Fluorescence In-Situ Hybridization (FISH) for Microbial Cells: Methods and Concepts 1071611143, 9781071611142

Citation preview

Methods in Molecular Biology 2246

Nuno F. Azevedo Carina Almeida Editors

Fluorescence In Situ Hybridization (FISH) for Microbial Cells Methods and Concepts

METHODS

IN

MOLECULAR BIOLOGY

Series Editor John M. Walker School of Life and Medical Sciences University of Hertfordshire Hatfield, Hertfordshire, UK

For further volumes: http://www.springer.com/series/7651

For over 35 years, biological scientists have come to rely on the research protocols and methodologies in the critically acclaimed Methods in Molecular Biology series. The series was the first to introduce the step-by-step protocols approach that has become the standard in all biomedical protocol publishing. Each protocol is provided in readily-reproducible step-bystep fashion, opening with an introductory overview, a list of the materials and reagents needed to complete the experiment, and followed by a detailed procedure that is supported with a helpful notes section offering tips and tricks of the trade as well as troubleshooting advice. These hallmark features were introduced by series editor Dr. John Walker and constitute the key ingredient in each and every volume of the Methods in Molecular Biology series. Tested and trusted, comprehensive and reliable, all protocols from the series are indexed in PubMed.

Fluorescence In Situ Hybridization (FISH) for Microbial Cells Methods and Concepts

Edited by

Nuno F. Azevedo LEPABE – Laboratory for Process Engineering, Environment, Biotechnology and Energy, Department of Chemical Engineering, University of Porto, Porto, Portugal

Carina Almeida INIAV – National Institute for Agrarian and Veterinarian Research, Rua dos Lagidos, Lugar da Madalena, Vairão, Vila do Conde, Portugal; CEB - Centre of Biological Engineering, University of Minho, Campus de Gualtar, Braga, Portugal; LEPABE – Laboratory for Process Engineering, Environment, Biotechnology and Energy, Department of Chemical Engineering, University of Porto, Porto, Portugal

Editors Nuno F. Azevedo LEPABE – Laboratory for Process Engineering Environment, Biotechnology and Energy Department of Chemical Engineering University of Porto Porto, Portugal

Carina Almeida INIAV – National Institute for Agrarian and Veterinarian Research Rua dos Lagidos, Lugar da Madalena, Vaira˜o Vila do Conde, Portugal CEB - Centre of Biological Engineering University of Minho, Campus de Gualtar Braga, Portugal LEPABE – Laboratory for Process Engineering Environment, Biotechnology and Energy Department of Chemical Engineering University of Porto Porto, Portugal

ISSN 1064-3745 ISSN 1940-6029 (electronic) Methods in Molecular Biology ISBN 978-1-0716-1114-2 ISBN 978-1-0716-1115-9 (eBook) https://doi.org/10.1007/978-1-0716-1115-9 © Springer Science+Business Media, LLC, part of Springer Nature 2021 This work is subject to copyright. All rights are reserved by the Publisher, whether the whole or part of the material is concerned, specifically the rights of translation, reprinting, reuse of illustrations, recitation, broadcasting, reproduction on microfilms or in any other physical way, and transmission or information storage and retrieval, electronic adaptation, computer software, or by similar or dissimilar methodology now known or hereafter developed. The use of general descriptive names, registered names, trademarks, service marks, etc. in this publication does not imply, even in the absence of a specific statement, that such names are exempt from the relevant protective laws and regulations and therefore free for general use. The publisher, the authors, and the editors are safe to assume that the advice and information in this book are believed to be true and accurate at the date of publication. Neither the publisher nor the authors or the editors give a warranty, expressed or implied, with respect to the material contained herein or for any errors or omissions that may have been made. The publisher remains neutral with regard to jurisdictional claims in published maps and institutional affiliations. This Humana imprint is published by the registered company Springer Science+Business Media, LLC, part of Springer Nature. The registered company address is: 1 New York Plaza, New York, NY 10004, U.S.A.

Preface Fluorescence in situ hybridization (FISH) has established itself as one of the cornerstone techniques in molecular biology for the study of microorganisms. Its unique ability to detect the presence of specific nucleic acid sequences inside a structurally intact target cell quickly found applications in microbial ecology studies and diagnostics, among many others areas. Having worked in applications of FISH in microorganisms for nearly 20 years, we felt it was time to provide a book that gathered the main protocols in this area. From the beginning, we decided that the book should be useful for both researchers that want to start working with the technique and more experienced researchers in FISH that want to learn about new protocols. As such, we included an introductory section (Chapters 1 and 2), where the basic principles of the classic FISH are described and the main variant techniques introduced. This is followed by a chapter that provides an easy-touse guide on how to design FISH probes for microorganisms. Chapters 4 and 5 contain the more “traditional” experimental protocols for the most common FISH techniques. More specifically, Chapter 4 provides protocols using DNA probes for targeting cells that are either in suspension or adhered to a surface. Chapter 5 provides protocols using nucleic acid mimics (NAMs) as probes as opposed to the standard DNA probes. Depending on the type of FISH variant technique or the application, it has been shown that the choice of the chemical nature of the probe (DNA vs NAM) has a critical impact on the robustness of the protocol. Other general-purpose protocols are provided in Chapters 6–8. Chapter 6 is dedicated to the in vivo delivery of nucleic acids into microbial cells, a FISH variant that aims to efficiently deliver the FISH probe in microbial cells when these cells are located inside the human body or the body of other higher-order animals. Chapter 7 describes the adaptation of the FISH method for adhered cells to the specific case where the microbial cells form biofilms, which are 3-D structures that consist of extracellular polymeric substances (EPS) surrounding the microbial cells. This EPS provides an extra diffusional barrier to the FISH probe, meaning that specific protocols must be devised to ensure efficient labeling in the entire biofilm structure. Chapter 8 describes the application of FISH on plant surfaces. Plants are well known for containing autofluorescence substances, and specialized FISH protocols are needed to overcome the effect of this autofluorescence on the probe signal. Chapters 9 and 10 provide protocols aiming at the improvement of fluorescence signals for an effective detection or quantification of low-copy-number targets inside the microbial cells (CARD-FISH and QD-FISH). This is for instance the case for slow-growing cultures of microorganisms containing minimum amounts of rRNA to be used as target, or for when the target is mRNA or genomic DNA instead of the more abundant rRNA. Yet another application of FISH is on the detection of bacteriophages (Chapter 11). Bacteriophages are known to be much smaller in size than bacteria, meaning that the amount of copies of target nucleic acid is also smaller and the individual detection of a phage almost impossible. Rather than improving the fluorescence signal, the strategy devised in this chapter is related to the multiplication of the phages inside the microbial host. When this occurs the number of copies of phage is sufficient for a FISH signal to be obtained, meaning that a relatively simple protocol can be applied to assess the presence of bacteriophages in microbial cells.

v

vi

Preface

Knowing the presence of a specific nucleic acid sequence inside a microbial cell provides us little information regarding the more general metabolic functions of that cell. As such, the following chapters (12–14) describe protocols that allow the assessment of functional information (i.e., the role that a certain microorganism might have in an ecosystem) associated with the phylogenetic analysis provided by the traditional FISH (such as MAR-FISH; GeneFISH and NanoSIMS). During the last years, significant effort has been devoted to developing FISH procedures that combine other technologies and equipment to improve the characterization aspects and automation of the FISH technique. FliFISH, for instance, is a method that allows the quantification of mRNA copies in intact bacterial cells by combining fluctuation localization imaging and FISH (Chapter 15). Additionally, the integration of FISH with microfluidics or with flow cytometry has allowed the development of more automated and reliable protocols to detect specific organisms in a sample (Chapters 16 and 17). The application of FISH embraces different areas, including the industrial (such as in food processing), environmental, and clinical areas. The subsequent chapters address the specific challenges faced by the application of FISH in these areas that range from the impact that food or environmental matrices have on microbial enrichment and the FISH protocol (Chapters 18 and 19) to quality control of FISH-based diagnostics in the clinical field (Chapter 20). The last chapter is dedicated to an area that is still emerging, but that has an immense potential in allowing researchers to develop their protocols in a more rational way or better explain the results obtained in their FISH experiments. This area is the computational modeling of the diffusion and hybridization aspects of the FISH protocol. As an example, the chapter reviews the main information that should be compiled to construct an agentbased model of FISH and provides a practical example of the application of the model consisting of a probe targeting the 23S rRNA of Escherichia coli. While selecting the main topics and chapters of the book, we were aware that many more techniques are available and could be included. Some of the most promising ones have been mentioned in Chapter 2, but FISH-based methods are continually being proposed and it is not straightforward to understand from the beginning which ones will be more successful. While this makes our role as editors a bit more difficult, it is also a testament to the vitality of this field. Nevertheless, we are confident that this book will serve as a valuable source of information to all researchers dealing with FISH. A final word of acknowledgment to all the authors that have kindly accepted to contribute to this book and that are helping to widen the range of FISH protocols. Vila do Conde, Portugal Porto, Portugal

Carina Almeida Nuno F. Azevedo

Contents Preface . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Contributors. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .

1 An Introduction to Fluorescence in situ Hybridization in Microorganisms . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Carina Almeida and Nuno F. Azevedo 2 FISH Variants . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . ˜ es, Nuno F. Azevedo, and Carina Almeida Nuno M. Guimara 3 Bioinformatic Tools and Guidelines for the Design of Fluorescence In Situ Hybridization Probes . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Helena Teixeira, Ana L. Sousa, and Andreia S. Azevedo 4 FISH in Suspension or in Adherent Cells . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Francesca Di Pippo, Diogo Queir
o s, Joana Pereira, Paulo C. Lemos, Luı´sa S. Serafim, and Simona Rossetti 5 Application of Nucleic Acid Mimics in Fluorescence In Situ Hybridization. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Ricardo Oliveira, Andreia S. Azevedo, and Luzia Mendes 6 Delivery of Oligonucleotides into Bacteria by Fusogenic Liposomes. . . . . . . . . . . ˜ es, Sara Pereira, Rita S. Santos, Luis Moreira, Nuno M. Guimara Kevin Braeckmans, Stefaan C. De Smedt, and Nuno F. Azevedo 7 Characterization of Social Interactions and Spatial Arrangement of Individual Bacteria in MultiStrain or Multispecies Biofilm Systems Using Nucleic Acid Mimics-Fluorescence In Situ Hybridization. . . . . . . Jontana Allkja and Andreia S. Azevedo 8 Leaf-FISH: In Situ Hybridization Method for Visualizing Bacterial Taxa on Plant Surfaces . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Elena L. Peredo and Sheri Simmons 9 CAtalyzed Reporter Deposition Fluorescence In Situ Hybridization (CARD-FISH) for Complex Environmental Samples . . . . . . . . . . . . . . . . . . . . . . . . ˜o Bruna Matturro, Simona Rossetti, and Patrı´cia Leita 10 Fluorescence In Situ Hybridization with Quantum Dot Labels in E. coli Cells . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Yang Liu, Zhiyuan Han, Suresh Sarkar, and Andrew M. Smith 11 Monitoring Bacteriophage Infection on Bacterial Cells Using FISH. . . . . . . . . . . Diana Vilas-Boas and Luı´s D. R. Melo 12 Linking Microbes to Their Genes at Single Cell Level with Direct-geneFISH. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Jimena Barrero-Canosa and Cristina Moraru 13 Assigning Function to Phylogeny: FISH-nanoSIMS. . . . . . . . . . . . . . . . . . . . . . . . . Katharina Kitzinger, Daniela Tienken, Sten Littmann, Abiel T. Kidane, Marcel M. M. Kuypers, and Jana Milucka

vii

v ix

1 17

35 51

69 87

97

111

129

141 157

169 207

viii

14 15

16 17

18 19

20

21

Contents

Assigning Function to Phylogeny: MAR-FISH . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Jeppe L. Nielsen Counting mRNA Copies in Intact Bacterial Cells by Fluctuation Localization Imaging-Based Fluorescence In Situ Hybridization (fliFISH) . . . . . Dehong Hu, Yi Cui, Lye M. Markillie, William B. Chrisler, Qian Wang, Roland Hatzenpichler, and Galya Orr Integration of FISH and Microfluidics. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . ˜ o M. Miranda Ce´lia F. Rodrigues, Nuno F. Azevedo, and Joa Flow-FISH Using Nucleic Acid Mimic Probes for the Detection of Bacteria . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Andreia S. Azevedo, Rui Rocha, and Nicolina Dias FISH in Food Samples . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Rui Rocha, Carina Almeida, and Nuno F. Azevedo Extraction of Microbial Cells from Environmental Samples for FISH Approaches. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Jennifer Pratscher Quality Control in Diagnostic Fluorescence In Situ Hybridization (FISH) in Microbiology . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Judith Kikhney and Annette Moter Computational Resources and Strategies to Construct Single-Molecule Models of FISH . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . ˜ es, Rita S. Santos, Nuno F. Azevedo, Beatriz T. Magalha and Ana´lia Lourenc¸o

Index . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .

225

237

249

263 279

291

301

317

331

Contributors JONTANA ALLKJA • LEPABE – Laboratory for Process Engineering, Environment, Biotechnology and Energy, Department of Chemical Engineering, Faculty of Engineering, University of Porto, Porto, Portugal CARINA ALMEIDA • INIAV – National Institute for Agrarian and Veterinarian Research, Rua dos Lagidos, Lugar da Madalena, Vaira˜o, Vila do Conde, Portugal; CEB – Centre of Biological Engineering, University of Minho, Campus de Gualtar, Braga, Portugal; LEPABE – Laboratory for Process Engineering, Environment, Biotechnology and Energy, Department of Chemical Engineering, Faculty of Engineering, University of Porto, Porto, Portugal ANDREIA S. AZEVEDO • LEPABE – Laboratory for Process Engineering, Environment, Biotechnology and Energy, Department of Chemical Engineering, Faculty of Engineering, University of Porto, Porto, Portugal; i3S – Instituto de Investigac¸a˜o e Inovac¸a˜o em Sau´de, Universidade do Porto, Porto, Portugal; IPATIMUP – Institute of Molecular Pathology and Immunology of the University of Porto, Porto, Portugal; CEB – Centre of Biological Engineering, University of Minho, Braga, Portugal NUNO F. AZEVEDO • LEPABE – Laboratory for Process Engineering, Environment, Biotechnology and Energy, Department of Chemical Engineering, Faculty of Engineering, University of Porto, Porto, Portugal JIMENA BARRERO-CANOSA • Department of Environmental Technology, Technische Universit€ at Berlin, Berlin, Germany KEVIN BRAECKMANS • Laboratory of General Biochemistry and Physical Pharmacy, Ghent University, Ghent, Belgium; Center for Advanced Light Microscopy, Ghent University, Ghent, Belgium WILLIAM B. CHRISLER • Environmental Molecular Sciences Laboratory, Pacific Northwest National Laboratory, Richland, WA, USA YI CUI • Environmental Molecular Sciences Laboratory, Pacific Northwest National Laboratory, Richland, WA, USA; MIT Media Lab, Cambridge, MA, USA STEFAAN C. DE SMEDT • Laboratory of General Biochemistry and Physical Pharmacy, Ghent University, Ghent, Belgium; Center for Advanced Light Microscopy, Ghent University, Ghent, Belgium FRANCESCA DI PIPPO • Water Research Institute C.N.R., Monterotondo, Italy NICOLINA DIAS • CEB – Centre of Biological Engineering, University of Minho, Braga, Portugal NUNO M. GUIMARA˜ES • LEPABE – Laboratory for Process Engineering, Environment, Biotechnology and Energy, Department of Chemical Engineering, Faculty of Engineering, University of Porto, Porto, Portugal ZHIYUAN HAN • Department of Bioengineering, University of Illinois at UrbanaChampaign, Urbana, IL, USA; Department of Materials Science & Engineering, University of Illinois at Urbana-Champaign, Urbana, IL, USA ROLAND HATZENPICHLER • Department of Chemistry and Biochemistry, Montana State University, Bozeman, MT, USA DEHONG HU • Environmental Molecular Sciences Laboratory, Pacific Northwest National Laboratory, Richland, WA, USA

ix

x

Contributors

ABIEL T. KIDANE • Department of Biogeochemistry, Max Planck Institute for Marine Microbiology, Bremen, Germany JUDITH KIKHNEY • Biofilmcenter, Institute for Microbiology, Infectious Diseases and Immunology, Charite´—Universit€ atsmedizin Berlin, Berlin, Germany; MoKi Analytics GmbH, Berlin, Germany KATHARINA KITZINGER • Department of Biogeochemistry, Max Planck Institute for Marine Microbiology, Bremen, Germany MARCEL M. M. KUYPERS • Department of Biogeochemistry, Max Planck Institute for Marine Microbiology, Bremen, Germany PATRI´CIA LEITA˜O • Cento de Recursos Naturais e Ambiente (CERENA), Faculty of Engineering, University of Porto, Porto, Portugal PAULO C. LEMOS • LAQV, REQUIMTE, Chemistry Department, Faculty of Science and Technology, University NOVA of Lisbon, Caparica, Portugal STEN LITTMANN • Department of Biogeochemistry, Max Planck Institute for Marine Microbiology, Bremen, Germany YANG LIU • Department of Bioengineering, University of Illinois at Urbana-Champaign, Urbana, IL, USA; Micro and Nanotechnology Laboratory, University of Illinois at Urbana-Champaign, Urbana, IL, USA ANA´LIA LOURENC¸O • Escuela Superior de Ingenierı´a Informa´tica (ESEI), University of Vigo, Ourense, Spain; Centro de Investigaciones Biome´dicas (CINBIO), University of Vigo, Vigo, Spain; Sistemas Informa´ticos de Nueva Generaci
on (SING) Research Group, Galicia Sur Health Research Institute (IIS Galicia Sur), SERGAS-UVIGO, Vigo, Spain; Centre of Biological Engineering (CEB), University of Minho, Braga, Portugal BEATRIZ T. MAGALHA˜ES • Laboratory for Process Engineering, Environment, Biotechnology and Energy (LEPABE), Department of Chemical Engineering, Faculty of Engineering of the University of Porto, Porto, Portugal LYE M. MARKILLIE • Environmental Molecular Sciences Laboratory, Pacific Northwest National Laboratory, Richland, WA, USA BRUNA MATTURRO • Water Research Institute (IRSA), National Research Council (CNR), Monterotondo St. (RM), Italy LUI´S D. R. MELO • Laborat
orio de Investigac¸a˜o em Biofilmes Rosa´rio Oliveira (LIBRO), Centre of Biological Engineering (CEB), University of Minho, Braga, Portugal LUZIA MENDES • FMDUP – Faculty of Dental Medicine, University of Porto, Porto, Portugal JANA MILUCKA • Department of Biogeochemistry, Max Planck Institute for Marine Microbiology, Bremen, Germany JOA˜O M. MIRANDA • CEFT – Transport Phenomena Research Center, Department of Chemical Engineering, Faculty of Engineering, University of Porto, Porto, Portugal CRISTINA MORARU • Institute for Chemistry and Biology of the Marine Environment, Oldenburg, Germany LUIS MOREIRA • LEPABE – Laboratory for Process Engineering, Environment, Biotechnology and Energy, Department of Chemical Engineering, Faculty of Engineering, University of Porto, Porto, Portugal ANNETTE MOTER • Biofilmcenter, Institute for Microbiology, Infectious Diseases and Immunology, Charite´—Universit€ atsmedizin Berlin, Berlin, Germany JEPPE L. NIELSEN • Department of Chemistry and Bioscience, Aalborg University, Aalborg, Denmark

Contributors

xi

RICARDO OLIVEIRA • LEPABE – Laboratory for Process Engineering, Environment, Biotechnology and Energy, Department of Chemical Engineering, Faculty of Engineering, University of Porto, Porto, Portugal; INIAV – National Institute for Agrarian and Veterinarian Research, Rua dos Lagidos, Lugar da Madalena, Vaira˜o, Vila do Conde, Portugal GALYA ORR • Environmental Molecular Sciences Laboratory, Pacific Northwest National Laboratory, Richland, WA, USA ELENA L. PEREDO • Marine Biological Laboratory, The Ecosystems Center, Woods Hole, MA, USA JOANA PEREIRA • CICECO – Aveiro Institute of Materials, Chemistry Department, University of Aveiro, Aveiro, Portugal SARA PEREIRA • LEPABE – Laboratory for Process Engineering, Environment, Biotechnology and Energy, Department of Chemical Engineering, Faculty of Engineering, University of Porto, Porto, Portugal; Laboratory of General Biochemistry and Physical Pharmacy, Ghent University, Ghent, Belgium JENNIFER PRATSCHER • The Lyell Centre, School of Energy, Geoscience, Infrastructure and Society, Heriot-Watt University, Edinburgh, UK DIOGO QUEIRO´S • CICECO – Aveiro Institute of Materials, Chemistry Department, University of Aveiro, Aveiro, Portugal RUI ROCHA • CISAS – Centre for Research and Development in Agrifood Systems and Sustainability, Escola Superior de Tecnologia e Gesta˜o, Instituto Polite´cnico de Viana do ´ vares, Viana do Castelo, Viana do Castelo, Portugal; Escola Industrial e Comercial Nun’A Castelo, Portugal CE´LIA F. RODRIGUES • LEPABE – Laboratory for Process Engineering, Environment, Biotechnology and Energy, Department of Chemical Engineering, Faculty of Engineering, University of Porto, Porto, Portugal SIMONA ROSSETTI • Water Research Institute (IRSA), National Research Council (CNR), Monterotondo St. (RM), Italy RITA S. SANTOS • LEPABE – Laboratory for Process Engineering, Environment, Biotechnology and Energy, Department of Chemical Engineering, Faculty of Engineering, University of Porto, Porto, Portugal SURESH SARKAR • Department of Bioengineering, University of Illinois at UrbanaChampaign, Urbana, IL, USA; Micro and Nanotechnology Laboratory, University of Illinois at Urbana-Champaign, Urbana, IL, USA LUI´SA S. SERAFIM • CICECO – Aveiro Institute of Materials, Chemistry Department, University of Aveiro, Aveiro, Portugal SHERI SIMMONS • Marine Biological Laboratory, The Ecosystems Center, Woods Hole, MA, USA ANDREW M. SMITH • Department of Bioengineering, University of Illinois at UrbanaChampaign, Urbana, IL, USA; Micro and Nanotechnology Laboratory, University of Illinois at Urbana-Champaign, Urbana, IL, USA; Department of Materials Science & Engineering, University of Illinois at Urbana-Champaign, Urbana, IL, USA; Carle Illinois College of Medicine, Urbana, IL, USA ANA L. SOUSA • LEPABE – Laboratory for Process Engineering, Environment, Biotechnology and Energy, Department of Chemical Engineering, Faculty of Engineering, University of Porto, Porto, Portugal; INIAV – National Institute for Agrarian and Veterinarian Research, Rua dos Lagidos, Lugar da Madalena, Vaira˜o, Vila do Conde, Portugal

xii

Contributors

HELENA TEIXEIRA • LEPABE – Laboratory for Process Engineering, Environment, Biotechnology and Energy, Department of Chemical Engineering, Faculty of Engineering, University of Porto, Porto, Portugal DANIELA TIENKEN • Department of Biogeochemistry, Max Planck Institute for Marine Microbiology, Bremen, Germany DIANA VILAS-BOAS • Laborat
orio de Investigac¸a˜o em Biofilmes Rosa´rio Oliveira (LIBRO), Centre of Biological Engineering (CEB), University of Minho, Braga, Portugal QIAN WANG • Land Resources & Environmental Sciences, Montana State University, Bozeman, MT, USA

Chapter 1 An Introduction to Fluorescence in situ Hybridization in Microorganisms Carina Almeida and Nuno F. Azevedo Abstract Fluorescence in situ hybridization (FISH) is a molecular biology technique that enables the localization, quantification, and identification of microorganisms in a sample. This technique has found applications in several areas, most notably the environmental, for quantification and diversity assessment of microorganisms and, the clinical, for the rapid diagnostics of infectious agents. The FISH method is based on the hybridization of a fluorescently labeled nucleic acid probe with a complementary sequence that is present inside the microbial cell, typically in the form of ribosomal RNA (rRNA). In fact, an hybridized cell is typically only detectable because a large number of multiple fluorescent particles (as many as the number of target sequences available) are present inside the cell. Here, we will review the major steps involved in a standard FISH protocol, namely, fixation/permeabilization, hybridization, washing, and visualization/ detection. For each step, the major variables/parameters are identified and, subsequently, their impact on the overall hybridization performance is assessed in detail. Key words FISH, Microorganism, Hybridization, Detection, Single-cell microbiology, Probes

1

Introduction During the 1940s, Chargaff observed that the concentration of the four DNA bases inside the cell was similar for the adenine-thymine and cytosine-guanine pairs, a work that laid the foundation for the selective base-pairing rules of nucleic acids [1]. This base recognition specificity is the basis of the principle of hybridization, a process where two complementary (or near complementary) sequences of nucleic acids are able to interact and stay together via the action of noncovalent forces. When the hybridization process occurs inside the cells, it is said to occur in situ. Whereas researchers have quickly seized the immense possibilities of in situ hybridization for biotechnological processes, namely, for detecting and tracking specific nucleic acid sequences, a robust method to report a successful hybridization was still needed. The most widely used method to detect hybridization at present

Nuno F. Azevedo and Carina Almeida (eds.), Fluorescence In Situ Hybridization (FISH) for Microbial Cells: Methods and Concepts, Methods in Molecular Biology, vol. 2246, https://doi.org/10.1007/978-1-0716-1115-9_1, © Springer Science+Business Media, LLC, part of Springer Nature 2021

1

2

Carina Almeida and Nuno F. Azevedo

involves coupling a fluorescent molecule, also known as fluorochrome or fluorophore, to the nucleic acid sequence of interest. Fluorescence in situ hybridization (FISH) is now routinely used to assess microbial diversity in environmental samples [2], rapidly detect specific pathogens in clinical diagnostics [3], or identify spoilage microorganisms in industrially relevant bioprocesses [4], among other applications. The origins of the detection of microorganisms by FISH can be traced back to the late 1980s, when DeLong et al. used oligonucleotides (i.e., short nucleic acid sequences containing typically between 15 and 35 bases) as probes to target the 16S ribosomal RNA (rRNA) of bacteria [5]. Previously, the same group had used radiolabeled probes to identify microorganisms [6], but concluded that using FISH, a higher resolution and faster analysis were obtained. In both studies, the FISH method involved 4 major steps: fixation/permeabilization, hybridization, washing, and visualization/detection. The fixation/permeabilization step objective is to render the cell wall permeable to the nucleic acid probe entry, while, at the same time, guaranteeing that cell lysis and extensive nucleic acid degradation will not occur. During hybridization, the probe is placed in contact with the target cells, and if complementary (or near-complementary) sequences are present, it hybridizes. The specificity of this binding is guaranteed by the stringency conditions set for the hybridization step and, later, at the washing step, where all loosely bound probes are washed away. Finally, visualization/detection by either fluorescence microscopy or flow cytometry allows the researcher to observe if a successful hybridization has occurred. In spite of being one of the most widely used molecular biology techniques at present, those working with FISH are aware that not everything is simple in protocol development. A large part of the problem has to do with the large number of experimental variables that need to be adjusted in order to obtain a successful hybridization. Most of these variables are interconnected, which means that alterations in one of them might implicate unexpected changes in others.

2

Parameters Involved in a FISH Method Each of the four steps involved in a FISH method and described in the previous section contains multiple variables (Table 1). Most of them can be user-defined (e.g., hybridization temperature and duration), but others are characteristics of the system that is being studied or are restricted to the conditions available in the laboratory (e.g., target microorganisms and type of equipment available).

An Introduction to FISH

3

Table 1 Variables typically involved in a standard FISH method, classified according to the steps to which they are relevant. A more thorough description on the specific impact of these and other variables can be found in the main text Step

Variables

Fixation Fixation or permeabilization Permeabilization agent Concentration of the agent Number and sequence of fixation/permeabilization agents Contact time Cell wall Cell spatial location Hybridization

Type of probe Probe sequence Probe length Hybridization Temperature Salt concentration Formamide concentration Probe self-complementarity Target molecule Cell physiological state rRNA target Contact time Cell spatial location Probe concentration pH Viscosity

Examples Ethanol, formaldehyde, and lysozyme 50% (vol/vol) ethanol, 4% (wt/vol) paraformaldehyde Typically 1–3 agents are used

5–60 min depending on the agent and sample Gram
and Gram + Adhered (membrane, slide) and suspension DNA, PNA, and LNA Self-complementarity, GC content, and Tm 15 to 35 bases 37–70  C 0–1 M 0–50% (vol/vol) Hairpin structures and number of selfcomplementary bases mRNA, rRNA, and DNA Latent, stationary, and exponential Secondary structure 30–180 min Adhered (membrane, slide) and suspension 200–400 nM 7–9 Presence of polymers in solution (e.g., dextran sulfate of different molecular weights)

Washing

Contact time Temperature pH

15–90 min 40–70  C 7–9

Visualization/ detection

Sample autofluorescence Equipment Fluorescence detection limit Filter wavelength

Type of sample and mounting media Fluorescent filters and light source Equipment settings; fluorochrome properties Band pass/long pass, fit to the fluorochromes excitation and emission spectra, and cross talk Cy3, FITC, Alexa Fluor User expertise, type of fluorochrome, and light source

Type of Fluorochrome Fluorochrome quenching

2.1 Fixation/ Permeabilization

There are several agents that can be employed during fixation/ permeabilization. The choice for the agents to be used in this step is mostly dependent on three factors: the type of microorganism (mainly the cell envelope), the type of nucleic acid probe used, and the target cells’ spatial location (i.e., whereas cells are in slides, filter

4

Carina Almeida and Nuno F. Azevedo

membranes, or in suspension). This multifactor dependency has caused all efforts to create a universal fixation method for microorganisms to fail [2, 7]. By far, the two most common fixation agents are ethanol and formaldehyde [e.g., 8, 9]. The first is a precipitating fixative and as such acts by reducing the solubility of macromolecules, which in turn inactivates enzymes and stabilizes nucleic acid structures. It also helps in dissolving the lipidic layer, and so ethanol can be regarded as a permeabilization agent too. The second, formaldehyde, induces the formation of covalent bonds between molecules, which will also inhibit the action of enzymes, but might decrease membrane permeability and consequently target accessibility. Typical concentrations for ethanol vary between 50% and 100% (vol/vol) with contact times of less than 30 min, whereas for formaldehyde, the concentration is usually very close to 4% (wt/vol) and exposure can take from as little as 10 min to last overnight, depending on the type of sample and the FISH technique [10, 11]. For the case of ethanol, it is not uncommon to subject the samples to solutions with increasing ethanol concentrations in order to obtain a complete dehydration without the destruction of cell structure [12]. In studies employing DNA probes, fixation is at times performed at low temperatures (~4  C). Other less common fixatives are methanol and glutaraldehyde, but their effect in the cell is expected to be quite similar to the one of ethanol and paraformaldehyde, respectively. As for permeabilization agents, the method of choice usually involves enzymes, even though mild acid hydrolysis has been used as well [12, 13]. Lysozyme is the most widely enzyme used and acts by degrading the peptidoglycan present in bacterial cell walls. Standard protocols tend to use this enzyme mainly on Gram+ bacteria at a concentration of 10 mg/ml, since these bacteria have a thicker peptidoglycan layer that might be hard to permeate. Lipase, proteinase K, mutanolysin, lysostaphin, or streptolysin have also been tested, particularly for microorganisms associated with permeabilization difficulties, such as those possessing mycolic acids in their cell walls, spore-forming bacteria, or mycobacteria [12, 14]. Other studies make use of a combination of enzymes that will act over different components of the cell envelope. For instance, FISH methods for Staphylococcus apply very often a mixture of lysozyme and lysostaphin [15]. Mixtures of mutanolysin/ lysozyme or lipase/proteinase K have been applied to mycolic acidcontaining bacteria (e.g., actinomycetes) [12]). These microorganisms are particularly difficult to permeabilize [12] because mycolic acids are long-chain fatty acids that form a capsule-like, high hydrophobic layer. The role of capsule and capsule-like external structures of the cell envelope in the permeabilization/fixation procedures is still poorly understood, especially because these layers are poorly characterized for many bacterial species. Other less

An Introduction to FISH

5

common permeabilization protocols might include detergents. Triton X-100, SDS, EDTA, and other detergents are frequently used to permeabilize the membranes by extracting the lipid membrane or destabilizing the lipopolysaccharides by removing divalent cations by chelation [16]. For combinations of particular microorganisms and nucleic acid probes, the use of permeabilization was found to be unnecessary [17, 18]. For instance, a protocol that uses peptide nucleic acid (PNA) probes to detect Staphylococcus aureus was able to dispense with this step altogether. In the case of PNA, this might be attributed to the neutral charge of the molecule and the small size of the probes (15 bp), which make cell penetration easier [18]. Nonetheless, similar results have been found for charged probes. LNA/ 20 -Omethyl RNA probes have been introduced into unfixed Helicobacter pylori cells [17], whereas DNA probes were applied to both Gram-positive (Bacillus sp.) and Gram-negative bacteria (Ruegeria sp. and Pseudovibrio sp.) [19]. Apart from H. pylori, which seems to have a more permeable envelope (as no additional permeation steps were applied), in other species the DNA probes were introduced into live microbial cells by means of chemical transformation. This showed that fixation and permeabilization steps might not be mandatory if innovative strategies are used to deliver probes inside cells. However, when cell fixation is not a concern (i.e., when viable cells are not necessary for further tests), fixation/permeabilization protocols are likely to deliver a more robust FISH outcome. 2.2 Hybridization and Washing

Hybridization is probably the most complex step of FISH due to the number of variables that can affect the binding of the probes to the sequence target—the duplex formation. This reaction is thought to proceed in two steps. First, a nucleation event leads to the formation of a few correct base pairs between the two complementary sequences and, then, a rapid zipping occurs, during which the rest of the base pairing proceeds [20]. Since the zipping step is very quick, it is thought that nucleation is the rate-limiting step, and so it is important to understand how FISH variables will affect this reaction. As FISH protocols usually use probe concentration in excess relative to the number of target sequences [21], the concentration of the target molecule (typically the rRNA) will have a more relevant impact on the FISH outcome.

2.2.1 The Target RNA

The physiological state of the microorganism as well as the species will define the content of target rRNA molecules in the cells. In growing bacterial cells, as many as 104–105 ribosomes per cell can be found [5]. For fast growing bacteria, such as Escherichia coli with a doubling time of 24 min, the number of rRNA copies per cell has been estimated at about 72,000 ribosomes/cell; nonetheless, the same E. coli species growing more slowly (doubling time, 100 min) contained about 6800 ribosomes/cell [22]. The slow-growing

6

Carina Almeida and Nuno F. Azevedo

bacteria Mycoplasma pneumoniae, Spiroplasma melliferum, and Rickettsia prowazekii have shown values of 300, 1000, and 1500 ribosomes per cell, respectively [23–25]. These values show the huge variation that one can face when applying FISH to different species and in different metabolic states. In environmental samples, rRNA content can be even more challenging as metabolic processes of cells can be reduced to minimal levels and because some matrices can present a strong background that requires brighter signals for a proper detection. As an example, the detection limit of conventional FISH with Cy3-labeled Eubacteria probe was found to be 370  45 16S rRNA molecules per cell for Escherichia coli hybridized on standard glass microscope slides, but increased to 1400  170 16S rRNA copies per E. coli cell in activated sludge [26]. Also, if an mRNA target is intended, the concentration is by default a problem when comparing with the rRNA content and also highly depends on the induction of a particular gene. In these cases, the number of hybridized probes will be obviously lower and cells might not be detected by a standard FISH procedure. Thus, researchers might need to resort to other FISH techniques intended to detect low copy numbers of the target. In addition to the concentration, the access to the target RNA is another parameter that will have a major impact on duplex formation. The rRNA naturally forms a complex secondary structure that might hinder the probe access to the target sequence. This structure has many loops and helices and embedded ribosomal proteins leaving some stretches of the rRNA more accessible to probes than others. Fuchs et al. have proposed an “accessibility map” of the E. coli 16S rRNA, describing regions with strong or weak accessibility that allows researchers to anticipate limitations on probe access to the target [27, 28]. Nonetheless, regions described as inaccessible can be made more accessible by using helperoligonucleotides (unlabeled oligonucleotides that bind in close vicinity to the target site of the labeled probe, opening the rRNA secondary structure) [27]. Accessibility to rRNA is also strongly affected by the salt concentration in solution (ionic strength), the denaturant, the temperature, and the type of nucleic acid probes being used. While the temperature and denaturant effects are quite obvious, as high values will destabilize the rRNA secondary structures making the probe access easier, the other two are more complex. Salts are essential for stabilizing both the rRNA secondary structure and the probe-target duplex. So, while the reduction in salts will help with accessibility, it might simultaneously affect the duplex (target-probe) formation. This is particularly true for natural nucleic acid probes, such as DNA probes, for which repulsive forces between the phosphate groups in the strand backbones need to be blocked/reduced by salts for a successful pairing. With the introduction of some apolar synthetic nucleic acids, this task has

An Introduction to FISH

7

become easier for several reasons, as detailed in the next sections. While accessibility to rRNA can be improved resorting to probes made of modified nucleic acids, the composition of the hybridization solution needs to be significantly adapted. Hybridization solutions are substantially different from technique to technique, which sometimes simply reflects the cumulative knowledge/experience of each research group but in many cases is related to the FISH variant applied. The composition of the hybridization solution, as well as the effect of salt, denaturants, and type of nucleic acid will be discussed below in more detail. 2.2.2 The Probes

Probe design is the parameter that has a stronger effect on the method performance, namely, on the specificity and sensitivity of the method. In diagnostic applications, specificity refers to the probe ability to correctly discriminate the target from the nontarget sequences/species, whereas sensitivity refers to the ability to correctly detect all strains/sequences within the same species/taxonomic group. In this regard, properties such as the length of the probe, GC percentage, melting temperature (Tm), specificity, sensitivity, self-complementarity, number of mismatches with close sequences should be assessed in the design stage to increase the odds of success of the method. The length of the probe and GC percentage have a direct impact on the probe Tm. It is well-known that duplex thermal stability is highly dependent on base composition as three hydrogen bonds occur between guanine and cytosine bases, in opposition to two hydrogen bonds between adenine and thymine bases. The importance of G/C content is typically more pronounced for shorter probes and in general is kept between 40% and 60%. Theoretically, the shorter the probe, the more rapidly diffusion across the cellular envelope occurs and the more discriminating the probe becomes. For instance, short oligonucleotides can discriminate between closely related target sequences that differ by as little as one base, simply because a single base will have a stronger effect on the overall affinity and Tm of a shorter molecule than in a longer one. However, the discriminating power might not translate into increased specificity for very short molecules, as very small sequences will present higher odds of being found in other organisms. As such, a balance between the discriminating power and specificity of the sequence should be achieved. Probes with as low as 10-bp have been described and proved to be specific [17]; but these low values would definitely need extra care in terms of specificity assessment. Most works use 12 to 20-bp long probes depending on the nature of the nucleic acid being used. For unnatural nucleic acids (nucleic acid mimics), the probe size is usually shorter (12 to 15 bp) because their thermal stability and affinity per base pair are usually higher.

8

Carina Almeida and Nuno F. Azevedo

Regarding target specificity, a probe is typically designed to have an exact match to the target. As such, one of the main parameters affecting probe performance is mismatches found in sequences of nontarget organisms. A mismatch is a position within the probe where the base is unable to bind with the base at the corresponding position in the target molecule. Mismatching delays the hybridization rate and may even inhibit hybridization altogether for shorter probes or if a significant number of mismatched positions are involved. Assuming a correct design, mismatches could be a result of point mutations in the target organism or simply a natural polymorphism of the sequence. They are very important for probe design as they can affect sensitivity. In this case, we want to avoid mismatches. However, they can (and should) be used to discriminate our target sequence/taxonomic group from closely related ones. As such, mismatched positions need to be well evaluated during probe design, and so we can both avoid them within the target sequences and look for them to choose regions with high discriminating power for closely related nontarget sequences. When mismatches are present, the probe performance will also be greatly dependent on two other features: (1) the stringency of the hybridization and (2) the type of probe being used. If the stringency is relaxed, mismatched probes will be able to hybridize with nonfully complementary sequences. The stringency of the system can be relaxed or increased by simply changing the hybridization temperature. The more the hybridization temperature is reduced below the probe Tm, the more mismatches will be tolerated [29]. However, if stringency is too high, probe binding is compromised even for perfect matches. In addition to temperature, stringency is affected by different factors that are mainly dependent on the composition of the hybridization and washing solutions to be used in FISH. The composition of those solutions and its effects on stringency will be explored in the next sections. Regarding the type of probe, as mentioned earlier, traditional FISH applications resort to probes made of natural nucleic acids, in particular, DNA. Advances in nucleic acid technology have resulted in the appearance of different generations of nucleic acid mimics, which are synthetic molecules that hybridize with natural nucleic acid obeying the same base-paring rules [30]. They provide several advantages over the traditional molecules, from which the most relevant for FISH application include (1) higher thermal stability and so higher hybridization temperature, which will help with accessibility; (2) higher affinity toward RNA than RNA or DNA, and so they might be able to displace secondary structures in the target region; (3) neutral backbone for some particular synthetic nucleic acids (in this particular case, PNA) that allow hybridization to occur with low or no salts at all (something that will also help with accessibility to the target rRNA); and (4) resistance to nuclease degradation due to their unnatural nature.

An Introduction to FISH

9

Among the synthetic molecules most used in FISH application, PNA, locked nucleic acids (LNA), and 20 -O-methyl-RNA (20 OMeRNA) play a prominent role [30, 31]. Modifications in these synthetic molecules are very diverse, including backbone substitutions, conformational lock, ring substitution, or the entire backbone replacement. The most singular advantage of PNA is related to its neutral backbone as mentioned above. It makes the probe-target duplex less dependent on the salt stabilization effect because there are no negatively charged phosphate groups on the probe. This effect is translated in the use of hybridization solutions with low salt, usually around 10 mM NaCl or with no NaCl at all. This will cause the rRNA secondary structure to open, improving drastically the accessibility. Regarding LNA, several studies have shown that these monomers can increase affinity toward DNA and RNA molecules, improving the overall signal-to-noise ratio, sensitivity, and specificity [32, 33]. Also, DNA duplexes containing LNA monomers present higher thermal stability and increase the melting temperature from about 2  C to 10  C (against RNA), per single LNA nucleotide incorporation [32, 34]. Because of the significant increase of the Tm per single LNA nucleotide incorporation, LNA probes are typically not completely made of LNA, as this would cause melting and hybridization temperatures to rise to values that could destroy the structure of the cells. Most often, LNA monomers are combined with DNA or with another RNA mimics, such as 20 OMe- 20 OMe-, which also has a great affinity for RNA/DNA targets, but increase on Tm is less prominent compared to LNA [35, 36]. Consequently, probe design with LNA or 20 OMe- has great flexibility, as mixed synthesis resorting to the intercalation of different monomers allows the fine-tuning of the probe thermodynamic parameters. This means that minor adjustments in the thermodynamic parameters can be achieved by changing the type of nucleotide at a particular position. In fact, such combinations are not limited to LNA and 20 OMe-, as there is an array of new nucleic acid mimics with potential for combination/ mixed synthesis that has been applied to FISH [30, 37] or even to antisense approaches (i.e., the use of complementary nucleic acids to bind/block a specific messenger RNA) [38, 39]. 2.2.3 The Hybridization Solution

Hybridization solutions have some common features among the different FISH procedures. In addition to the probe, their basic composition should always include a denaturant, a buffer, and salt. As described above in the “target RNA” section, salts play an important role in the stability of the duplex probe-target and in the rRNA secondary structures. The buffer role is to keep the pH of the hybridization step fairly constant, usually between 7 and 9. While the pH effect has not been fully disclosed, it is known to

10

Carina Almeida and Nuno F. Azevedo

affect the ionization of the nucleotides [40]. From pH 5 to 9, all bases are uncharged, and so hybridization should occur without interference. For lower values, such as pH 4, the hybridization has proved possible, for instance, for LNA probes [17]. However, it is known that some common fluorochromes are affected by pH and such values might compromise their performance [41]. For values higher than 10, hybridization/pairing might be disfavored as most bases will be deprotonated. Nonetheless, a pH around 10 has shown to be beneficial for the hybridization of PNA probes in some Gram-negative bacteria. This effect was probably linked with the viscosity of the hybridization solution that decreased for higher pH. Temperature and denaturant concentration are typically the variables that are first changed when trying to reach the desired performance. Formamide at concentrations ranging from 30% to 50% (vol/vol) is by far the most commonly denaturant applied in FISH procedures. Nonetheless, some studies have attempted to optimize this parameter and have shown that the concentration range can be broader (5–70% vol/vol), depending on the type of probe and cell envelope of the microorganisms [42]. Formamide destabilizes the double-stranded molecules by interfering with hydrogen bond formation. Thus, it reduces the Tm of the probe and, consequently, the hybridization temperature (Th) in a linear manner (Th is expected to decrease from 0.75 to 1.0  C for each 1% (vol/vol) of formamide added) [43]. It is, therefore, common to use Th near 50 and 60  C, using probes that have theoretical Tm above 70  C. However, one should be aware that formamide is toxic and volatile at the temperatures commonly used in hybridization procedures. In the last decade, other chemicals have been introduced either to use nontoxic compounds or to try to accelerate the nucleation reaction. In the first case, urea has been the elected denaturant of choice for replacing the toxic formamide, especially when in vivo applications are intended [44]. Usually, its concentration ranges from 0.5 to 6 M. Regarding the acceleration of the hybridization kinetics, ethylene carbonate has been tested and proved to be efficient for targeting specific sequences in human tissue samples [45]. However, until now, its superiority over urea or formamide for application in microorganisms is not apparent [46, 47]. Denaturants, salts, and buffers were listed above as the basic components of the hybridization solution, but these solutions might include several other compounds that can interact with each other. These might include detergents/surfactants, chelators, or polymers. For instance, the anionic polymer dextran sulfate is a common ingredient in many hybridization solutions. It appears to increase the rate of hybridization by forcing the probe into the cell, as it will fill the physical space creating an osmotic pressure that will

An Introduction to FISH

11

drive probes toward the target [47]. However, dextran sulfate, in particular, the ones with high molecular weight, will render the hybridization solution highly viscous, which that might also interfere with macromolecular diffusion [48]. Dextran sulfate with molecular weights ranging from 0.5 to 500 kDalton and concentrations from 2% to 10% (wt/v) has been applied. Both the molecular weight and concentration might be adjusted according to the target cell properties [47]. A similar effect can be obtained with the inclusion of Denhardt’s solution, which is part of several hybridization solutions. This solution includes a mixture of high-molecular weight polymers that artificially increase the concentration of available probe; but it also helps reducing background signal. It acts as a blocking agent, since polymers are capable of saturating nonspecific binding sites reducing nonspecific binding. While these reagents are more common in Northern and Southern Blot techniques (to block membrane unspecific binding), they persist in some FISH recipes, but, apart from techniques requiring enzymatic conjugates for signal amplification or the techniques performed in filter membranes, their role is probably not crucial for most FISH variants. 2.2.4 Washing

Washing steps ensure that excess probe molecules are washed away and, when properly optimized, prevent unspecific binding by removing also loosely bound probes. Parameters such as temperature, salt, and washing time are manipulated to ensure a stringent wash. Depending on the nature of the probe and the stability of the duplex, these 3 parameters might need adjustments, but their values are in general very similar to the ones used in the hybridization step for each experiment. Other components of the washing solution are buffers and detergents, such as SDS, Tween-20, or Nonidet P-40. Denaturants can also be part of the washing buffer solutions, with formamide being the most commonly used compound. Adjustments in this step usually depend on the FISH outcome. For instance, if cross-hybridization is noticed, temperature can be increased or denaturant can be added. If a foggy image is obtained (or if impurities are noticed), the number and time of washes can be increased. Protocols resorting to both 3 short washings and one longer wash are quite common for different FISH variants.

2.3 Visualization/ Detection

The importance of the visualization/detection step in the FISH procedure is often underestimated, but the selection of appropriate settings can be crucial for a successful detection of the cells. FISH samples are typically evaluated by fluorescence microscopy, resorting to filter units containing excitation and emission filters that allow the discrimination of different color channels (usually blue, green, and red channels). The properties of the filters should be evaluated even before ordering the probes, as ordering a probe with

12

Carina Almeida and Nuno F. Azevedo

a red fluorochrome does not mean it will work efficiently with any red filter set. This is even more relevant now because of the wide range of fluorochromes available. If the fluorochromes do not fit perfectly the filter settings, the excitation efficiency can be reduced drastically, which will compromise the emission signal. Equally, if the emission signal is not collected at the peak of the fluorochrome emission spectrum, fluorescence intensity might be very low. At present, many fluorescence systems make use of lasers for excitation, and so instead of an excitation range, they have a fixed excitation wavelength. The relevance of the equipment settings described here for microscopy is also true for other fluorescence detection systems such as flow cytometry, as they rely on the same excitation/ emission strategies. The choice of equipment to be used for visualization/detection will mainly depend on laboratory resources/access to equipment and also on the main goal of the study. If a quantification is intended, flow cytometry will provide a more straightforward approach; while for observing complex population threedimensional structures, microscopy is the obvious choice. Equipment has observed a huge evolution in the last decade. Flow cytometry systems offer the possibility of sorting cells with a specific fluorescence signal (a functionality usually called as fluorescenceactivated cell sorting [19, 49]), while advanced microscopy systems allow the 3D observation of microbial communities and present highly multiplexed (i.e., simultaneous detection of multiple fluorochomes) capabilities. The visualization equipment, in particular microscopy, can also be combined with other techniques so that, in addition to genetic information, data on the cells’ metabolic activity are collected. These techniques usually resort to labeled substrates [50]. They provide functional analysis at a single-cell level in complex microbial communities. One of the first methods being introduced was MAR-FISH. This well-established approach has been followed by Raman- or SIMS-FISH. Differences between the methods rely on the techniques used to detect the labeled substrates, which resort to Secondary Ion Mass Spectrometry measurements for SIMS-FISH, bright- or phase-contrast microscopy for MAR-FISH, or Raman spectroscopy for Raman-FISH. Within the technology advances in fluorescence imaging, works focusing on super-resolution microscopy have also opened up a new window into bacterial cells, allowing the study of intracellular processes at an unprecedented level of spatial resolution. Single-molecule fluorescence tracking is now possible and is teaching us about dynamic cellular processes of microorganism. An overview on single-molecule fluorescence imaging and subsequent application to the study of bacteria biology can be found in Gahlmann et al. [51].

An Introduction to FISH

3

13

Conclusions In this chapter, basic concepts of nucleic acid hybridization, as well as parameters that influence FISH performance/outcome, were reviewed. Overall, it is clear that probe design, type of fixation/ permabilization, and composition of hybridization solution are steps with a major influence on FISH. Nonetheless, information that was taken for granted a few years ago is now being put into question. The need for a fixation/permeation step, the role of denaturants, or, even, the optimal salt concentrations for a successful hybridization are all examples of parameters that had very welldefined ranges, which are now much more flexible. For decades, the development of FISH techniques has resorted to an empirical experimental design that was mainly based on previous observations. However, further developments in (1) the nucleic acid properties, (2) hybridization modeling/prediction, (3) fluorescence imaging/detection systems, and (4) the databases of genomic data, have triggered a new systematic approach on FISH development. New design strategies have emerged for nucleic acid mimics, and modeling its hybridization kinetics became a necessity for exploring the properties of this new array of molecules and their behavior with the different FISH reagents. At the same time, the resolution of fluorescence detection and imaging systems has faced a huge evolution boosting the design of new FISH strategies. In that field, the emergence of fluorochromes with brighter, stable, and narrow emission spectra has also played a major role. The detection and separation of single cells with a particular FISH profile became a reality, and subsequently, the quantification of RNA content and the tracking of single molecules were also possible. Last but not the least, the databases of genomic data, as well as the bioinformatics tools, have perfected probe design, allowing the expansion to other genomic regions with higher specificity. All these advances have paved the way for the diversity of FISH procedures available today and are still playing a major role in FISH progress.

Acknowledgements This work was financially supported by (a) Base Funding—UIDB/ 00511/2020 of the Laboratory for Process Engineering, Environment, Biotechnology, and Energy—LEPABE—funded by national funds through the FCT/MCTES (PIDDAC); (b) Projects POCI-01-0145FEDER-016678 (Coded-FISH), POCI-01-0145-FEDER-031011 (μFISH), and POCI-01-0145-FEDER-029961 (ColorISH), funded by FEDER funds through COMPETE2020—Programa Operacional Competitividade e Internacionalizac¸˜ao (POCI) and by national funds

14

Carina Almeida and Nuno F. Azevedo

(PIDDAC) through FCT/MCTES; (c) BioTecNorte operation (NORTE-01-0145-FEDER-000004) funded by the European Regional Development Fund under the scope of Norte2020—Programa Operacional Regional do Norte; (d) European Union’s Horizon 2020 research and innovation program under grant agreement No 810685 (DelNAM). References 1. Chargaff E (1950) Chemical specificity of nucleic acids and mechanism of their enzymatic degradation. Experientia 6(6):201–209 2. Amann R, Fuchs BM (2008) Single-cell identification in microbial communities by improved fluorescence in situ hybridization techniques. Nat Rev Microbiol 6(5):339–348 3. Forrest GN (2007) PNA FISH: present and future impact on patient management. Expert Rev Mol Diagn 7(3):231–236 4. Diaz M et al (2010) Application of flow cytometry to industrial microbial bioprocesses. Biochem Eng J 48(3):385–407 5. Delong EF, Wickham GS, Pace NR (1989) Phylogenetic stains - ribosomal RNA-based probes for the identification of single cells. Science 243(4896):1360–1363 6. Giovannoni SJ et al (1988) Phylogenetic group-specific oligodeoxynucleotide probes for identification of single microbial-cells. J Bacteriol 170(2):720–726 7. Rocha R, Almeida C, Azevedo NF (2018) Influence of the fixation/permeabilization step on peptide nucleic acid fluorescence in situ hybridization (PNA-FISH) for the detection of bacteria. PLoS One 13(5):e0196522 8. Thurnheer T, Gmur R, Guggenheim B (2004) Multiplex FISH analysis of a six-species bacterial biofilm. J Microbiol Methods 56(1):37–47 9. Perry-O’Keefe H et al (2001) Identification of indicator microorganisms using a standardized PNA FISH method. J Microbiol Methods 47 (3):281–292 10. Guimaraes N et al (2007) Development and application of a novel peptide nucleic acid probe for the specific detection of Helicobacter pylori in gastric biopsy specimens. J Clin Microbiol 45(9):3089–3094 11. Wu S et al (2009) Direct viable count combined with fluorescence in situ hybridization (DVC-FISH) for specific enumeration of viable Escherichia coli in cow manure. Microbes Environ 24(1):33–38 12. Carr EL et al (2005) Improved permeabilization protocols for fluorescence in situ hybridization (FISH) of mycolic-acid-containing

bacteria found in foams. J Microbiol Methods 61(1):47–54 13. Macnaughton SJ, O’Donnell AG, Embley TM (1994) Permeabilization of mycolic-acid-containing actinomycetes for in situ hybridization with fluorescently labelled oligonucleotide probes. Microbiology 140(Pt 10):2859–2865 14. Frickmann H et al (2017) Fluorescence in situ hybridization (FISH) in the microbiological diagnostic routine laboratory: a review. Crit Rev Microbiol 43(3):263–293 15. Lawson TS et al (2011) Optimization of a two-step permeabilization fluorescence in situ hybridization (FISH) assay for the detection of Staphylococcus aureus. J Clin Lab Anal 25 (5):359–365 16. Kubota K (2013) CARD-FISH for environmental microorganisms: technical advancement and future applications. Microbes Environ 28(1):3–12 17. Fontenete S et al (2015) Towards fluorescence in vivo hybridization (FIVH) detection of H. pylori in gastric mucosa using advanced LNA probes. PLoS One 10(4):e0125494 18. Hartmann H et al (2005) Rapid identification of Staphylococcus aureus in blood cultures by a combination of fluorescence in situ hybridization using peptide nucleic acid probes and flow cytometry. J Clin Microbiol 43(9):4855–4857 19. Batani G et al (2019) Fluorescence in situ hybridization (FISH) and cell sorting of living bacteria. Sci Rep 9(1):18618 20. Sikorav JL, Orland H, Braslau A (2009) Mechanism of thermal renaturation and hybridization of nucleic acids: Kramers’ process and universality in Watson-Crick base pairing. J Phys Chem B 113(12):3715–3725 21. Yilmaz LS, Noguera DR (2004) Mechanistic approach to the problem of hybridization efficiency in fluorescent in situ hybridization. Appl Environ Microbiol 70(12):7126–7139 22. Bremer H, Dennis PP (1996) Modulation of chemical composition and other parameters of the cell by growth rate. In: Neidhardt FC et al (eds) Escherichia coli and Salmonella: cellular and molecular biology, 2nd edn. ASM Press, Washington, DC, pp 1553–1569

An Introduction to FISH 23. Ortiz JO et al (2006) Mapping 70S ribosomes in intact cells by cryoelectron tomography and pattern recognition. J Struct Biol 156 (2):334–341 24. Pang H, Winkler HH (1994) The concentrations of stable RNA and ribosomes in Rickettsia prowazekii. Mol Microbiol 12(1):115–120 25. Yus E et al (2009) Impact of genome reduction on bacterial metabolism and its regulation. Science 326(5957):1263–1268 26. Hoshino T et al (2008) Quantification of target molecules needed to detect microorganisms by fluorescence in situ hybridization (FISH) and catalyzed reporter deposition-FISH. Appl Environ Microbiol 74(16):5068–5077 27. Fuchs BM et al (2000) Unlabeled helper oligonucleotides increase the in situ accessibility to 16S rRNA of fluorescently labeled oligonucleotide probes. Appl Environ Microbiol 66 (8):3603–3607 28. Fuchs BM et al (1998) Flow cytometric analysis of the in situ accessibility of Escherichia coli 16S rRNA for fluorescently labeled oligonucleotide probes. Appl Environ Microbiol 64 (12):4973–4982 29. Fontenete S et al (2016) Prediction of melting temperatures in fluorescence in situ hybridization (FISH) procedures using thermodynamic models. Crit Rev Biotechnol 36(3):566–577 30. Cerqueira L et al (2008) DNA mimics for the rapid identification of microorganisms by fluorescence in situ hybridization (FISH). Int J Mol Sci 9(10):1944–1960 31. Fontenete S et al (2015) Mismatch discrimination in fluorescent in situ hybridization using different types of nucleic acids. Appl Microbiol Biotechnol 99(9):3961–3969 32. Petersen M, Wengel J (2003) LNA: a versatile tool for therapeutics and genomics. Trends Biotechnol 21(2):74–81 33. Thomsen R, Nielsen PS, Jensen TH (2005) Dramatically improved RNA in situ hybridization signals using LNA-modified probes. RNA 11(11):1745–1748 34. Braasch DA, Corey DR (2001) Locked nucleic acid (LNA): fine-tuning the recognition of DNA and RNA. Chem Biol 8(1):1–7 35. Kierzek E et al (2005) The influence of locked nucleic acid residues on the thermodynamic properties of 2’-O-methyl RNA/RNA heteroduplexes. Nucleic Acids Res 33 (16):5082–5093 36. Majlessi M, Nelson NC, Becker MM (1998) Advantages of 2’-O-methyl oligoribonucleotide probes for detecting RNA targets. Nucleic Acids Res 26(9):2224–2229 37. Silverman AP, Kool ET (2007) Oligonucleotide probes for RNA-targeted fluorescence in situ hybridization. Adv Clin Chem 43:79–115

15

38. Kurupati P et al (2007) Inhibition of gene expression and growth by antisense peptide nucleic acids in a multiresistant beta-lactamaseproducing Klebsiella pneumoniae strain. Antimicrob Agents Chemother 51(3):805–811 39. Lima JF et al (2018) Targeting miR-9 in gastric cancer cells using locked nucleic acid oligonucleotides. BMC Mol Biol 19(1):6 40. Vieregg JR (2010) Encyclopedia of analytical chemistry: applications, theory and instrumentation. Wiley, New York 41. Hermanson GT (2013) Bioconjugate techniques, 3rd edn. Academic Press, London, pp 395–463 42. Santos RS et al (2014) Optimization of a peptide nucleic acid fluorescence in situ hybridization (PNA-FISH) method for the detection of bacteria and disclosure of a formamide effect. J Biotechnol 187:16–24 43. Farrell R (2010) Laboratory guide for isolation and characterization. In: RNA methodologies, 4th edn. Elsevier, Boston 44. Fontenete S et al (2016) Fluorescence in vivo hybridization (FIVH) for detection of Helicobacter pylori infection in a C57BL/6 mouse model. PLoS One 11(2):e0148353 45. Matthiesen SH, Hansen CM (2012) Fast and non-toxic in situ hybridization without blocking of repetitive sequences. PLoS One 7(7): e40675 46. Azevedo AS et al (2019) Optimizing locked nucleic acid/2’-O-methyl-RNA fluorescence in situ hybridization (LNA/2’OMe-FISH) procedure for bacterial detection. PLoS One 14(5):e0217689 47. Rocha R et al (2016) Optimization of peptide nucleic acid fluorescence in situ hybridization (PNA-FISH) for the detection of bacteria: the effect of pH, dextran sulfate and probe concentration. J Biotechnol 226:1–7 48. Zustiak SP, Nossal R, Sackett DL (2011) Hindered diffusion in polymeric solutions studied by fluorescence correlation spectroscopy. Biophys J 101(1):255–264 49. Kalyuzhnaya MG et al (2006) Fluorescence in situ hybridization-flow cytometry-cell sortingbased method for separation and enrichment of type I and type II methanotroph populations. Appl Environ Microbiol 72(6):4293–4301 50. Musat N et al (2012) Detecting metabolic activities in single cells, with emphasis on nanoSIMS. FEMS Microbiol Rev 36(2):486–511 51. Gahlmann A, Moerner WE (2014) Exploring bacterial cell biology with single-molecule tracking and super-resolution imaging. Nat Rev Microbiol 12(1):9–22

Chapter 2 FISH Variants Nuno M. Guimara˜es, Nuno F. Azevedo, and Carina Almeida Abstract FISH has gained an irreplaceable place in microbiology because of its ability to detect and locate a microorganism, or a group of organisms, within complex samples. However, FISH role has evolved drastically in the last few decades and its value has been boosted by several advances in signal intensity, imaging acquisitions, automation, method robustness, and, thus, versatility. This has resulted in a range of FISH variants that gave researchers the ability to access a variety of other valuable information such as complex population composition, metabolic activity, gene detection/quantification, or subcellular location of genetic elements. In this chapter, we will review the more relevant FISH variants, their intended use, and how they address particular challenges of classical FISH. Key words FISH, CARD-FISH, Gene-FISH, Phage-FISH, MAR-FISH, FISH-NanoSIMS, CLASIFISH, DOPE-FISH, Leaf-FISH, Flow-FISH, Microfluidic-FISH, NAM-FISH, QD-FISH

1

Introduction Fluorescence in situ hybridization (FISH) has become one of the most routinely used molecular techniques in microbiology. Traditional FISH involves the binding of short, fluorescently labeled DNA or nucleic acid mimic oligonucleotides to the ribosomal RNA of bacteria with subsequent analysis by fluorescence microscopy. The reasons why this technique has become an invaluable tool for microbiologists are easy to understand. FISH allows the detection of a microorganism, or a group of organisms, within a sample regardless of its cultivability [1], and the in situ detection of the microorganisms can give us insights into the microbial localization within the communities, which may in turn be important information to unveil their functions in that community [2]. Despite all these features, FISH has some drawbacks that limited the use and application of this technique [3]. One of the main limitations that FISH suffers is the low signal intensity that often occurs. The rRNA of the bacteria was the main target in the classic FISH for bacterial detection, not only because it is a genetic marker and is present in

Nuno F. Azevedo and Carina Almeida (eds.), Fluorescence In Situ Hybridization (FISH) for Microbial Cells: Methods and Concepts, Methods in Molecular Biology, vol. 2246, https://doi.org/10.1007/978-1-0716-1115-9_2, © Springer Science+Business Media, LLC, part of Springer Nature 2021

17

18

Nuno M. Guimara˜es et al.

Fig. 1 FISH variants (blue lines) developed to improve some of the characteristics (green boxes) of classic FISH. CARD-FISH catalyzed reporter deposition, MAR MicroAutoRadiography, SIMS secondary ion mass spectrometry, CLASI combinatorial labeling and spectral imaging, DOPE double labeling oligonucleotide probe, NAMs nucleic acid mimics, QD quantum dots

all microorganisms but also because in normal conditions, it is present in high copy numbers, which generates a high intensity fluorescence signal. Throughout the years, the basis of FISH remains the same, but several improvements and adaptations have been added. These have made the technique more robust and have opened the range of different applications. The development of new types of probes and the increase in genomic and bioinformatic resources, coupled with important advances in the fields of fluorescence microscopy and digital imaging, were responsible for an improvement of FISH specificity, sensitivity, resolution, and applicability. In fact, several FISH variants have been developed and optimized to answer problems that classic FISH alone was not able to address (Fig. 1). For instance, CARD-FISH includes signal amplification allowing us to detect low copy number targets such as genes; while Gene-FISH allows gene quantification and subcellular location. Phage-FISH aims at the visualization of viral replication inside bacterial cells or even the free viral particles. MAR-FISH and NanoSIMS-FISH established a correlation and differentiation of metabolic activity and phylogenetic identification. The ability to identify several microorganisms at the same time in a multiplex assay was improved with the development of CLASI-FISH, DOPE-FISH, and LEAF-FISH. The versatility and robustness of FISH were upgraded with the appearance of brighter reporting molecules (QD-FISH), new nucleic acid mimics (NAM-FISH), alternative detection methods (Flow-FISH), and new platforms for the assays (microfluidics-based FISH). In this chapter, we will cover the main FISH variants and the main purpose for their use.

FISH Variants

2 2.1

19

FISH Variants CARD-FISH

The low signal intensity is one of the major drawbacks of the classical FISH and is partly attributed to the low levels of the target nucleic acids, in particular, for microorganisms with low metabolic activity or when detection of low copy genetic elements is intended [4]. CARD-FISH (catalyzed reporter deposition), also known as TSA-FISH (tyramide signal amplification), was developed with the purpose of improving the sensitivity of standard FISH protocols through enzyme-mediated signal amplification. CARD was initially designed for enzyme immunoassays [5, 6] and only a few years later was applied in FISH [4, 7]. The basic principle of CARD consists in the conversion of tyramide into a radical intermediate by horseradish peroxidase (HRP) in the presence of hydrogen peroxide. Traditionally, CARD was performed using streptavidin-HRP that catalyzed the deposition of biotin-tyramine on nitrocellulose membrane-bound proteins [5, 6]. Scho¨nhuber et al. described a direct visualization method where oligonucleotides labeled with HRP and fluorescently labeled tyramides were used [4]. First, the oligonucleotides covalently linked to a horseradish peroxidase (HRP) hybridize with the target, and then fluorescently labeled tyramides are added, leading to signal amplification. Lebaron et al. described an indirect visualization method using biotinylated oligonucleotides and streptavidin labeled with HRP [7]. In this case, the hybridization of the biotinylated oligonucleotides to the target occurs first, and, then, HRP-labeled streptavidin is added, which will bind to the biotin of the oligonucleotides. The signal obtained through both approaches was much higher than the one obtained using just oligonucleotides labeled with a single fluorochrome, due to the fact that one single HRP is able to react with multiple tyramides. One of the major drawbacks of CARD-FISH is that it requires extra optimizations and additional steps when compared to the traditional FISH. The HRP-labeled oligonucleotides used in CARD-FISH have a much lower ability to penetrate the bacterial cell wall because HRP is a large molecule, with about 40 kDa, when compared to the fluorochromes used to label the traditional oligonucleotides (which are usually around 400–800 Da). Consequently, the permeabilization and fixation steps are of utmost importance and have to be optimized in accordance with the intended application/target. The permeabilization step often involves enzymatic treatments, with lysozyme being the most commonly used enzyme in these processes [8, 9]. The CARD procedure is based on the activity of the HRP; therefore, it is crucial that all endogenous peroxidases that might be present are inactivated in order to avoid false positives. Despite the existence of several possible treatments to inactivate the peroxidases [8, 10, 11], the

20

Nuno M. Guimara˜es et al.

optimization of this step should be performed for each sample because none of the methods work for all possible environments. The background fluorescence was another recurrent problem due to high concentrations of oligonucleotide used, leading to nonspecific fluorescence signal or nonspecific fluorescence deposition [11]. The development and optimization of CARD-FISH increased the range of application when compared to the classic FISH. The rRNA was no longer the only target for FISH. New applications have since appeared aiming for different targets such as mRNA, plasmids, or bacterial genes in the chromosome. 2.2

Gene-FISH

The plating techniques have been for years the gold standard methodology to identify and study microorganisms. But some microorganisms are hard to recover from environmental samples. The rapid advance of sequencing techniques allowed the retrieval of many sequences from yet to be cultured microorganisms. A large number of these sequences are from genomic fragments with no phylogenetic marker, which makes it difficult to correlate them with the microorganisms from where they were originated. In 2010, Moraru et al. described a new technique, geneFISH, which directly links the presence of genes with the cell identity in environmental samples [12]. The geneFISH consists in a dual hybridization combining CARD-FISH with the detection of gene fragments using short polynucleotide probes. The taxonomic identification of the bacterial cells is obtained through CARD-FISH using horseradish peroxidase (HRP)-labeled oligonucleotides targeting ribosomal RNA. Afterward, gene detection is performed using multiple double-stranded DNA polynucleotide probes labeled with digoxigenin and a combined antibody–CARD signal amplification system. The use of two CARD steps provides a very stable signal, due to the covalent binding of fluorescence tyramides in the cells, and a high sensitivity. The main disadvantages of this approach are the long and laborious protocol and the inability to quantify the gene copy number inside individual cells. In fact, the fluorescence signal is not directly related to the gene copy numbers present, because the tyramide deposition in the cells is not linear with the number of targets [13]. These problems were addressed, and more recently, in 2017, a new protocol for geneFISH was published avoiding the use of the CARD technique [14]. In this new approach, the CARD procedure was replaced by the use of probes directly labeled with fluorochromes and super-resolution microscopy for the simultaneous detection of rRNA and genes in a single microbial cell. The gene detection was obtained using double stranded polynucleotide probes synthesized by PCR and labeled with fluorochromes. These probes must be designed and conditions optimized to work in the same stringency conditions as the normal rRNA-targeted oligonucleotides. Regarding the visualization, both structure illumination

FISH Variants

21

microscopy (SIM) and wide-field microscopy were used successfully. The detection and quantification of gene signals per cell copy number simply require a well-equipped epifluorescence microscope, with high efficiency filters, high numerical aperture objectives, and sensitive cameras. The major advantages of this new geneFISH approach are the much faster and simplified protocol, the quantification of gene copy number per cell, and, when combined with the super resolution microscopy, the subcellular localization of the genes. 2.3

Phage-FISH

Bacteriophages, also known as phages, are viruses that infect bacteria and are among the most common and diverse organisms on earth; it is estimated that there are 10 phages for each bacterial cell [15]. Phages, due to their high diversity, can play different roles in complex microbial populations, having a high impact on the ecological balance and microbial life balance. Being a type of virus, phages must infect bacteria in order to replicate, and they use a variety of mechanisms to do so. One of those mechanisms causes the death of bacteria during the replication stage [16], whereas in the other cases, phages are incorporated into the bacterial genome, shaping the bacterial population genome through horizontal gene transfer [17]. Understanding phage-bacteria interactions is critical for several areas, such as the clinical, particularly when we bear in mind the possible therapeutic applications of phages. However, the knowledge of 3D structures combining both entities has been limited, especially due to technical limitations. Genomic studies have generated an enormous amount of data that allowed the identification of a huge number of bacteria and phages; however, the plating techniques do not follow the evolution of the populations, and many of the bacteria identified remain unculturable [18]. The evolution of molecular methods allows us to use alternatives to culture techniques to study, at a single cell level, the interactions of the phages with their respective target bacteria, but determination of spatial location remained elusive [19, 20]. In 2013, a new method, phageFISH, resulting from the adaptation of geneFISH [12], was described for targeting phages [21]. PhageFISH combines bacterial identification using rRNAtargeted oligonucleotides with phage identification using polynucleotide probes targeting phage specific genes [21]. The major optimization was the increase in the number of polynucleotides probe targets, up to twelve 300 bp regions of the same gene instead of one 350 bp gene region used in geneFISH. This raised the detection efficiency to 98% [22]. One of the main challenges for phageFISH was probe design, in particular, for complex samples that contained a large number of phage cells and bacteria. However, the number of available genomic datasets is constantly growing, and, with the help of bioinformatic tools, it is in general possible to

22

Nuno M. Guimara˜es et al.

identify highly conserved phage gene targets required for suitable probe design. A more recent FISH technique applied to bacteriophages has used nucleic acid mimic probes to target the mRNA coding for capsid proteins, allowing the direct visualization of the phage replication phase [23]. During the phage replication inside its host, multiple copies of viral mRNA are produced to ensure the correct assembly of the phage progeny. Targeting the conserved regions of the phage genome highly expressed in the transcription phase, both viral DNA and mRNA transcripts, will allow a natural amplification in the FISH fluorescence signal. This approach means that no extra amplification steps are required and just the direct hybridization of the target was enough to obtain an improved detection limit when compared to classic FISH. This technique has provided excellent results in the quantification of phage-infected bacteria in microbial biofilms [23]. The high sensitivity and versatility of the FISH techniques applied to phages have proved to be efficient in the quantification of the infected cell fraction and the measurement of the infection dynamics, from early to late stages [21, 23]. 2.4

MAR-FISH

In nature, the majority of microorganisms live together in microbial communities such as biofilms or microbial aggregates. These are dynamic ecosystems that change in accordance with different stimulus. The study of these microbial habitats requires not only the identification and localization of the microorganisms but also the assessment of the physiological role that each one plays in the ecosystem. While traditional FISH gives us important phylogenetic information even in complex and heterogeneous microbial communities such as biofilms [24, 25], it does not provide any metabolic details of the identified microorganisms. FISH evolution has provided an alternative on this regard. It is possible to identify, locate, and characterize complex communities to the functional level, without the need to cultivate the microorganisms. The metabolic activity of a microorganism can be assessed in situ using microautoradiography (MAR), a technique that has been used since 1966 to study the uptake of specific radioisotopes by individual microorganisms [26]. The direct correlation of metabolic function with microbial identity was established later, with the successful combination of microautoradiography technique with FISH [27–29]. This approach is commonly named as MAR-FISH (MicroAutoRadiography-Fluorescence In Situ Hybridization) and correlates the 16S rRNA phylogenetic information derived from the FISH with the specific metabolic activity of individual cells obtained through MAR. In spite of the huge potential of MAR-FISH, some limitations need to be addressed during protocol development. For instance, the incorporation rate of the radiolabeled compounds may vary depending on the microorganism and its activity, and as such, the substrate selected will influence the

FISH Variants

23

sensitivity of the method. These factors must be optimized in advance and generally by a time-consuming trial and error approach. The price of the radiolabeled substrates and the prohibition of the use of some of them in some countries have also contributed to a restricted application of MAR-FISH [30]. 2.5

FISH-NanoSIMS

Secondary ion mass spectrometry (SIMS) is a technique used since the 70s for surface analysis that has been adapted and applied to the study of microbial ecology [31]. Since its first application on the biological field, several optimizations and improvements were made, resulting in an enhanced SIMS technique with higher lateral resolution and sensitivity called NanoSIMS (Nanoscale secondary ion mass spectrometry) [32]. NanoSIMS is used to analyze metabolic activities of specific microorganisms within a microbial community, such as denitrification, desulfurization, or biomineralization [33–35]. The analysis is performed using isotope-labeled compounds and assessing their incorporation into cells [36]. The full understanding of metabolic pathways requires a correlation of the metabolic activity with the identification of the organism present, which is not easy since many of the microorganisms present in communities are still uncultured. Exploring the complementarity of NanoSIMS and FISH, these techniques were combined to simultaneously assess metabolic and phylogenetic information of the analyzed community [31, 37, 38]. FISHNanoSIMS hence allows the differentiation of activity between phylogenetic similar microorganisms occupying different spatial niches [37]. The combined FISH-NanoSIMS approach offers other advantages over other single-cell approaches, including higher spatial resolution, multielement or isotope analysis, and quantification analysis [39]. The major downfalls of FISHNanoSIMS are the complex and expensive sample preparation involved and the cost of the equipment.

2.6

CLASI-FISH

FISH has proved to be an efficient tool for identification, quantification, and localization of the microorganisms within complex community structure [2, 3, 40, 41]; but the analysis and ability to differentiate multiple microorganisms at the same time is limited by the fluorescence acquisition process by either microscopy or flow cytometry. The fluorescence acquisition is usually performed using bandpass filters for efficient selection of excitation and emission wavelengths according to the fluorophore used. There are a large number of fluorophores available; however, many of them have highly overlapping excitation and emission spectra, which hinder the correct differentiation between them. Due to this, FISH only typically allows the differentiation of a limited number of microorganisms simultaneously [42]. The CLASI-FISH (combinatorial labeling and spectral imaging—fluorescence in situ hybridization) has been developed to increase the number of microorganisms that

24

Nuno M. Guimara˜es et al.

can be detected simultaneously. The technique uses two probes with different associated fluorophores for each species, with the identification based on the spectral properties of the combined fluorophores [43]. Combined with recent advances in fluorescence spectral image acquisition and the application of linear unmixing to spectrally recorded image data, this approach allows the unambiguous identification of fluorophores with overlapping spectra [44]. One major requirement for CLASI-FISH to work is the knowledge of the reference spectra of each fluorophore combination used in the experiment. The linear unmixing algorithm requires this information for a correct assignment of fluorophore identities to every pixel of the spectral image. A highly dynamic range in signal intensities of the images acquired can also be a problem because images acquired where brighter cells are overexposed, in order to visualize the dimmer cells, are not compatible with the linear unmixing [42]. The identification of multiple targets at the same time requires several oligonucleotides to hybridize at the same conditions; otherwise, the sensitivity of the assay may decrease due to modifications in the spectral composition of poorly hybridized oligonucleotides [45, 46]. Despite all the challenges, CLASI-FISH is being constantly optimized and it has been initially designed to distinguish 15 different target organisms at once; but so far, the same research group is able to discriminate up to 120 targets simultaneously [47]. 2.7

DOPE-FISH

As CLASI-FISH, DOPE-FISH also addresses the limited number of different organisms that can be detected simultaneously in traditional FISH. While the DOPE-FISH protocol is similar to classic FISH, the major difference is that it uses 50 - and 30 -doubly labeled oligonucleotide probes that have been shown to approximately double the signal intensity without losing specificity [48]. Benham et al., using multicolored, double-labeled oligonucleotide probes, were able to detect up to six microorganisms in a single FISH experiment [46], and more recently, using the same multilabeled oligonucleotide approach, seven different microorganisms were detected [49]. DOPE-FISH has shown to be a simple, potential alternative and straightforward approach for multitargeting; however, the number of microorganisms that can be detected simultaneously is far from the numbers reached with CLASI-FISH.

2.8

Leaf-FISH

The plant phyllosphere harbors a diverse microbial community that is dependent on several factors such as host-plant genotype, seasonal changes, and geography [50, 51]. The full understanding of these communities requires not only the identification of the microorganisms but also information of the dynamics of microorganism colonization and microorganism-plant interaction. The identification of the bacteria in phyllosphere has been traditionally performed using sequencing-related methods where the spatial

FISH Variants

25

localization of the microorganisms is lost. On the other hand, the spatial localization of naturally occurring leaf bacteria has been performed using nonspecific dyes coupled with different microscopic techniques [52, 53]. FISH could solve this problem because it could be applied directly in the leaves, allowing the identification of the microorganisms and their spatial localization; however, the high autofluorescence of the leaves hinders the application of the traditional FISH technique. Several optimizations were tested and described, but leaf-FISH has been described as one of the most robust methods to visualize multispecies microbial communities, generating in planta tridimensional images [42, 54]. The leafFISH relies on the same principles of CLASI-FISH and takes advantage of the spectral analysis principle of the technique to solve the leaf autofluorescence problem. In this way, it is possible to separate the fluorescence signals of the labeled probes used for FISH from the ones of plant autofluorescence. Nonetheless, a pretreatment performed prior to the fixation step might still be necessary to decrease autofluorescence. In short, this methodology allows the direct visualization and identification of microbial communities on plant leaves and could represent an image protocol that can be adapted for other applications and samples where the background autofluorescence is a limiting factor for analysis. 2.9

Flow-FISH

The classical FISH result assessment relies on the microscopy analyses of the samples. However, these observations can introduce some bias associated with interobserver variability, can be very time-consuming for a larger number of samples, and makes cell counting very demanding, meaning that the use of FISH for diagnostic procedures is somewhat lagging behind other techniques such as PCR and immune-based detection methods. Flow-FISH was the method established to answer these problems, where the identification potential of FISH is coupled with the fast data acquisition potential of flow cytometry. Several works have been published showing that the efficiency of Flow-FISH is the same as the classic FISH, but with the faster acquisition times and without the subjectivity of the microscopy, predisposing this method for use in automated formats [55–57]. Flow-FISH can be an important improvement on the identification of microorganisms related to clinical infections, allowing a faster diagnostics when compared to traditional identification systems. Trnovsky et al. have optimized a flow-FISH method that allows the identification of Candida albicans in just 1 h, when the existing methods take on average 24–48 h [56, 58]. The detection of Staphylococcus aureus in blood cultures without the need of a subculturing step was also possible with flowFISH [57]. Flow-FISH also allowed us to distinguish between methicillin-resistant S. aureus (MRSA) and methicillin-susceptible S. aureus (MSSA) isolates [55]. Flow-FISH presents great advantages even when compared to other alternative methods, such as

26

Nuno M. Guimara˜es et al.

real time PCR, which provides whole cells analysis with a simplified sample preparation, and some equipment allows direct visual confirmation of cell morphology by microscopy and a potentially lower cost per test. Also, some specific flow cytometers allow the cells to be sorted according to a specific fluorescence signature, and those cells can be further used for other assessments. The main limitation of flow-FISH implementation is the fact that usually expensive flow cytometers are not standard equipment in routine laboratories. 2.10 Microfluidic-FISH

The widespread use of FISH in clinical and diagnostic settings has been slowed down by the lack of automation of the method. The requirement of well-trained personnel and the large number of fixation, hybridization, and washing steps, together with the need for extra pre-enrichment steps for some clinical and food samples, are some of those challenges. Developments in microfluidics design and fabrication make this a promising approach to be conjugated with FISH in order to overcome some of the issues of the classical FISH. In fact, microfluidic devices are now widespread in bioanalysis and clinical diagnostics, such as protein, nucleic acid, and tissue analysis [59]. In addition to automation, the adaptation of FISH methods onto microfluidic platforms can bring other advantages such as reduced assay time, reagent and probe volumes, and smaller instrumentation footprint. During the last few years, several approaches have been reported regarding lab-on-chip-based FISH assays. The main differences between the published strategies are related to the level of FISH method integration, the sample immobilization strategy, and the type of target cell [60]. Some authors opted to use simple devices that interface with existing equipment and workflow, while others follow a more complex route aiming to a fully autonomous FISH-on-chip device where the whole FISH procedure can be executed [61, 62]. Actually, a variety of approaches have been presented at a proof-of-concept stage for chromosome abnormalities, circulating tumor cells and tissue cells [60, 63, 64]. The adaptation of FISH-on-chip for bacteria has also been reported in a few studies, showing important improvements in sample detection limit and sample volume used [61, 65, 66]. However, the efficient trapping of small size cells, such as bacteria, remains a significant challenge, and the ability to have a signal amplification incorporated for their detection (to avoid the need of advanced equipment for imaging acquisition) is one of the major challenges for the development of FISH-on-chip devices suitable for bacterial detection. Apart from technical issues, the high cost of some chips and the lack of validation are also limiting steps that have to be overcome for future implementation on the diagnostics market.

FISH Variants

27

2.11

NAM-FISH

The probe selection is one of the key factors for the efficiency of the FISH technique, and several factors must be taken into account. The probe length is an important element because, if it is too short, the odds of binding to unwanted targets might increase; but it should be short enough to allow single mismatches to have a greater impact on the probe performance. In this way, the probe will have a better diffusion though the cell envelope and a superior discriminatory power when compared to the longer probes. Also, the possibility of formation of intramolecular structures will decrease, as well as the probe production cost [67]. Other important parameters are affinity and thermal stability. Usually, the higher the affinity and thermal stability the better; but this should always be balanced with specificity. The classic probes used in FISH were made from DNA or RNA single strand sequences, with lengths from 15 to 30 nucleotides and linked to a single fluorescent dye [68]. During FISH development and optimization, several issues were described related to the use of DNA and RNA probes that hinder a wider implementation of the technique. The main problems described were the low affinity between probe and target (which leads to a weak fluorescence signal), low diffusion through the cell envelope, and the degradation of the probes by proteases and endonucleases [69, 70]. In order to overcome FISH shortcomings related to the use of DNA probes, a new class of synthetic molecules has emerged that mimic the action of DNA and RNA, which can be collectively named as nucleic acid mimics (NAM) [71]. The first NAM to be described for FISH application was peptide nucleic acid (PNA) [72]. In the meantime, several PNA-FISH methods for the detection of different bacteria were published [e.g., 25, 41, 73]. In recent years, new nucleic acid mimics have been optimized and adapted to FISH, with locked nucleic acid (LNA) and 20 -OMethyl-RNA (20 OMe) being some of the most common ones [74, 75]. NAMs have improved the robustness of FISH method mainly due to their ability to resist the attack of enzymes, due to their synthetic nature, and their higher affinity for the target, which allows more successful hybridizations and easier discrimination of single-base mismatches [76, 77]. In addition to the resistance to degradation and higher affinity than DNA probes, PNA also improves cell penetration because of its neutral nature. Despite the promising results obtained with NAM-FISH, the higher synthesis costs compared to the DNA counterparts have been limiting a wider implementation of NAM-FISH.

2.12

QD-FISH

Quantum dots (QDs) are inorganic nanometer-sized crystals with important fluorescence properties that have been used in several biological applications, in particular, for biological detection and tagging [78]. QDs present several advantages over fluorophores and organic dyes, like narrow emission and broad absorption

28

Nuno M. Guimara˜es et al.

spectra and high photo-bleaching resistance [79]. These characteristics, together with their resistance to metabolic degradations, made QDs a suitable and potential complement or alternative to the well-established fluorophores traditionally used in bioanalytical assays, cell imaging, and in vivo animal targeting [80]. In fact, QDs have been successfully applied in a vast array of techniques in eukaryotic cells for disease detection, fluorescence assays for drug discovery, single protein tracking, and intracellular reporting [78]. FISH is one of the techniques where the use of QDs can be useful to solve some of the limitations that are related to the use of fluorophores, mainly their photo-bleach susceptibility and limited multiplex options. QD-FISH application to bacteria has been limited, but it is a field that is developing with promising results regarding the detection of genes in bacterial cells using QD-labeled DNA oligonucleotides [80] and QD-labeled molecular beacons [81]. Developments in the quantum dot synthesis and architecture have improved their biocompatibility and uptake by the microorganisms, which is important for a wider application of QD-FISH in bacteria in the future [82].

3

Conclusions FISH has gained a major role as a diagnostic and localization method for microorganisms, being applied in the analysis of both environmental and clinical samples. Through the years, the FISH technique has been the target of several optimizations and improvements, which allowed several limitations to be overcome and opened the door to new applications. The different environments, microorganisms, and targets where we want to apply the FISH method present their unique aspects and challenges that require specific strategies. The signal amplification obtained with CARDFISH paved the way for Gene-FISH and Phage-FISH appearance and the ability to target and quantify genes in bacteria. The correlation of metabolic activity with bacteria identification was obtained with MAR-FISH and NanoSIMS-FISH. CLASI-FISH and DOPEFISH increase the applicability of FISH in multiplex experiments. The versatility and robustness of FISH are also constantly being improved with the continuous appearance of new promising alternatives for oligonucleotides synthesis (NAM-FISH) and fluorescent labels (QD-FISH), together with the simplification of the method (microfluidic-FISH) and with the increase of data acquisition processing (Flow-FISH). The diversification of the classic FISH protocol is hence a very active field of research, which shows the ability of FISH to renew itself and to remain in the forefront of clinical and environmental research.

FISH Variants

29

Acknowledgements This work was financially supported by (a) Base Funding—UIDB/ 00511/2020 of the Laboratory for Process Engineering, Environment, Biotechnology, and Energy—LEPABE—funded by national funds through the FCT/MCTES (PIDDAC); (b) Projects POCI01-0145-FEDER-016678 (Coded-FISH), POCI-01-0145FEDER-029841 (POLY-PREVENTT), and POCI-01-0145FEDER-029961 (ColorISH), funded by FEDER funds through COMPETE2020—Programa Operacional Competitividade e Internacionalizac¸˜ao (POCI) and by national funds (PIDDAC) through FCT/MCTES; (c) BioTecNorte operation (NORTE-010145-FEDER-000004) funded by the European Regional Development Fund under the scope of Norte2020—Programa Operacional Regional do Norte. References 1. Levsky JM, Singer RH (2003) Fluorescence in situ hybridization: past, present and future. J Cell Sci 116(Pt 14):2833–2838. https://doi. org/10.1242/jcs.00633 2. Drescher J, Schlafer S, Schaudinn C, Riep B, Neumann K, Friedmann A, Petrich A, Gobel UB, Moter A (2010) Molecular epidemiology and spatial distribution of Selenomonas spp. in subgingival biofilms. Eur J Oral Sci 118 (5):466–474. https://doi.org/10.1111/j. 1600-0722.2010.00765.x 3. Frickmann H, Zautner AE, Moter A, Kikhney J, Hagen RM, Stender H, Poppert S (2017) Fluorescence in situ hybridization (FISH) in the microbiological diagnostic routine laboratory: a review. Crit Rev Microbiol 43 (3):263–293. https://doi.org/10.3109/ 1040841X.2016.1169990 4. Schonhuber W, Fuchs B, Juretschko S, Amann R (1997) Improved sensitivity of whole-cell hybridization by the combination of horseradish peroxidase-labeled oligonucleotides and tyramide signal amplification. Appl Environ Microbiol 63(8):3268–3273 5. Bobrow MN, Harris TD, Shaughnessy KJ, Litt GJ (1989) Catalyzed reporter deposition, a novel method of signal amplification. Application to immunoassays. J Immunol Methods 125(1–2):279–285 6. Bobrow MN, Shaughnessy KJ, Litt GJ (1991) Catalyzed reporter deposition, a novel method of signal amplification. II. Application to membrane immunoassays. J Immunol Methods 137 (1):103–112 7. Lebaron P, Catala P, Fajon C, Joux F, Baudart J, Bernard L (1997) A new sensitive, whole-cell hybridization technique for

detection of bacteria involving a biotinylated oligonucleotide probe targeting rRNA and tyramide signal amplification. Appl Environ Microbiol 63(8):3274–3278 8. Pernthaler A, Pernthaler J, Amann R (2002) Fluorescence in situ hybridization and catalyzed reporter deposition for the identification of marine bacteria. Appl Environ Microbiol 68 (6):3094–3101. https://doi.org/10.1128/ aem.68.6.3094-3101.2002 9. Pavlekovic M, Schmid MC, SchmiderPoignee N, Spring S, Pilhofer M, Gaul T, Fiandaca M, Loffler FE, Jetten M, Schleifer KH, Lee NM (2009) Optimization of three FISH procedures for in situ detection of anaerobic ammonium oxidizing bacteria in biological wastewater treatment. J Microbiol Methods 78(2):119–126. https://doi.org/ 10.1016/j.mimet.2009.04.003 10. Ishii K, Mussmann M, MacGregor BJ, Amann R (2004) An improved fluorescence in situ hybridization protocol for the identification of bacteria and archaea in marine sediments. FEMS Microbiol Ecol 50(3):203–212. https://doi.org/10.1016/j.femsec.2004.06. 015 11. Sekar R, Pernthaler A, Pernthaler J, Warnecke F, Posch T, Amann R (2003) An improved protocol for quantification of freshwater Actinobacteria by fluorescence in situ hybridization. Appl Environ Microbiol 69 (5):2928–2935. https://doi.org/10.1128/ aem.69.5.2928-2935.2003 12. Moraru C, Lam P, Fuchs BM, Kuypers MM, Amann R (2010) GeneFISH—an in situ technique for linking gene presence and cell identity in environmental microorganisms. Environ

30

Nuno M. Guimara˜es et al.

Microbiol 12(11):3057–3073. https://doi. org/10.1111/j.1462-2920.2010.02281.x 13. Tanke HJ, Vrolijk J, Raap AK (1994) Image analysis of fish stained specimens - a new diagnostic tool in genetics and oncology. In: Proceedings - annual meeting, Microscopy Society of America, pp 78–79 14. Barrero-Canosa J, Moraru C, Zeugner L, Fuchs BM, Amann R (2017) DirectgeneFISH: a simplified protocol for the simultaneous detection and quantification of genes and rRNA in microorganisms. Environ Microbiol 19(1):70–82. https://doi.org/10.1111/ 1462-2920.13432 15. Azeredo J, Sutherland IW (2008) The use of phages for the removal of infectious biofilms. Curr Pharm Biotechnol 9(4):261–266. https://doi.org/10.2174/ 138920108785161604 16. Catalao MJ, Gil F, Moniz-Pereira J, Sao-Jose C, Pimentel M (2013) Diversity in bacterial lysis systems: bacteriophages show the way. FEMS Microbiol Rev 37 (4):554–571. https://doi.org/10.1111/ 1574-6976.12006 17. Brussow H, Canchaya C, Hardt WD (2004) Phages and the evolution of bacterial pathogens: from genomic rearrangements to lysogenic conversion. Microbiol Mol Biol Rev 68 (3):560–602, table of contents. https://doi. org/10.1128/MMBR.68.3.560-602.2004 18. Rappe MS, Giovannoni SJ (2003) The uncultured microbial majority. Annu Rev Microbiol 57:369–394. https://doi.org/10.1146/ annurev.micro.57.030502.090759 19. Tadmor AD, Ottesen EA, Leadbetter JR, Phillips R (2011) Probing individual environmental bacteria for viruses by using microfluidic digital PCR. Science 333(6038):58–62. https://doi.org/10.1126/science.1200758 20. Kenzaka T, Tani K, Nasu M (2010) Highfrequency phage-mediated gene transfer in freshwater environments determined at singlecell level. ISME J 4(5):648–659. https://doi. org/10.1038/ismej.2009.145 21. Allers E, Moraru C, Duhaime MB, Beneze E, Solonenko N, Barrero-Canosa J, Amann R, Sullivan MB (2013) Single-cell and population level viral infection dynamics revealed by phageFISH, a method to visualize intracellular and free viruses. Environ Microbiol 15 (8):2306–2318. https://doi.org/10.1111/ 1462-2920.12100 22. Dang VT, Sullivan MB (2014) Emerging methods to study bacteriophage infection at the single-cell level. Front Microbiol 5:724. https://doi.org/10.3389/fmicb.2014.00724

23. Vilas Boas D, Almeida C, Sillankorva S, Nicolau A, Azeredo J, Azevedo NF (2016) Discrimination of bacteriophage infected cells using locked nucleic acid fluorescent in situ hybridization (LNA-FISH). Biofouling 32 (2):179–190. https://doi.org/10.1080/ 08927014.2015.1131821 24. Azevedo AS, Almeida C, Pereira B, Madureira P, Wengel J, Azevedo NF (2015) Detection and discrimination of biofilm populations using locked nucleic acid/20 -O-methylRNA fluorescence in situ hybridization (LNA/20 OMe-FISH). Biochem Eng J. https://doi.org/10.1016/j.bej.2015.04. 024 25. Almeida C, Azevedo NF, Santos S, Keevil CW, Vieira MJ (2011) Discriminating multi-species populations in biofilms with peptide nucleic acid fluorescence in situ hybridization (PNA FISH). PLoS One 6(3):e14786. https://doi. org/10.1371/journal.pone.0014786 26. Brock TD, Brock ML (1966) Autoradiography as a tool in microbial ecology. Nature 209 (5024):734–736. https://doi.org/10.1038/ 209734a0 27. Ouverney CC, Fuhrman JA (1999) Combined Microautoradiography-16S rRNA probe technique for determination of radioisotope uptake by specific microbial cell types in situ. Appl Environ Microbiol 65(4):1746–1752 28. Lee N, Nielsen PH, Andreasen KH, Juretschko S, Nielsen JL, Schleifer KH, Wagner M (1999) Combination of fluorescent in situ hybridization and microautoradiography - a new tool for structure-function analyses in microbial ecology. Appl Environ Microbiol 65 (3):1289–1297 29. Cottrell MT, Kirchman DL (2000) Natural assemblages of marine proteobacteria and members of the Cytophaga-Flavobacter cluster consuming low- and high-molecular-weight dissolved organic matter. Appl Environ Microbiol 66(4):1692–1697. https://doi.org/10. 1128/aem.66.4.1692-1697.2000 30. Okabe S, Kindaichi T, Ito T (2004) MAR-FISH—an ecophysiological approach to link phylogenetic affiliation and in situ metabolic activity of microorganisms at a single-cell resolution. Microbes Environ 19(2):83–98. https://doi.org/10.1264/jsme2.19.83 31. Orphan VJ, House CH, Hinrichs KU, McKeegan KD, DeLong EF (2001) Methaneconsuming archaea revealed by directly coupled isotopic and phylogenetic analysis. Science 293(5529):484–487. https://doi.org/10. 1126/science.1061338 32. Guerquin-Kern JL, Wu TD, Quintana C, Croisy A (2005) Progress in analytical imaging

FISH Variants of the cell by dynamic secondary ion mass spectrometry (SIMS microscopy). Biochim Biophys Acta 1724(3):228–238. https://doi. org/10.1016/j.bbagen.2005.05.013 33. Lechene CP, Luyten Y, McMahon G, Distel DL (2007) Quantitative imaging of nitrogen fixation by individual bacteria within animal cells. Science 317(5844):1563–1566. https:// doi.org/10.1126/science.1145557 34. Fike DA, Gammon CL, Ziebis W, Orphan VJ (2008) Micron-scale mapping of sulfur cycling across the oxycline of a cyanobacterial mat: a paired nanoSIMS and CARD-FISH approach. ISME J 2(7):749–759. https://doi.org/10. 1038/ismej.2008.39 35. Slaveykova VI, Guignard C, Eybe T, Migeon HN, Hoffmann L (2009) Dynamic NanoSIMS ion imaging of unicellular freshwater algae exposed to copper. Anal Bioanal Chem 393 (2):583–589. https://doi.org/10.1007/ s00216-008-2486-x 36. Popa R, Weber PK, Pett-Ridge J, Finzi JA, Fallon SJ, Hutcheon ID, Nealson KH, Capone DG (2007) Carbon and nitrogen fixation and metabolite exchange in and between individual cells of Anabaena oscillarioides. ISME J 1 (4):354–360. https://doi.org/10.1038/ ismej.2007.44 37. Dekas AE, Connon SA, Chadwick GL, Trembath-Reichert E, Orphan VJ (2016) Activity and interactions of methane seep microorganisms assessed by parallel transcription and FISH-NanoSIMS analyses. ISME J 10 (3):678–692. https://doi.org/10.1038/ ismej.2015.145 38. Li T, Wu TD, Mazeas L, Toffin L, GuerquinKern JL, Leblon G, Bouchez T (2008) Simultaneous analysis of microbial identity and function using NanoSIMS. Environ Microbiol 10 (3):580–588. https://doi.org/10.1111/j. 1462-2920.2007.01478.x 39. Gao D, Huang X, Tao Y (2016) A critical review of NanoSIMS in analysis of microbial metabolic activities at single-cell level. Crit Rev Biotechnol 36(5):884–890. https://doi. org/10.3109/07388551.2015.1057550 40. Schmiedel D, Kikhney J, Masseck J, Rojas Mencias PD, Schulze J, Petrich A, Thomas A, Henrich W, Moter A (2014) Fluorescence in situ hybridization for identification of microorganisms in acute chorioamnionitis. Clin Microbiol Infect 20(9):O538–O541. https://doi. org/10.1111/1469-0691.12526 41. Stender H (2003) PNA FISH: an intelligent stain for rapid diagnosis of infectious diseases. Expert Rev Mol Diagn 3(5):649–655. https:// doi.org/10.1586/14737159.3.5.649

31

42. Valm AM, Mark Welch JL, Borisy GG (2012) CLASI-FISH: principles of combinatorial labeling and spectral imaging. Syst Appl Microbiol 35(8):496–502. https://doi.org/10. 1016/j.syapm.2012.03.004 43. Valm AM, Mark Welch JL, Rieken CW, Hasegawa Y, Sogin ML, Oldenbourg R, Dewhirst FE, Borisy GG (2011) Systems-level analysis of microbial community organization through combinatorial labeling and spectral imaging. Proc Natl Acad Sci U S A 108 (10):4152–4157. https://doi.org/10.1073/ pnas.1101134108 44. Zimmermann T, Rietdorf J, Pepperkok R (2003) Spectral imaging and its applications in live cell microscopy. FEBS Lett 546 (1):87–92. https://doi.org/10.1016/s00145793(03)00521-0 45. Lukumbuzya M, Schmid M, Pjevac P, Daims H (2019) A multicolor fluorescence in situ hybridization approach using an extended set of fluorophores to visualize microorganisms. Front Microbiol 10:1383. https://doi.org/ 10.3389/fmicb.2019.01383 46. Behnam F, Vilcinskas A, Wagner M, Stoecker K (2012) A straightforward DOPE (double labeling of oligonucleotide probes)-FISH (fluorescence in situ hybridization) method for simultaneous multicolor detection of six microbial populations. Appl Environ Microbiol 78(15):5138–5142. https://doi.org/10. 1128/aem.00977-12 47. Valm AM, Oldenbourg R, Borisy GG (2016) Multiplexed spectral imaging of 120 different fluorescent labels. PLoS One 11(7):e0158495. https://doi.org/10.1371/journal.pone. 0158495 48. Stoecker K, Dorninger C, Daims H, Wagner M (2010) Double labeling of oligonucleotide probes for fluorescence in situ hybridization (DOPE-FISH) improves signal intensity and increases rRNA accessibility. Appl Environ Microbiol 76(3):922–926. https://doi.org/ 10.1128/AEM.02456-09 49. Schimak MP, Kleiner M, Wetzel S, Liebeke M, Dubilier N, Fuchs BM (2016) MiL-FISH: Multilabeled Oligonucleotides for Fluorescence In Situ Hybridization Improve Visualization of Bacterial Cells. Appl Environ Microbiol 82(1):62–70. https://doi.org/10.1128/ AEM.02776-15 50. Redford AJ, Bowers RM, Knight R, Linhart Y, Fierer N (2010) The ecology of the phyllosphere: geographic and phylogenetic variability in the distribution of bacteria on tree leaves. Environ Microbiol 12(11):2885–2893. https://doi.org/10.1111/j.1462-2920.2010. 02258.x

32

Nuno M. Guimara˜es et al.

51. Knief C, Ramette A, Frances L, Alonso-BlancoC, Vorholt JA (2010) Site and plant species are important determinants of the Methylobacterium community composition in the plant phyllosphere. ISME J 4(6):719–728. https://doi. org/10.1038/ismej.2010.9 52. Fett WF, Cooke PH (2003) Scanning electron microscopy of native biofilms on mung bean sprouts. Can J Microbiol 49(1):45–50. https://doi.org/10.1139/W03-002 53. Morris CE, Monier JM, Jacques MA (1997) Methods for observing microbial biofilms directly on leaf surfaces and recovering them for isolation of culturable microorganisms. Appl Environ Microbiol 63(4):1570–1576 54. Peredo EL, Simmons SL (2017) Leaf-FISH: Microscale Imaging of Bacterial Taxa on Phyllosphere. Front Microbiol 8:2669. https://doi. org/10.3389/fmicb.2017.02669 55. Shrestha NK, Scalera NM, Wilson DA, BrehmStecher B, Procop GW (2011) Rapid identification of Staphylococcus aureus and methicillin resistance by flow cytometry using a peptide nucleic acid probe. J Clin Microbiol 49 (9):3383–3385. https://doi.org/10.1128/ jcm.01098-11 56. Trnovsky J, Merz W, Della-Latta P, Wu F, Arendrup MC, Stender H (2008) Rapid and accurate identification of Candida albicans isolates by use of PNA FISHFlow. J Clin Microbiol 46 (4):1537–1540. https://doi.org/10.1128/ JCM.00030-08 57. Hartmann H, Stender H, Schafer A, Autenrieth IB, Kempf VA (2005) Rapid identification of Staphylococcus aureus in blood cultures by a combination of fluorescence in situ hybridization using peptide nucleic acid probes and flow cytometry. J Clin Microbiol 43(9):4855–4857. https://doi.org/10.1128/JCM.43.9.48554857.2005 58. Verweij PE, Breuker IM, Rijs AJMM, Meis JFGM (1999) Comparative study of seven commercial yeast identification systems. J Clin Pathol 52(4):271–273 59. Kovarik ML, Gach PC, Ornoff DM, Wang Y, Balowski J, Farrag L, Allbritton NL (2012) Micro total analysis systems for cell biology and biochemical assays. Anal Chem 84 (2):516–540. https://doi.org/10.1021/ ac202611x 60. Rodriguez-Mateos P, Azevedo NF, Almeida C, Pamme N (2020) FISH and chips: a review of microfluidic platforms for FISH analysis. Med Microbiol Immunol. https://doi.org/10. 1007/s00430-019-00654-1 61. Liu P, Meagher RJ, Light YK, Yilmaz S, Chakraborty R, Arkin AP, Hazen TC, Singh

AK (2011) Microfluidic fluorescence in situ hybridization and flow cytometry (muFlowFISH). Lab Chip 11(16):2673–2679. https://doi.org/10.1039/c1lc20151d 62. Sieben VJ, Marun CSD, Pilarski PM, Kaigala GV, Pilarski LM, Backhouse CJ (2007) FISH and chips: chromosomal analysis on microfluidic platforms. IET Nanobiotechnol 1 (3):27–35 63. Sato K (2015) Microdevice in cellular pathology: microfluidic platforms for fluorescence in situ hybridization and analysis of circulating tumor cells. Anal Sci 31(9):867–873 64. Kwasny D, Vedarethinam I, Shah P, Dimaki M, Silahtaroglu A, Tumer Z, Svendsen WE (2012) Advanced microtechnologies for detection of chromosome abnormalities by fluorescent in situ hybridization. Biomed Microdevices 14 (3):453–460 65. Packard MM, Shusteff M, Alocilja EC (2012) Microfluidic-based amplification-free bacterial DNA detection by dielectrophoretic concentration and fluorescent resonance energy transfer assisted in situ hybridization (FRET-ISH). Biosensors (Basel) 2(4):405–416. https://doi. org/10.3390/bios2040405 66. Liu W, Kim HJ, Lucchetta EM, Du W, Ismagilov RF (2009) Isolation, incubation, and parallel functional testing and identification by FISH of rare microbial single-copy cells from multi-species mixtures using the combination of chemistrode and stochastic confinement. Lab Chip 9(15):2153–2162. https://doi.org/ 10.1039/b904958d 67. Bottari B, Ercolini D, Gatti M, Neviani E (2006) Application of FISH technology for microbiological analysis: current state and prospects. Appl Microbiol Biotechnol 73 (3):485–494. https://doi.org/10.1007/ s00253-006-0615-z 68. Bauman JG, Wiegant J, Borst P, van Duijn P (1980) A new method for fluorescence microscopical localization of specific DNA sequences by in situ hybridization of fluorochromelabelled RNA. Exp Cell Res 128(2):485–490. https://doi.org/10.1016/0014-4827(80) 90087-7 69. Cummins LL, Owens SR, Risen LM, Lesnik EA, Freier SM, Mcgee D, Guinosso CJ, Cook PD (1995) Characterization of fully 2’modified oligoribonucleotide heteroduplex and homoduplex hybridization and nuclease sensitivity. Nucleic Acids Res 23 (11):2019–2024 70. Yilmaz LS, Noguera DR (2004) Mechanistic approach to the problem of hybridization efficiency in fluorescent in situ hybridization. Appl Environ Microbiol 70(12):7126–7139.

FISH Variants https://doi.org/10.1128/AEM.70.12.71267139.2004 71. Cerqueira L, Azevedo NF, Almeida C, Jardim T, Keevil CW, Vieira MJ (2008) DNA mimics for the rapid identification of microorganisms by fluorescence in situ hybridization (FISH). Int J Mol Sci 9(10):1944–1960. https://doi.org/10.3390/ijms9101944 72. Nielsen PE, Egholm M, Berg RH, Buchardt O (1991) Sequence-selective recognition of DNA by strand displacement with a thyminesubstituted polyamide. Science 254 (5037):1497–1500 73. Guimaraes N, Azevedo NF, Figueiredo C, Keevil CW, Vieira MJ (2007) Development and application of a novel peptide nucleic acid probe for the specific detection of Helicobacter pylori in gastric biopsies. J Clin Microbiol 45 (9):3089–3094 74. Fontenete S, Guimara˜es N, Leite M, Figueiredo C, Wengel J, Filipe Azevedo N (2013) Hybridization-based detection of Helicobacter pylori at human body temperature using advanced locked nucleic acid (LNA) probes. PLoS One 8:e81230. https://doi. org/10.1371/journal.pone.0081230 75. Petersen M, Wengel J (2003) LNA: a versatile tool for therapeutics and genomics. Trends Biotechnol 21(2):74–81. https://doi.org/10. 1016/s0167-7799(02)00038-0 76. Lehtola MJ, Loades CJ, Keevil CW (2005) Advantages of peptide nucleic acid oligonucleotides for sensitive site directed 16S rRNA fluorescence in situ hybridization (FISH) detection of Campylobacter jejuni, Campylobacter coli and Campylobacter lari. J Microbiol Methods 62(2):211–219

33

77. Fontenete S, Carvalho D, Guimaraes N, Madureira P, Figueiredo C, Wengel J, Azevedo NF (2016) Application of locked nucleic acidbased probes in fluorescence in situ hybridization. Appl Microbiol Biotechnol 100 (13):5897–5906. https://doi.org/10.1007/ s00253-016-7429-4 78. Rosenthal SJ, Chang JC, Kovtun O, McBride JR, Tomlinson ID (2011) Biocompatible quantum dots for biological applications. Chem Biol 18(1):10–24 79. Kaul Z, Yaguchi T, Kaul SC, Hirano T, Wadhwa R, Taira K (2003) Mortalin imaging in normal and cancer cells with quantum dot immuno-conjugates. Cell Res 13(6):503–507. https://doi.org/10.1038/sj.cr.7290194 80. Wu SM, Zhao X, Zhang ZL, Xie HY, Tian ZQ, Peng J, Lu ZX, Pang DW, Xie ZX (2006) Quantum-dot-labeled DNA probes for fluorescence in situ hybridization (FISH) in the microorganism Escherichia coli. ChemPhysChem 7(5):1062–1067. https://doi.org/ 10.1002/cphc.200500608 81. Wu SM, Tian ZQ, Zhang ZL, Huang BH, Jiang P, Xie ZX, Pang DW (2010) Direct fluorescence in situ hybridization (FISH) in Escherichia coli with a target-specific quantum dot-based molecular beacon. Biosens Bioelectron 26(2):491–496. https://doi.org/10. 1016/j.bios.2010.07.067 82. Baruah S, Ortinero C, Shipin OV, Dutta J (2012) Manganese doped zinc sulfide quantum dots for detection of Escherichia coli. J Fluoresc 22(1):403–408. https://doi.org/10. 1007/s10895-011-0973-5

Chapter 3 Bioinformatic Tools and Guidelines for the Design of Fluorescence In Situ Hybridization Probes Helena Teixeira, Ana L. Sousa, and Andreia S. Azevedo Abstract Fluorescence in situ hybridization (FISH) is a well-established technique that allows the detection of microorganisms in diverse types of samples (e.g., clinical, food, environmental samples, and biofilm communities). The FISH probe design is an essential step in this technique. For this, two strategies can be used, the manual form based on multiple sequence alignment to identify conserved regions and programs/software specifically developed for the selection of the sequence of the probe. Additionally, databases/software for the theoretical evaluation of the probes in terms of specificity, sensitivity, and thermodynamic parameters (melting temperature and Gibbs free energy change) are used. The purpose of this chapter is to describe the essential steps and guidelines for the design of FISH probes (e.g., DNA and Nucleic Acid Mimic (NAM) probes), and its theoretical evaluation through the application of diverse bioinformatic tools. Key words Probe design, Bioinformatic tools, Fluorescence in situ hybridization, DNA, Nucleic Acid Mimics, LNA, PNA, 2’OMe

1

Introduction Fluorescence in situ hybridization (FISH) is a powerful molecular technique used for a variety of purposes from detection, quantification, and location of pathogens in clinical, environmental, and food samples [1, 2], characterization of social interactions and spatial arrangement of microorganisms in complex microbial communities [3, 4], study of gene expression [5], etc. Typically, FISH is based on the hybridization of fluorescently labeled oligonucleotides (commonly called probes) with a conserved rRNA sequence [6]. The rRNA (16S rRNA sequences for Bacteria or Archaea domain and the 18S rRNA sequences for Eukaryote domain [7]) is a common target molecule due to its high abundance in the cell, its relatively high stability of sequences, and the presence of regions with low and high conservation of sequences, which allows a distinction between specific microorganisms [8, 9]. In addition, as each cell

Nuno F. Azevedo and Carina Almeida (eds.), Fluorescence In Situ Hybridization (FISH) for Microbial Cells: Methods and Concepts, Methods in Molecular Biology, vol. 2246, https://doi.org/10.1007/978-1-0716-1115-9_3, © Springer Science+Business Media, LLC, part of Springer Nature 2021

35

36

Helena Teixeira et al.

contains many ribosomes, a high fluorescence intensity is obtained as a result [6–8]. However, probes might also hybridize with other specific RNA targets, such as mRNA, miRNA, and even DNA [9, 10]. The probes used in FISH are generally between 15 and 30 bp and are covalently labeled with a fluorescent dye molecule attached to the 30 or 50 -end (e.g., cyanine dyes (Cy 3 and Cy 5), FluoresceinIsothiocyanate (FITC), Fluorescein amidite (FAM), Alexa Fluor, Texas Red, and rhodamine derivatives [6, 11–13]). The probe design/selection is one of the most important factors in the successful performance of FISH [14]. The selection of the optimal probe must take into account several parameters: – Probe length: Since the specificity, hybridization temperature, and time are partly dependent on probe length, this parameter is critical for the successful hybridization. Probes typically have 15–30 nucleotides in length. If the probe is too long, the selectivity toward the target decreases, and the possibility of formation of intramolecular structures increases; in addition, low synthesis yields, longer hybridization times, and the cost of the synthesis of the probe is greater. Conversely, probes with few bp in length might lack specificity; the target sequence might be present in a larger number of biological targets [15, 16]. The length of DNA probes is at least 18 nucleotides [17], PNA probes can contain 13–18 bases, [18] and LNA probes can contain 10–15 nucleotides in length [19]. – Overall Gibbs free energy change (ΔG overall): ΔG overall is a thermodynamic parameter that can be used as indicator of whether a reaction is thermodynamically favorable or not. A negative ΔG value (ΔG < 0) is an indicator of a thermodynamically favorable reaction; in fact, the more the negative value, the more the favorable reaction. It is well-established that for DNA probes ΔG should be between 13 and 20 kcal/mol to maximize hybridization efficiency without compromising specificity [15]. – Melting Temperature (Tm): Tm is defined as the temperature at which 50% of the nucleic acid strands form a double helix and the other 50% are single stranded [20]. A higher predicted Tm usually provides better results than probes with lower Tm. Using improved thermodynamic values given by SantaLucia et al., [21] an estimate of Tm can be obtained. A recent study has established a correlation between melting and hybridization temperature [22]. – Pyrimidine content (GC): the GC content might provide information about the strength of hybridization. For probes with a low GC content, it may be necessary to extend the probe sequence to keep the Tm in the recommended range since GC content and Tm are strictly dependent on one another [20, 23].

Probe Design for FISH

37

– Complementary probe sequences: self-complementary regions with more than three nucleotides within the probe should be avoided. – Secondary Structure: the presence of secondary structures is an important factor to consider when designing a probe. This greatly decreases the number of nucleotides available for binding in the reaction. The presence of hairpin loops in the probe reduces the FISH efficiency, limiting the probe ability to bind to the target site [24]. – Specificity: a high specificity value means that the probe only detects the target for which it was designed [25]. – Sensitivity: a high sensitivity value means that the probe detects all strains of the target taxa for which the probe was designed [25]. Specific databases (e.g., GenBank [26], European Ribosomal RNA Database [27], ARBsilva [28], and Ribosomal Database Project (RDP) [29]) are available online for the selection of sequences of the microorganisms of interest. Thousands of sequences of small and large subunit ribosomal RNA covering not only pure cultures but also mostly rRNA genes of so far uncultured bacteria are deposited in these public databases [30]. For the design/selection of the probes, the web-based tools described in Table 1 can be used. On the other hand, the design/selection of the probe sequence can also be done manually, using a multiple sequence alignment program, such as a ClustalW [36]. Concerning the types of nucleic acid probes, the traditional FISH uses labeled DNA probes; however, the FISH technique suffers from limitations associated with their use, including low affinity of the probes for its targets, low robustness, and degradation of probe by endonucleases of living cells [37]. For certain applications, particularly in clinical diagnostics [38], food safety, [39] and characterization of complex biofilm communities [40], some authors have showed that FISH limitations could be overcome by the use of nucleic acid mimics (NAMs), including Peptide Nucleic Acid (PNA), Locked Nucleic Acid (LNA), and 2’-Omethyl-RNA (2’OMe). These molecules present higher affinity for RNA sequences with a lower number of base pairs, which leads not only to a more successful hybridization but also to easier discrimination of single-base mismatches [37, 41]. Furthermore, LNA and 2’OMe probes offer higher design flexibility, meaning that with LNA/2’OMe probes, a more thorough control of the thermodynamic parameters (e.g., melting temperature) is possible by intercalating LNA and 2’OMe monomers at the desired locations. This might improve the FISH efficiency for multiplex approaches (detection of multiple targets simultaneously [40]) or detection of microorganisms at specific hybridization temperatures [42].

38

Helena Teixeira et al.

Table 1 Description of web-based resources/software for the probe design/selection Software name

Description

Reference

Unique Probe Selector

– Online service [31] – Freely accessible – Intuitive web interface – Nonspecific for FISH probe design – The algorithms applied include thermodynamic models, GC content, secondary structure of probes, and some other features used by lab researchers

PRIMROSE

– Download is required, but it is executable on Microsoft windows [32] operating systems – Fast and easy to use through a simple and intuitive graphical interface – It can also be used to design FISH probes – It uses data from the above-mentioned rRNA databases (Arb-silva, RDP, Genbank, or European ribosomal RNA database) to find potentially useful oligonucleotides with up to two degenerate positions – It is able to identify degenerate oligonucleotides – It is no longer maintained

PRImer Selector 2 (PRISE2)

[33] – Download is required – Intuitive and easy graphical interface – Freely accessible for noncommercial use – It can also be used to design FISH probes – The program extracts several candidate probes that meet certain criteria specified by the user (length, GC content, etc.). These candidate probes are processed by a module that removes probes that are not likely to bind in a predictable manner – The software selects the final small collection of probes that optimizes the resolution

DECIPHER’s Design Probes

[34] – Download is required – It can also be used to design FISH probes – It allows us to choose one group of rRNA sequences in the presence of many nontarget groups, as the target – It generates two different outputs: a list of all possible single probes and a list of the top 100 dual probe pairs – Additional parameters can be controlled such as specifying a region within the sequence alignment for the target in the probe design

iFISH Probe Designer

[35] – Freely accessible – User-friendly web interface – It can also be used to design DNA FISH probes – It allows design probes of variable size and number along the genome of interest – It is possible to see probes, previously tested, and request them for an evaluation

Probe Design for FISH

39

In this chapter, we will describe a protocol providing the essential steps and guidelines for the DNA and NAM probe design/ selection and its theoretical evaluation, applying diverse bioinformatic tools. While the protocol will focus on the design of rRNAtargeted probes for microbial detection, it can be easily adapted to other targets of interest (mRNA and miRNA), changing the initial database (e.g., TargetScan or miRbase) [43].

2

Materials

2.1 Probe Design/ Selection 2.1.1 Databases for the Selection of Sequences of Interest of Microorganisms 2.1.2 Software for Multiple Sequence Alignment

1. ARB Silva, available at https://www.arb-silva.de/. 2. Ribosomal Database (RDP), available at https://rdp.cme.msu. edu/. 3. Genbank, available at ftp://ftp.ncbi.nih.gov/genbank/. 4. European Ribosomal RNA Database, available at http://bioin formatics.psb.ugent.be/webtools/rRNA/. 1. ClustalW, available at https://www.ebi.ac.uk/Tools/msa/ clustalo/. 2. Geneious, available at https://www.geneious.com/. 3. MEGA, available at https://www.megasoftware.net/.

2.1.3 Web-Based Resources for the Probe Design/Selection

1. Unique Probe Selector, available at http://array.iis.sinica.edu. tw/ups/. 2. PRIMROSE, distributed under the terms of the GNU General Public License (http://www.gnu.org/licenses/gpl-3.0.html) and can be downloaded as a Microsoft Windows 95/NT/ 2000 executable, without charge, from https://www.cardiff. ac.uk/biosciences. 3. PRImer Selector 2 (PRISE2), available at http://alglab1.cs.ucr. edu/OFRG/PRISE2.php. 4. DECIPHER’s Design Probes, available at http://www2.deci pher.codes/DesignProbes.html. 5. iFISH Probe Designer, available at http://ifish4u.org/.

2.2 Theoretical Probe Evaluation 2.2.1 Databases for the Evaluation of the Theoretical Specificity and Sensitivity

1. ProbeCheck, available at http://131.130.66.200/cgibin/ probecheck/probecheck.pl. 2. Probe Match, available probematch/search.jsp. 3. TestProbe, available testprobe/.

at

at

https://rdp.cme.msu.edu/

https://www.arb-silva.de/search/

4. BLAST, available at http://blast.ncbi.nlm.nih.gov/Blast.cgi.

40

Helena Teixeira et al.

2.2.2 Databases for the Melting Temperature and Gibbs Free Energy Change Determination

1. Biocalculator, available at http://www.metabion.com/sup port-and solution/biocalculator/. 2. PNA Tool, available at https://www.pnabio.com/support/ PNA_Tool.htm. 3. Oligo Tools, available at https://www.exiqon.com/oligo-tools. 4. LNA/2’OMe Calculator, available at http://rnachemlab.ibch. poznan.pl/calculator2.php. 5. OligoAnalyzer Tool, available at https://eu.idtdna.com/pages/ tools/oligoanalyzer.

2.2.3 Tools for the Evaluation of Secondary Structures

1. Unafold/Mfold, available at http://unafold.rna.albany.edu/. 2. RNAstructure, version 6.1, available at https://rna.urmc. rochester.edu/RNAstructure.html. 3. OligoDesign, program is freely accessible at https://bio.tools/ lnatools.

2.2.4 Tools for the Evaluation of Self-Complementary Regions Within Probes

1. OligoAnalyzer Tool, available at https://eu.idtdna.com/pages/ tools/oligoanalyzer.

2.3 Database of Existing rRNA-Targeted Oligonucleotide Probes

1. ProbeBase, available at http://www.probebase.net.

3

2. OLIGO Primer Analysis Software, available at https://www. oligo.net/downloads.html.

Methods Before proceeding with probe design/selection, it is recommended to search for probes that have already been described and published. The published probes can be found on the “probeBase”, which is a curated database of rRNA-targeted oligonucleotide probes (see Note 1). If the probe of interest has not been found, we can proceed with its design either manually through multiple sequence alignments to identify conserved regions (see Subheading 3.1) or more automatically using specific web-based resources (see Subheading 3.2).

3.1 Probe Design/ Selection Using Sequence Alignment

The design/selection of probes using multiple sequence alignment (see Note 2) typically involves the following steps: 1. To identify regions of interest, the 16S rRNA (SSU) or 23S rRNA (LSU) gene sequences of the microorganisms of interest and of phylogenetically similar microorganisms may be selected from the ARB Silva database (or from any of the programs of

Probe Design for FISH

41

Fig. 1 Partial alignment of 16S RNA sequences for E. coli probe selection. 16S rRNA sequence of target is aligned to nontarget sequences to identify regions of consensus among targets that are not a match to the nontarget sequence. Base differences between the E. coli sequences and other species sequences are highlighted

Subheading 2.1.1). For that, only target sequences with >1200 bp, high sequence quality, and high alignment quality are used. 2. Align the rRNA sequences of target taxa with nontarget sequences to identify regions of consensus among targets that are not a match to the nontarget sequence. For this purpose, several tools for multiple sequence alignment (see programs of Subheading 2.1.2) might be used. From our experience, a suitable number of strains for the target organism should be used (see Note 3). For example, for the design of an Escherichia coli probe, 10 E. coli sequences, 7 sequences of species that belong to the genus Escherichia, and 7 sequences of related species belonging to the family Enterobacteriaceae can be used to start looking for conserved regions among E. coli. 3. After that, conserved regions in the target microorganisms, which are not present in the nontarget microorganisms, should be selected from the alignment obtained by the software being used (see Fig. 1 for example). 4. Finally, the reverse complementary of these regions is used as candidate probes after their evaluation according to Subheading 3.3. 3.2 Web-Based Resources for the Probe Design/ Selection

Alternatively, probes can be designed using specific software, such as Unique Probe Selector, PRIMROSE, PRImer Selector 2 (PRISE2), DECIPHER’s Design Probes web tool, and iFISH Probe Designer (Table 1). In this chapter, we will provide some details of these tools widely used for this purpose.

3.3 Theoretical Probe Evaluation

Once a sequence probe has been found, databases/software mentioned in Subheading 2.2 are used to test the potentially of that probe.

42

Helena Teixeira et al.

Table 2 Tools for the determination of the thermodynamic parameters of different types of NAM probes Type of probe

Database

DNA

http://www.metabion.com/support-and-solution/biocalculator/ https://eu.idtdna.com/pages/tools/oligoanalyzer

RNA

http://www.metabion.com/support-and-solution/biocalculator/ https://eu.idtdna.com/pages/tools/oligoanalyzer

PNA

https://www.pnabio.com/support/PNA_Tool.htm

LNA/DNA

https://eu.idtdna.com/pages/tools/oligoanalyzer https://www.exiqon.com/oligo-tools

2’OMe

http://rnachemlab.ibch.poznan.pl/calculator1.php https://eu.idtdna.com/pages/tools/oligoanalyzer

LNA/2’OMe

http://rnachemlab.ibch.poznan.pl/calculator2.php https://eu.idtdna.com/pages/tools/oligoanalyzer

1. The estimation of the probe Tm and ΔG could be performed using the equations described by Fontenete et al [22]. As NAMs have different thermodynamic properties from traditional DNA and RNA oligonucleotides, the appropriate tool for this purpose should be selected, Table 2 (see Note 4). 2. It is also important to check secondary structure in the probe design. There are many software and tools available for this purpose. In this chapter, we will provide some details of three of them: Unafold/Mfold, RNAstructure, and OligoDesign (Table 3). 3. In addition, hairpins, dimers, and self-complementarity in probes generally impair intended hybridization reactions, and so efforts to predict and avoid such structures should be employed in probe design. For this purpose, there are several tools described that gives critical insight into the behavior and properties of the probe sequences in study (Table 4). 4. Finally, the theoretical specificity and sensitivity of the probe can be evaluated in silico using ProbeCheck and Testprobe programs available at ARB Silva database website or using Probe Match software available at RDP II (see Notes 5, 6, and 7). The determination of the theoretical specificity is based on Eq. 1: specificity ¼

nPs  100, tPs

ð1Þ

Probe Design for FISH

43

Table 3 Description of bioinformatics tools for secondary structure evaluation Software name

Description

Reference

Unafold/Mfold

This is the most popular tool to predict the secondary structure of RNA [44] and DNA. The servers compute multiple foldings and dot plots for single sequences. It is also possible to redesign structures to produce more favorable results. This tool also has an additional web server called “DI-nucleic acid hybridization and melting prediction”, which can be useful to predict the strength of possible secondary structures on probes

RNAstructure, version 6.1

It allows RNA and DNA secondary structure prediction and analysis. It [45] also includes algorithms for secondary structure prediction, including facility to predict base pairing probabilities

OligoDesign

It includes bioinformatics tools for the prediction of self-annealing, melting temperature, and secondary structure for LNA-substituted oligonucleotides, as well as secondary structure prediction of the target nucleotide sequence

[46]

Table 4 Description of tools for the evaluation of self-complementary regions within FISH probes Software name

Description

Reference

OligoAnalyzer tool, in integrated DNA technologies

The use of this tool is simple: enter the sequence, adjust the [47] required parameters, and choose the function you want to obtain (dimer formation inclusive). BLAST analysis can also be done directly from this tool. It is possible to insert LNA sequences

OLIGO primer analysis software

For each sequence, Oligo’s various analysis windows show a [48] multitude of useful data, such as dimer formation, false priming and homology, internal stability, composition, and physical properties

where nPs stands for the total number of nontarget strains that did not react with the probe and tPS is the total nontarget strains examined. The determination of the theoretical sensitivity is based on Eq. 2: sensitivity ¼

sS  100, TSs

ð2Þ

where sS stands for the number of bacterial strains detected by the probe and TSs is the total number of bacterial strains present in the databases.

44

Helena Teixeira et al.

Table 5 Different types of NAM probes and their distinct applications on microbial detection by FISH Type of probe

Applications on microbial detection

References

PNA

Biofilm characterization (see Note 8) Single microbial detection Mismatch discrimination

[49] [18] [50, 51]

LNA

Multiplexed FISH Single mismatch discrimination Detection of microorganisms at specific conditions

[40] [52–54] [55]

LNA/2’OMe

Multiplex experiments Detection of microorganisms at specific conditions

[41, 55, 56] [40, 55]

3.4 Applying Nucleic Acid Mimics on Probe Design

As referred above, the use of NAMs on probe design can be applied to overcome some of the drawbacks that have been associated with DNA, improving their performance in FISH procedure. According to some previous works, the types of NAMs used in probes are determined by the type of target/aim of the study (Table 5).

3.4.1 PNA Probes

PNA probes have been used for characterization of biofilm structures in terms of analysis of spatial and quantification of biofilm population (e.g., [49]), for single microbial detection (e.g [18]) and for single-mismatch discrimination (e.g., [50, 51]). During the design of PNA probes, the following guidelines should take into account the following: 1. PNA probes of 13–18 bases are most common, because of high Tm of PNA probes. In fact, PNA/DNA-duplex generally has higher Tm than corresponding DNA/DNA-duplex, approximately 1  C per base pair. In addition, due to high binding affinity to its complementary DNA sequences, long PNA probes are not needed for hybridization. 2. As PNA/PNA interaction is stronger than PNA/DNA interaction, it is strongly recommended to avoid any selfcomplementary sequences. 3. Try to limit the purine content lower than 60% for PNA probes, because purines present low solubility in aqueous solution. 4. PNA probes for single-mismatch discrimination, and the mutation point/mismatch should be located in the center of the probe [50]. 5. For PNA probes, there are no guidelines related to the minimum ΔG ; therefore, we recommend to adjust the ΔG according to previous studies (e.g., [57, 58]).

Probe Design for FISH 3.4.2 LNA and 2’OMe Probes

45

LNA probes have been used for single-mismatch discrimination [52–54], multiplexed FISH [40], and detection of microorganisms at specific conditions [55]. Since LNA and 2’OMe bases can be positioned wherever desired within a probe sequence, its properties can be altered and adjusted as needed. Hence, LNA and 2’OMe probes offer higher design flexibility comparative to the PNA and DNA probes, because with these synthetic molecules, it is possible to control the thermodynamic parameters of molecules, designing probes that work at specific hybridization conditions (e.g., probes that work at the same hybridization conditions [40], low pH, and human body temperature [55]). The below rules should be followed for all assays that include LNA probes: 1. It is possible to design LNA probes with 10–15 pb, maintaining the desired Tm (see Note 4). 2. For single-mismatch discrimination using LNA/DNA probes, a triplet of LNA should be positioned directly at the mismatch site to improve the discrimination [52, 53]. In addition, an LNA/2’OMe probe can be used for this purpose. In this case, LNAs can be incorporated at every third 2’OMe monomer, as previously reported by Kierzek et al. 2005 [52] and Fontenete et al. 2015 [59], since the use of a higher number of LNA bases in each probe can improve the mismatch discrimination [52] (see Note 9). 3. Using LNA probes, it is possible to incorporate mixed/degenerated bases, i.e., have one, some, or all of the bases at the same particular position of the probe, allowing a decrease in the number of probes. However, in the literature, there are no references to FISH probes, where only references to degenerate primers exist [60]. 4. Avoid self-complementarity and complementarity to other LNA probes (in the case of multiplex assay) since LNA will bind very tightly to other LNA monomers. 5. Avoid stretches of more than 4 LNA bases, because LNA hybridizes very tightly when several consecutive LNA bases are present within probe.

4

Notes 1. Note that the probes deposited in probeBase should be evaluated according to Subheading 3.3. 2. Note that multiple sequence alignment strategy for the design of probes might be time consuming.

46

Helena Teixeira et al.

3. Too many sequences make the process very complex and less likely to find suitable candidate probes, whereas few sequences can be insufficient to find a specific probe. 4. The Tm for PNA/DNA duplexes is higher than that for DNA/DNA; the Tm increases 1.5  C per bp for PNA/DNA and for PNA/RNA duplexes [56, 61]. DNA duplexes containing LNA nucleotides have the ability to increase the Tm between 2 and 4  C per single LNA nucleotide, whereas the Tm of LNA/RNA duplexes increases by 2–10  C per single LNA nucleotide substitution [13, 62]. 5. If the probes will be applied in ex vivo studies (e.g., mouse and humans), check the specificity against these microorganisms. This analysis can be done using BLAST. 6. If the probe is designed for the 16 rRNA sequences, the existence of possible cross-hybridization with the 23S rRNA sequences (and vice versa) should also be evaluated using the above-mentioned rRNA databases. 7. It is also important to check that the selected probe has low number of sequences of other microorganisms with one mismatch. 8. PNA probes are very effective in FISH for characterization of biofilms due to their neutral backbone; in fact, the neutrally charger PNA allows the penetration ability, resulting in an enhanced diffusion into the biofilm matrix (negatively charged [63]). However, LNA/2’OMe probes were successfully applied in a biofilm study [40]. 9. Avoid to positionate the mismatch at the very 30 or 50 end in order to not compromise the discrimination. You should introduce LNA bases at the positions where specificity and discrimination are needed.

Acknowledgements This work was financially supported by (a) Base Funding—UIDB/ 00511/2020 of the Laboratory for Process Engineering, Environment, Biotechnology and Energy—LEPABE—funded by national funds through the FCT/MCTES (PIDDAC); (b) Projects POCI01-0145-FEDER-016678 (Coded-FISH), POCI-01-0145FEDER-03043 (CLASInVivo), and POCI-01-0145-FEDER028659 (NAM4toxins), funded by FEDER funds through COMPETE2020—Programa Operacional Competitividade e Internacionalizac¸˜ao (POCI), and by national funds (PIDDAC) through FCT/MCTES; and (c) BioTecNorte operation (NORTE-010145-FEDER-000004) funded by the European Regional Development Fund under the scope of Norte2020—Programa Operacional Regional do Norte.

Probe Design for FISH

47

References 1. Amann RI, Ludwig W, Schleifer KW (1995) Phylogenetic identification and in situ detection of individual microbial cells without cultivation. Microbiol Rev 59:143–169 2. PGI D, Rmusk R (2018) Fluorescence in situ hybridization (FISH) in food pathogen detection. Int J Mol Biol 3(3):143–149. https:// doi.org/10.15406/ijmboa.2018.03.00066 3. Lukumbuzya M, Schmid M, Pjevac P, Daims H (2019) A multicolor fluorescence in situ hybridization approach using an extended set of Fluorophores to visualize microorganisms. Front Microbiol 10:1383. https://doi.org/ 10.3389/fmicb.2019.01383 4. Valm AM, Mark JL, Rieken CW, Hasegawa Y, Sogin ML, Oldenbourg R, Dewhirst FE, Borysi GG (2011) Systems-level analysis of microbial community organization through combinatorial labeling and spectral imaging. Proc Natl Acad Sci U S A 108:4152–4157. https://doi.org/10.1073/pnas.1101134108 5. Wyart M, Botstein D, Wingreen NS (2010) Evaluating gene expression dynamics using pairwise RNA FISH data. PLoS Comput Biol 6(11):e1000979. https://doi.org/10.1371/ journal.pcbi.1000979 6. Amann R, Fuchs BM, Behrens S (2001) The identification of microorganisms by fluorescence in situ hybridisation. Curr Opin Biotechnol 12:231–236. https://doi.org/10.1016/ S0958-1669(00)00204-4 7. Fukuda K, Ogawa K, Taniguchi H, Saito M (2016) Molecular approaches to studying microbial communities: targeting the 16S ribosomal RNA gene. J UOEH 38:223–232. https://doi.org/10.7888/juoeh.38.223 8. Amann R, Fuchs BM (2008) Single-cell identification in microbial communities by improved fluorescence in situ hybridization techniques. Nat Rev Microbiol 6:339–348. https://doi. org/10.1038/nrmicro1888 9. Woese CR (1987) Bacterial evolution. Microbiol Rev 51:221–271 10. Pernthaler A, Amann R (2004) Simultaneous fluorescence in situ hybridization of mRNA and rRNA in environmental bacteria. Appl Environ Microbiol 70:5426–5433. https:// doi.org/10.1128/AEM.70.9.5426-5433. 2004 11. Moter A, Go¨bel UB (2000) Fluorescence in situ hybridization (FISH) for direct visualization of microorganisms. J Microbiol Methods 41:85–112. https://doi.org/10.1016/ S0167-7012(00)00152-4

12. Silverman AP, Kool ET (2007) Oligonucleotide probes for RNA-targeted fluorescence in situ hybridization. Adv Clin Chem 43:79–115. https://doi.org/10.1016/S0065-2423(06) 43003-1 13. Cerqueira L, Azevedo NF, Almeida F, Jardim T, Keevil CW, Vieira MJ (2008) DNA mimics for the rapid identification of microorganisms by fluorescence in situ hybridization (FISH). Int J Mol Sci 9:1944–1960. https:// doi.org/10.3390/ijms9101944 14. Frickmann H, Zautner AE, Moter A, Kikhney J, Hagen RM, Stender H, Poppert S (2017) Fluorescence in situ hybridization (FISH) in the microbiological diagnostic routine laboratory: a review. Crit Rev Microbiol 43:263–293. https://doi.org/10.3109/ 1040841X.2016.1169990 15. Yilmaz LS, Noguera DR (2004) Mechanistic approach to the problem of hybridization efficiency in fluorescent in situ hybridization. Appl Environ Microbiol 70:7126–7139. https:// doi.org/10.1128/AEM.70.12.7126-7139. 2004 16. Prudent E, Raoult D (2019) Fluorescence in situ hybridization, a complementary molecular tool for the clinical diagnosis of infectious diseases by intracellular and fastidious bacteria. FEMS Microbiol Rev 43:88–107. https://doi. org/10.1093/femsre/fuy040 17. Acton QA (2013) Nucleic acid probes advances in research and application, Atlanta, Georgia 18. Cerqueira L, Fernandes RM, Ferreira RM, Carneiro F, Ribeiro DM, Figueiredo C, Keevil CW, Azevedo NF, Vieira MJ (2011) PNA-FISH as a new diagnostic method for the determination of clarithromycin resistance of Helicobacter pylori. BMC Microbiol 11:101. https://doi.org/10.1186/14712180-11-101 19. Fontenete S, Carvalho D, Guimara˜es N, Madureira P, Figueiredo C, Wengel J, Azevedo NF (2016) Application of locked nucleic acidbased probes in fluorescence in situ hybridization. Appl Microbiol Biotechnol 100:5897–5906. https://doi.org/10.1007/ s00253-016-7429-4 20. Muro MA (2005) Probe design, production, and applications. In: Walker JM, Rapley R (eds) Medical biomethods handbook. Humana Press, Totowa, NJ 21. SantaLucia J, Allawi HT, Seneviratne PA (1996) Improved nearest-neighbor parameters

48

Helena Teixeira et al.

for predicting DNA duplex stability. Biochemistry 35:3555–3562. https://doi.org/10. 1021/bi951907q 22. Fontenete S, Guimara˜es N, Wengel J (2015) Prediction of melting temperatures in fluorescence in situ hybridization (FISH) procedures using thermodynamic models. Crit Rev Biotechnol 36:1–12. https://doi.org/10.3109/ 07388551.2014.993589 23. Abd-Elsalam KA (2003) Bioinformatic tools and guideline for PCR primer design. Afr J Biotechnol 5:91–95. https://doi.org/10. 5897/AJB2003.000-1019 24. Apte A, Daniel S (2009) PCR primer design. Cold Spring Harb Protoc 2009(3):pdb.ip65. https://doi.org/10.1101/pdb.ip65 25. Rocha R, Sousa JM, Cerqueira L, Vieira JM, Almeida C, Azevedo NF (2019) Development and application of peptide nucleic acid fluorescence in situ hybridization for the specific detection of Listeria monocytogenes. Food Microbiol 80:1–8. https://doi.org/10.1016/ j.fm.2018.12.009 26. Benson DA, Karsch-Mizrachi I, Lipman DJ, Ostell J, Wheeler DL (2007) GenBank. Nucleic Acids Res 35:D21–D25. https://doi. org/10.1093/nar/gks1195 27. Wuyts J, Perrie`re G, Van De Peer Y (2004) The European ribosomal RNA database. Nucleic Acids Res 32:D101–D103. https://doi.org/ 10.1093/nar/gkh065 28. Quast C, Pruesse E, Yilmaz P, Gerken J, Schweer J, Yarza P, Glockner FO (2013) The SILVA ribosomal RNA gene database project: improved data processing and web-based tools. Nucleic Acids Res 41:D590–D596. https:// doi.org/10.1093/nar/gks1219 29. Cole JR, Wang Q, Fish JA, Chai B, McGarrell DM, Sun Y, Brown CT, Porras-alfaro A, Kuske CR, Tiedje JM (2014) Ribosomal Database Project: data and tools for high throughput rRNA analysis. Nucleic Acids Res 42: D633–D642. https://doi.org/10.1093/nar/ gkt1244 30. Kent and Riegel’s handbook of industrial chemistry and biotechnology. Google - Livros. https://books.google.pt/books? id¼AYjFoLCNHYUC&pg¼PA1315& lpg¼PA1315&dq¼Hundreds+of+sequences +have+been+deposited+in+the+public+data base&source¼bl&ots¼GRZuTD5wDD& sig¼ACfU3U3wf-DzSpq8ihqwrCzaXldSUqP0w&hl¼pt-PT&sa¼X& ved¼2ahUKEwjl9cG918rjAhUj2uAKHaXzA dAQ6AEwA. Accessed 23 July 2019

31. Chen SH, Lo CZ, Tsai MC, Hsiung CA, Lin CY (2007) The unique probe selector: a comprehensive web service for probe design and oligonucleotide arrays. BMC Bioinformatics 9 Suppl 1(Suppl 1):S8. https://doi.org/10. 1186/1471-2105-9-S1-S8 32. Ashelford KE, Weightman AJ, Fry JC (2002) PRIMROSE: a computer program for generating and estimating the phylogenetic range of 16S rRNA oligonucleotide probes and primers in conjunction with the RDP-II database. Nucleic Acids Res 30:3481–3489. https:// doi.org/10.1093/nar/gkf450 33. Huang YT, Yang J, Chrobak M, Borneman J (2014) PRISE2: software for designing sequence-selective PCR primers and probes. BMC Bioinformatics 15:317. https://doi. org/10.1186/1471-2105-15-317 34. Noguera DR, Wright ES, Camejo P, Yilmaz LS (2014) Mathematical tools to optimize the design of oligonucleotide probes and primers. Appl Microbiol Biotechnol 98:9595–9608. https://doi.org/10.1007/s00253-014-6165x 35. Gelali E, Girelli G, Matsumoto M, Wernersson E, Custodio J, Mota A, Schweitzer M, Ferenc K, Li X, Mirzazadeh R, Agostini F, Schell JP, Lanner F, Crosseto N, Bienko M (2019) iFISH is a publically available resource enabling versatile DNA FISH to study genome architecture. Nat Commun 10:1636. https://doi.org/10.1038/s41467-01909616-w 36. Thompson JD, Higgins DG, Gibson TJ (1994) CLUSTAL W: improving the sensitivity of progressive multiple sequence alignment through sequence weighting, position-specific gap penalties and weight matrix choice. Nucleic Acids Res 22:4673–4680. https:// doi.org/10.1093/nar/22.22.4673 37. Kubota K, Ohashi A, Imachi H, Harada H (2006) Improved in situ hybridization efficiency with locked-nucleic-acid-incorporated DNA probes. Appl Environ Microbiol 72:5311–5317. https://doi.org/10.1128/ AEM.03039-05 38. Almeida C, Azevedo NF, Bento JC, Cerca N, Ramos H, Vieria MJ, Keevil CW (2013) Rapid detection of urinary tract infections caused by Proteus spp. using PNA-FISH. Eur J Clin Microbiol Infect Dis 32:781–786. https:// doi.org/10.1007/s10096-012-1808-2 39. Almeida C, Cerqueira L, Azevedo NF, Vieira MJ (2013) Detection of Salmonella enterica serovar Enteritidis using real time PCR,

Probe Design for FISH immunocapture assay, PNA FISH and standard culture methods in different types of food samples. Int J Food Microbiol 161:16–22. https:// doi.org/10.1016/j.ijfoodmicro.2012.11.014 40. Azevedo AS, Almeida C, Pereira B, Madureira P, Wengel J (2015) Detection and discrimination of biofilm populations using locked nucleic acid/2’-O-methyl-RNA fluorescence in situ hybridization (LNA/2’OMeFISH). Biochem Eng J 104:64–73. https:// doi.org/10.1016/j.bej.2015.04.024 41. Fontenete S, Leite M, Guimara˜es N, Madureira P, Ferreira RM, Figueiredo C, Wenge J, Azevedo NF (2015) Towards Fluorescence In Vivo Hybridization (FIVH) Detection of H. pylori in Gastric Mucosa Using Advanced LNA Probes. PLoS One 10:4. https://doi.org/10.1371/journal.pone. 0125494 42. Fontenete S, Carvalho D, Guimara˜es N, Madureira P, Ferreira RM, Figueiredo C, Wenge J (2016) Application of locked nucleic acid-based probes in fluorescence in situ hybridization. Appl Microbiol Biotechnol 100:5897–5906. https://doi.org/10.1089/ dna.1991.10.233 43. Lima JF, Cerqueira L, Figueiredo C, Oliveira C, Azevedo NF (2018) Anti-miRNA oligonucleotides: a comprehensive guide for design. RNA Biol 15:338–352. https://doi. org/10.1080/15476286.2018.1445959 44. Mathews DH, Sabina J, Zuker M, Turner DH (1999) Expanded sequence dependence of thermodynamic parameters improves prediction of RNA secondary structure. J Mol Biol 5:911–941. https://doi.org/10.1006/jmbi. 1999.2700 45. Reuter JS, Mathews DH (2010) RNAstructure : software for RNA secondary structure prediction and analysis. BMC Bioinformatics 15:11–129. https://doi.org/10.1186/14712105-11-129 46. Tolstrup N, Nielsen PS, Kolberg JG, Frankel AM, Vissing H, Kauppinen S (2003) OligoDesign: optimal design of LNA (locked nucleic acid) oligonucleotide capture probes for gene expression profiling. Nucleic Acids Res 31 (13):3758–3762 47. Dwivedi B, Schmieder R, Goldsmith DB, Edwards RA, Breitbart M (2012) PhiSiGns: an online tool to identify signature genes in phages and design PCR primers for examining phage diversity. BMC Bioinformatics 13:37. https://doi.org/10.1186/1471-2105-13-37 48. Rychlik JDO (2003) OLIGO Primer Analysis Software. In: Womble DD, Krawetz SA (eds) Introduction to bioinformatics a theoretical

49

and practical approach. Humana Press, Totowa, NJ 49. Almeida C, Azevedo NF, Santos S, Keevil CW, Vieira MJ (2011) Discriminating multi-species populations in biofilms with peptide nucleic acid fluorescence in situ hybridization (PNA FISH). PLoS One 6:e14786. https://doi. org/10.1371/journal.pone.0014786 50. Ghosh S, Mishra S, Banerjee T, Mukhopadhyay R (2013) Facilitating mismatch discrimination by surface-affixed PNA probes via ionic regulation. Langmuir 29:3370–3379. https://doi. org/10.1021/la400125x 51. Hur D, Kim MS, Song M, Jung J, Park H (2015) Detection of genetic variation using dual-labeled peptide nucleic acid (PNA) probe-based melting point analysis. Biol Proced Online 17:14. https://doi.org/10. 1186/s12575-015-0027-5 52. You Y, Moreira BG, Behlke MA, Owczarzy R (2006) Design of LNA probes that improve mismatch discrimination. Nucleic Acids Res 34:60. https://doi.org/10.1093/nar/gkl175 53. Owczarzy R, You Y, Groth CL, Tataurov AV (2011) Stability and mismatch discrimination of locked nucleic acid–DNA duplexes. Biochemistry 50:9352–9367. https://doi.org/ 10.1021/bi200904e ¨ hrmalm C, Jobs M, Eriksson R, Golbob S, 54. O Elfaitouri A, Benachenhou F, Strømme M, Blomberg J (2010) Hybridization properties of long nucleic acid probes for detection of variable target sequences, and development of a hybridization prediction algorithm. Nucleic Acids Res 38:e195. https://doi.org/10.1093/ nar/gkq777 55. Fontenete S, Guimara˜es N, Leite M, Figueiredo C, Wengel J, Azevedo NF (2013) Hybridization-based detection of Helicobacter pylori at human body temperature using advanced locked nucleic acid (LNA) probes. PLoS One 8:e81230. https://doi.org/10. 1371/journal.pone.0081230 56. Mitchel J (1997) Nucleic acids: thermal stability and denaturation. http://citeseerx.ist.psu. edu/viewdoc/download?doi¼10.1.1.612. 3148&rep¼rep1&type¼pdf. Accessed 27 July 2019 57. Guimara˜es N, Azevedo NF, Figueiredo C, Keevil CW, Vieira MJ (2007) Development and application of a novel peptide nucleic acid probe for the specific detection of Helicobacter pylori in gastric biopsy specimens. J Clin Microbiol 45:3089–3094. https://doi.org/ 10.1128/JCM.00858-07 58. Cerqueira L, Fernandes RM, Ferreira RM, Oleastro M, Carneiro F, Branda˜o C, Nunes

50

Helena Teixeira et al.

PP, Ribeiro DM, Figueiredo C, Keevil CW, Vieira MK, Azevedo NF (2013) Validation of a fluorescence in situ hybridization method using peptide nucleic acid probes for detection of Helicobacter pylori clarithromycin resistance in gastric biopsy specimens. J Clin Microbiol 51:1887–1893. https://doi.org/10.1128/ JCM.00302-13 59. Fontenete S, Barros J, Madureira P, Figueiredo C, Wengel J, Azevedo NF (2015) Mismatch discrimination in fluorescent in situ hybridization using different types of nucleic acids. Appl Microbiol Biotechnol 9:3961–3969. https://doi.org/10.1007/ s00253-015-6389-4 60. Burbano CS, Reinhold-Hurek B, Hurek T (2010) LNA-substituted degenerate primers improve detection of nitrogenase gene transcription in environmental samples. Environ

Microbiol Rep 2:251–257. https://doi.org/ 10.1111/j.1758-2229.2009.00107.x 61. Shakeel S, Karim S, Ali A (2006) Peptide nucleic acid (PNA) — a review. J Chem Technol Biotechnol 6:892–899. https://doi.org/ 10.1002/jctb.1505 62. Oligo modifications for increased duplex stability & nuclease resistance. https://pdfs. semanticscholar.org/73b2/ 92e4cd314676657b0ce0b3fa8279efd00d66. pdf. Accessed 28 July 2019 63. Drobniewski FA, More PG, Harris GS (2000) Differentiation of Mycobacterium tuberculosis complex and nontuberculous mycobacterial liquid cultures by using peptide nucleic acidfluorescence in situ hybridization probes. J Clin Microbiol 38:444–447

Chapter 4 FISH in Suspension or in Adherent Cells Francesca Di Pippo, Diogo Queiro´s, Joana Pereira, Paulo C. Lemos, Luı´sa S. Serafim, and Simona Rossetti Abstract Fluorescence in situ hybridization (FISH) enables the detection and enumeration of microorganisms in a diversity of samples. Short-length oligonucleotide DNA probes complementary to 16S or 23S rRNA sequences are generally used to target different phylogenetic levels. The protocol for the application of FISH to aggregated or suspended cells in mixed microbial communities is described in this chapter, with a special emphasis on environmental samples. Key words FISH, Suspension cells, Adherent cells, Unculturable bacteria, Environmental samples, Oligonucleotide probes

1

Introduction In their natural environment, microorganisms grow in community surrounded by potential competitors or collaborators. Between them, there is a complex exchange of metabolites, signals, or structure. The number of possible interactions grows exponentially with the local diversity, and up to hundreds of genotypes may coexist in a community [1]. Most single strains cannot be characterized in isolation and occupy structurally complex microenvironments that are difficult to replicate in laboratory [2]. For this reason, the majority of bacteria cannot be cultivated in laboratory using the standard methods, and it is believed that only 1% of bacteria are potentially cultivated in vitro [3]. Back in 1995, Amann et al. reviewed the first methods developed to characterize the unculturable bacteria and accounted them for diverse environments [4]. While 1–15% of bacteria were considered cultivable in activated sludge samples, this value decreased to 0.25% in fresh water samples and decreased drastically to 0.01–0.001% in seawater samples [4]. Generations of microbiologists reported the discrepancy between the number of colonies obtained after standard plating

Nuno F. Azevedo and Carina Almeida (eds.), Fluorescence In Situ Hybridization (FISH) for Microbial Cells: Methods and Concepts, Methods in Molecular Biology, vol. 2246, https://doi.org/10.1007/978-1-0716-1115-9_4, © Springer Science+Business Media, LLC, part of Springer Nature 2021

51

52

Francesca Di Pippo et al.

of an environmental sample and the number of viable-cell counting under the microscope of the same sample [4]. The so-called “great plate count anomaly” [5] was, then, explained by the existence of two groups of cells: one constituted by known species for which the traditional cultivation techniques were simply not appropriate or were in a nonculturable state and the other formed by unknown species never cultured before due to lack of suitable methods [5]. With the uprise of molecular biology in the 1990s, more specifically, methods using oligonucleotide probes in fluorescence in situ hybridization (FISH) enabled the detection and characterization of unculturable taxa among mixed populations [3]. Nowadays, the number of unculturable bacteria is still very high. As reviewed by Solden et al., the different approaches using rRNA allowed identifying 89 bacterial and 20 archaeal phyla, but it is believed that the total number of phyla can reach 1500 [6]. FISH involves the selective hybridization of fluorescently labeled short-length DNA fragments (probes) to complementary 16S or 23S rRNA sequences in the target bacterial cells. Some probes used for FISH can be accessed at probeBase (http:// probebase.csb.univie.ac.at/). Depending on the nucleotide composition, probes can be designed to target different phylogenetic levels, from species to higher phylogenetic groups [7]. The specificity of the hybridization is high, and the hybridized cells retain the FISH probe becoming cells fluorescently labeled. With quantitative FISH, cell numbers or biovolumes of equivalent cell quantities are quantified preferentially by digital imaging. A relative abundance, as percentage, is estimated based on comparing the hybridization with a first specific probe against a second probe usually targeting most Bacteria [8] (Fig. 1). FISH in microbial community analysis is useful for the phylogenetic identification of cells in association with their cell and microcolony morphology and spatial distribution or localization

Fig. 1 Double hybridization of biomass using probes (a) EUBmix (6-FAM, Bacteria domain) (b) Bet42a (CY3, Betaproteobacteria class), and (c) overlay of images where yellow resulted from the combination of red and green positive signals

FISH for Environmental Samples

53

Fig. 2 Hybridization of biomass aggregates with EUBmix (Bacteria domain) probes with 6-FAM fluorophore (green) where different morphologies could be observed

within activated sludge flocs or biofilms (Fig. 2). Poor bacterial cell permeability, low cell ribosomal content, ribosome inaccessibility, low hybridization affinity, sensitivity to nuclease, and sample autofluorescence can affect negatively the method [7, 9]. For this reason, previous knowledge of sample nature and origin is recommended, which together with the limitation on the existing probes represents more difficulties for FISH application [8]. Apart from this DNA-FISH method, in recent years, some alternative FISH-associated techniques have been developed. Among them, we can consider those that are based on nucleic acid analogues as peptide nucleic acids (PNA-FISH), locked-nucleic acids (LNA-FISH), and the utilization of 2’-Omethyl-RNA in conjunction with locked-nucleic acids (LNA/ 2’OMe-FISH). Other methods used to increase sensitivity are catalyzed reporter deposition-FISH (CARD-FISH) and recognition of individual gene-FISH (RING-FISH) [10]. Recent advances also allowed the utilization/detection of more than two probes/ organism using multicolor fluorescence in situ hybridization approach with an extended set of fluorophores (up to eight) [11]. Overall, despite the constraints of probe availability and development, FISH remains a strong technique for the in situ, spatial and temporal assessment of the structure of microbial communities [12]. The phylogenetic information obtained with FISH can be correlated with phenotypic information by coupling it to other techniques. Levantesi et al. were able to correspond the phylogenetic groups identified to the phenotypes observed in a

54

Francesca Di Pippo et al.

phosphorus-removing population by using postFISH staining for polyphosphate and polyhydroxyalkanoates (PHA) [13]. By coupling Microautography (MAR) with FISH, Albuquerque et al. correlated the consumption of the different substrates with the probe-related populations [14]. With the recent developments in high throughput 16S rRNA gene sequencing (NGS) and the decrease of the service cost, much research work recurs to this technique for microbial identification. This approach does not replace the FISH method as apart from identification, abundance and localization of a given organism in a sample/floc/biofilm can also be obtained. FISH analysis with rRNA-targeted oligonucleotide probes consists of the following main steps: 1. Fixation and dehydration of the samples. 2. Hybridization of the samples with the probes and washing. 3. Microscopic evaluation. So far, FISH has been successfully applied in many different types of environmental samples that require specific protocols depending on the way of aggregation and origin of the microbial communities. The protocols for the application of FISH analysis to aggregated (e.g., activated sludge and biofilms) or suspended mixed microbial communities (e.g., groundwater and cells extracted form complex matrix such as soil and sediment) are provided in this chapter.

2

Materials All solutions and the material used for FISH have to be sterilized. Prepare all solutions using ultrapure water (prepared by purifying deionized water, to attain a sensitivity of 18 MΩ-cm at 25
C) and analytical grade reagents. Prepare and store all reagents at room temperature (unless indicated otherwise).

2.1 Reagents for Sample fixation and Dehydration

1. Phosphate buffer: 20:80 (v/v) mixture of 200 mM NaH2PO4 and 200 mM Na2HPO4, pH of the buffer mixture should be 7.2–7.4). 2. Phosphate Buffered Saline (PBS): 130 mM NaCl, 10 mM sodium phosphate buffer, pH 7.2 (Table 1). 3. 3x PBS: 390 mM NaCl, 15% (v/v) phosphate buffer, pH 7.2–7.4. 4. 1x PBS: 130 mM NaCl, 5% (v/v) phosphate buffer, pH 7.2–7.4. 5. 4% (w/v) paraformaldehyde (PFA) solution (see Note 1): heat 65 mL of high purity water to 60
C. Add 4 g of PFA. Add a drop of 2 M NaOH solution and stir rapidly until the solution

FISH for Environmental Samples

55

Table 1 Phosphate Buffered Saline (for pH 7.2, the ratios of disodium:sodium phosphates must be 7.2:2.8) 100

30

Concentration (M) Concentration (g/L) Concentration (M) Concentration (g/L) Na2HPO4.12H2O

0.72

257.9

0.216

77.37

NaH2PO4.2H2O

0.28

43.7

0.084

13.1

NaCl

13

754

3.9

226.2

has nearly clarified (ca. 1–2 min). Remove from the heat source and add 33 mL of 3x PBS. Adjust pH to 7.2 with HCl and remove crystals by sterile filtration (0.2 μm). Quickly cool to 4
C and store at this temperature. Store the solution at 20
C in small aliquots. 6. 2 M NaOH. 7. 50%, 80%, and 96% (v/v) ethanol solutions for sample dehydration. 8. Double distilled water (ddH2O). 9. 96% (v/v) ethanol for fixation. 10. Ice. 2.2 Reagents for Coating Microscope Slides

1. Ethanolic KOH: 10% (w/v) KOH in 96% (w/v) ethanol. 2. Gelatin solution: 0.1% (w/v) gelatin, 0.01% (w/v) chromium potassium sulfate. 3. Acidic ethanol: 1% (v/v) HCl in 70% (v/v) ethanol. 4. 0.01% poly-L-lysine.

2.3 Reagents for In Situ Hybridization

1. 5 M NaCl. 2. Tris buffer: 1 M Tris/HCl, pH 8.0. 3. Formamide (see Note 2). 4. 10% (w/v) sodium dodecyl sulfate (SDS). 5. 0.5 M ethylenediaminetetraacetate (EDTA), pH 8.0. 6. Hybridization buffer (preparation procedure detailed in Subheading 3.4). 7. Washing buffer (preparation procedure detailed in Subheading 3.4). 8. Fluorescently labeled rRNA-targeted oligonucleotide probes (see Note 3). 9. Unlabelled competitor oligonucleotides (the same concentration as corresponding probe).

56

Francesca Di Pippo et al.

10. Ice-cold Milli-Q. 11. Antifading agent Vectashield).

(e.g.,

Citifluor

AF1,

Citifluor,

or

12. Hand-warm 0.5–1% (w/v) agarose. 13. SYBR Green I or DAPI (40 ,6-diamidino-2-phenylindole) dissolved in distilled water, final concentration, 1 μg mL1. 2.4

Equipment

1. 1.5 mL microcentrifuge plastic tubes. 2. 50 mL screw cap tubes. 3. 0.2 μm filters and syringes. 4. Microscope slides. Teflon-coated slides partitioned into 6–12 wells are used for FISH analysis (Fig. 3). This allows analyzing more samples on the same slide. 5. Cover slips. 6. Tissue paper. 7. Rack to hold 50 mL screw cap tubes. 8. Analytical balance. 9. Gloves, dust mask. 10. Centrifuge for 1.5 mL tubes. 11. Fridge (4
C) and freezer (20
C). 12. Magnetic stirrer with heater (60–70
C). 13. Drying oven (46
C). 14. Water bath (48
C). 15. Tweezers. 16. Fume hood. 17. Oil-free compressed air.

Fig. 3 FISH analysis procedure steps before hybridization phase. (a) Samples are poured on the Teflon coated slides, (b) slide is carefully placed in the hybridization chamber containing the tissue paper moistened with the hybridization buffer, and (c) the hybridization chamber is placed at a horizontal position onto a rack before the incubation

FISH for Environmental Samples

57

Table 2 Excitation and emission wavelengths of fluorescent dyes suitable for labeling rRNA-targeted oligonucleotide probes

Fluorochrome

Excitation (nm)

Emission (nm)

Fluorescence color

Oregon green 488

490, 493

514, 520

Green

5(6)-carboxyfluorescein-Nhydroxysuccinimide ester (FLUOS)

492

518

Green

Cy3

514

566

Orange-red

Tetramethylrhodamine isothiocyanate (TRITC)

550

573

Red

Cy5

649

666

Near infrared

18. Epifluorescence or confocal laser scanning microscope, filters and lasers that match the excitation and emission wavelengths of the fluorochromes (Table 2). 19. Image analysis software capable to detect objects in images and to measure their area (in pixels) and brightness. 2.5 Additional Reagents and Equipment for FISH on Planktonic Communities (Membrane Filters)

1. Clean glass bottles for sampling. 2. Plastic Petri dish (5 cm diameter). 3. White polycarbonate membrane filters (47 mm diameter; 0.2 m pore size). 4. Cellulose nitrate support filters (47 mm diameters; 0.45 m pore size). 5. Filter towers for 47 mm membrane filters. 6. Vacuum pump. 7. Particle-free 37% (w/v) formaldehyde solution.

2.6 Additional Reagents and Equipment for FISH on Soil Samples

3 3.1

1. 0.5% Tween 20. 2. OptiPrep™, density gradient medium used for cell isolation (density 1.3 g mL1).

Methods Fixation of Cells

The fixation of the sample is one of the most critical steps of the protocol and must occur soon after the sampling. This step is essential to preserve the morphological integrity of the cells during the exposition to high temperature, detergents, and osmotic gradients. Moreover, the fixation of the sample allows higher

58

Francesca Di Pippo et al.

permeability of cells to labeled oligonucleotide probes. The analysis of environmental samples requires the use of both PFA- and ethanol-fixed aliquots to keep the intracellular RNA during the sample storage in both Gram-negative and Gram-positive microorganisms. Cell walls of Gram-negative prokaryotes are strengthened by fixation with the crosslinking agent PFA. On the contrary, the fixation with PFA renders the cell walls of many Gram-positive impermeable to oligonucleotide probes. Therefore, the FISH detection of Gram-positive cells requires the fixation with ethanol as detailed below. In addition, an alternative rapid fixation procedure to be adopted for the analysis of samples is reported. This procedure does not allow a long-term storage of the sample, but it is useful in the case of field sampling without the possibility to easily reach an equipped laboratory. 3.1.1 Fixation of Gram-Negative Cells

1. Add 3 volumes of PFA fixative to 1 volume of sample and hold at 4
C for 3–12 h. 2. Pellet the cells by centrifugation (5000  g) and replace supernatant with ice-cold 1xPBS. Repeat this step 2–3 times to remove fixative. 3. Resuspend the sample in 1 volume of ice-cold 1xPBS. 4. Add 1 volume of ice-cold 96% (v/v) ethanol and mix. 5. Fixed cells can be spotted onto glass slides or stored at 20
C. Samples fixed according to this protocol can be stored for several months to years. The rapid fixation procedure is reported in Note 4.

3.1.2 Fixation of Gram-Positive Cells

1. Add 1 volume of ice-cold 96% (v/v) ethanol fixative to 1 volume of sample and hold at 4
C for 4–16 h. 2. Centrifuge the sample (5000  g) and remove the fixative. 3. Wash the cells in 1 PBS and resuspend in 1 PBS to have approximately 109 cells mL1. 4. Add one volume of ice cold ethanol 96% and mix. 5. Store the sample at 20
C. The rapid fixation procedure is reported in Note 4.

3.2 Coating of Microscope Slides

Microscope slides can be coated with either gelatin or poly-L-lysine in order to improve the adhesion of sample material to the glass surface (see Note 5). This procedure is generally adopted for quantitative FISH analysis only or in the case of samples that adhere poorly to the glass surface.

FISH for Environmental Samples 3.2.1 Coating with Gelatin

59

1. Clean the slides in ethanolic KOH for 1 h and air-dry the slides. 2. Warm gelatin solution (60–70
C). 3. Dip the slides for a few seconds into the gelatin solution. 4. Air-dry the slides for at least 3 h.

3.2.2 Coating with Poly-L-Lysine

1. Clean the slides in acidic ethanol for 5 min. 2. Dip the slides for 5 min at room temperature into 0.01% polyL-lysine. 3. Dry the slides for 1 h at 46
C.

3.3 Application of Samples to Slides and Dehydration of Fixed Samples

1. Apply 3–30 μL of PFA- or ethanol-fixed sample onto one field of a Teflon-coated slide (Fig. 3). 2. Dry for 15 min at 46
C or longer at room temperature. 3. Dip slide for 3 min each into 50%, 80%, and 96% (v/v) ethanol. 4. Dry the slides for a couple of minutes at 46
C. 5. For some Gram-positive cells, additional enzymatic pretreatment might be necessary to enhance the cell permeability prior to whole-cell probing. Different enzymatic pretreatment protocols are available for the detection of specific microorganism. This step should be therefore experimentally optimized if different targeted prokaryotes are analyzed [15, 16].

3.4 In Situ Hybridization

1. Prepare 2 mL of fresh hybridization buffer (see Note 6). The hybridization buffer is prepared in 2 mL microcentrifuge tubes at the time of use. In a 2 mL microcentrifuge tube, add the following reagents: l 360 μL of 5 M NaCl (autoclaved). Final concentration ¼ 0.9 M. l

l

l l

40 μL of 1 M Tris/HCl concentration ¼ 20 mM.

(autoclaved).

Final

2 μL of 10% (w/v) SDS (not autoclaved) placed in the lid of the centrifuge tube. Final concentration ¼ 0.1% (v/v). X μL of formamide (Table 3) (see Note 7). Y μL of autoclaved milli-Q water (depending on amount of formamide, Table 3).

2. Add 8 μL of hybridization buffer to each well on the slide. 3. Add 0.5 μL of probe at 50 ng μL1 and mix carefully (see Note 8). 4. Put a piece of tissue paper into a 50 mL screw top plastic tube and pour the remaining hybridization buffer onto the tissue paper. 5. Place immediately the slide horizontally into the 50 mL tube containing the moistened tissue (Fig. 1). Close and place the

60

Francesca Di Pippo et al.

Table 3 Amount of formamide (FA) and milli-Q water depending on the concentration of formamide in the hybridization buffer Volume of FA (μL)

% FA (v/v)

Volume of milli-Q water (μL)

0

0

1598

100

5

1498

200

10

1398

300

15

1298

400

20

1198

500

25

1098

600

30

998

700

35

898

800

40

798

900

45

698

1000

50

598

tube at a horizontal position onto a rack and incubate it in an oven at 46
C for 1–5 h, but an incubation time of 90 min is sufficient in most cases (see Note 9). 6. Prepare 50 mL of washing buffer. In a 50 mL tube, add the following: l z μL of 5 M NaCl (autoclaved) (Table 4). l

1 mL of 1 M Tris/HCl (autoclaved).

l

Fill to the 50 mL mark with Milli-Q water and mix.

l

Add 50 μL of 10% SDS (not autoclaved, see Note 10).

l

If the hybridization buffer contains 20% (v/v) or more formamide, 0.5 M EDTA has to be added to the washing buffer (see Note 11).

7. Warm the washing buffer in the bath at 48
C during the hybridization. 8. After hybridization, remove carefully the slide from the screw cap tube. 9. Wash away the hybridization buffer with a small volume of prewarmed washing buffer and transfer the slide into the washing buffer tube (see Note 12). 10. Put the tube containing the washing buffer and the slide into the water bath and incubate for 10–15 min at 48
C. 11. Take the slide out of the tube and rinse gently for 2–3 s in ice-cold Milli-Q from a wash bottle. Water is directed above

FISH for Environmental Samples

61

Table 4 Amount of 5 M NaCl in the washing buffer to achieve different washing stringencies % FA

NaCl (M)

Volume of 5 M NaCl (μL)

0

0.900

9000

5

0.636

6300

10

0.450

4500

15

0.318

3180

20

0.225

2150

25

0.159

1490

30

0.112

1020

35

0.080

700

40

0.056

460

45

0.040

300

50

0.028

180

wells and allowed to flood over them. Wash both sides of the slide to remove residual washing buffer from the slide (see Note 13). 12. Air-dry the slide as quickly as possible (the use of compressed air is recommended) (see Note 14). 3.5

Mounting Slides

1. Mount the slide in an antifading mounting solution (Citifluor or Vectashield). A very thin film of antifading is used. 2. Alternatively, Vectashield Mounting Medium containing DAPI (1.5 μg mL1) can be used to also highlight the total DAPI stained cells. 3. Put a microscope cover slip on top and wait until the antifading has spread over the whole slide (see Note 15).

3.6 Viewing Slides and Quantification of Microorganisms

The qualitative analysis of complex aggregated samples (e.g., activated sludge, granules, and biofilms) can be performed by epifluorescence microscopy. The quantification of microorganisms by FISH analysis requires the use of Confocal Laser Scanning Microscopy. 1. Randomly select 20–30 different microscope fields and acquire the digital images. 2. Calculate the areas covered by hybridized cells and by cells stained with DAPI separately for each field. Different software packages for image analysis are available both freeware (Image J; Daime) and commercial ones. Microorganisms are expressed as a percentage contribution to the total biomass.

62

Francesca Di Pippo et al.

CLSM analysis can be used to quantify the biovolume and mean thickness of the biofilm. Different programs for the quantification of three-dimensional biofilm structures are available. These programs are generally menu controlled and user-friendly and analyze stacks of images acquired using confocal laser-scanning microscopy. The resulting value from image analyses is biovolume divided by substratum area (μm3/μm2). Before using the chosen program: 1. Establish a threshold value to each set of optical sections (or rather, to each sample). The threshold helps the program identify the presence or absence of biomass in each pixel from every image. 2. Establish the number of optical sections (they will vary between samples, according to the thickness of the floc/biofilm). 3. Analyze the sections of all samples and select a threshold where only the floc/biofilm could be quantified by the program, making the easily identified background turn into a black base.

4

FISH on Planktonic Cells FISH can also be applied to samples characterized by low cell concentration (i.e., planktonic cells in water). In this case, samples are concentrated by filtration and FISH analysis is performed directly on filters.

4.1 Fixation and Dehydration of Planktonic Samples

1. Add formaldehyde to a water sample to a final concentration of 2–4% and fix for at least 2 but not longer than 24 h at 4
C. 2. Place a moistened support filter (0.45 μm pore size, cellulose nitrate, 47 mm diameter) and a membrane filter (0.2 μm pore size, white polycarbonate, 47 mm diameter) into a filtration tower. Filter a known volume of the fixed sample by applying gentle vacuum. Support filters may be utilized for several samples. For cell numbers of around 105 mL1, 10 mL of sample are generally sufficient. A hand-operated vacuum pump and several autoclavable plastic filter towers that can be linked together for parallel sample processing make an inexpensive filtration apparatus for field work. 3. After complete sample filtration, wash with 10–20 mL of sterile water; remove water by filtration. 4. Put membrane filter in petri dish and allow to air-dry. 5. Store at 20
C until processing. Filters can be stored frozen for several months without apparent loss of hybridization signal. 6. Dehydrate membrane filter for 3 min each into 50%, 80%, and 96% (v/v) ethanol.

FISH for Environmental Samples

4.2 Hybridization of Cells on Membrane Filters

63

1. Prepare 2 mL of hybridization buffer in a microfuge tube as described in Subheading 3.5, Table 3. 2. Cut sections from membrane filters with a sterile razor blade. A 47 mm diameter filter should allow the preparation of 16–20 individual hybridizations. 3. Label filter sections with a pencil, e.g., by numbering them. 4. Put filter sections on glass slides (upside facing up); several filter sections can be placed on one slide and hybridized simultaneously with the same probe. 5. Prepare hybridization mixture by adding 0.5 μL of probe at 50 ng μL1 to 8 μL of hybridization buffer in a 0.5 mL microfuge tube. Keep probe solutions dark and on ice. 6. Put a piece of tissue paper into a polyethylene tube and soak it with the remaining hybridization buffer. 7. Add hybridization mix on the samples and place the slide with filter sections into the polyethylene tube (at a horizontal position). 8. Incubate at 46
C for at least 90 min (maximal 3 h). 9. Prepare 50 mL of washing buffer in a polyethylene tube (see Subheading 3.5, Table 4). 10. Quickly transfer filter sections into preheated washing buffer and incubate for 15 min at 48
C in the water bath. 11. Pour washing buffer with filter sections into a petri dish. Pick filter sections and rinse them by placing them into a petri dish with Milli-Q for several seconds; then let them air-dry on tissue paper. 12. Put filter sections on a glass slide. Mount the slides according to Subheading 3.6. 13. Viewing samples and quantification of microorganisms on membrane filters: cell numbers of planktonic microorganisms can be determined by direct cell count on randomly selected microscopic fields. In this case, a quantitative estimation of the cell abundance per volume of the sample (e.g., cells mL1 of filtered water) is performed.

5 5.1

FISH on Soil Samples Sample Fixation

1. Place about 1 g of soil in a sterile 10 mL test tube. 2. Add 9 mL of filter-sterilized PBS containing 0.5% Tween 20, 2% formaldehyde, and 1 g L1 sodium pyrophosphate with a final pH of 7.4. 3. Store soil samples at 4
C until further processing to detach and separate cells from the matrices.

64

Francesca Di Pippo et al.

5.2 Cell Extraction from Soil Samples

1. Mix the fixed samples for 15 min in an orbital shaker (400 rpm) to detach particle-associated bacteria. 2. Add 1 vol of OptiPrep™ (density approx. 1.3 g mL1) to 1 vol of the sample (slurry), using a syringe needle with adequate length to reach the bottom of the tube. 3. Centrifuge the samples in a swing-out rotor (14,000  g for 90 min at 4
C). 4. After centrifugation, four distinct phases are formed (from the bottom of the tube: (a) pellet, (b) OptiPrep™, (c) detached cell layer, and (d) supernatant) based on their buoyant density. The fraction containing most of the detached cells (layer c) is present as a thin, translucent layer on top of the OptiPrep™ “cushion” and thus clearly visible when the sample is observed against a light source. Carefully collect and filter the aliquots from layer “c” on 0.2-μm polycarbonate membrane (Millipore, 25 mm diameter) by gentle vacuum (