 glutaraldehyde 90, 98, 113 gold nanoparticle (AuNP) 250, 262, 264 gold nanowire 277 graphene 242 green fluorescent protein (GFP) 150 guanine 1, 4, 74 haem group 39 half-wave potential 121 handheld blood glucose meter 141 hapten 48 hard template synthesis 252 hexagonal boron nitride (hBN) 242 high electrical resistance membrane 232 high-performance liquid chromatography (HPLC) 17 His-tag 95 horseradish peroxidase (HRP) 42, 151, 167, 180–181 human chorionic gonadotropin (hCG) 174 HyBeacon 18, 208 hydrodynamic voltammetric technique 123 hydrogel 84, 142 hydrothermal synthesis 260

283

Illumina microarray 218 Illumina sequencing 220 immobilisation 83, 103, 171 immunoassay 163, 267 immunoblotting 267 immunoglobulin (Ig) 49, 52, 175 immunoprecipitation 164 immunosensor 74, 163 indicator electrode 106 Infinium® whole-genome genotyping assay 218 inhibition reaction 160 integral method 69 intron 6 Ion Torrent 230 ion-exchange resin 79 ionic current 232 ion-selective electrode (ISE) 109, 113 ion-selective membrane 111 ion-sensitive field-effect transistor (ISFET) sensor 230 iron–sulphur cluster 40 irreversible immobilization 77 isoelectric point (pI) 23 Jablonski diagram 149 key–lock mechanism 33 kinetic analytical method 66 label-based optical detection 144 label-free immunosensor 189 label-free optical detection 144 lactate 63, 159 lactate dehydrogenase 63, 159 lateral flow immunoassay (LFIA) 172, 174–175 light source 148 lipid vesicle 89 liposome 89 localised surface plasmon resonance (LSPR) 263 long-read next-generation sequencing 230 LSPR 263 luciferase 151, 159 luciferin 151 luminol 151, 156 lysine 23, 92

284

Index

macroporous gold electrode 257 magnetic beads ECL immunoassay 186 magnetic microbead 186 mass spectrometry 262 mass transport 103, 116 mass transport limitation 121, 132 mediated electron transfer (MET) 131 melting curve 12, 205–206, 265, 270 melting temperature 11, 205, 266 membrane amperometric electrode 125 membrane potential 109 messenger RNA (mRNA) 7 metal nanoparticle 250, 262 metal nanostar 251 metal nanotube 253 metal nanowire 253 metal–thiolate bond 261 Michaelis–Menten constant 44, 104, 135 Michaelis–Menten equation 45, 134 Michaelis–Menten plot 46, 135–136 microbead flow cytometry 184 microbead-based immunoassay 183 microemulsion 251 microplate 62 microsphere templated synthesis 257 migration 116 minor groove 4 minor groove binder (MGB) 208 missense mutation 10 molecular beacon 209 monoclonal antibody 48 motor protein 233 multiplexing analysis 184, 206, 208, 217, 266 multi-wall carbon nanotube (MWCNT) 239 multiwell plate 62 MXenes 243 Nafion 88, 104 nanoelectrode array (NEA) 276 nanoelectrode ensemble (NEE) 253 nanofiber 241, 248 nanoimprint lithography (NIL) 248 nanolithography 245 nanomaterial 237 nanomaterial characterisation 262 nanomaterial functionalization 260 nanomaterial synthesis 244 nanoparticle (NP) 237, 260 nanopore 232

nanoporous membrane 252 nanorod 240 nanostructure 243 nanotechnology 237 nanowire 241, 252 Nernst equation 107, 120 Nernst–Planck equation 117 next-generation sequencing (NGS) 221 nicotinamide adenine dinucleotide (NADH) 38, 64, 158 nicotinamide adenine dinucleotide phosphate (NADPH) 38 Nile red fluorescent dye 157 non-competitive ELISA sandwich ELISA non-covalent interaction 31, 78 nonsense mutation 10 NovaSeq 6000 230 N-terminus 26 nucleic acid 1, 199 nucleotide 1, 7, 12 one-dimensional nanomaterial 239 open pore current 232 optical fibre 145 optical glucose biosensor 156 optode 148 osmotic shock 199 Oxford Nanopore Technologies sequencing 230 oxidase 41 oxidoreductase 40, 128 oxygenase 42 Pacific Biosciences sequencing 230 papain 51 paratope 47, 53, 171 passivating group 98 patterned flow cell 225, 227 PCR efficiency 203 PCR primer 202 peak current 122 pepsin 51 peptide bond 24 peroxidase 42 pH indicator 152, 160 pH meter 109 phosphodiester linkage 3 phospholipid 89 phosphoramidite 16, 19, 217 photolithography 245

Index

photon detector 148 photoresist 245 phycoerythrin 185 physical adsorption 78 physical entrapment 77, 79, 83–84 piezoelectric crystal 194 piezoelectric effect 193 plasmon 190, 263 plasmonic enzyme-linked immunoassay 267 point mutation 9 Poisson distribution 214 polarography 123 poly(3,4-ethylenedioxythiophene) (PEDOT) 86 polyacrylamide gel 84 polyacrylamide gel electrophoresis (PAGE) 17, 177 polyaniline (PANI) 86 polycarbonate membrane 252 polyclonal antibody 48 polyelectrolyte 88 polymerase chain reaction (PCR) 201 polymeric gel 79 polypyrrole (PPy) 86 polysaccharide 87 polystyrene bead 183, 218 post-synthetic labelling 19 potentiometric biosensor 106, 113 potentiometric cell 108 potentiometry 106 pregnancy test 174 primary structure 26 primer annealing 202 protein 19, 26 protein denaturation 32 protein nanopore 232 purine 1, 9, 12 pyrimidine 1, 9, 12 QCM immunosensor 195 qualitative assay 61 quantification cycle (Cq) 203 quantitative assay 61 quantitative PCR (qPCR) 203 quantum dot 238 quantum wire 241 quartz crystal 193 quartz crystal microbalance (QCM) 195 quaternary structure 31

285

radial diffusion 126, 274 radioimmunoassay (RIA) 165 Randles–Sevcik equation 122, 274 random flow cell 225, 227 real-time PCR 18, 203 redox mediator 130, 140, 181 reference electrode 106, 118 refractive index 145, 190 restriction endonuclease 200 reverse transcription real-time PCR (qRT-PCR) 210 reversible immobilization 76 reversible terminator 225 rhodamine 150 ribonucleic acid (RNA) 5 ribosome 7 RNA polymerase 6 rolling circle amplification (RCA) 230 rotating disc electrode (RDE) 125 sandwich ELISA 169 sandwich LFIA 174 Sanger sequencing 219 SARS-CoV-2 175, 210 Sauerbrey equation 194 scan rate 118, 122, 133 scanning electron microscope (SEM) 261 scanning probe lithography (SPL) 247 scanometric assay 267 Scatchard equation 58 scatterplot 229 secondary antibody (sec-Ab) 165, 170, 179, 184 secondary structure 27 second-generation glucose biosensor 140, 142 second-generation sequencing 221 seed-mediated growth 258 self-assembled array 218 self-assembled monolayer (SAM) 79, 96, 191 self-complementary DNA 15 sensorgram 192 sequencing adapter 222 sequencing by synthesis (SBS) 222 sequencing primer 225 SERS 264, 269 short-read next-generation sequencing 220 silane 88, 98 silanisation 98, 261 silent mutation 10

286

Index

silver nanoparticle 250 single nucleotide polymorphism (SNP) 218 single-wall carbon nanotube (SWCNT) 239 SMRT (single molecule real time) 230 soft template synthesis 250 solid–phase DNA synthesis 16, 19 sol–gel 87 Southern blotting 199 specific orientation 94 sphere segment void (SSV) substrate 257, 264, 269 splicing 6 SPR biosensor 190 steady-state kinetics 43 steady-state voltammetric technique 123 stem–loop 209 stem–loop Scorpion primer 210 stereospecificity 36 Stern–Volmer equation 155 streptavidin 74, 82, 186 substrate specificity 36 supporting electrolyte 117 surface functionalisation 96 surface plasmon resonance (SPR) 190 surface-enhanced Raman spectroscopy (SERS) 258 SYBR Green 206, 213 Taq polymerase 202 TaqMan hydrolysis probe 207, 212–213 target DNA 200, 264 template synthesis 249 tertiary structure 30, 103 third-generation glucose biosensor 140 third-generation sequencing 221, 230 three-dimensional nanomaterial 243 threshold cycle (CT) 203 thymine 1, 4

top-down nanomaterial fabrication 244–245 total internal reflection 145, 190 tracer antibody 174 transcription 6 transducer 71, 74, 76, 105 transfer RNA (tRNA) 7 transition metal dichalcogenide (TMD) 243 translation 7 transmission electron microscopy (TEM) 262 tripropylamine (TPA) 188 TruSeq™ DNA PCR-Free 222 turnover number 46 two-dimensional nanomaterial 241 ultramicroelectrode 126 uracil 1 UV melting 11 UV spectroscopy 64 UV–visible spectroscopy 262 valence 54, 58 van’t Hoff plot 13 vibration frequency 194 Watson–Crick base pair 3 Western blotting 177 whole Human Genome Project 220 working electrode 118 Xenes 243 zero-dimensional nanomaterial 237 zero-mode waveguide (ZMW) 231 zwitterion 23 α-helix 27 β-pleated sheet 28 32 P-labelled DNA 219