Oral Biology: Molecular Techniques and Applications (Methods in Molecular Biology, 2588) 1071627791, 9781071627792
This fully revised new edition explores advances in the prevention and treatment of oral diseases. Beyond the updated ch
110 72 19MB
English Pages 543 [523] Year 2022
Preface
Contents
Contributors
Part I: Saliva and Other Oral Fluids
Chapter 1: RNA Sequencing Analysis of Saliva exRNA
1 Introduction
2 Available Methods for Saliva Collection, Pre- and Post-processing, for RNA-Sequencing
2.1 Saliva Collection
2.2 RNA Extraction Methods
2.2.1 RNA Quantification and Quality Controls (QCs)
2.3 cDNA Library Construction Methods
2.4 Sequencing and Bioinformatic Analysis of RNA-Seq Data
2.4.1 Library Quality Control and Sequencing
2.4.2 Alignment Settings
2.5 Uniqueness of Salivary RNA-Seq Analysis
References
Chapter 2: Proteome Analysis of Oral Biofluids in Periodontal Health and Disease Using Mass Spectrometry
1 Introduction
2 Materials
2.1 GCF Collection
2.2 Qubit Assay
2.3 FASP
2.4 Software for Proteomic Data Analysis
2.5 Equipment for Proteomic Data Analysis
3 Methods
3.1 Gingival Crevicular Fluid (GCF) Collection
3.2 GCF Supernatant Preparation
3.3 Saliva Supernatant Preparation and Total Protein Quantification
3.4 FASP Digestion for the GCF or Saliva Supernatant
3.5 LC-MS/MS Analysis
3.6 Protein Search, Identification, and Label-Free Quantification
4 Notes
References
Chapter 3: Saliva Diagnosis Using Small Extracellular Vesicles and Salivaomics
1 Introduction
2 Materials
2.1 Saliva Collection and Storage
2.2 2.2 Salivary sEVs Isolation Using SEC Columns
2.3 Salivary Genomic DNA Isolation
2.4 16S rRNA Library Preparation and NGS
2.5 Salivary Total RNA Isolation
2.6 RNA Library Preparation and Sequencing
2.7 Salivary Genomic DNA Isolation
2.8 Salivary gDNA Library Preparation
2.9 Materials for Salivary Proteome
3 Methods
3.1 Unstimulated Whole Saliva Collection and Storage
3.2 Salivary sEVs Isolation Using SEC Columns
3.3 Salivary Genomic DNA Isolation
3.4 16S rRNA Gene Amplification and Sequencing
3.5 Salivary Total RNA Isolation Using Trizol and ABI PicoPure Columns
3.6 Salivary RNA Library Preparation and Illumina RNA-Sequencing
3.7 Salivary gDNA Isolation
3.8 Salivary gDNA Library Preparation (see Fig. 3)
3.9 MeDIP, Library Amplification, and Sequencing
3.10 Salivary Proteome Using Liquid Chromatography-Tandem Mass Spectrometry (LC-MS/MS)
4 Notes
References
Chapter 4: Antioxidant Micronutrients and Oxidative Stress Biomarkers
1 Introduction
2 Materials
2.1 Determination of Ascorbic Acid and Dehydroascorbic Acid
2.2 Measurement of Protein Carbonyls
2.3 Preparation of White Cells for 8-OHdG Analyses
2.3.1 3% Gelatin in PBS
2.4 Comet Assay
2.5 Serum Preparation
2.6 Plasma Preparation
2.7 Metaphosphoric Acid (See Note 1)
2.8 Collection of Gingival Crevicular Fluid (GCF)
2.9 Collection of Saliva
2.10 Measurement of Lipid Oxidation Products
3 Methods
3.1 Determination of Ascorbic Acid and Dehydroascorbic Acid in Human Plasma
3.2 Carotenoid Analyses (See Notes 5, 6, and 7)
3.3 Measurement of Protein Carbonyls to Detect Oxidative Protein Biomarkers
3.3.1 Preparation of ELISA Standards
3.3.2 Method
3.4 Carbonyl ELISA Method
3.5 8-OHdG Analyses or Comet Assay to Determine Oxidative DNA Damage (See Notes 12 and 13)
3.5.1 Preparation of White Cells (Buffy Coats)
3.5.2 3% Gelatin in PBS (150 Bloom from Pig Skin) for Preparation of Buffy Coats
3.6 Comet Assay to Determine Oxidative DNA Damage (See Note 16)
3.6.1 Day 1
3.6.2 Day 2 (See Note 17)
3.6.3 Day 3
3.7 Serum Preparation to Determine Antioxidant Capacity (See Note 20)
3.8 Determination of Antioxidant Capacity (See Note 22)
3.8.1 Plasma Preparation
3.8.2 33.5% Metaphosphoric Acid; for Plasma Vitamin C Analyses (See Notes 23 and 24)
3.9 Collection of Gingival Crevicular Fluid (GCF) to Measure Antioxidant Activity (See Note 25)
3.10 Collection of Saliva Samples to Measure Antioxidant Activity (See Notes 26, 27 and 28) [20]
3.11 Analysis of Phospholipid Oxidation Products (See Notes 29 and 30)
3.11.1 Lipid Extraction
3.11.2 Lipid Analysis
4 Notes
References
Part II: Molecular Biosciences
Chapter 5: The Oral Microbiota in Health and Disease: An Overview of Molecular Findings
1 Introduction
2 Nucleic Acid Technologies
3 Diversity and Taxonomy of Oral Bacteria
4 Refined Bacterial Taxonomy Associated with Oral Diseases
4.1 Dental Caries
4.2 Halitosis
4.3 Periodontal Disease
4.4 Apical Periodontitis
5 Concluding Remarks
References
Chapter 6: The Long and Short of Genome Sequencing: Using a Hybrid Sequencing Strategy to Sequence Oral Microbial Genomes
1 Introduction
2 Purification of Genomic DNA from Oral Microorganisms
2.1 Growth Media and Incubation Conditions
2.2 Purification of Genomic DNA
3 Post-sequencing Bioinformatics
3.1 Genome Assembly Using Canu
3.2 How Do You Solve a Problem Like ONT? The Need for a Hybrid Sequencing Strategy
3.3 Hybrid Genome Assembly Using SPAdes
3.4 Concluding Remarks
4 Notes
References
Chapter 7: Microbial Community Profiling Using Terminal Restriction Fragment Length Polymorphism (T-RFLP) and Denaturing Gradi...
1 Introduction
2 Materials
2.1 DNA Extraction
2.2 Terminal Restriction Fragment Length Polymorphism
2.2.1 PCR Amplification of the 16S rRNA Gene
2.2.2 T-RFLP Analysis
2.3 Denaturing Gradient Gel Electrophoresis
2.3.1 PCR Amplification of 16S rRNA Gene
2.3.2 DGGE Analysis
3 Methods
3.1 DNA Extraction
3.2 Terminal Restriction Fragment Length Polymorphism
3.2.1 PCR Amplification of 16S rRNA Gene
3.2.2 T-RFLP Analysis for ABI PRISM 310 Genetic Analyser
3.3 Denaturing Gradient Gel Electrophoresis
3.3.1 PCR Amplification of 16S rRNA Gene
3.3.2 DGGE Analysis
4 Notes
References
Chapter 8: Bioinformatic Approaches for Describing the Oral Microbiota
1 Introduction
2 Microbiota Analysis Pipeline #1 Using QIIME2
2.1 Import Data into QIIME2
2.2 Remove Primers
2.3 Merge Paired-End Sequences
2.4 Denoise the Data
2.5 OTU Picking
2.6 Taxonomic Classification of OTUs
2.7 Building a Phylogenetic Tree (Optional)
2.8 Filter Steps
2.9 Normalize Sequence Reads per Sample via Rarefaction
2.10 Core Diversity Analysis with Figures (See Note 7)
3 Microbiota Analysis Pipeline #2 Using R and DADA2
3.1 Importing Data into R
3.2 Quality Filtering
3.3 Merge Paired-End Reads
3.4 Chimera Removal
3.5 Taxonomic Analysis
3.6 Core Diversity Analysis via Phyloseq
3.7 Loading in Sample Metadata
3.8 Basic Analyses
4 Notes
References
Chapter 9: Adhesion of Yeast and Bacteria to Oral Surfaces
1 Introduction
2 Materials
2.1 Radiolabeling of Yeast and Bacterial Cells and Cell Culture
2.2 Blot Overlay Assay to Demonstrate Adhesion of Yeast Cells to Immobilized Proteins
2.3 Adhesion of C. albicans Cells to Saliva-Coated Hydroxyapatite
2.4 Adhesion of Saliva-Treated C. albicans Cells to Epithelial Cells
2.5 Adhesion of C. albicans or S. epidermidis Cells to Saliva-Coated Medical Grade Silicone or to Denture Prosthetic Materials
2.6 Adhesion of S. epidermidis Cells to Denture Prosthetic Materials Under Flow Conditions
2.7 C. albicans Biofilm Formation on Denture Acrylic Flag Strips Suspended in Microtiter Wells
3 Methods
3.1 Radiolabeling of Yeast and Bacterial Cells and Cell Culture
3.1.1 To Prepare Inocula for Pre-culture of Yeast or Bacteria
3.1.2 Preparation of C. albicans Cells Radioactively Labeled with 35S-methionine
3.1.3 Preparation of S. epidermidis Cells Radioactively Labeled with 3H-thymidine
3.2 Blot Overlay Assay to Investigate Adhesion of Yeast Cells to Immobilized Proteins
3.2.1 SDS-PAGE Analysis
3.2.2 Electroblotting
3.2.3 Radiolabeled Yeast Overlay
3.3 Adhesion of C. albicans Cells to Saliva-Coated Hydroxyapatite
3.4 Adhesion of Saliva-Treated C. albicans Cells to Epithelial Cells
3.4.1 Epithelial Cell Monolayers
3.4.2 Adherence Assay Conditions
3.4.3 Confocal Microscopy
3.5 Adhesion of C. albicans or S. epidermidis Cells to Saliva-Coated Medical Grade Silicone or to Denture Prosthetic Materials
3.6 Adhesion of S. epidermidis to Denture Prosthetic Materials Under Flow Conditions
3.6.1 Bacteria: (S. epidermidis)
3.6.2 Preparation of Denture Prosthetic Material Surfaces
3.6.3 Parallel Plate Flow Chamber Set Up
3.6.4 Bacterial Deposition
3.7 C. albicans Biofilm Formation on Denture Acrylic Flag Strips Suspended in Microtiter Wells
3.7.1 Preparation of Inocula for Biofilm Experiments
3.7.2 Fabrication of Acrylic Flag Strips
3.7.3 Coating of Acrylic Flags with Saliva
3.7.4 Initial Deposition of Yeast Cells on Acrylic Flags
3.7.5 Biofilm Formation
3.7.6 Biofilm Quantification Using Crystal Violet (CV) Staining
4 Notes
References
Chapter 10: Quantitative Analysis of Periodontal Pathogens Using Real-Time Polymerase Chain Reaction (PCR)
1 Introduction
2 Materials
2.1 Samples
2.2 Positive Controls (Standard Curve)
2.3 DNA Extraction
2.4 qPCR Amplification
2.5 Multiplex qPCR
3 Methods
3.1 Biosafety Measures in Handling Clinical Samples
3.2 Sample Collection
3.3 Positive Controls (Standard Curve)
3.4 DNA Extraction
3.4.1 DNA Extraction from Pure Cultures
3.4.2 DNA Extraction from GCF Samples
3.4.3 DNA Extraction from Blood Samples
3.4.4 DNA Extraction from Any Other Biological Solid Samples, for Example, Atheromatous Plaques or Brain Samples
3.5 Preparation of Standard Curves for qPCR
3.6 qPCR Assay
3.7 Data Analysis
4 Notes
References
Chapter 11: Methods to Study Antagonistic Activities Among Oral Bacteria
1 Introduction
2 Materials
2.1 Bacteriocin Assay
2.2 Biofilm Assay and Confocal Laser Scanning Microscopy
2.3 H2O2 Detection with Indicator Plates
2.3.1 Enzymatic H2O2 Detection
2.3.2 Nonenzymatic H2O2 Detection
2.4 Isolation and Purification of Bacteriocin
2.5 Derivatization of Lantibiotics
2.6 Cloning and Other Genetic Techniques
3 Methods
3.1 Competition Assay on Plate Culture
3.2 Competition Assay in Biofilms
3.3 H2O2 Production Assay
3.4 Bacteriocin Activity Assay by Deferred Antagonism (Plate Overlay)
3.5 Isolation of Bacteriocin
3.6 Purification of Bacteriocin
3.7 Sequencing of the Purified Bacteriocin
3.8 Isolation of Bacteriocin Structural Genes by Reverse Genetics (See Note 10)
3.9 Mutagenesis via Single and Double Crossover
3.10 Gene Expression Analysis by Reporter Fusions
3.11 Luciferase Assay Using Live Cells
4 Notes
References
Chapter 12: Generation of Multispecies Oral Bacteria Biofilm Models
1 Introduction
2 Materials
3 Methods
3.1 Initial Biofilm Baseline Colonization for the Development of All Different Biofilm Models
3.2 Culture Specifics for the Different Biofilm Models
3.2.1 The Dental Caries Model
3.2.2 The Denture Biofilm Model
3.2.3 The Gingivitis and Periodontitis Biofilm Model
3.2.4 Further Analysis and Utility of the Biofilm Models
4 Notes
References
Chapter 13: Markerless Genome Editing in Competent Streptococci
1 Introduction
2 Materials
2.1 Competence Induction and Transformation
2.2 PCR
2.3 Agarose Gel Electrophoresis
2.4 Primers
3 Methods
3.1 Construction of Markerless Amplicons
3.2 Markerless Transformation Protocol Using XIP in S. mutans
3.3 Markerless Transformation Protocol Using CSP in S. pneumoniae
3.4 Examples of Applications
3.4.1 Example 1. Eight-Basepair Inversion
3.4.2 Example 2. Thirty-Nine-Basepair Deletion
3.4.3 Example 3. Single-Base Substitution in S. mutans
3.4.4 Example 4: Single-Base Substitution in S. pneumoniae
4 Notes
References
Chapter 14: A Protocol to Produce Genetically Edited Primary Oral Keratinocytes Using the CRISPR-Cas9 System
1 Introduction
2 Materials
2.1 Cell Culture
2.2 Plasmid Transformation and Purification
2.3 Transfection and Gene Edition Reagents:
2.4 PCR and Sequencing
3 Methods
3.1 Target Sequence Design
3.2 Plasmid Transformation and Purification
3.2.1 Plasmid Transformation
3.2.2 Plasmid Purification
3.3 Transfection
3.4 Cell Selection and Expansion
3.5 Confirmation of CRISPR-Cas9-Mediated Genetic Edition
4 Notes
References
Chapter 15: Size-Based Method for Enrichment of Circulating Tumor Cells from Blood of Colorectal Cancer Patients
1 Introduction
2 Materials
2.1 Cell Lines and Culture Conditions
2.2 Preparation of 10x Red Blood Cell (RBC) Lysis Buffer
2.3 Description of MetaCell CTC Enrichment Kit
2.4 Preparation of 4% Paraformaldehyde (Fixative)
2.5 Preparation of Permeabilization Buffer (0.2% Triton X-100 in DPBS)
2.6 Antibody Buffer
2.7 Blocking Solution (10% Normal Donkey Serum, 3% BSA, in Permeabilization Buffer)
2.8 Primary Antibody Solution
3 Methods
3.1 Thawing Frozen Cell Lines
3.2 Spiking Experiments to Test the MetaCell Method of Enriching for CTCs
3.3 Immunostaining of CRC Cells Retained on the MetaCell Membrane
3.4 Benchmarking Strategy for MetaCell
3.4.1 Recovery Rate
3.4.2 WBC Depletion Rate
3.5 Patient Sampling and blood processing
4 Notes
References
Chapter 16: Strategy for RNA-Seq Experimental Design and Data Analysis
1 Introduction
2 Materials
3 Methods
3.1 RNA-Sequencing Experimental Design
3.1.1 Single-End or Paired-End
3.1.2 Stranded or Unstranded Library
3.1.3 Power Analysis
3.1.4 Higher Coverage or More Replicates
3.1.5 Number of Replicates
3.1.6 Long vs. Short Reads
3.1.7 Total RNA + Ribodepletion vs. mRNA
3.2 Analysis of RNA-Sequencing Data
3.2.1 Setting Up the Computational Environment and Essential Bioinformatic Tools
3.2.2 Acquisition of Datasets from Publicly Available Sources
3.2.3 Assessment of Sequencing Data Quality, RNA-Seq Library Biases, and Processing
3.2.4 Alignment to the Reference Genome
3.2.5 Quantifying All the Transcripts/Genes in Individual Samples to Report Abundance
3.2.6 Differential Expression Analysis
4 Notes
References
Chapter 17: Characterization of the Expression and Role of Histone Acetylation and Deacetylation in Dental Pulp Cells
1 Introduction
2 Materials
2.1 Histochemical Analysis
2.2 Dental Pulp Cell (DPC) Mineralization Culture
2.3 Western Blotting Analysis
3 Methods
3.1 Immunohistochemical Analysis
3.1.1 Preparation of the Tissue Section
3.1.2 Immunostaining
3.2 Mineralizing Cell Culture of Dental Pulp Cells
3.2.1 Cell Culture and mineralization of Rat DPCs
3.2.2 HDACi Supplementation
3.2.3 Alizarin Red Staining to Evaluate Mineralization
3.3 Western Blot Analysis of HDAC Expression
3.3.1 Cell Lysis
3.3.2 Western Blotting
4 Notes
References
Chapter 18: Genome-Wide Analysis of Periodontal and Peri-implant Cells and Tissues
1 Introduction
2 Materials
2.1 Source Materials
2.1.1 Tissue Samples
2.1.2 Primary Mononuclear Cells Isolated from Patient Blood
2.1.3 Cultured Cells
2.2 Extraction and Purification of Nucleic Acids (and Protein)
2.3 Quantitation and Purity Assessment
2.4 High-Throughput Analysis
2.4.1 Microarray Platforms
2.4.2 Next-Generation Sequencing
3 Methods
3.1 Source Materials
3.1.1 Tissue Samples
3.1.2 Primary Mononuclear Cells Isolated from Patient Blood
3.1.3 Cultured Cells
3.2 Extraction and Purification of Nucleic Acids (and Protein)
3.3 Quantitation and Purity Assessment
3.4 High-Throughput Analysis
3.4.1 Microarray Platforms
3.4.2 Next-Generation Sequencing
4 Notes
References
Chapter 19: Differential Expression, Functional and Machine Learning Analysis of High-Throughput -Omics Data Using Open-Source...
1 Introduction
2 Materials
2.1 Hardware
2.2 Software
2.3 Manifests, Annotations, Genome Files
2.4 Targets File
2.5 Raw Data
3 Methods
3.1 Pre-processing of Array Data
3.2 Pre-processing of Sequencing Data
3.3 Differential Expression Analysis
3.4 Functional Analysis
3.5 Upload to Repositories
3.6 Use of Supervised Learning Algorithms for the Distinction of Formerly Classified Aggressive and Chronic Periodontitis Base...
3.7 Identification of Novel Classes of Periodontitis Based on mRNA Expression Profiles Using Unsupervised Clustering
4 Notes
References
Chapter 20: Micro-RNA Profiling in Dental Pulp Cell Cultures
1 Introduction
2 Materials
2.1 DPC Isolation and Culture
2.2 Mineralizing Cell Culture (see Note 1)
2.3 Epigenetic Modifier (HDACi and DNMTi) Solutions
2.4 miRNA Isolation
2.5 RNA Quality Analysis
2.6 Alizarin Red S Staining and Quantification
2.7 RNA Sequencing ``As a Service´´ and ``In-House´´ Data Analysis
3 Methods
3.1 DPC Isolation
3.2 Sub-culture of Confluent Cells
3.3 Sub-culture of Confluent Cells into 6-Well Plates
3.4 Mineralizing Culture (see Notes 2 and 3)
3.5 miRNA Isolation (see Notes 4 and 5)
3.6 RNA Quality Analysis
3.7 Alizarin Red S Staining of Cell Cultures
3.8 Alizarin Red S Quantification
3.9 RNA Sequencing Analysis
3.10 RNA Sequencing Data Analysis
3.10.1 Experiment Creation
3.10.2 Experimental Design
3.10.3 Interpretations
3.10.4 Quantification
3.10.5 Differential Expression Analysis
3.10.6 Target Gene Prediction (see Note 11)
3.10.7 GO Enrichment and Pathway Analysis (see Note 11)
4 Notes
References
Part III: Cells and Tissues
Chapter 21: Oral Epithelial Cell Culture Model for Studying the Pathogenesis of Chronic Inflammatory Disease
1 Introduction
1.1 Three-Dimensional (3D) Cell Culture Techniques
2 Materials
2.1 Epithelial Cell Culture Media and Reagents
2.2 Cell Culture Methodology (Fig. 5)
2.3 Bacterial Growth (See Note 1)
2.4 Immunocytochemistry of Cells Grown on Multi-well Slides
2.5 High-Throughput Immuno-cytochemistry
2.6 mRNA for Gene Expression
2.7 Polymerase Chain Reaction (PCR)
2.8 Agarose Gel Electrophoresis
3 Methods
3.1 Production and Maintenance of Epithelial Cell Model System
3.2 Bacterial Culture/Growth
3.3 Cell Culture
3.3.1 Cell Passage (See Notes 2 and 3)
3.3.2 Cell Growth on Glass Multi-well Slides
3.3.3 Growing Cells in 96-Well Plates
3.4 Immunocytochemical Analyses of NF-kB
3.4.1 Staining Procedure (See Note 4)
3.4.2 Quantification of Cell Translocation
3.5 High-Throughput Immuno-cytochemistry
3.5.1 Fixing of Cell Monolayer
3.5.2 Staining
3.5.3 Data Acquisition and Analysis
3.6 mRNA for Gene Expression
3.6.1 RNA Isolation
3.6.2 Reverse Transcription (RT)
3.6.3 Concentration and Purification of cDNA
3.6.4 Quantification of RNA and DNA
3.7 Polymerase Chain Reaction
3.8 Agarose Gel Electrophoresis
3.8.1 Preparation of Agarose Gel
3.8.2 Gel Electrophoresis
4 Notes
References
Chapter 22: A Cell Culture Method for the Isolation and Study of Primary Human Dental Pulp Cells
1 Introduction
2 Materials
2.1 Cell Culture Media and Related Solutions
2.2 Immunocytochemistry Reagents
2.3 Equipment
3 Method: (Fig. 1a-d)
3.1 Tooth Collection and Transfer to Tissue Culture Laboratory
3.2 Isolation of Human Dental Pulp Cells: Dental Pulp Explant Method
3.3 Cell Passaging: Trypsinization
3.4 Cell Counting
3.4.1 Calculating Total Number of Cells
3.5 Cell Cryopreservation
3.6 Thawing and Revival of Cryopreserved Cells
3.7 Immunocytochemistry
4 Notes
References
Chapter 23: Culturing Adipose-Derived Stem Cells Under Serum-Free Conditions
1 Introduction
2 Materials
2.1 Cell Culture Components
3 Methods
3.1 Isolating ADSC
3.2 Cell Culture and Serum Free Adaptation
3.3 Cell Passage
3.4 Cryopreservation
4 Notes
References
Chapter 24: Quantitative Real-Time Gene Profiling of Human Alveolar Osteoblasts Using a One-Step System
1 Introduction
2 Materials
2.1 Primary Human Alveolar Osteoblast Isolation and Culture
2.2 Experimental Treatment of Osteoblasts
2.3 Total RNA Extraction and DNase 1 Treatment
2.4 One Step - Reverse Transcription and qPCR
2.5 Data Analysis
3 Methods
3.1 Primary Human Alveolar Osteoblast Isolation and Culture
3.2 Experimental Treatment of Osteoblasts
3.3 Total RNA Recovery in TRIzol
3.4 Total RNA Extraction and DNase I Treatment
3.4.1 Total RNA Extraction
3.4.2 DNase 1 Treatment
3.4.3 Elution
3.5 One-Step Reverse Transcription and qPCR
3.6 Data Analysis
3.6.1 Volcano Plot of Relative Gene Expression
3.6.2 Volcano Plot of Selected Regulation
3.6.3 Graph Demonstrating Expression Levels of an Individual Gene (2-ΔCq)
3.6.4 Graph Demonstrating the Fold Regulation of an Individual Gene (2-ΔΔCq)
4 Notes
References
Chapter 25: Fabrication and Characterization of Decellularized Periodontal Ligament Cell Sheet Constructs
1 Introduction
2 Materials
2.1 Primary Human Periodontal Ligament Cell (hPDLC) Harvesting and Expansion
2.2 Melt Electrospun PCL Carrier Membrane Fabrication
2.3 Cell Sheet Fabrication and Harvesting
2.4 Perfusion Decellularization Components
2.5 Cell Sheet Fixation and Preparation for Immunostaining and Confocal Imaging
2.6 Growth Factor Extraction
3 Methods
3.1 Primary Human Periodontal Ligament Cell (hPDLC) Harvesting and Expansion
3.2 Melt Electrospun PCL Carrier Membrane Fabrication
3.3 Cell Sheet Fabrication and Harvesting
3.4 Cell Sheet Construct Decellularization
3.5 Immunostaining of Cell Sheet Constructs
3.6 Growth Factor Extraction
4 Notes
References
Chapter 26: Immunohistochemistry and Immunofluorescence
1 Introduction
2 Materials
2.1 Tissue and Slide Preparation
2.2 Antigen Retrieval (Heat-Induced Method)
2.3 Antigen Retrieval (Enzyme Digestion Method)
2.4 Immunohistochemistry
2.4.1 Double Immunohistochemistry
2.5 Immunofluorescence
3 Methods
3.1 Tissue and Slide Preparation
3.2 Manual Antigen Retrieval (Heat-Induced) Method
3.3 Antigen Retrieval (Enzyme Digestion Method)
3.4 Preparation of Primary Antibody (Optimization of Antibody)
3.5 Primary Antibody Incubation and Secondary Antibody Detection (Single Immunostaining)
3.6 Qualitative and Quantification Analysis of Immunostaining
3.7 Double Immunostaining Immunohistochemistry
3.8 Immunofluorescence Staining
4 Notes
References
Chapter 27: Characterization, Quantification, and Visualization of Neutrophil Extracellular Traps
1 Introduction
2 Materials
2.1 Isolation of Neutrophils from Human Peripheral Blood by Density Gradient Centrifugation
2.2 Isolation of Neutrophils from Human Peripheral Blood by Negative Selection
2.3 Isolation of Neutrophils from the Human Oral Cavity
2.4 Neutrophil ROS Assays
2.5 NET Assays
2.6 Visualization of NETs by SEM
2.7 Visualization of NETs by Fluorescence Microscopy and HCA
2.8 Neutrophil Activation
3 Methods
3.1 Isolation of Neutrophils from Human Peripheral Blood by Density Gradient Centrifugation
3.2 Isolation of Neutrophils from Human Peripheral Blood by Negative Selection
3.3 Isolation of Neutrophils from the Human Oral Cavity
3.4 Chemiluminescence to Measure Neutrophil ROS
3.5 Quantification of NET-DNA
3.6 Quantification of NET-Bound Components
3.6.1 Production of NETs and NET-Bound Components
3.6.2 Measuring NET-Bound Neutrophil Elastase (NE)
3.6.3 Measuring NET-Bound Myeloperoxidase (MPO)
3.6.4 Measuring NET-Bound Cathepsin G (CG)
3.7 Quantification of NET-Entrapped Bacteria
3.8 Quantification of NET-Mediated Killing of Bacteria
3.9 Quantification of NET Degradation by Human Plasma
3.10 Scanning Electron Microscopy (SEM) of NETs
3.11 Immunofluorescence and Fluorescence Microscopy of NETs
3.12 NET Quantification and Visualization with HCA
3.13 Bacterial Culture
4 Notes
References
Chapter 28: Cell Seeding on 3D Scaffolds for Tissue Engineering and Disease Modeling Applications
1 Introduction
2 Materials
2.1 Primary Human Osteoblasts Cell (hOB) Harvesting and Expansion
2.2 Melt Electrowritten Scaffold Fabrication
2.3 Cell Seeding
2.4 Seeded-Scaffold Culture
2.5 3D Construct Imaging
3 Methods
3.1 Primary Human Osteoblasts Cell (hOB) Harvesting and Expansion
3.2 Melt Electrowritten Scaffold Fabrication
3.3 Surface Modification
3.4 Cell Seeding
3.5 Seeded-Scaffold Culture
3.6 Imaging of 3D Construct
3.6.1 Alizarin Red Staining
3.6.2 Picrosirius Red Staining
3.6.3 Immunostaining for Confocal Microscopy
4 Notes
References
Chapter 29: Workflow for Fabricating 3D-Printed Resorbable Personalized Porous Scaffolds for Orofacial Bone Regeneration
1 Introduction
2 Materials
2.1 Computational Modeling of the Patient Matched Scaffold
2.2 3D Printing of the Patient Matched Scaffold
3 Methods
3.1 Computational Modeling of the Patient Matched Scaffold
3.2 3D Printing of the Patient Matched Scaffold
4 Notes: 3D Printing of the Patient Matched Scaffold
References
Chapter 30: Methacrylated Gelatin as an On-Demand Injectable Vehicle for Drug Delivery in Dentistry
1 Introduction
2 Materials
2.1 Gelatin Methacrylation
2.2 GelMA Purification
2.3 Lyophilization
2.4 Hydrogel Fabrication
3 Methods
3.1 Gelatin Methacrylation
3.2 GelMA Purification
3.3 Lyophilization
3.4 Hydrogel Fabrication
3.4.1 Utilization of the Biodegradability of GelMA for Controlled Delivery of Chlorohexidine
3.4.2 An Antibiotic-Eluting Hydrogel for Oral Infection Ablation
4 Notes
References
Chapter 31: In Vitro Biological Testing of Dental Materials
1 Introduction
1.1 Classification of Dental Materials
1.2 Material Characterization Methods
1.2.1 Chemical Characterization
1.3 In Vitro Biological Testing
2 Materials
2.1 Preparation of Growth Media
2.2 Preparation of Differentiation Medium (DM)
2.3 Cell Seeding and Cell Growth
2.4 Subculturing (Passaging) Cells
2.5 Cell Counting
2.6 Cryopreserving Cells
2.7 Reviving Frozen Cells and Material Sterilization
2.8 Cell Seeding on to the Test Material
2.9 Cell Viability/Cytotoxicity Assay (LIVE/DEAD Assay)
2.10 The MTS Cell Proliferation Assay
2.11 Alkaline Phosphatase (ALP) Assay
3 Methods
3.1 Preparation of Growth Media (GM) (See Note 1)
3.2 Preparation of Differentiation Medium (DM)
3.3 Reviving Frozen Cells, Cell Seeding, and Cell Growth
3.4 Subculturing (Passaging) Cells
3.5 Cell Counting
3.6 Cryopreserving/Freezing Cells
3.7 Dental Material Preparation and Sterilization
3.8 Cell Seeding on to the Dental Material Surface
3.9 Cell Viability/Cytotoxicity Assay (LIVE/DEAD Assay)
3.10 Cell Proliferation (MTS Assay)
3.11 Alkaline Phosphatase (ALP) Assay
4 Notes
References
Correction to: Characterization, Quantification, and Visualization of Neutrophil Extracellular Traps
Index
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Methods in Molecular Biology 2588
Gregory J. Seymour Mary P. Cullinan Nicholas C.K. Heng Paul R. Cooper Editors
Oral Biology Molecular Techniques and Applications Third Edition
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.
Oral Biology Molecular Techniques and Applications Third Edition
Edited by
Gregory J. Seymour and Mary P. Cullinan School of Dentistry, The University of Queensland, Herston, QLD, Australia
Nicholas C. K. Heng and Paul R. Cooper Faculty of Dentistry, University of Otago, Dunedin, New Zealand
Editors Gregory J. Seymour School of Dentistry The University of Queensland Herston, QLD, Australia
Mary P. Cullinan School of Dentistry The University of Queensland Herston, QLD, Australia
Nicholas C. K. Heng Faculty of Dentistry University of Otago Dunedin, New Zealand
Paul R. Cooper Faculty of Dentistry University of Otago Dunedin, New Zealand
ISSN 1064-3745 ISSN 1940-6029 (electronic) Methods in Molecular Biology ISBN 978-1-0716-2779-2 ISBN 978-1-0716-2780-8 (eBook) https://doi.org/10.1007/978-1-0716-2780-8 © The Editor(s) (if applicable) and The Author(s), under exclusive license to Springer Science+Business Media, LLC, part of Springer Nature 2023, Corrected Publication 2023 This work is subject to copyright. All rights are solely and exclusively licensed 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. Cover Illustration Caption: The image shows the visualization (20x magnification) of lymphocytes with anti-CD25 single immunostaining positivity with diaminobenzidine (DAB; brown). The cell surface and cytoplasmic staining is visible without any positive staining of the nucleus (no nuclear staining). Image provided by H. Hussaini. 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 The biological and physical sciences remain the research base that underpins advances in the prevention and treatment of oral diseases, and it is in this context that this third edition continues the theme of the very successful previous editions of Oral Biology: Molecular Techniques and Applications. As in previous editions, it is recognized that it is not possible to include all possible techniques in a single volume. Nevertheless, many of the techniques covered in the previous editions remain valid and have been extensively updated and included in this edition together with 14 new chapters. In recent years, there have been major advances in the field of regenerative biology such that two of the new chapters cover 3D printing and cell seeding of 3D scaffolds. Chapters on gene editing and the use of CRISPR in oral biology, and histone acetylation and deacetylation techniques are also in this edition, further reflecting advances in the application of molecular techniques to oral biology. In presenting a selection of up-to-date molecular methods applicable to the study of oral health and disease, it is hoped that this third edition of Oral Biology: Molecular Techniques and Applications will continue to be a basic resource not only for new researchers but also for experienced scientists wishing to expand their research platform into new areas of oral biology. Herston, QLD, Australia
Gregory J. Seymour Mary P. Cullinan Nicholas C. K. Heng Paul R. Cooper
Dunedin, New Zealand
v
Contents Preface . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Contributors. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .
PART I
SALIVA AND OTHER ORAL FLUIDS
1 RNA Sequencing Analysis of Saliva exRNA. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Karolina Elz˙bieta Kaczor-Urbanowicz and David T. W. Wong 2 Proteome Analysis of Oral Biofluids in Periodontal Health and Disease Using Mass Spectrometry . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Nagihan Bostanci and Kai Bao 3 Saliva Diagnosis Using Small Extracellular Vesicles and Salivaomics . . . . . . . . . . . Pingping Han, Xiang Li, Wei Wei, and Sasˇo Ivanovski 4 Antioxidant Micronutrients and Oxidative Stress Biomarkers . . . . . . . . . . . . . . . . . Irundika H. K. Dias, Helen R. Griffiths, Mike R. Milward, Martin R. Ling, Iain L. C. Chapple, and Melissa M. Grant
PART II
v xi
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13 25 41
MOLECULAR BIOSCIENCES
5 The Oral Microbiota in Health and Disease: An Overview of Molecular Findings . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Jose´ F. Siqueira Jr. and Isabela N. Roˆc¸as 6 The Long and Short of Genome Sequencing: Using a Hybrid Sequencing Strategy to Sequence Oral Microbial Genomes . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Nicholas C. K. Heng and Jo-Ann L. Stanton 7 Microbial Community Profiling Using Terminal Restriction Fragment Length Polymorphism (T-RFLP) and Denaturing Gradient Gel Electrophoresis (DGGE) . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Jose´ F. Siqueira Jr, Mitsuo Sakamoto, and Alexandre S. Rosado 8 Bioinformatic Approaches for Describing the Oral Microbiota. . . . . . . . . . . . . . . . Kristi Biswas, Michael W. Taylor, and David T. J. Broderick 9 Adhesion of Yeast and Bacteria to Oral Surfaces . . . . . . . . . . . . . . . . . . . . . . . . . . . . Richard D. Cannon, Karl M. Lyons, Kenneth Chong, Kathryn Newsham-West, Kyoko Niimi, and Ann R. Holmes 10 Quantitative Analysis of Periodontal Pathogens Using Real-Time Polymerase Chain Reaction (PCR) . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Mª. Jose´ Marin, Elena Figuero, David Herrera, and Mariano Sanz 11 Methods to Study Antagonistic Activities Among Oral Bacteria. . . . . . . . . . . . . . . Fengxia Qi and Jens Kreth 12 Generation of Multispecies Oral Bacteria Biofilm Models . . . . . . . . . . . . . . . . . . . . Jason L. Brown, Mark C. Butcher, Chandra Lekha Ramalingam Veena, Safa Chogule, William Johnston, and Gordon Ramage
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91 105 131
157 171 187
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Contents
Markerless Genome Editing in Competent Streptococci . . . . . . . . . . . . . . . . . . . . . Roger Junges, Rabia Khan, Yanina Tovpeko, Heidi A. Åmdal, Fernanda C. Petersen, and Donald A. Morrison A Protocol to Produce Genetically Edited Primary Oral Keratinocytes Using the CRISPR-Cas9 System. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Sven E. Niklander and Keith D. Hunter Size-Based Method for Enrichment of Circulating Tumor Cells from Blood of Colorectal Cancer Patients. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Sai Shyam Vasantharajan, Edward Barnett, Elin S. Gray, Euan J. Rodger, Michael R. Eccles, Sharon Pattison, Fran Munro, and Aniruddha Chatterjee Strategy for RNA-Seq Experimental Design and Data Analysis . . . . . . . . . . . . . . . Gregory Gimenez, Peter A. Stockwell, Euan J. Rodger, and Aniruddha Chatterjee Characterization of the Expression and Role of Histone Acetylation and Deacetylation in Dental Pulp Cells . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Yukako Yamauchi and Henry F. Duncan Genome-Wide Analysis of Periodontal and Peri-implant Cells and Tissues. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Moritz Kebschull, Annika Therese Kroeger, and Panos N. Papapanou Differential Expression, Functional and Machine Learning Analysis of High-Throughput –Omics Data Using Open-Source Tools. . . . . . . . . . . . . . . . Moritz Kebschull, Annika Therese Kroeger, and Panos N. Papapanou Micro-RNA Profiling in Dental Pulp Cell Cultures. . . . . . . . . . . . . . . . . . . . . . . . . . Michaela Kearney and Henry F. Duncan
PART III 21
22
23 24
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CELLS AND TISSUES
Oral Epithelial Cell Culture Model for Studying the Pathogenesis of Chronic Inflammatory Disease . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Mike R. Milward, Martin R. Ling, Melissa M. Grant, Joanna Batt, and Iain L. C. Chapple A Cell Culture Method for the Isolation and Study of Primary Human Dental Pulp Cells . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Shelly Arora, Benedict Seo, Lara Friedlander, and Haizal Mohd Hussaini Culturing Adipose-Derived Stem Cells Under Serum-Free Conditions . . . . . . . . Diogo Godoy Zanicotti, Trudy J. Milne, and Dawn E. Coates Quantitative Real-Time Gene Profiling of Human Alveolar Osteoblasts Using a One-Step System . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Dawn E. Coates, Sobia Zafar, and Trudy J. Milne
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Contents
Fabrication and Characterization of Decellularized Periodontal Ligament Cell Sheet Constructs . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Amro Farag, Cedryck Vaquette, Dietmar W. Hutmacher, P. Mark Bartold, and Sasˇo Ivanovski 26 Immunohistochemistry and Immunofluorescence. . . . . . . . . . . . . . . . . . . . . . . . . . . Haizal Mohd Hussaini, Benedict Seo, and Alison M. Rich 27 Characterization, Quantification, and Visualization of Neutrophil Extracellular Traps . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Josefine Hirschfeld, Ilaria J. Chicca, Carolyn G. J. Moonen, Phillipa C. White, Martin R. Ling, Helen J. Wright, Paul R. Cooper, Mike R. Milward, and Iain L. C. Chapple 28 Cell Seeding on 3D Scaffolds for Tissue Engineering and Disease Modeling Applications . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Fanny Blaudez, Cedryck Vaquette, and Sasˇo Ivanovski 29 Workflow for Fabricating 3D-Printed Resorbable Personalized Porous Scaffolds for Orofacial Bone Regeneration . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Cedryck Vaquette, Danilo Carluccio, Martin Batstone, and Sasˇo Ivanovski 30 Methacrylated Gelatin as an On-Demand Injectable Vehicle for Drug Delivery in Dentistry . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . W. Benton Swanson, Abdel Hameed Mahmoud, Seth Woodbury, and Marco C. Bottino 31 In Vitro Biological Testing of Dental Materials . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Jithendra Ratnayake, Josette Camilleri, T. Nethmini Haththotuwa, and Jeffrey Huang Correction to: Characterization, Quantification, and Visualization of Neutrophil Extracellular Traps. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .
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Index . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .
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439
451
473
485
493
505
C1 525
Contributors HEIDI A. ÅMDAL • Department of Oral Biology, Faculty of Dentistry, University of Oslo, Oslo, Norway SHELLY ARORA • Sir John Walsh Research Institute, Faculty of Dentistry, University of Otago, Dunedin, New Zealand KAI BAO • Section of Oral Health and Periodontology, Division of Oral Diseases, Department of Dental Medicine, Karolinska Institutet, Stockholm, Sweden EDWARD BARNETT • Department of Pathology, Dunedin School of Medicine, University of Otago, Dunedin, New Zealand P. MARK BARTOLD • Colgate Australian Clinical Dental Research Centre, Dental School, University of Adelaide, Adelaide, Australia MARTIN BATSTONE • Herston Biofabrication Institute, Metro North Hospital and Health Service, Brisbane, QLD, Australia JOANNA BATT • The School of Dentistry, University of Birmingham, Birmingham, UK KRISTI BISWAS • Department of Surgery, Faculty of Medical and Health Sciences, University of Auckland, Auckland, New Zealand FANNY BLAUDEZ • School of Dentistry, The University of Queensland, Brisbane, Australia NAGIHAN BOSTANCI • Section of Oral Health and Periodontology, Division of Oral Diseases, Department of Dental Medicine, Karolinska Institutet, Stockholm, Sweden MARCO C. BOTTINO • Department of Cariology, Restorative Sciences, and Endodontics, University of Michigan School of Dentistry, Ann Arbor, MI, USA; Department of Biomedical Engineering, College of Engineering, University of Michigan, Ann Arbor, MI, USA DAVID T. J. BRODERICK • Department of Surgery, Faculty of Medical and Health Sciences, University of Auckland, Auckland, New Zealand; School of Biological Sciences, University of Auckland, Auckland, New Zealand JASON L. BROWN • Oral Sciences Research Group, College of Medical, Veterinary and Life Sciences, Glasgow University, Glasgow, UK; Glasgow Biofilm Research Network, Glasgow, UK MARK C. BUTCHER • Oral Sciences Research Group, College of Medical, Veterinary and Life Sciences, Glasgow University, Glasgow, UK; Glasgow Biofilm Research Network, Glasgow, UK JOSETTE CAMILLERI • School of Dentistry, Institute of Clinical Sciences, College of Dental and Medical Sciences, University of Birmingham, Birmingham, UK RICHARD D. CANNON • Sir John Walsh Research Institute, Faculty of Dentistry, University of Otago, Dunedin, New Zealand DANILO CARLUCCIO • Herston Biofabrication Institute, Metro North Hospital and Health Service, Brisbane, QLD, Australia IAIN L. C. CHAPPLE • The School of Dentistry, University of Birmingham, Birmingham, UK; Department of Periodontology, Birmingham Dental School and Hospital, University of Birmingham, Birmingham, UK ANIRUDDHA CHATTERJEE • Department of Pathology, Dunedin School of Medicine, University of Otago, Dunedin, New Zealand; School of Health Sciences, UPES University, Dehradun, India
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Contributors
ILARIA J. CHICCA • Clinical Immunology Service, Medical School, University of Birmingham, Birmingham, UK SAFA CHOGULE • Oral Sciences Research Group, College of Medical, Veterinary and Life Sciences, Glasgow University, Glasgow, UK; Glasgow Biofilm Research Network, Glasgow, UK KENNETH CHONG • Sir John Walsh Research Institute, Faculty of Dentistry, University of Otago, Dunedin, New Zealand DAWN E. COATES • Sir John Walsh Research Institute, Faculty of Dentistry, University of Otago, Dunedin, New Zealand PAUL R. COOPER • Faculty of Dentistry, University of Otago, Dunedin, New Zealand IRUNDIKA H. K. DIAS • Aston Medical School, Aston University, Birmingham, UK HENRY F. DUNCAN • Division of Restorative Dentistry and Periodontology, Dublin Dental University Hospital, Trinity College Dublin, Dublin, Ireland MICHAEL R. ECCLES • Department of Pathology, Dunedin School of Medicine, University of Otago, Dunedin, New Zealand AMRO FARAG • School of Dentistry, The University of Queensland, Brisbane, Australia ELENA FIGUERO • Oral Research Laboratory, Faculty of Dentistry, University Complutense, Madrid, Spain; Etiology and Therapy of Periodontal and Peri-implant Diseases (ETEP) Research Group, University Complutense, Madrid, Spain LARA FRIEDLANDER • Sir John Walsh Research Institute, Faculty of Dentistry, University of Otago, Dunedin, New Zealand GREGORY GIMENEZ • Department of Pathology, Dunedin School of Medicine, University of Otago, Dunedin, New Zealand MELISSA M. GRANT • The School of Dentistry, University of Birmingham, Birmingham, UK ELIN S. GRAY • Centre for Precision Health, Edith Cowan University, Joondalup, Australia HELEN R. GRIFFITHS • Faculty of Health & Medical Sciences, University of Surrey, Surrey, UK PINGPING HAN • School of Dentistry, The University of Queensland, Brisbane, Australia; School of Dentistry, Centre for Orofacial Regeneration, Reconstruction and Rehabilitation (COR3), The University of Queensland, Brisbane, Australia T. NETHMINI HATHTHOTUWA • Department of Anatomy, School of Biomedical Sciences, University of Otago, Otago, New Zealand NICHOLAS C. K. HENG • Sir John Walsh Research Institute, Faculty of Dentistry, University of Otago, Dunedin, New Zealand DAVID HERRERA • Etiology and Therapy of Periodontal and Peri-implant Diseases (ETEP) Research Group, University Complutense, Madrid, Spain JOSEFINE HIRSCHFELD • Department of Periodontology, Birmingham Dental School and Hospital, University of Birmingham, Birmingham, UK ANN R. HOLMES • Sir John Walsh Research Institute, Faculty of Dentistry, University of Otago, Dunedin, New Zealand JEFFREY HUANG • Department of Anatomy, School of Biomedical Sciences, University of Otago, Otago, New Zealand KEITH D. HUNTER • Unit of Oral and Maxillofacial Medicine and Pathology, School of Clinical Dentistry, University of Sheffield, Sheffield, UK; Oral Biology and Pathology, University of Pretoria, Pretoria, South Africa; Liverpool Head and Neck Centre, University of Liverpool, Liverpool, UK
Contributors
xiii
HAIZAL MOHD HUSSAINI • Sir John Walsh Research Institute, Faculty of Dentistry, University of Otago, Dunedin, New Zealand; School of Dentistry, University of Otago, Dunedin, New Zealand DIETMAR W. HUTMACHER • Centre for Regenerative Medicine, Institute of Health and Biomedical Innovation, Queensland University of Technology, Brisbane, Australia ˇ IVANOVSKI • School of Dentistry, The University of Queensland, Brisbane, Australia; SASO School of Dentistry, Centre for Orofacial Regeneration, Reconstruction and Rehabilitation (COR3), The University of Queensland, Brisbane, Australia; Herston Biofabrication Institute, Metro North Hospital and Health Service, Brisbane, Australia WILLIAM JOHNSTON • Oral Sciences Research Group, College of Medical, Veterinary and Life Sciences, Glasgow University, Glasgow, UK; Glasgow Biofilm Research Network, Glasgow, UK ROGER JUNGES • Department of Oral Biology, Faculty of Dentistry, University of Oslo, Oslo, Norway KAROLINA ELZ˙BIETA KACZOR-URBANOWICZ • Center for Oral and Head/Neck Oncology Research, UCLA School of Dentistry, University of California at Los Angeles, Los Angeles, CA, USA; UCLA Institute for Quantitative and Computational Biosciences, University of California at Los Angeles, Los Angeles, CA, USA; UCLA Section of Orthodontics, University of California at Los Angeles, Los Angeles, CA, USA; Section of Biosystems and Function, UCLA School of Dentistry, University of California at Los Angeles, Los Angeles, CA, USA MICHAELA KEARNEY • Division of Restorative Dentistry and Periodontology, Dublin Dental University Hospital, Trinity College Dublin, Dublin, Ireland MORITZ KEBSCHULL • Periodontal Research Group, Institute of Clinical Sciences, College of Medical & Dental Sciences, The University of Birmingham, Birmingham, UK; Division of Periodontics, Section of Oral, Diagnostic and Rehabilitation Sciences, Columbia University College of Dental Medicine, New York, NY, USA; Birmingham Community Healthcare NHS Trust, Birmingham, UK RABIA KHAN • Department of Oral Biology, Faculty of Dentistry, University of Oslo, Oslo, Norway JENS KRETH • School of Dentistry, Oregon Health & Science University, Portland, OR, USA ANNIKA THERESE KROEGER • Birmingham Community Healthcare NHS Trust, Birmingham, UK; Department of Oral Surgery, School of Dentistry, University of Birmingham, Birmingham, UK XIANG LI • Department of Neurosurgery, Zhongnan Hospital, Wuhan University, Wuhan, China; Medical Research Institute, Wuhan University, Wuhan, China; Department of Neurosurgery and Brain Research Center, Zhongnan Hospital, Wuhan University, Wuhan, China MARTIN R. LING • Oral Health R&D, GSK, Weybridge, UK; Birmingham Dental School and Hospital, University of Birmingham, Birmingham, UK KARL M. LYONS • Department of Oral Rehabilitation, University of Otago, Dunedin, New Zealand ABDEL HAMEED MAHMOUD • Department of Cariology, Restorative Sciences, and Endodontics, University of Michigan School of Dentistry, Ann Arbor, MI, USA Mª. JOSE´ MARIN • Oral Research Laboratory, Faculty of Dentistry, University Complutense, Madrid, Spain; Etiology and Therapy of Periodontal and Peri-implant Diseases (ETEP) Research Group, University Complutense, Madrid, Spain
xiv
Contributors
TRUDY J. MILNE • Sir John Walsh Research Institute, Faculty of Dentistry, University of Otago, Dunedin, New Zealand MIKE R. MILWARD • The School of Dentistry, University of Birmingham, Birmingham, UK; Department of Periodontology, Birmingham Dental School and Hospital, University of Birmingham, Birmingham, UK CAROLYN G. J. MOONEN • Academic Centre for Dentistry Amsterdam (ACTA), University of Amsterdam and Vrije Universiteit Amsterdam, Amsterdam, the Netherlands DONALD A. MORRISON • Department of Biological Sciences, College of Liberal Arts and Sciences, University of Illinois at Chicago, Chicago, IL, USA FRAN MUNRO • Department of Surgical Sciences, Dunedin School of Medicine, University of Otago, Dunedin, New Zealand KATHRYN NEWSHAM-WEST • Department of Oral Rehabilitation, University of Otago, Dunedin, New Zealand KYOKO NIIMI • Sir John Walsh Research Institute, Faculty of Dentistry, University of Otago, Dunedin, New Zealand SVEN E. NIKLANDER • Unidad de Patologı´a y Medicina Oral, Facultad de Odontologia, Universidad Andres Bello, Vin˜a del Mar, Chile PANOS N. PAPAPANOU • Division of Periodontics, Section of Oral, Diagnostic and Rehabilitation Sciences, Columbia University College of Dental Medicine, New York, NY, USA SHARON PATTISON • Department of Medicine, Dunedin School of Medicine, University of Otago, Dunedin, New Zealand FERNANDA C. PETERSEN • Department of Oral Biology, Faculty of Dentistry, University of Oslo, Oslo, Norway FENGXIA QI • University of Oklahoma Health Sciences Center, Oklahoma City, OK, USA GORDON RAMAGE • Oral Sciences Research Group, College of Medical, Veterinary and Life Sciences, Glasgow University, Glasgow, UK; Glasgow Biofilm Research Network, Glasgow, UK JITHENDRA RATNAYAKE • Faculty of Dentistry, Department of Oral Sciences, University of Otago, Dunedin, New Zealand ALISON M. RICH • School of Dentistry, University of Otago, Dunedin, New Zealand; School of Medicine, University of Otago, Dunedin, New Zealand ˆ ¸ AS • Department of Endodontics and Molecular Microbiology, Iguac¸u ISABELA N. ROC University, Rio de Janeiro, Brazil EUAN J. RODGER • Department of Pathology, Dunedin School of Medicine, University of Otago, Dunedin, New Zealand ALEXANDRE S. ROSADO • Institute of Microbiology Prof. Paulo de Goes, Federal University of Rio de Janeiro, Rio de Janeiro, Brazil MITSUO SAKAMOTO • Microbe Division/Japan Collection of Microorganisms, RIKEN BioResource Center, Saitama, Japan MARIANO SANZ • Etiology and Therapy of Periodontal and Peri-implant Diseases (ETEP) Research Group, University Complutense, Madrid, Spain BENEDICT SEO • Sir John Walsh Research Institute, Faculty of Dentistry, University of Otago, Dunedin, New Zealand; School of Dentistry, University of Otago, Dunedin, New Zealand JOSE´ F. SIQUEIRA JR • Department of Endodontics and Molecular Microbiology, Iguac¸u University, Rio de Janeiro, Brazil JO-ANN L. STANTON • Department of Anatomy, University of Otago, Dunedin, New Zealand
Contributors
xv
PETER A. STOCKWELL • Department of Pathology, Dunedin School of Medicine, University of Otago, Dunedin, New Zealand W. BENTON SWANSON • Department of Biologic and Materials Science and Division of Prosthodontics, University of Michigan School of Dentistry, Ann Arbor, MI, USA MICHAEL W. TAYLOR • School of Biological Sciences, University of Auckland, Auckland, New Zealand YANINA TOVPEKO • Department of Biological Sciences, College of Liberal Arts and Sciences, University of Illinois at Chicago, Chicago, IL, USA CEDRYCK VAQUETTE • School of Dentistry, The University of Queensland, Brisbane, Australia; School of Dentistry, Centre for Orofacial Regeneration, Reconstruction and Rehabilitation (COR3), The University of Queensland, Brisbane, Australia; Herston Biofabrication Institute, Metro North Hospital and Health Service, Brisbane, Australia SAI SHYAM VASANTHARAJAN • Department of Pathology, Dunedin School of Medicine, University of Otago, Dunedin, New Zealand CHANDRA LEKHA RAMALINGAM VEENA • Oral Sciences Research Group, College of Medical, Veterinary and Life Sciences, Glasgow University, Glasgow, UK; Glasgow Biofilm Research Network, Glasgow, UK WEI WEI • Department of Neurosurgery, Zhongnan Hospital, Wuhan University, Wuhan, China; Department of Neurosurgery and Brain Research Center, Zhongnan Hospital, Wuhan University, Wuhan, China PHILLIPA C. WHITE • Birmingham Dental School and Hospital, University of Birmingham, Birmingham, UK DAVID T. W. WONG • Center for Oral and Head/Neck Oncology Research, UCLA School of Dentistry, University of California at Los Angeles, Los Angeles, CA, USA; Section of Biosystems and Function, UCLA School of Dentistry, University of California at Los Angeles, Los Angeles, CA, USA; UCLA’s Jonsson Comprehensive Cancer Center, Los Angeles, CA, USA SETH WOODBURY • Department of Biologic and Materials Science and Division of Prosthodontics, University of Michigan School of Dentistry, Ann Arbor, MI, USA HELEN J. WRIGHT • Birmingham Dental School and Hospital, University of Birmingham, Birmingham, UK YUKAKO YAMAUCHI • Division of Restorative Dentistry and Periodontology, Dublin Dental University Hospital, Trinity College Dublin, Dublin, Ireland SOBIA ZAFAR • Sir John Walsh Research Institute, Faculty of Dentistry, University of Otago, Dunedin, New Zealand DIOGO GODOY ZANICOTTI • Sir John Walsh Research Institute, Faculty of Dentistry, University of Otago, Dunedin, New Zealand
Part I Saliva and Other Oral Fluids
Chapter 1 RNA Sequencing Analysis of Saliva exRNA Karolina Elz˙bieta Kaczor-Urbanowicz and David T. W. Wong Abstract Next-generation sequencing (NGS) methodologies are rapidly developing. However, RNA Sequencing of saliva is challenging due to low abundance and integrity of extracellular RNA, as well as large amounts of bacterial RNAs that may be encountered in saliva. In addition, the literature about human salivary extracellular RNA is very scarce. Therefore, in our chapter, we present the most appropriate protocols for saliva collection, pre- and post-processing, including bioinformatic analysis of salivary RNA Sequencing data. However, the choice of the proper method for RNA extraction, cDNA library preparation, and computational pipeline can make a significant impact on the final quality of data and their interpretation. Key words RNA Sequencing, Saliva, Salivary RNAs, Transcriptome, Bioinformatic analysis
1
Introduction RNA Sequencing (RNA-Seq) is a rapidly developing tool to transcriptome profiling that uses deep-sequencing technologies and is becoming the major tool in analyzing gene expression [1]. This is a new high-throughput method for both mapping and quantifying transcriptomes. It provides more detailed information about the levels of transcripts and their isoforms than other methods [2]. Thus, revealing the transcriptome is crucial for investigating the functional elements of the genome, the molecular constituents of cells and tissues, and also for understanding development and disease [2]. RNA-Seq has clear advantages over existing approaches. It can be used for detection of known and novel complex transcripts as well as for precise localization of transcription boundaries, to a single-base resolution [3, 4]. Having very low background signal, RNA-Seq significantly differs from microarray platforms by allowing unique mapping to the genome of interest [5]. It also enables
Gregory J. Seymour et al. (eds.), Oral Biology: Molecular Techniques and Applications, Methods in Molecular Biology, vol. 2588, https://doi.org/10.1007/978-1-0716-2780-8_1, © The Author(s), under exclusive license to Springer Science+Business Media, LLC, part of Springer Nature 2023
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Karolina Elz˙bieta Kaczor-Urbanowicz and David T. W. Wong
identification of different transcript variants and quantification of allele-specific expression within an individual [6–8]. Finally, it has an accurate and large dynamic range of expression levels with high reproducibility [3, 5]. However, there are also some problems associated with RNASeq technology, specifically with RNA isolation and complementary DNA (cDNA) library construction as they include several manipulation stages, thus introducing technical noise [2, 8]. In addition, to be compatible with most deep-sequencing technologies, longer RNAs have to be fragmented into smaller pieces (200–500 bp) by including a number of steps (RNA fragmentation, cDNA synthesis, adapter ligation, etc.) that might introduce biases into the resulting data [5, 8, 9]. RNA-Seq also brings bioinformatic challenges associated with storage, retrieval, and processing of large amounts of data. For example, the alignment of long RNA-Seq reads can be complicated due to nonunique mapping to multiple locations in the genome. Furthermore, the detection of rare transcripts requires costly deeper sequencing [2]. In addition, high abundance of ribosomal RNA (rRNA) in total RNA imposes the process of selective sequencing of poly-A-tailed mRNA transcripts in eukaryotic cells or depletion of rRNA [8, 10]. Poly(A) selection typically requires a relatively high proportion of mRNA with minimal degradation. However, many biological samples (such as tissue biopsies) cannot be obtained in adequate quantity or good mRNA integrity to produce good poly (A) RNA-seq libraries and therefore require rRNA depletion such as in the case of salivary rRNA [8, 9]. Although RNA-Seq is still in the early stages of use, it has clear advantages over previously developed transcriptomic methods such as microarray profiling [2]. It has a wide variety of applications, but no single analysis pipeline can be used for all biofluids [8]. Specifically, RNA-Seq of saliva is challenging, including difficult RNA isolation step as well as laborious RNA-Seq library construction stage, inclusion of spike in standards and controls, sequencing of data, and data storage and analysis [11–13]. The literature about characterization of human salivary extracellular RNA by NGS is scarce [10, 14–16]. Therefore, in our chapter, we present a protocol for saliva collection, pre- and postprocessing, for RNA-Seq methods.
2 Available Methods for Saliva Collection, Pre- and Post-processing, for RNASequencing 2.1
Saliva Collection
After unstimulated human saliva collection by spitting method, the samples should be thawed, centrifuged at 2600 g for 15 min at 4 C, stored at 80 C until further analysis, and treated for the concurrent stabilization of proteins and RNA by the inclusion of a
RNA Sequencing Analysis of Saliva exRNA
5
protease inhibitor cocktail (aprotinin, PMSF, and sodium orthovanadate) and RNase inhibitor (SUPERaselIn; Ambion, Austin, TX) based on the published saliva standard operating procedure (SOP) [17, 18]. 2.2 RNA Extraction Methods
Most of the commercial kits used for RNA isolation provide RNA yield diluted in big volumes. Thus, if needed small volume for further analysis, RNA of low concentration prevents adequate library construction and receiving RNA-Seq reads of good quality. Therefore, it is essential that all the preparation stages before sequencing will be performed at the highest level. Specifically, saliva requires special attention since RNA load in saliva is much lower than in blood. Currently, a wide variety of commercial kits are available for RNA isolation for NGS. They are based on organic extraction, silica-membrane-based spin column technology, or paramagnetic particle technology [15]. However, each of these methods has some advantages as well as disadvantages. For example: the phenol-Guanidine Isothiocyanate (GITC)-based organic extraction is often much more contaminated with proteins and other cellular materials, organic solvents, as well as with DNA, compared to silica column and paramagnetic particle-based RNA isolation systems [16]. However, the latter ones can also cause DNA contamination [15]. In the recent publication, Li et al. [19] compared several commercially available kits for total RNA isolation from cell-free saliva (CFS) including phenol-based [miRNeasy micro Kit (Qiagen), TRIzol® Plus RNA Purification Kit (Invitrogen), and mirVana miRNA Isolation Kit (Ambion)] as well as non-phenol-based kits [Quick-RNA™ MicroPrep kit (Zymo Research), QIAamp Viral RNA Mini Kit (Qiagen), and NucleoSpin miRNA (MachereyNagel)]. TRIzol® Plus RNA Purification Kit (Invitrogen) utilizes organic extraction, whereas QIAamp Viral RNA Mini Kit (Qiagen) and NucleoSpin miRNA (Macherey-Nagel) are based on silicamembrane spin column technology. Specifically, the TRIzol® Plus RNA Purification Kit (Invitrogen) uses Trizol, a GITC-containing chaotropic lysis-buffer premixed with phenol for precipitation of nucleic acids from the sample, and uses RNase-free DNase for the subsequent RNA purification. The miRNeasy micro Kit (Qiagen) utilizes phenol/guanidine-based lysis of samples and silicamembrane-based purification of total RNA. In turn, the QuickRNA™ MicroPrep kit (Zymo Research) combines a unique buffer system with Zymo-Spin column, while mirVana miRNA Isolation Kit (Ambion) employs organic extraction and spin column technology (glass fiber filter) [20]. It appeared that the use of the miRNeasy Micro Kit (Qiagen) (total RNA yield ¼ 31 ng) and the NucleoSpin miRNA kit (Macherey-Nagel) (total RNA yield ¼ 32 ng) seemed to be the best methods for human CFS
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NGS, producing RNA yield at least 3-fold higher than the other methods. Specifically, the recovery of miRNAs and piRNAs was the highest in the case of the miRNeasy micro Kit (Qiagen) [100 copies/μL for miRNA148a, 84 copies/μL for miRNA21, 100 copies/ μL for miRNA26a, and 100 copies/μL for piR-001184] and NucleoSpin miRNA (Macherey-Nagel) [100 copies/μL, 100 copies/μL, 49 copies/μL, and 54 copies/μL]. The Quick-RNA MicroPrep kit (Zymo Research) (total RNA yield ¼ 9 ng) and the QIAamp Viral RNA Mini Kit (Qiagen) (total RNA yield ¼ 11 ng) revealed low RNA recovery [19]. 2.2.1 RNA Quantification and Quality Controls (QCs)
For assessing RNA quality and yield, A260/A280 and A260/A230 ratios for RNA preparation samples should be analyzed with a Nano-Drop® ND-1000 spectrophotometer (NanoDrop Technologies). An A260/A280 ratio >1.8 indicates good quality RNA with a low level of DNA and protein contamination [21, 22]. An A260/ A230 ratio >1.8 denotes RNA with a low level of polysaccharide contamination. In addition, quantification of total RNA yield and assessment of its integrity can be done with a 2100 Bioanalyzer (Agilent Systems). Once the total RNA amount is >5 ng, it is recommended to proceed with the next step, which is library construction (RNA yield should be about 50–80 ng/mL CFS) [13, 19].
2.3 cDNA Library Construction Methods
Similarly, there are known several cDNA library construction kits to RNA-Seq [10, 14] including systems with lost RNA sense and antisense strand information (non-stranded protocols) or with preserved strand information (stranded protocols). Non-stranded protocols generally are less expensive and faster, but may lose antisense transcription information [23]. Li et al. [19] also evaluated the performance of various cDNA library generation methods for RNA-Seq including three for long RNA library generation [NEBNext Ultra Directional RNA Library Prep kit for Illumina® (NEBNext), SMARTer Stranded RNA-Seq Kit (Clontech), and Ovation® RNA-Seq System V2 (Nugen)] (Table 1) and two for small ncRNA library preparation [NEBNext® Multiplex Small RNA library Prep Set for Illumina® (NEBNext) and Ovation® Ultralow Library System V2 (Nugen)] [19]. They decided to investigate those systems because of their efficiency in library yield recovery in case of biofluids with very low amounts of RNA such as in CFS [19]. For each library, 500 ng of total RNA should be used as an input. Importantly, a predefined amount of synthetic spike-in RNAs should also be added into each RNA sample as internal standards and normalization of data across different samples. The comparison of those kits revealed that the NEBNext cDNA library preparation methods performed best (5649–6813 long RNAs in human CFS (1 RPKM threshold)), yielded the
RNA Sequencing Analysis of Saliva exRNA
7
Table 1 Comparison of different cDNA long library preparation kits NEBNext Ultra Directional cDNA library RNA Library Prep kit for generation kit Illumina® [NEBNext]
SMARTer Stranded RNA-Seq The Ovation® RNA-Seq Kit [CLONTECH] (Clontech, System V2 [NUGEN] (NuGEN, San Carlos, CA) Mountain View, CA)
cDNA synthesis
Random primers
Random and oligo (dT) primers
Fragmentation RNA by heat method
RNA by heat
cDNA by Covaris shearing (ultrasonication)
Strand selection
Yes
Yes
No
Size of RNAs
Long RNAs
Long RNAs (150–1000 bases)
Long RNAs (200–1500 bases)
RNA input
Total RNA (5 ng to 1 μg)
1 ng depleted RNA (to 100 ng)
0.5 ng depleted RNA (500 pg to 100 ng)
rRNA depletion required
Yes
No
Yes
Random and oligo (dT) primers
highest transcriptional coverage of short and long RNAs, while the Ovation RNA-Seq V2 systems and SMARTer Stranded (only 1471–6156 long RNAs) library preparation methods seemed to provide much fewer promising results – therefore they should not be recommended for CFS [19]. In turn, the small RNA library was composed of mainly bacterial exRNAs (~59%) including 6% miRNAs and 7% piRNAs. The results for short RNAs were: 482–696 miRNAs and 190–214 other small RNAs for NEBNext® Multiplex Small RNA library Prep Set for Illumina® (NEBNext), while for Ovation® Ultralow Library System V2 (Nugen)] the numbers were 453–644 and 152–212, respectively [19]. 2.4 Sequencing and Bioinformatic Analysis of RNA-Seq Data 2.4.1 Library Quality Control and Sequencing
Libraries may be quantified by qPCR using the Qubit® dsDNA BR Assay Kit (Invitrogen, Carlsbad, CA, USA) and assessed using the DNA High Sensitivity LabChip kit on an Agilent Bioanalyzer. Small RNA library is expected to have a major peak of 140–200 bp, whereas long RNA library to have a major peak of 300–400 bp. Libraries may be sequenced on HiSeq2000 Illumina System using 150 base-length read chemistry in a paired-end mode. Compared to other human biofluids, saliva is unique, as it contains abundant microbial species, while most other physiological fluids (blood, etc.) are considered to be sterile or to contain just a few bacteria [12]. Therefore, only recently, different approaches to bioinformatic analysis of RNA-Seq data in human saliva were developed [24].
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After the first quality control (QC) of raw reads (i.e., FastQC, RSeQC, etc.), the initial steps of analyzing salivary RNA-Seq data involve trimming adapters, filtering out contaminants, ribosomal RNAs (rRNAs), and calibration. For long RNAs, the next step includes the alignment to the specific genomes of interest [24]: l
For identification of microbial exRNAs, the first step should follow the alignment of the reads to the human genome (i.e., hg38) and subsequently aligning the unmapped reads to the bacterial genomes (i.e., 16S rRNA, HOMD, etc.);
l
For identification of human exRNAs, the reads should first be aligned to microbial genomes (i.e., 16S rRNA and HOMD), then removed and the unmapped reads aligned to the human genome (i.e., hg38).
In the case of short RNAs, there are currently three proposed different approaches to follow:
2.4.2
Alignment Settings
2.5 Uniqueness of Salivary RNA-Seq Analysis
l
Indirect mapping to bacterial genome(s) (including removing the previously mapped human RNA-Seq reads)
l
Direct mapping to bacterial genome(s) (without previous alignment to human genome)
l
Direct mapping to transcriptome (i.e., miRBase, piRNABank, etc.)
Various aligner platforms (Bowtie1, Bowtie2, TopHat, Star, HISAT2, etc.) enable different ways of setting the stringency for the mapping parameters such as in the case of Bowtie1, it is possible to limit the number of mismatches either in the entire read region (option -v) or only in the seed region (option –n). Increasing the mapping stringency noticeably decreases the number of the mapped reads, but, simultaneously, reduces the error rate in the base alignment. However, if too many reads are left without RNA database annotations (intergenic alignments), it can lead to misleading results such as DNA contamination. Processing human saliva samples for RNA studies revealed a unique property of saliva, compared to other biofluids due to low RNA abundance, low integrity of extracellular RNA (exRNA), and large amounts of exogenous RNA-Seq reads, mostly bacterial RNAs (90.4%), including Proteobacteria (34.8%) and Firmicutes (24%). Among the most abundant exogenous species present in human saliva were Rothia mucilaginosa (16711 RPM), Rothia aeria (7605 RPM), and Streptococcus sanguinis (7136 RPM) [24]. Among other exogenous species present in human saliva are commonly reported Neisseria flavescens and Streptococcus salivarius (67.3 8.8%) [11]. Furthermore, Veillonella parvula, Prevotella melaninogenica, Fusobacterium periodonticum, and Streptococcus mitis were also very prevalent in human saliva [25, 26].
RNA Sequencing Analysis of Saliva exRNA
9
Lastly, the vast majority of total RNA yield in human CFS constituted rRNA, which is consistent with the current literature (80–90% of total RNA) [1, 27]. rRNA provides little information about the transcriptome. Thus, removing rRNA enables a significant increase in the gene coverage and better detection of proteincoding genes, noncoding RNAs, snRNAs, snoRNAs, etc. [28]. There are many methods for performing rRNA depletion through enrichment of a non-ribosomal fraction, digestion of highly abundant transcripts, amplification of a non-rRNA fraction, etc. [1, 27]. In the human salivary RNA study, optimized rRNA depletion resulted in significantly greater sensitivity of human gene detection (41% human reads with rRNA depletion vs. 14% human reads without rRNA depletion), including human lncRNAs (13% with versus 5% without rRNA depletion) [19]. To conclude, the choice of the suitable and most reliable method for performing RNA isolation and cDNA library preparation may greatly influence the quality of recovered data as well as affect the final results and interpretation of transcriptomic data. This chapter provides guidelines for choosing the proper methods and protocols for salivary RNA-Seq studies.
Acknowledgments This work was supported by the Public Health Service (PHS) grant from the National Institutes of Health (NIH): UH3 TR000923, UG3/UH3 TR002978, 5 R25 DE030117, SEED grant from the UCLA Jonsson Comprehensive Cancer Center / Ali Jassim Family Cancer Research Fund (Y.K.), the QCBio Collaboratory Fellowship 2019-2022 from the Institute for Quantitative & Computational Biosciences at the University of California, Los Angeles (K.E. K-U), and UCLA Jonsson Comprehensive Cancer Center’s (JCCC) Postdoctoral Fellowship Award (K.E.K-U). Conflicts of Interest David Wong is consultant to GlaxoSmithKlein, PeriRx, Wrigley, and Colgate-Palmolive. David Wong holds equity in RNAmeTRIX Inc. and Liquid Diagnostics LLC. The University of California also holds equity in RNAmeTRIX. None of the other authors have a conflict of interest in relation to this study.
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References 1. Choy JY, Boon PL, Bertin N, Fullwood MJ (2015) A resource of ribosomal RNA-depleted RNA-Seq data from different normal adult and fetal human tissues. Sci Data 2:150063 2. Wang Z, Gerstein M, Snyder M (2009) RNA-Seq: a revolutionary tool for transcriptomics. Nat Rev Genet 10:57–63 3. Cloonan N et al (2008) Stem cell transcriptome profiling via massive-scale mRNA sequencing. Nat Methods 5:613–619 4. Morin R et al (2008) Profiling the HeLa S3 transcriptome using randomly primed cDNA and massively parallel short-read sequencing. Biotechniques 45:81–94 5. Mortazavi A, Williams BA, McCue K, Schaeffer L, Wold B (2008) Mapping and quantifying mammalian transcriptomes by RNA-Seq. Nat Methods 5:621–628 6. Pastinen T (2010) Genome-wide allele-specific analysis: insights into regulatory variation. Nat Rev Genet 11:533–538 7. Kumasaka N, Knights AJ, Gaffney DJ (2016) Fine-mapping cellular QTLs with RASQUAL and ATAC-seq. Nat Genet 48(2):206–213 8. Conesa A, Madrigal P, Tarazona S, GomezCabrero D, Cervera A, McPherson A, Szczes´niak MW, Gaffney DJ, Elo LL, Zhang X, Mortazavi A (2016) A survey of best practices for RNA-seq data analysis. Genome Biol 17:13 9. Nagalakshmi U et al (2008) The transcriptional landscape of the yeast genome defined by RNA sequencing. Science 320:1344–1349 10. Head SR, Komori HK, LaMere SA, Whisenant T, Van Nieuwerburgh F, Salomon DR et al (2014) Library construction for nextgeneration sequencing: overviews and challenges. Biotechniques 56:61–64 11. Takeshita T, Kageyama S, Furuta M, Tsuboi H, Takeuchi K, Shibata Y, Shimazaki Y, Akifusa S, Ninomiya T, Kiyohara Y, Yamashita Y (2016) Bacterial diversity in saliva and oral healthrelated conditions: the Hisayama Study. Sci Rep 24(6):22164 12. Yeri A, Courtright A, Reiman R, Carlson E, Beecroft T, Janss A, Siniard A, Richholt R, Balak C, Rozowsky J, Kitchen R, Hutchins E, Winarta J, McCoy R, Anastasi M, Kim S, Huentelman M, Van Keuren-Jensen K (2017) Total extracellular small RNA profiles from plasma, saliva, and urine of healthy subjects. Sci Rep 17(7):44061 13. Majem B, Li F, Sun J, Wong DT (2017) RNA sequencing analysis of salivary extracellular RNA. Methods Mol Biol 1537:17–36
14. Levin JZ, Yassour M, Adiconis X, Nusbaum C, Thompson DA, Friedman N, Gnirke A, Regev A (2010) Comprehensive comparative analysis of strand-specific RNA sequencing methods. Nat Methods 7:709–715 15. Tavares L, Alves PM, Ferreira RB, Santos CN (2011) Comparison of different methods for DNA-free RNA isolation from SK-N-MC neuroblastoma. BMC Res Notes 4:3 16. Esser KH, Marx WH, Lisowsky T (2005) Nucleic acid-free matrix: regeneration of DNA binding columns. Biotechniques 39: 270–271 17. St John MA, Li Y, Zhou X, Denny P, Ho CM, Montemagno C, Shi W, Qi F, Wu B, Sinha U, Jordan R, Wolinsky L, Park NH, Liu H, Abemayor E, Wong DT (2004) Interleukin 6 and interleukin 8 as potential biomarkers for oral cavity and oropharyngeal squamous cell carcinoma. Arch Otolaryngol Head Neck Surg 130(8):929–935 18. Henson BS, Wong DT (2010) Collection, storage, and processing of saliva samples for downstream molecular applications. Methods Mol Biol 666:21–30 19. Li F, Kaczor-Urbanowicz KE, Sun J, Majem B, Lo HC, Kim Y, Koyano K, Rao SL, Kang SY, Kim SM, Kim KM, Kim S, Chia D, Elashoff D, Grogan TR, Xiao X, Wong DTW (2018) Characterization of human salivary extracellular RNA by next-generation sequencing. Clin Chem 64(7):1085–1095 20. Fromm B, Harris PD, Bachmann L (2011) MicroRNA preparations from individual monogenean Gyrodactylus salaris-a comparison of six commercially available totalRNA extraction kits. BMC Res Notes 4:217 21. Sambrook J, Fritsch EF, Maniatis T (1989) Molecular cloning: a laboratory manual. Cold Spring Harbor Press, New York 22. Manchester KL (1996) Use of UV methods for measurement of protein and nucleic acid concentrations. Biotechniques 20:968–970 23. Zhu YY, Machleder EM, Chenchik A, Li R, Siebert PD (2001) Reverse transcriptase template switching: a SMART approach for fulllength cDNA library construction. Biotechniques 30:892–897 24. Kaczor-Urbanowicz KE, Kim Y, Li F, Galeev T, Kitchen RR, Gerstein M, Koyano K, Jeong SH, Wang X, Elashoff D, Kang SY, Kim SM, Kim K, Kim S, Chia D, Xiao X, Rozowsky J, Wong DTW (2018) Novel approaches for bioinformatic analysis of salivary RNA sequencing data for development. Bioinformatics 34(1):1–8
RNA Sequencing Analysis of Saliva exRNA 25. Diaz PI, Dupuy AK, Abusleme L, Reese B, Obergfell C, Choquette L, DongariBagtzoglou A, Peterson DE, Terzi E, Strausbaugh LD (2012) Using high throughput sequencing to explore the bio-diversity in oral bacterial communities. Mol Oral Microbiol 27: 182–201 26. Pereira JV, Leomil L, RodriguesAlburquerque F, Pereira JO, Astolfi-Filho S (2012) Bacterial diversity in the saliva of patients with different oral hygiene indexes. Braz Dent J 23:409–416
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27. O’Neil D, Glowatz H, Schlumpberger M (2013) Ribosomal RNA depletion for efficient use of RNA-seq capacity. Curr Protoc Mol Biol 2013:Chapter 4:Unit 4.19. https://doi.org/ 10.1002/0471142727.mb0419s103 28. Cui P, Lin Q, Ding F, Xin C, Gong W, Zhang L, Geng J, Zhang B, Yu X, Yang J, Hu S, Yu J (2010) A comparison between ribo-minus RNA-sequencing and polyAselected RNA-sequencing. Genomics 96: 259–265
Chapter 2 Proteome Analysis of Oral Biofluids in Periodontal Health and Disease Using Mass Spectrometry Nagihan Bostanci and Kai Bao Abstract Mass spectrometry-based proteomic approaches permit the high-throughput assessment of proteins from oral biofluids, therefore, allowing a deeper insight into the mechanistic study of periodontal disease. Here we describe an entire experimental design of proteomic workflow for oral biofluids, exemplified by saliva and gingival crevicular fluid collected from periodontal health or disease subjects and using a label-free quantification strategy for mass spectrometric data acquisition. Key words Saliva, Gingival crevicular fluid, Proteomics, Label-free quantification, Periodontal disease, Mass spectrometry
1
Introduction Periodontal diseases continue to act as “silent epidemics” and are the sixth most prevalent diseases worldwide, with a heavy burden to human health and national health systems with high socioeconomic impact [1]. Periodontal diseases span from gingivitis to advanced periodontitis, where there is loss of bone and connective tissues that support the teeth. The early detection of periodontitis is essential in terms of disease prevention, and in this context, the monitoring of proteins in oral biofluids has attracted much interest. Saliva and gingival crevicular fluid (GCF) are considered as the most applicable fluids for protein biomarker discovery due to their noninvasive, painless collection, and ready availability for real-time monitoring. GCF is a serum transudate or an inflammatory exudate that can be collected in as little as 30 s from both healthy and diseased tissues. Although GCF was the first choice as a potential diagnostic aid in periodontics, due to practical difficulties related to its collection in the whole mouth, saliva became a more favorable matrix for rapid screening of periodontal diseases [2].
Gregory J. Seymour et al. (eds.), Oral Biology: Molecular Techniques and Applications, Methods in Molecular Biology, vol. 2588, https://doi.org/10.1007/978-1-0716-2780-8_2, © The Author(s), under exclusive license to Springer Science+Business Media, LLC, part of Springer Nature 2023
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The vast amount of information on the protein content of GCF and saliva has come from candidate protein analysis using antibody detection-based methods including conventional ELISAs and in more recent years via multiplexed immunoarrays [3–6]. Despite the great capacity of multiplex immunoassays to investigate the protein composition of saliva and GCF, these approaches have provided only a tiny glance into the complex proteins that exist in these complex biofluids. The rapid innovations in mass spectrometry (MS) techniques and bioinformatics advances have altered the existing protein identification and quantification paradigm in oral biofluids [7–9]. The majority of studies of GCF and saliva are based on “bottom-up” proteomic strategies, performing analysis at the peptide level generated by proteolytic digestion of intact proteins [10]. It combines the use of high-performance liquid chromatography to separate highly complex peptide mixtures and the subsequent identification of their sequences [11]. MS-based proteomic analyses of saliva and GCF, similar to other biological fluids, are technically demanding due to the dynamic range of protein concentrations spanning several orders of magnitude [8]. While depletion of the abundant proteins might help unmask the low-abundance proteins, it may also result in co-removal of several others that are structurally bound to those removed [12]. Moreover, pre-analytical processing of the samples, i.e., the collection method, sampling duration, and sample storage, may have a significant impact on the proteome results. Salivary composition varies with age, gender, daily rhythm, hormonal alterations, and dietary habits. These limitations have to be borne in mind when collecting and analyzing saliva for proteomic applications. The selection of a proteomic method should be dictated by sample type and study design. However, one needs to note that many factors can affect sensitivity, such as sample preparation, the type of MS used, and the type of database search employed. Individual projects may benefit from the optimization of their own sample collection and preparation prior to MS analysis. In our laboratories, “bottom-up”, label-free quantitative proteomics is the preferred choice for GCF and saliva analysis since they enable the parallel analysis of large sample sizes [9, 13–15]. Nevertheless, “labelled” approaches still have some benefits as data are faster to acquire while allowing quantitation of a small sample size [16]. A typical workflow of quantitative proteomics is presented below (Fig. 1).
Proteome Analysis of Oral Biofluids in Periodontal Health and Disease. . .
15
Fig. 1 A typical workflow for label-free quantitative proteome analysis of gingival crevicular fluid (GCF) and saliva
2
Materials Prepare all solutions using Milli-Qwater (prepared by purifying deionized water, to attain a sensitivity of 18 MΩ-cm at 25 C) and analytical grade reagents. Filter-aided Sample Preparation (FASP) digestion should be carried out at room temperature unless indicated otherwise.
2.1
GCF Collection
1. GCF collection strips (PerioPaper, OraFlow Inc., Plainview, New York, USA) 2. Sterile low protein binding microcentrifuge tubes, DNAse, RNAse-free 3. Periotron 8000TM (Oraflow Inc)
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2.2
Nagihan Bostanci and Kai Bao
Qubit Assay
1. Qubit™ Protein Assay Kits (ThermoFisher Scientific) 2. Qubit® assay tubes (ThermoFisher Scientific)
2.3
FASP
1. UA: 8 M urea in 100 mM Tris/HCl pH 8.2 2. DTT solution: 0.1 M DTT in in UA 3. IAA solution: 0.05 M iodoacetamide in UA 4. 0.5 M NaCl in Water 5. TEAB: 0.05 M Triethylammoniumbicarbonate in water 6. Reverse phase cartridges solid-phase extraction (SPE) C18 (see Note 1)
2.4 Software for Proteomic Data Analysis
1. Progenesis QI for proteomics (Waters | Nonlinear Dynamics)
2.5 Equipment for Proteomic Data Analysis
1. Speed vacuum concentrator
2. Mascot Server v2.4.1 search engine (MatrixScience Ltd.) 3. Scaffold v4.0 (Proteome Software)
2. Low protein binding collection tubes 3. Bath Sonicator 4. Benchtop microcentrifuge 5. Mass spectrometers with high mass resolution capability (see Note 2) 6. Ultra-high pressure LC-systems (see Note 3) 7. Analytical HPLC column analytical nano-flow HPLC columns with C18 solid-phase (see Note 4) 8. Trap columns suited for nano-flow with reversed-phase selectivity (see Note 5)
3
Methods
3.1 Gingival Crevicular Fluid (GCF) Collection
1. Prior to GCF collection, the sampling area should be cleared of supragingival plaque isolated with cotton roles and dried by a gentle stream of air to prevent saliva contamination; 2. Gently insert sterile filter paper strips into the gingival crevice or periodontal pocket and leave in place for 30 s (see Note 6); 3. Measure GCF volume using an electronic device prior to the transfer of each strip to a separate Eppendorf tube. The readings from this electronic device are converted to an actual volume (μl) by reference to the standard curve; 4. Add GCF collection strips from each patient into an Eppendorf tube.
Proteome Analysis of Oral Biofluids in Periodontal Health and Disease. . .
3.2 GCF Supernatant Preparation
17
1. Apply 80 μL of 1X PBS per collection strip. 2. Elute GCF from the strips by shaking at 600 rpm at 4 C overnight; 3. Centrifuge the Eppendorf tube at 500 g for 60 min at 4 C to separate strips from the supernatant; 4. Transform the supernatant into a new Eppendorf tube (see Note 7); 5. Centrifuge the combined supernatant further for 10 min 13,000 g 4 C and collect supernatants; 6. Dilute the Qubit® Protein Reagent 1:200 in Qubit® Protein Buffer (see Note 8) in a plastic tube (see Note 9) each time you prepare Qubit® working solution. 7. Add 10 μL of Qubit® standard with 190 μL of Qubit® working solution; 8. Add 1–20 μL of GCF supernatant (see Note 10) with Qubit® working solution to the final volume in each tube of 200 μL; 9. Vortex the tube and spin down the suspension; 10. Incubate the tube at room temperature for 15 min, avoid light; 11. Measure the protein concentration on a Qubit fluorometer; 12. Store the supernatants in a 20 C freezer, then move them into 80 C for longer storage.
3.3 Saliva Supernatant Preparation and Total Protein Quantification
1. Collect fresh saliva for 5 min on ice in a 50 mL Flacon tube. 2. Centrifuge the samples at 3,500 g for 20 min at 4 C. 3. Aliquot the supernatants to new low-protein binding Eppendorf tubes (500 μL each). 4. Store both supernatant and pellet in
80 C for further use.
5. Before experiments, thaw one of the frozen stocks on ice and centrifuge further for 10 min at 13,000 g 4 C to collect debris-free supernatants. 6. Dilute the Qubit® Protein Reagent 1:200 in Qubit® Protein Buffer (see Note 8) in a plastic tube (see Note 9) each time you prepare Qubit® working solution. 7. Add 10 μL of Qubit® standard with 190 μL of Qubit® working solution. 8. Add 1–20 μL of saliva supernatant (see Note 10) with Qubit® working solution to the final volume in each tube of 200 μL. 9. Vortex the tube and spin down the suspension. 10. Incubate the tube at room temperature for 15 min, avoid light. 11. Measure the protein concentration on a Qubit fluorometer. 12. Store supernatant in
20 C freezer.
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3.4 FASP Digestion for the GCF or Saliva Supernatant
1. Centrifuge the supernatant further for 15 min 13,000 g. 2. Aliquot sufficient sample with 20 μg (see Note 11) of proteins into a new Eppendorf tube. 3. Incubate samples at 95 C, and mix at 900 rpm in a Thermomixer (Eppendorf) for 5 min (see Note 12). 4. Chill the tubes on ice for 40 s. 5. Vortex the tube and spin down the suspension. 6. Sonification for 10 min. 7. Chill the tubes at room temperature for 3 min. 8. Repeat steps 6 and 7 twice. 9. Vortex the tube and centrifuge at max speed for 20 min. 10. Aliquot supernatant with 200 μL DTT solution and load to the filter unit (see Note 13). 11. Centrifuge the filter unit at 14,000 g for 20 min at room temperature or 35 C. 12. Discard flow-through. 13. Add 200 μL of UA to the filter unit. 14. Centrifuge the filter unit at 14,000 g for 17 min at room temperature or 35 C. 15. Discard flow-through. 16. Add 100 μL IAA solution to the filter unit. 17. Mix the filter unit at 600 rpm in the thermomixer for 1 min. 18. Incubate without mixing for 5 min. 19. Centrifuge the filter unit at 14,000 g for 17 min at room temperature or 35 C. 20. Add 100 μL of UA to the filter unit. 21. Centrifuge the filter unit at 14,000 g for 17 min at room temperature or 35 C. 22. Repeat steps 21 and 22 two more times. 23. Add 100 μL of 0.5 M NaCl to the filter unit. 24. Centrifuge the filter unit at 14,000 g for 17 min at room temperature or 35 C. 25. Repeat step 25 one more time (see Note 14). 26. Transfer the filter units to new collection tubes for the trypsin digestion step. 27. Resuspend 0.4 ug (see Note 15) trypsin in 120 μL TEAB and load this 120 μL mixture to filter unite. 28. Mix at 600 rpm in the thermomixer for 1 min. 29. Digest proteins by incubating the units overnight on room temperature;
Proteome Analysis of Oral Biofluids in Periodontal Health and Disease. . .
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30. Centrifuge the filter units at 14,000 g for 15–20 min (see Note 16). 31. Acidify the digested peptides with 5%TFA Solution to a final concentration of 0.5% TFA in new Eppendorf tubes. 32. Make sure the final solution is acidic (see Note 17). 33. Dilute samples with 3% ACN, 0.1% TFA into 500 μL. 34. Activate C18 columns by load 1 mL 100% MeOH. 35. Equilibrate columns by load 1 mL 60% ACN, 0.1% TFA. 36. Equilibrate columns by load 2 mL 3% ACN, 0.1% TFA. 37. Load samples on the columns, collect flow-through, and load it on columns again. 38. Wash columns by load 6 mL 3% ACN, 0.1% TFA. 39. Elute samples from columns with 500 μL 60% ACN, 0.1% TFA. 40. Dry samples on a speed vacuum (see Note 18). 3.5 LC-MS/MS Analysis
1. Resuspend pellet with 30 μL of 3% ACN, 0.1% FA. 2. Sonicate sample in a water bath for 10 min. 3. Spin down sample at 14,000 g for 5 min. 4. Inject 2–4 μL of each pooled sample into the HPLC system. 5. LC-MS/MS settings: l
3.6 Protein Search, Identification, and Label-Free Quantification
Gradients on trap column: 15 μL/min flow rate, pressure limit: 0–14,000 psi
l
Gradients on analytical column: 2–24% of ACN containing 0.1% formic acid for 80 min at 300 nL/min; 24–36% of ACN containing 0.1% formic acid for 10 min at 300 nL/ min; 36–95% of ACN containing 0.1% formic acid for 5 min at 300 nL/min
l
Electrospray voltage of 1.9 kV in the positive ion mode
l
MS acquisition method: Acquisition in the data-dependent acquisition (DDA) mode using the Orbitrap analyzer, isolation of top 20 precursors in a survey mass spectrum at 120,000 mass resolution in the range of m/z 300–1,500, maximum allowed injection time of 500 ms, dynamic exclusion of 10 ppm for 25 s, MS2 isolation width of 1.6 m/z units with HCD of 30% at mass resolution of 50,000, and maximum injection time of 80 ms with a single scan
1. Label-free quantification is performed using the ProgenesisQI software V4.0 (Nonlinear Dynamics, UK). l
“Reviewing alignment” – The pool of all tryptic digests is used as a reference for alignment.
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– The alignment is generated automatically; however, experiments with alignment scores 30 mins. 10. Quantify gDNA using nanodrop and Qubit dsDNA assay kit to obtain concentration and OD 260/080 (see Note 12).
3.4 16S rRNA Gene Amplification and Sequencing
1. Hypervariable regions V3–V4 of 16S are amplified from the salivary gDNA using unique primers (GeneWiz patent) (see Note 13). A barcode adaptor sequence for the Illumina sequencing platform is added to the end of the PCR product to create the amplicons. 2. The library is purified with magnetic beads and fragment size detected by agarose gel electrophoresis (~500bp). 3. 10 nM of the library is subject to PE250/FE300 paired-end sequencing using Illumina MiSeq/Novaseq (Illumina). 4. The MiSeq Control Software (MCS)/Novaseq Control Software (NCS) is used to process all raw sequencing data. 5. Data analysis: (i) Quality filter the reads with larger than 200 bp by removing barcodes, primers, ambiguous bases (Ns), and chimeric amplicons.
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(ii) Amplicon sequences are clustered and operational taxonomic units (OTU) picked by VSEARCH clustering sequence 1.9.6 (sequence similarity is set to 97%) against 16 s rRNA reference database Silva 132. (iii) The final taxonomic assignment is performed using Ribosomal Database Program (RDP) classifier using the Bayesian algorithm for OTU species taxonomy analysis. (iv) Alpha diversity index – Shannon, Chao1, and beta diversity indexes – principal components analysis (PCA), principal coordinates analysis (PCoA), and NMDS (nonmetric multidimensional scaling) are analyzed using weighted unifrac analysis. 3.5 Salivary Total RNA Isolation Using Trizol and ABI PicoPure Columns
1. 500 μL of Trizol is added to 200 μL saliva and vortexed for 1 min 2. 0.2 volume of chloroform is added and vortexed for 1 min prior to 14,000 g centrifuge for 15 mins at 4 C. 3. The aqueous layer is transferred to a new Eppendorf tube and an equal volume of RNAse-free 70% Ethanol added. 4. 300 μL of the supernatant/70% EtOH mix is transferred to a preconditioned PicoPure column for RNA binding on the column membrane. 5. The column is treated with DNase and Proteinase K to remove DNA and protein contamination. 6. RNA is eluted from the column and stored RNA in –80 C freezer. 7. RNA quality and quantity is determined using Nanodrop and Qubit HS RNA assay. 8. Bioanalyzer is used to assess the RNA integrity with an RNA integrity number (RIN) >6.5 for salivary RNA (see Fig. 2 and Notes 14 and 15).
[FU] 4000
RIN: 7.8
200
2000 1000
100
500 200 25
0 25
200
1000
4000
[nt]
Fig. 2 An example of bioanalyzer analysis of salivary RNA quality from Trizol + ABI PicoPure kit, with a RIN value at 7.8
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3.6 Salivary RNA Library Preparation and Illumina RNASequencing
1. Starting material: 250 pg – 100 ng of total RNA. TAKARA SMRTer Pico V4 RNA-seq kit allows 250 pg – 10 ng of low salivary RNA as start material for RNA library preparation. 2. Ribosomal RNA is depleted from total RNA using Ribo-Zero magnetic beads. 3. rRNA-depleted RNA is purified and fragmented to ~200 bp using an ultrasonicator. 4. A 30 SR adaptor is ligated to 30 of RNA and then SR RT primer hybridized to the excess of 30 SR adapters (to avoid the adapterdimer formation and transform single-stranded DNA adaptor to a double-stranded DNA molecule) (see Note 16). 5. The 50 Illumina SR adapter is ligated to RNA to perform reverse transcription to obtain a cDNA library. 6. The cDNA library is amplified using the Illumina primers (set 1 primer has 12 different indices) provided with this kit. 7. After cDNA library amplification, quality control (QC) is performed using AMPure XP beads (1:1.3 ratio; see Note 17). 8. Each library is quantified using an Agilent Bioanalyzer and Qubit Fluorometric quantification using a dsDNA highsensitivity kit (see Note 18). 9. RNA-seq libraries are sequenced using the Illumina HiSeq 2000 (Illumina) instrument with 150 bp paired-end reads (PE150) at GENEWIZ. 10. Raw data of FastQ file for an average of 10–12 libraries per lane of HiSeq2000 with 10–12 indices. 11. Data analysis: (i) The TopHat package53 is used to align the reads to the hg19 reference genome, followed by HTseq and DESEQ2 for transcript assembly and quantification of RNA expression. (ii) Based on these raw count tables, edgeR is adopted to perform the differential expression analysis between groups. (iii) EdgeR uses a trimmed mean of M values to compute scale factors for library size normalization. Genes with counts per million >1 in at least three samples were kept for downstream analysis. (iv) Differentially expressed genes between two groups are identified when the false discovery rate was 5 were considered as differentially methylated peaks between conditions. 3.10 Salivary Proteome Using Liquid ChromatographyTandem Mass Spectrometry (LC-MS/ MS)
1. The proteome method is adopted from our recent published protocol [15] (see Notes 25 and 26). 2. Salivary protein is isolated using protein lysis buffer and the quantification is determined using a Pierce™ BCA Protein Assay Kit. 3. 100 μg samples are reduced with 10 mM DTT, alkylated with 20 mM IAA and digested with 2 μg trypsin for 16 h at 37 C (see Note 27). 4. The digested samples are dried and resuspended in 50 μL of loading buffer. 5. The 10 μL samples are injected onto a peptide trap for pre-concentration and desalted with 0.1% formic acid, 2% ACN, at 5 μL/min for 10 min. 6. The peptide trap is then switched into line with the analytical column. 7. Peptides are eluted from the column using a linear solvent gradient with steps, from 98% Buffer A (0.1% formic acid) and 2% Buffer B (99.9% acetonitrile, 0.1% formic acid) to 90% Buffer A and 10% Buffer B for 10 min, then to 65% Buffer A and 35% Buffer B at 550 nL/min over a 100 min period. 8. The eluent is subject to positive ion nanoflow electrospray MS (Triple TOF 5600, AB Sciex) analysis in an informationdependant acquisition mode (IDA) at m/z 350–1200 for 0.25 s. 9. MS/MS spectra are accumulated for 100 ms (m/z 100–1500) with rolling collision energy. 10. Data analysis: (i) The LC-MS/MS data are searched using ProteinPilot V4.2 (AB Sciex) in thorough mode against the Swissprot Rat Database. (ii) Data are extracted using PeakView. Shared peptides were excluded. (iii) After data processing, peptides (max 50 peptides per protein) with confidence >99% and FDR 100,000 basepair) sequence reads concomitant with an incredible reduction in cost per sequenced basepair. Unfortunately, the ultra-high sequence yields of third-generation sequencers are compromised by their inherent sequencing error rates, prompting an alternative sequencing strategy, i.e., a hybrid sequencing strategy, which combines PacBio/ ONT primary datasets with complementary datasets generated by mainstream short-read NGS platforms, e.g., Illumina or Ion Torrent. Although the concept of a hybrid sequencing strategy is not new, existing yields and accuracy of ultra-long and short-read sequencing technologies makes such a strategy achievable, resulting in complete genome sequences in one hit. In this chapter, we describe our updated laboratory and bioinformatic protocols that will allow the average research group to obtain complete oral microbial genome sequences assembled from a combination of DNA sequence data generated by NGS and thirdgeneration platforms. Key words Oral bacterial genome sequencing, Streptococcus, Genomic DNA purification, Nanopore sequencing, Semiconductor (Ion Torrent) sequencing, Single-molecule real-time (SMRT) sequencing, Illumina, Bioinformatics, Linux, SPAdes genome assembler, RAST gene annotation software
1
Introduction The human oral cavity is home to >700 microbial species, collectively known as the oral microbiota [1]. Although many bacterial species are culturable in the laboratory and can be studied to great detail, much is still unknown of the functions and activities of most, if not all, oral species. One of the most direct steps to redress the paucity of information regarding the activities of oral microbes is to perform whole-genome sequencing on important species. Despite the availability of databases such as the Human Oral Microbiome
Gregory J. Seymour et al. (eds.), Oral Biology: Molecular Techniques and Applications, Methods in Molecular Biology, vol. 2588, https://doi.org/10.1007/978-1-0716-2780-8_6, © The Author(s), under exclusive license to Springer Science+Business Media, LLC, part of Springer Nature 2023
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Database (http://www.homd.org), which contains hundreds of completed genome sequences, there is much diversity in the ways microbial genomes are organized, even within the same species. It can thus be said that every molecular geneticist regards a complete genome sequence of their organism of interest as a prerequisite for further investigation, as the genome sequence serves as an exclusive reference for downstream studies that encompass (i) functional genomics, (ii) transcriptomics, (iii) proteomics, and (iv) metabolomics. Nearly 80 years ago, Avery et al. [2] reported experimental evidence supporting DNA as the genetic material of cells. Nearly a decade later, in 1953, the molecular structure of DNA was described by Watson and Crick [3]. Ever since that landmark paper, much research has been invested into finding more effective and cost-efficient ways to determine the nucleotide sequence of any DNA molecule. In 1977, two competing sequencing methods were introduced: (i) the chemical modification/cleavage method of Maxam and Gilbert [4], (ii) the dideoxynucleotide chaintermination method of Sanger et al. [5], of which the latter prevailed (now called “Sanger sequencing”). Even more than 40 years since its invention, and despite considerable advances in sequencing chemistries and instrumentation (yielding >900 basepairs (bp) of reliable sequence data) [6], Sanger sequencing is still relatively expensive and labor-intensive and is thus largely limited to confirmatory sequencing applications, e.g., of cloned DNA fragments in recombinant plasmids. Significant advancements in miniaturization technology in the new millennium meant that it was only a matter of time before cost-effective high-throughput DNA sequencing technologies would become a reality. Nevertheless, Sanger sequencing remains the ‘gold standard’ for nucleotide sequencing with unsurpassed accuracy. The first decade of the current millennium saw the introduction of the first mainstream next-generation DNA sequencing (NGS) technologies, namely pyrosequencing (454) [7], sequencing-by-synthesis (Solexa/Illumina) [8], and semiconductor sequencing (Ion Torrent) [9]. These technologies revolutionized DNA sequencing by generating unprecedented yields of sequence data (up to a billion-fold higher than Sanger) at a greatly reduced cost [6]. However, the maximum read lengths of these NGS technologies were only a fraction of that achievable by Sanger sequencing, i.e., 300–700 bp for Illumina and pyrosequencing, respectively. This read length limitation makes large genes, including those with many repeat sequences, very difficult to sequence. The introduction and establishment of third-generation sequencing (TGS) platforms, namely single-molecule, real-time (SMRT) sequencing developed by Pacific Biosciences (PacBio) and the highly innovative (‘sequencing on a USB stick’) nanopore sequencing system pioneered by Oxford Nanopore Technologies (ONT), signaled the end of the pyrosequencing era (for more detail
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on the sequencing methods, the reader is referred to the review by Athanasopoulou et al. [10]). Both PacBio and ONT took sequence read lengths to an unprecedented level, with PacBio achieving >32,000 bp per sequence and the ONT MinION system capable of generating sequences of >325,000 bp in length [11]. Despite both TGS technologies being in mainstream use, their Achilles’ heel is the sequencing error rate inherent to their respective chemistries, and both platforms are affected by homopolymer runs (i.e., runs of the same base which, if sequenced incorrectly, will lead to frameshift mutations). On the other hand, the main strength of the Illumina and Ion Torrent NGS technologies is their accuracy, and this allows them to maintain their presence in the sequencing market. At the time of writing, the Illumina NovaSeq 6000 system (https://www.illumina.com/systems/sequenc ing-platforms/novaseq.html) boasts yields of up to 6 trillion bp of sequence data. The current ability of NGS and TGS platforms to generate millions to billions of DNA sequence reads leads to the question: “how does one deal with this massive data overload?” Fortunately, advances in computer storage hardware to store the data, along with the development of faster computer processing units and algorithms to analyze gigabasepairs of data, have kept pace with NGS/TGS development. Furthermore, sophisticated assembly software such as the overlap-based MIRA [12] and De Bruijn graph-based SPAdes [13] are now able to handle huge volumes of DNA/RNA sequence data, and this is only limited by the computer hardware resources dedicated to the software. Since our chapter in the First Edition of this book [14], we have undertaken a variety of genome sequencing projects of oral microbes, employing either the high-throughput short-read Illumina and Ion Torrent systems or the ultra-long-read PacBio and ONT technologies [11, 15]. For Illumina- and Ion Torrent-based sequence data, SPAdes (currently version 3.15.4) [13] has been very useful in assembling contigs very quickly (i.e., in a matter of hours). When assembling genomes from PacBio or ONT datasets, Canu (currently version 2.2) [16] has been our algorithm of choice as it specializes in processing, correcting, and assembling ultra-long sequence reads. In this chapter, we describe our updated laboratory and bioinformatic protocols that will allow the average research group to undertake a hybrid sequencing strategy to obtain complete oral microbial genome sequences assembled from a combination of DNA sequence data generated by NGS and TGS platforms, namely Ion Torrent and ONT, respectively. While it should be noted that a plethora of genome assembly software packages are available, we have only described here those packages that are (a) appropriate for the sequence data generated, and (b) compatible with the computing resources available in our laboratory.
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Purification of Genomic DNA from Oral Microorganisms For any genome sequencing project, the quality of the genomic DNA impacts directly the overall quality of the sequence data generated. The importance of obtaining highly purified, high molecular weight genomic DNA from your strain of interest cannot be emphasized enough. Although the following steps are standard procedures in our laboratory for the cultivation and purification of genomic DNA from oral streptococci, the growth media and incubation conditions can be adapted for your organism of interest.
2.1 Growth Media and Incubation Conditions
1. Grow the bacterial strain of interest either on BHI/BHY agar or blood agar at 37 C under anaerobic conditions (see Note 1). Ensure that well-isolated colonies are obtained. 2. Transfer a single bacterial colony into a 10 mL BHI/BHY broth. Incubate overnight (16–18 h). Pre-warm and pre-reduce a 15-mL BHI/BHY broth under the same conditions. 3. The next day, inoculate the 15-mL BHI/BHY broth with 150 μL (1% inoculum) of overnight culture. Incubate the broth culture anaerobically at 37 C until an optical density at 600 nm (OD600) of 0.5–0.6 is reached (see Note 2). 4. Transfer an 850 μL aliquot of cells into a 1.5-mL cryovial. Add 150 μL of sterile 100% glycerol (final concentration of 15%) and mix by vortexing. Store the cryovial into 80 C. This is now the stock culture of the genome strain. The storage of multiple cryovials of the strain is highly recommended. 5. The remainder of the culture is used for the genomic DNA purification step.
2.2 Purification of Genomic DNA
Although there are many options available for genomic DNA purification, we have obtained consistent high-quality results using the NucleoSpin Microbial DNA Mini kit (Macherey-Nagel GmbH, Du¨ren, Germany) (see Note 3). The following protocol works especially well for oral streptococci. 1. Harvest the cells from the remainder of the culture (from the previous section) by centrifugation (8000 g for 10 min) in 15 mL Falcon centrifuge tubes (see Note 4). 2. Wash the cells twice (10-mL volumes) in sterile phosphatebuffered saline (PBS), pH 7.4, by vortexing and centrifuging as above (see Note 5). 3. Follow the DNA extraction (cell lysis) and purification steps as recommended by the manufacturer (for our modification of the NucleoSpin lysis protocol, see Note 6).
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4. Elute the genomic DNA in 100 μL of the elution buffer provided by the manufacturer. 5. Quantify the amount of genomic DNA using a microvolume spectrophotometer. Ideally, the purity of the DNA (A260/A280 ratio) should be ~1.8. 6. The purified genomic DNA is now ready for sequencing (see Note 7). For ONT- and Ion Torrent-based sequencing, 5 μg is usually sufficient. However, if the DNA is to be sequenced by the PacBio RS-II system, up to 20 μg may be required (see Note 8).
3
Post-sequencing Bioinformatics Current post-sequencing bioinformatic algorithms require that all DNA sequence data be (ideally) provided in FASTQ (“.fastq”) format with any sequencing linkers and barcodes (for multiplex sequencing) removed (see Note 9). Your DNA sequencing service should be able to provide you with the requisite “.fastq” files comprising raw (unprocessed) and filtered (linkers and barcodes removed) data. The filtered “.fastq” files will be used in the following sections. Here, we will highlight two examples: (a) assembly of a single long-read-length ONT dataset with Canu version 2.2, and (b) a hybrid assembly of ONT and short-read-length Ion Torrent sequence datasets using SPAdes version 3.15.4 (see Notes 10 and 11).
3.1 Genome Assembly Using Canu
Canu is a genome assembler that is designed for long-read-length datasets generated by platforms such as the PacBio RS-II and ONT MinION [16]. Its installation is relatively straightforward (see https://canu.readthedocs.io/en/latest/) and is available as pre-compiled packages for Linux and MacOS (Darwin) (see Note 12). In this section, we use a single ONT MinION sequencing run as an example. The basic command structure is as follows and should be typed as is (including the backslash \): canu \ -p MyFavoriteBug -d MyFavoriteBug_Canu \ genomeSize=2.0m \ -nanopore /home/FAK63811.fastq
where will activate the Canu command environment and everything after the \ will be a Canu-specific command,
canu \
will give your project a name and will save the assembly results in / MyFavoriteBug_Canu,
-p MyFavoriteBug -d MyFavoriteBug_Canu \
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Nicholas C. K. Heng and Jo-Ann L. Stanton genomeSize ¼ 2.0 m \
will give Canu an idea of how big your genome is likely to be – this parameter is optional unless you know the approximate genome size.
-nanopore /home/FAK63811.fastq is the
all-important parameter to tell Canu what type of data are being assembled and where the .fastq file containing all the sequencing reads for the project resides (see Note 13).
Canu also offers a myriad of other parameters that the user can specify, but these do not always confer any advantage with regard to assembly speed or quality of the results. Depending on the amount of input sequence data and the estimated size of the genome, the assembly can take up to 2 h, depending on the computing resources employed (see Note 14). Canu will perform the assembly and generate the reports in the MyFavoriteBug_Canu directory specified. Output files are named using the {project name} prefix, so the file containing the assembled contiguous sequences (contigs) will be saved as “MyFavoriteBug.fasta”. Each contig will be listed in standard FASTA format with a header, for example “>tig00000001 len¼9741 reads¼378”, followed by the associated nucleotide sequence. In this example, contig #1 has a length of 9741 bp comprising 378 assembled reads. Note that the order of contigs is random, i.e., the first contig may not be the largest. Post-assembly, gene annotation is carried out with the Rapid Annotation using Subsystem Technology (RAST; https://rast. nmpdr.org/) server [17], which is a fully automated annotation service for prokaryotic genomes and our annotation tool of choice. RAST can annotate whole and partial genomes and can generate the results in GenBank and other common sequence formats. We routinely use RAST to annotate our draft and final genome sequences as it gives us a good overall assessment of the genome, including %G + C content, along with any frameshifts, ribosomal RNA operons and tRNA genes, and other features (see Note 15). If applicable, RAST also features graphical representations of metabolic pathways present in the genome annotated. 3.2 How Do You Solve a Problem Like ONT? The Need for a Hybrid Sequencing Strategy
One of the biggest advantages of ONT sequencing is the extremely long read lengths generated, sometimes >100 kbp in a single sequence. However, the Achilles’ heel of ONT sequencing is the sequencing error rate, mainly due to homopolymer runs (see Note 16). Using the RAST-annotated Canu-assembled ONT-sequenced genome above (see Subheading 3.1) as an example, the first gene of the genome, dnaA (specifying the DNA replication initiation protein), manifests in several parts as evidenced by the multiple translated amino acid sequences (Fig. 1). Despite this inherent error rate, ONT-only genome assemblies are very useful as they usually provide a single genomic scaffold showing the correct order of
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A CDS
CDS
CDS
CDS
complement(1034533..1034811) /translation="MIPIEKIQTEVGKFYNVSVNEMKGSRRVQNIVLARQVAMYLARE MTDNSLPRIGREFGGKDHTTVMHAYEKIKGMIEIDDNLRLEIQTIKKN" /product="Chromosomal replication initiator protein DnaA" complement(1034865..1035068) /translation="MTQNITPPDFETRIAILRNKIEDLDFTFPDDTLEYLAGQFDSNV RDLEGALNDISLVARVKKSKILQ" /product="Chromosomal replication initiator protein DnaA" complement(1035238..1035498) /translation="MEINGLWALAVSENLATTYNPLFIYGGPGLGKTHLLNAIGNQIM ENYPNARVKYIPAESFINEFLERLRLNDMDNFKKPIETWICS" /product="Chromosomal replication initiator protein DnaA" complement(1035583..1035717) /translation="MTKVILTAGFEIYAVELTISYQFNLEEEEEEKEFVPFQKRIEII " /product="Chromosomal replication initiator protein DnaA"
B CDS
complement(755383..756753) /translation="MSREETFWNRVIELSKKTFKKEIFDYFVLTSRLIKVDQQEAIIY LDAEVKKLFWEENMTKVILTAGFEIYAVELTISYQFNLEEEEEEKEFVPLSETNRDYI VSHAPIIDLPPIQTGLRKKYTFDNFVSGDGNQWALAAALAVSENLATTYNPLFIYGGP GLGKTHLLNAIGNQIMENYPNARVKYIPAESFINEFLERLRLNDMDNFKKTYRNLDLL LIDDIQSLGGKKVTTQEEFFNTFNALYGDNKQIVLTSDRSPDHLDSLEERLVTRFKWG LTQNITPPDFETRIAILRNKIEDLDFTFPDDTLEYLAGQFDSNVRDLEGALNDISLVA RVKKIKDITIDVAAEAIRARKNEALQITVIPIEKIQTEVGKFYNVSVNEMKGSRRVQN IVLARQVAMYLAREMTDNSLPRIGREFGGKDHTTVMHAYEKIKGMIEIDDNLRLEIQT IKKKLK" /product="Chromosomal replication initiator protein DnaA"
Fig. 1 The impact of sequencing errors during ONT sequencing resulting in frameshift mutations. (a) Multiple open reading frames encoding portions of DnaA, the protein that initiates chromosomal replication, from the Canu assembly of ONT-only sequence data; (b) the complete DnaA protein now encoded by a single open reading frame after a hybrid assembly with ONT and Ion Torrent sequence data
genes in the genome (see Note 17). Nonetheless, it is highly recommended that genome sequencing using either ONT or PacBio technologies is paired with a highly accurate short-read-length NGS technology such as Illumina or Ion Torrent. In our laboratory, we now use a standard hybrid sequencing strategy combining ONT and Ion Torrent technologies. 3.3 Hybrid Genome Assembly Using SPAdes
SPAdes is a “Swiss army knife” sequence assembly package that contains a variety of assembly pipelines (e.g., metaSPAdes for metagenomic data, rnaSPAdes for assembling transcriptomes, etc.) depending on the user’s needs [13]. More importantly, SPAdes can generate hybrid assemblies from data originating from different NGS sources (see Note 18). For that reason, SPAdes is our assembler of choice. SPAdes is available as regularly updated pre-compiled packages for Linux and MacOS (Darwin) (see Note 19). In this section, we will perform a hybrid assembly using
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SPAdes version 3.15.4 with two sequence datasets generated from the same genomic DNA preparation: (i) filtered reads from an ONT MinION sequencing run (e.g., FAK63811.fastq), and (ii) filtered reads from an Ion Torrent 318™ Chip V2 sequencing run. The basic SPAdes command structure, presuming that the correct PATH for the SPAdes executable scripts has been specified, is typed as a single line as follows (see Notes 20 and 21): spades.py --iontorrent –s {Ion Torrent datafile}.fastq -nanopore {nanopore datafile}.fastq –o {output directory name}
For example: spades.py --iontorrent –s MyFavoriteBug_IonTor.fastq --nanopore FAK63811.fastq –o /home/MyFavoriteBug_HybridAssembly
where spades.py
activates the SPAdes assembly algorithm
is the parameter specifying unpaired (unidirectional) Ion Torrent sequence reads (see Note 22)
--iontorrent --s
--nanopore is the parameter
to tell SPAdes that the data originate
from an ONT run -o /home/MyFavoriteBug_HybridAssembly
is the output
directory name The SPAdes algorithm will commence and provide you with a constant stream of what the algorithm is doing at each assembly step. All of these steps can be found in the “spades.log” file that will be saved in the /home/MyFavoriteBug_HybridAssembly directory. The most important file in this directory will be “contigs.fasta”, which contains the assembled contigs (or NODEs) listed in order of the longest to the shortest, for example: >NODE_1_length_2039092_cov_387.996116 >NODE_2_length_4146_cov_1614.350834
where Contig #1 is 2,039,092 bp in length with an estimated 388-fold coverage of each base, and contig #2 is 4146 bp with an estimated 1614-fold coverage. Note: These are actual values from one of our hybrid sequencing projects in which contig #1 represents the chromosome of our Streptococcus strain of interest and contig #2 represents its indigenous plasmid (estimated by coverage values to be present at 4 copies per cell). As mentioned in Note 17, large (>6 kbp) genes, especially those that encode cell surface adhesins with many repeated amino acid motifs, are invariably distributed across several contigs when
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sequenced using a short-read-length NGS platform (Fig. 2a). These issues are resolved when a hybrid sequencing strategy is implemented as the long (but relatively inaccurate) sequence reads provide a scaffold to which the shorter (and much more accurate) reads can be mapped, resulting in single open reading frames (Fig. 2b). 3.4 Concluding Remarks
In this chapter, we have highlighted the range of NGS technologies available today as well as benefits of a hybrid sequencing strategy incorporating long- and short-read-length NGS technologies. Although the hybrid sequencing strategy is costlier as two sequencing runs are required per genome, it ultimately pays for itself as the researcher obtains a highly accurate complete genome without the need for further costs associated with gap closure procedures.
A Contig #12 CDS
Contig #3 CDS
complement(2..2956) /db_xref="SEED:fig|28037.1498.peg.103" /translation="MIFKRSNGEFRETDRVTRFKLIKSGKNWLRAATSNFGLLKVIRG QVEETVVAEVREDAVSTKSRHLLKGILVAAAVLSATTVANTTFADEVGNEVVSSSTAM SAPSVSTEAASLTEEASKEVSAPQNETATETKPSVASSSQVVESAVASDDKAILEQNA SEAALLNQIAEKYASNHQNEEQKAAIKEAVAKVQAELPTKEASTNSGENAPSYAEQRS RLNKSVDDMMATLKDAGFNGNTTVNGAPAISAQLAPISTSTTGSVDTNPVISNANGAT IEDAAFNKSGYALDPNADRYTFGVWQFLKTDHATGAKTNFDYYATLAVDRSAITGSLS ANPDVYLRIVKKSDGTEAYTRTIHAGESNISLPSYITNNNSDVTNTLTYAAANGTNPG NVTFSLVNANLEYETLQIRDTNFPGNEGEDKPNVQTSVPYAVADQTTHYKVVDSSKYT EGQPYTPTGNETVLASYTQKGLAGQQFTASNNREIPGYKQVPSTTDSTQKTSGILGKG VVGQKLVELQGGANHYYVKRISEVVDTEGSTVTKFYALDPSQVSNFSASDVGTEDVSK YTLIYTSKVNKAGEEWKADESKVTKVNSKNGDYHIDVTETAGSKSLVITGWQSNTEKV NLTHDETKVTEAFDKPFTGAPDPSLTPAGKGNNAWNLIAGRNADIVAKTKVTQPDGSI TYTELKGYSSFSNNYSVPSAVKPSTDVNYYFVKSDEKGNVYVHYKDTEGNELKTSVTD EAAQPINKAYDTVVDNRPQTIEKDSKTYELVPAGNYTVGEVDDEGHLKSSDSTTGLVA KEDKNVTYIYKLKEEPKGNVYVHYVDTEGKTIKSDVTDEDAQPVDKDYDTVVDNRPQE IAFEGKTYKLVSAGTYTVGEVDDQGHLKSTDSTTGKVIEGDKNVTYVYKLKEEPKGNV YVHYVDTEGKTIKSDVTDEEAQPVDKAYDTVVDNRPQKIEFEGKTYELVPAGNYTVGE VDDQGHLKSTDST" /product="hypothetical protein"
complement(242351..243019) /db_xref="SEED:fig|28037.1498.peg.1283" /translation="TYVYKEVKEEPTQPKGNVYVHYVDTEGKTIKSDVTDEENQPVDK DYNTVVDNRPQTIEKDGKTYELVPAGNYTVGEVDDEGHLKSTDPTTGKVIEGDKNVTY VYKLKETPDKPVEPTPDKPVEPTPDKPVEPTPDKPVDPTPGKPVNPTPDKPVDPTPGK PVDPTPDKPVDPTSGKPVESTPAKGQLPNTGETTSVGSTVLGLVAGLVGLAALGRRKE EDEK" /product="hypothetical protein"
Fig. 2 The main disadvantage of short-read-length NGS technology in relation to large genes. (a) A SPAdes assembly of Ion Torrent-only data showing the locus encoding a putative cell surface protein being distributed across at least two contigs; (b) a single 6.4-kbp open reading frame encoding the entire protein resulting from a SPAdes hybrid assembly incorporating ONT and Ion Torrent data
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B Single chromosomal contig CDS complement(1433024..1439413) /translation="MIFKRSNGEFRETDRVTRFKLIKSGKNWLRAATSNFGLLKVIRG QVEETVVAEVREDAVSTKSRHLLKGILVAAAVLSATTVANTTFADEVGNEVVSSSTAM SAPSVSTEAASLTEEASKEVSAPQNETATETKPSVASSSQVVESAVASDDKAILEQNA SEAALLNQIAEKYASNHQNEEQKAAIKEAVAKVQAELPTKEASTNSGENAPSYAEQRS RLNKSVDDMMATLKDAGFNGNTTVNGAPAISAQLAPISTSTTGSVDTNPVISNANGAT IEDAAFNKSGYALDPNADRYTFGVWQFLKTDHATGAKTNFDYYATLAVDRSAITGSLS ANPDVYLRIVKKSDGTEAYTRTIHAGESNISLPSYITNNNSDVTNTLTYAAANGTNPG NVTFSLVNANLEYETLQIRDTNFPGNEGEDKPNVQTSVPYAVADQTTHYKVVDSSKYT EGQPYTPTGNETVLASYTQKGLAGQQFTASNNREIPGYKQVPSTTDSTQKTSGILGKG VVGQKLVELQGGANHYYVKRISEVVDTEGSTVTKFYALDPSQVSNFSASDVGTEDVSK YTLIYTSKVNKAGEEWKADESKVTKVNSKNGDYHIDVTETAGSKSLVITGWQSNTEKV NLTHDETKVTEAFDKPFTGAPDPSLTPAGKGNNAWNLIAGRNADIVAKTKVTQPDGSI TYTELKGYSSFSNNYSVPSAVKPSTDVNYYFVKSDEKGNVYVHYKDTEGNELKTSVTD EAAQPINKAYDTVVDNRPQTIEKDSKTYELVPAGNYTVGEVDDEGHLKSSDSTTGLVA KEDKNVTYIYKLKEEPKGNVYVHYVDTEGKTIKSDVTDEDAQPVDKDYDTVVDNRPQE IAFEGKTYKLVSAGTYTVGEVDDQGHLKSTDSTTGKVIEGDKNVTYVYKLKEEPKGNV YVHYVDTEGKTIKSDVTDEEAQPVDKAYDTVVDNRPQKIEFEGKTYELVPAGNYTVGE VDDQGHLKSTDSTTGKVIEGDKNVTYVYKLKEEPKGNVYVHYVDTEGKTIKSDVTDED AQPVDKDYDTVVDNRPQEIAFEGKTYELVPAGNYTVGEVDDQGHLKSTDPTTGKVIEG DKNVTYVYKLKETPAEPKGNVYVHYVDTEGKTIKSDVTDEDQQPVDKDYDTVVDNRPQ EIAFEGKTYELVPAGTYTVGEVDNEGHLKSTDPTTGKVIEGDKNVTYVYKLKETPAEP KKGEVIITYVDENGKEIEKPRQDTPNSPYDTPYNTTEEGEKPNTIKTPDGKTYKIVPK GDYPVGKVDEDGHLESSDPVKGKVDKPKSIITYVYKEVKEEPTQPKGNVYVHYVDTEG KTIKSDVTDEDQQPVDKDYDTVVDNRPQEIAFEGKTYELVPAGTYTVGEVDNEGHLKS TDPTTGKVIEGDKNVTYVYKLKETPAEPKKGEVIITYVDENGKEIEKPRQDTPNSPYD TPYNTTEEGEKPNTIKTPDGKTYKIVPKGDYPVGKVDEDGHLESSDPVKGKVDKPKSI ITYVYKEVKEEPTQPKGNVYVHYVDTEGKTIKSDVTDEDQQPVDKDYDTVVDNRPQEI AFEGKTYELVPAGTYTVGEVDNEGHLKSTDPTTGKVIEGDKNVTYVYKLKETPAEPKK GEVIITYVDENGKEIEKPRQDTPNSPYDTPYNTTEEGEKPNTIKTPDGKTYKIVPKGD YPVGKVDEDGHLESSDPVKGKVDKPKSIITYVYKEVKEEPTQPKGNVYVHYVDTEGKT IKSDVTDEDAQPVDKDYDTVVDNRPQEIAFEGKTYELVPAGNYTVGEVDDEGHLKSTD PTTGKVIEGDKNVTYVYKLKETPAEPKKGEVIITYVDENGKEIEKPRQDTPNSPYDTP YNTTEEGEKPNTIKTPDGKTYKIVPKGDYPVGKVDEDGHLESSDPVKGKVDKPKSIIT YVYKEVKEEPTQPKGNVYVHYVDTEGKTIKSDVTDEENQPVDKDYNTVVDNRPQTIEK DGKTYELVPAGNYTVGEVDDEGHLKSTDPTTGKVIEGDKNVTYVYKLKETPDKPVEPT PDKPVEPTPDKPVEPTPDKPVEPTPDKPVDPTPGKPVNPTPDKPVDPTPGKPVDPTPD KPVDPTSGKPVESTPAKGQLPNTGETTSVGSTVLGLVAGLVGLAALGRRKEEDEK" /product="hypothetical protein"
Fig. 2 (continued)
The latter advantage is especially desirable for genome sequencing projects involving new species or metagenome-associated genomes. NGS technologies are continuously being improved and there is no doubt that future technologies will be even more cost- and timeefficient, yielding even larger datasets, while minimizing sequencing errors with better sequencing chemistries and detection mechanisms. Finally, despite the huge selection of genome assembly packages available to the scientific community, e.g., MIRA [12], Unicycler [18], and the more recently developed Trycycler [19] to name a few, our current hybrid assembler of choice is SPAdes as it is easy to install and use, fast and accurate, but more importantly able to work with multiple types of NGS datasets in order to get the job done.
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Notes 1. Brain heart infusion (BHI) medium is a general-purpose medium designed to grow fastidious microorganisms. BHY medium consists of BHI containing 0.5% yeast extract (5 g/ L). Sterilize the media by autoclaving at 121 C for 20 min. BHI/BHY can also be used for culturing highly fastidious gram-negative bacteria, e.g., the periopathogen Porphyromonas gingivalis, but supplementation with vitamin K (menadione; 1 g/L) and hemin (1 g/L) is essential. We use Columbia sheep blood agar (supplied by a New Zealand-based manufacturer) for subculturing oral bacteria, including P. gingivalis. Bacteria are incubated anaerobically at 37 C either in a GasPak™ EZ Anaerobe Container System (Becton Dickinson & Co.) or equivalent. 2. Mid-logarithmic growth phase (OD600 ¼ 0.5–0.6) cells are preferred over stationary phase cells as (i) actively growing cells are easier to lyse, and (ii) the genomic DNA will be of higher quality as it is less likely to be degraded by nucleases released (e.g., by species such as Streptococcus pyogenes) during stationary phase. 3. We have used several genomic DNA purification kits, namely the DNeasy (Qiagen GmbH, Germany), PureLink (Thermo Fisher Scientific, USA), and Geneaid Bacteria Genomic DNA kit (Geneaid, Taiwan). With these kits, there are not only additional lysis steps involved (using 10 mg/mL lysozyme), but the overall quality is inferior to that of NucleoSpin. Furthermore, the manufacturers of the DNeasy and PureLink kits do not provide the enzymatic/digestion (lysis) buffer and the end-user will have to prepare this beforehand. 4. Although the manufacturers of all genomic DNA purification kits recommend processing cells harvested from a 5-mL bacterial culture, doubling (or even tripling) the number of cells harvested does not affect downstream steps, resulting in a higher yield of genomic DNA (5–20 μg). The DNA-binding capacity of the spin columns is usually 15–20 μg. 5. This is a wash buffer to remove most of the growth media components during harvesting of the bacterial cells. Some suppliers do provide PBS with different compositions (e.g., potassium phosphate instead of sodium phosphate) or pH values, but any of these are also suitable. Do not use water to wash the cells as some species may lyse as a result of osmotic shock. 6. Although the NucleoSpin protocol specifies an agitator or swing mill for the bead-beating step, a regular vortex device
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is suitable. For Gram-positive bacteria such as Streptococcus and Lactobacillus, we have found that a bead-beating (disruption) time of 8 min is sufficient. 7. It would be prudent to perform additional experiments to verify that the genomic DNA preparation is indeed correct. This can include sequencing of the 16S rRNA gene (to confirm the identity of the strain) and/or verifying (by PCR) the presence of known genes in the strain. Moreover, these experiments may detect any impurities, e.g., residual ethanol, that may affect downstream sequencing procedures. 8. At the time of writing, PacBio-based sequencing is not available in New Zealand. Some sequencing service providers, e.g., Macrogen Corp. (based in South Korea), require additional genomic DNA for their internal quality control procedures. Our most recent PacBio sequencing jobs using the Macrogen service required 16 μg of purified bacterial genomic DNA. 9. The FASTQ file format contains not only the DNA sequence but also the quality score of each sequence. The latter is used by all current genome assembly algorithms when assembling the data. 10. Although commercially available DNA sequence processing software packages are available for mainstream operating systems such as Microsoft Windows and MacOS, they can be costly. Open-source genome assembly algorithms, e.g., Canu and SPAdes, are distributed free-of-charge, faster and much more efficient with computing resources, and are usually supported continuously by the programmer(s). However, Canu and SPAdes are only available on Linux- or Unix-based (includes MacOS) computers, therefore possibly requiring an additional computing platform. Microsoft Windows-based users will have to set up a virtual machine (e.g., VirtualBox), emulating the Linux environment in order to run Canu and SPAdes. 11. Our laboratory bioinformatics workstation is a modestly configured system comprising an Intel-based 3.3-GHz hexa-core Core i7-3960X CPU, 64 gigabytes of RAM, a 500-gigabyte solid state disk (SSD) drive, and a 2-terabyte regular hard disk drive running 64-bit Ubuntu Linux 19.04. The genome assemblers described in this chapter are expected to run on any flavor of Linux (e.g., Fedora or OpenSUSE) as long as the software’s requirements (e.g., g++, Python, etc.) are satisfied. If they are not, you will have to install the required dependency. For example, the minimum Linux requirements to run Canu version 2.2 include GCC 4.5 (GCC 7 or newer strongly recommended), Perl version 5.12.0, and Java SE 8.
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12. Pre-compiled Canu packages can be found at https://github. com/marbl/canu/releases. The program can be installed in any directory within your Linux environment, but it is essential that you specify the correct directory that contains the Canu binary files using the Linux command export PATH¼$PATH: {Canu}/bin where {Canu} is the main Canu directory and / bin contains the Canu algorithm scripts. Otherwise, you will get an error message. You can embed the export PATH command within your environment initialization (e.g., /bashrc.) file. The .bashrc file is a hidden login file that provides the operating system with configuration options including PATH (names of folders where the operating system will look for files, commands, etc.). 13. ONT MinION output files for a particular sequencing run usually comprise a consecutive series of 25- to 50-megabyte FASTQ text files with a unique identifier, e.g., FAK63811_b3980. . .ef6_1.fastq, FAK63811_b3980. . .ef6_2. fastq, FAK63811_b3980. . .ef6_3.fastq, and so on. You may wish to combine (concatenate) all those FAKxxx.fastq files into a single .fastq file by going into the folder containing those FAKxxx files and using the command: cat *.fastq > {new filename}.fastq
For example, cat *.fastq > FAK63811.fastq
The resulting “FAK63811.fastq” text file will be very large (e.g., 10 gigabytes!) Note that the final line in this command block does not have the backslash \. This tells Canu to proceed with the assembly process. If you are using data generated by a PacBio instrument, then substitute -nanopore with -pacbio. PacBio output files usually comprise only a few subread.fastq files and so are more convenient. 14. On our current bioinformatics workstation (see Note 11), assembly of an ONT dataset of a 2-Mbp Streptococcus mitis genome takes approximately 2 h to complete. Previously, a smaller PacBio-sequenced dataset of a 2.5-Mbp Staphylococcus aureus genome was assembled in approximately 1 h. 15. In order to access RAST, you will need to set up an account. When you have entered the RAST server, you can “Upload New Job” using the “contigs.fasta” file generated by Canu or SPAdes. RAST will only accept FASTA (.fasta) files. There are many options available to the user, e.g., specifying the Taxonomy ID (if the species is known). We usually specify the default
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options to progress through the various steps. Depending on the Job Load at the server, it can take anywhere from 30 min to 8 h for your genome sequence to be annotated. The results are usually downloaded in GenBank format. 16. In our experience, the error rate of PacBio sequencing is considerably lower than that of ONT sequencing. Nevertheless, errors do exist and we have had to verify certain genomic regions of interest by conventional Sanger sequencing of PCR amplicons. 17. This ONT-derived scaffold is especially important when determining the organization of large (>6 kbp) genes and those with highly repetitive sequences, e.g., streptococcal adhesinencoding genes. 18. Although SPAdes accepts combinations of datasets, assemblies using only long-read-length (PacBio or ONT) datasets are not recommended. 19. Pre-compiled SPAdes packages can be downloaded directly from https://cab.spbu.ru/software/spades/. Like Canu, you need to specify the installation directory using export PATH¼ $PATH:{SPAdes}/bin. 20. It is essential that you ensure that the two dashes in --iontorrent and --nanopore are typed, with single dashes for the other parameters. The double-dash tells SPAdes what kind of data it will be working with. 21. For our assembly purposes, the basic SPAdes command essentially remains unchanged except for the filenames of the Ion Torrent or ONT datasets. SPAdes features many parameters that can be tweaked. One particular parameter that extends the k-mer range for the algorithm to 127 (21–99 is default) could potentially increase the accuracy of the assembly. The modified command line to increase the k-mer range becomes: spades.py –k 21,33,55,77,99,127 --iontorrent –s {Ion Torrent datafile}.fastq --nanopore {nanopore datafile}.fastq –o {output directory name}
Another SPAdes parameter that we have tried is --careful (inserted before the –k parameter), but the assembly output remained unchanged. 22. The parameter –s indicates that it is a file comprising unidirectional sequence reads. If paired-end Illumina sequence reads are to be assembled, then parameters such as --s1 {filename of forward reads}.fastq --s2 {filename of reverse
may be used. The reader is referred to Prjibelski et al. [20] and to https://cab.spbu.ru/files/ release3.15.4/manual.html for more detail on the pipeline parameters available.
reads}.fastq
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Acknowledgments This research was supported financially by the Otago Medical Research Foundation, the New Zealand Lottery Grants Board (Lottery Health), the New Zealand Dental Research Foundation, and the Sir John Walsh Research Institute. References 1. Dewhirst FE, Chen T, Izard J, Paster BJ, Tanner AC, Yu WH, Lakshmanan A, Wade WG (2010) The human oral microbiome. J Bacteriol 192(19):5002–5017 2. Avery OT, Macleod CM, McCarty M (1944) Studies on the chemical nature of the substance inducing transformation of pneumococcal types: induction of transformation by a desoxyribonucleic acid fraction isolated from pneumococcus type III. J Exp Med 79(2):137–158 3. Watson JD, Crick FH (1953) Molecular structure of nucleic acids. Nature 171:737–738 4. Maxam AM, Gilbert W (1977) A new method for sequencing DNA. Proc Natl Acad Sci U S A 74(2):560–564 5. Sanger F, Nicklen S, Coulson AR (1977) DNA sequencing with chain-terminating inhibitors. Proc Natl Acad Sci U S A 74(12):5463–5467 6. Liu L, Li Y, Li S, Hu N, He Y, Pong R, Lin D, Lu L, Law M (2012) Comparison of nextgeneration sequencing systems. J Biomed Biotechnol 2012:251364. https://doi.org/10. 1155/2012/251364 7. Margulies M, Egholm M, Altman WE, Attiya S, Bader JS, Bemben LA, Berka J, Braverman MS, Chen YJ, Chen Z et al (2005) Genome sequencing in microfabricated highdensity picolitre reactors. Nature 437(7057): 376–380 8. Bennett S (2004) Solexa Ltd. Pharmacogenomics 5:433–438 9. Rothberg JM, Hinz W, Rearick TM, Schultz J, Mileski W, Davey M, Leamon JH, Johnson K, Milgrew MJ, Edwards M et al (2011) An integrated semiconductor device enabling non-optical genome sequencing. Nature 475(7356):348–352 10. Athanasopoulou K, Boti MA, Adamopoulos PG, Skourou PC, Scorilas A (2021) Thirdgeneration sequencing: the spearhead towards the radical transformation of modern genomics. Life (Basel) 12(1):30 11. Heng NCK, Stanton J-AL (2020) Nextgeneration DNA sequencing of oral microbes at the Sir John Walsh Research Institute: technologies, tools and achievements. J R Soc N Z 50(1):91–107
12. Chevreux B, Pfisterer T, Wetter T, Suhai S (1999) Genome sequence assembly using trace signals and additional sequence information. Comp Sci Biol: Proc German Conf Bioinf (GCB) 99:45–56 13. Bankevich A, Nurk S, Antipov D, Gurevich AA, Dvorkin M, Kulikov AS, Lesin VM, Nikolenko SI, Pham S, Prjibelski AD et al (2012) SPAdes: a new genome assembly algorithm and its applications to single-cell sequencing. J Comput Biol 19(5):455–477 14. Heng NCK, Stanton J-AL (2010) Oral bacterial genome sequencing using the highthroughput Roche Genome Sequencer FLX System. Methods Mol Biol 666:197–218 15. Heng NCK, Haji-Ishak NS, Kalyan A, Wong AY, Lovric M, Bridson JM, Artamonova J, Stanton JA, Wescombe PA, Burton JP et al (2011) Genome sequence of the bacteriocinproducing oral probiotic Streptococcus salivarius strain M18. J Bacteriol 193(22): 6402–6403 16. Koren S, Walenz BP, Berlin K, Miller JR, Bergman NH, Phillippy AM (2017) Canu: scalable and accurate long-read assembly via adaptive k-mer weighting and repeat separation. Genome Res 27(5):722–736 17. Aziz RK, Bartels D, Best AA, DeJongh M, Disz T, Edwards RA, Formsma K, Gerdes S, Glass EM, Kubal M, Meyer F, Olsen GJ, Olson R, Osterman AL, Overbeek RA, McNeil LK, Paarmann D, Paczian T, Parrello B, Pusch GD, Reich C, Stevens R, Vassieva O, Vonstein V, Wilke A, Zagnitko O (2008) The RAST server: rapid annotations using subsystems technology. BMC Genomics 9:75 18. Wick RR, Judd LM, Gorrie CL, Holt KE (2017) Unicycler: resolving bacterial genome assemblies from short and long sequencing reads. PLoS Comput Biol 13(6):e1005595 19. Wick RR, Judd LM, Cerdeira LT, Hawkey J, Me´ric G, Vezina B, Wyres KL, Holt KE (2021) Trycycler: consensus long-read assemblies for bacterial genomes. Genome Biol 22(1):266 20. Prjibelski A, Antipov D, Meleshko D, Lapidus A, Korobeynikov A (2020) Using SPAdes de novo assembler. Curr Protoc Bioinform 70:e102
Chapter 7 Microbial Community Profiling Using Terminal Restriction Fragment Length Polymorphism (T-RFLP) and Denaturing Gradient Gel Electrophoresis (DGGE) Jose´ F. Siqueira Jr, Mitsuo Sakamoto, and Alexandre S. Rosado Abstract In their natural environments, microorganisms usually live in organized communities. Profiling analysis of microbial communities has recently assumed special relevance as it allows a thorough understanding of the diversity of the microbiota, its behavior over time, and the establishment of patterns associated with health and disease. The application of molecular biology approaches holds the advantage of including culturedifficult and as-yet-uncultivated phylotypes in the profiles, providing a more comprehensive picture of the microbial community. This chapter focuses on two particular techniques, namely terminal restriction fragment length polymorphism (T-RFLP) and denaturing gradient gel electrophoresis (DGGE), both of which have been widely used in environmental studies and have been recently successfully used by the authors in the study of the oral microbial communities associated with conditions of health and disease. Key words Human oral microbiota, 16S rRNA gene, Terminal restriction fragment length polymorphism (T-RFLP), Denaturing gradient gel electrophoresis (DGGE)
1
Introduction Microbial community profiling techniques are genetic fingerprinting approaches that can be used to determine the structure and diversity of microbial communities living in a given environment and to monitor changes in the community over time, including after antimicrobial treatment. Species identification can also be obtained with these techniques. There are several molecular methods for community profiling. Of these, the terminal restriction fragment length polymorphism (T-RFLP) and the denaturing gradient gel electrophoresis (DGGE) have been widely used in the study of oral communities in health and disease [1–12].
Gregory J. Seymour et al. (eds.), Oral Biology: Molecular Techniques and Applications, Methods in Molecular Biology, vol. 2588, https://doi.org/10.1007/978-1-0716-2780-8_7, © The Author(s), under exclusive license to Springer Science+Business Media, LLC, part of Springer Nature 2023
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T-RFLP allows the assessment of the diversity of complex bacterial communities and rapid comparison of the community structure from different ecosystems [13]. T-RFLP analysis measures the size polymorphism of terminal restriction fragments from a PCR-amplified marker. When T-RFLP is used to analyze bacterial communities, PCR is first carried out to amplify the 16S rRNA gene from different species in the sample. One of the PCR primers is labeled with a fluorescent dye [14]. PCR amplicons are then digested with restriction enzymes, generating fluorescently labeled fragments of different lengths (the terminal fragments). These fragments are separated on high-resolution sequencing gels in an automated DNA sequencer, which is used to read both the size and the intensity of terminally labeled restriction fragments (T-RF), creating a typical profile. In such a profile, size is represented on the horizontal axis and intensity (relative to the abundance of a given fragment size) is represented on the vertical axis [15]. In theory, each T-RF represents a single species. Extensive databases exist for 16S rRNA gene sequences and can be used to identify all T-RFs predicted from known sequences, considering a given set of primers and restriction enzymes [16]. T-RF lengths are predicted by finding the restriction site closest to the site where the labeled primer will anneal and counting the number of nucleotides in between. Multiple restriction enzymes (usually 4 or 5) are necessary to provide reliable identification since distinct species may generate the same T-RF when only one enzyme is used [17]. The DGGE technique is based on electrophoretic separation of PCR-amplified 16S rRNA gene (or other genes) fragments in polyacrylamide gels containing a linearly increasing gradient of DNA denaturants (a mixture of urea and formamide). As the PCR product migrates in the gel, it encounters increasing concentrations of denaturants and, at some position in the gel, it will become partially- or fully denatured. Partial denaturation causes a significant decrease in the electrophoretic mobility of the DNA molecule. Molecules with different sequences may have a different melting behavior and will therefore stop migrating at different positions in the gel. The position in the gel at which the DNA melts is determined by its nucleotide sequence and composition [18]. Therefore, in DGGE, PCR products of the same length but with different sequences can be separated [19, 20]. A GC-rich sequence (or GC-clamp) is added to the 50 -end of one of the primers used in the PCR reaction and makes the DNA unable to denature completely in the gel [21]. DNA bands in DGGE can be visualized using ethidium bromide, SYBR™ Green, or silver staining. If species identification is desired, specific bands can be excised from the gels, re-amplified by PCR and sequenced [22].
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Materials DNA Extraction
1. Buffer A: 10 mM Tris–HCl (pH 8.0), 50 mM ethylenediamine tetraacetic acid (EDTA). 2. Lysis buffer: 0.5% (w/v) lysozyme and 0.1% (w/v) N-acetylmuramidase in buffer A. Store in aliquots at 20 C. 3. TE buffer: 10 mM Tris–HCl, pH 8.0, 1 mM EDTA. 4. Alternatively, other techniques may be used for DNA extraction (see Note 1).
2.2 Terminal Restriction Fragment Length Polymorphism 2.2.1 PCR Amplification of the 16S rRNA Gene
1. Forward primer 8F: 50 -AGA GTT TGA TCC TGG CTC AG-30 . This primer is labeled at the 50 end with 60 -carboxyfluorescein (6-FAM) (see Note 2). 2. Reverse primer 1492R: 50 -GGT TAC CTT GTT ACG ACT T-30 . 3. Tris-acetate-EDTA (TAE) buffer (50): 2 M Tris (do not adjust pH), 2 M glacial acetic acid, 0.05 M EDTA, pH 8.0. 4. Polyethyleneglycol (PEG) solution: 40% (w/v) PEG 6000, 10 mM MgCl2 (see Note 3).
2.2.2
T-RFLP Analysis
1. Capillaries: 310 Capillary 47 cm, 3130xl & 3100 Capillary Array 36 cm, 3130xl & 3100 Capillary Array 50 cm (Applied Biosystems, Foster City, CA, USA) (see Note 4). 2. Polymers: POP-4 (for the ABI Genetic Analyzer 310 and ABI PRISM 3100 instruments); POP-7 (for the ABI 3130xl Genetic Analyzer) (Applied Biosystems). 3. Running buffer: Biosystems).
Buffer
(10)
with
EDTA
(Applied
4. Size standards: GeneScan 500 ROX Size Standard, GeneScan 1000 ROX Size Standard, GeneScan 1200 LIZ Size Standard (all supplied by Applied Biosystems). 5. Template preparation reagent: Hi-Di Formamide (Applied Biosystems). 2.3 Denaturing Gradient Gel Electrophoresis 2.3.1 PCR Amplification of 16S rRNA Gene
1. Forward primer 968F: 5’-AAC GCG AAG AAC CTT AC-30 , containing a 40-base GC clamp (5’-CGC CCG CCG CGC GCG GCG GGC GGG GCG GGG GCA CGG GGG G-30 ) added to its 50 -end, which makes it suitable for DGGE. 2. Reverse primer 1401R: 5’–GCG TGT GTA CAA GAC CC-30 . 3. Deionized formamide (see below). 4. 1% (w/v) bovine serum albumin (BSA). Store in aliquots of 50 μL at 20 C. 5. DNA polymerase kit (including buffers) for PCR (e.g., ExTaq Hot Start DNA polymerase [Takara Japan]).
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1. TAE buffer: 20 mM Tris-acetate, pH 7.4, 10 mM sodium acetate, 0.5 mM disodium EDTA. Store at room temperature. 2. Deionized formamide: add 12.5 g of AG 501-X8 resin (Bio-Rad) to 250 mL formamide 100%. Stir for 1 h at room temperature. Remove beads by passing the solution through folded filter paper in a funnel. Store in the dark at 4 C. 3. 10% (w/v) ammonium persulfate (APS) in deionized water. Store in 800 μL aliquots at 20 C. 4. N, N, N, N0 -tetra-methyl-ethylenediamine (TEMED). 5. Loading buffer 6: 1.5 mL glycerol and 12.5 mg bromophenol blue (BPB) in 5 mL deionized water. Store at 4 C. 6. Gel-dye: 0.05 g bromophenol blue in 10 mL 1 TAE. 7. Acrylamide/bis-acrylamide, 40% solution for electrophoresis, 37.5:1 (Sigma-Aldrich). 8. Zero percentage UF (urea/formamide) in 6% acrylamide/bis: 15% (v/v) acrylamide/bis-acrylamide, 40% solution for electrophoresis (37.5, 1), 2% (v/v) TAE buffer 50. Store at 4 C in a dark bottle (stable up to 6 months) (see Note 5). 9. 100% UF in 6% acrylamide/bis: 42% (w/v) urea P.A., 40% (v/v) deionized formamide, 15% (v/v) acrylamide/bisacrylamide, 40% solution for electrophoresis, 2% (v/v) TAE buffer 50. The final volume must be made up to 100 mL after dissolving the urea (see Note 6). 10. Staining solution for DGGE: SYBR™ Green® in deionized water in the proportion of 1:10,000 (this solution should be prepared fresh and kept in the dark or in an amber vial).
3 3.1
Methods DNA Extraction
1. An aliquot of 0.5 mL of clinical sample (saliva, pus, and plaque or root canal contents suspended in Tris-EDTA buffer) is diluted with buffer A in a 1:2 ratio (v/v), and washed with the same buffer (see Note 1). 2. The bacterial cell pellet obtained is resuspended in 0.5 mL of the lysis buffer. After incubation at 37 C for 1 h, proteinase K and sodium dodecyl sulfate (SDS) are added to a final concentration of 2 mg/mL and 1% (w/v), respectively. The mixture is incubated at 50 C for 2 h. 3. Nucleic acid is released by three cycles of freezing in a 80 C freezer followed by thawing in a 65 C water bath. 4. The nucleic acid is then extracted with equal volumes of phenol (saturated with 10 mM Tris–HCl, pH 8.0) and phenol:chloroform:isoamyl alcohol (25:24:1).
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5. Bulk nucleic acids are precipitated from solution with 0.1 volume of 3 M sodium acetate and 0.8 volume of isopropyl alcohol followed by centrifugation (16,000 g for 15 min). 6. The DNA precipitate is washed with 70% ethanol and resuspended in 100 μL TE. 7. RNase is added to a final concentration of 10 μg/mL and the mixture is incubated at 37 C for 1 h. 8. The mixture is then treated with equal volumes of phenol and phenol:chloroform:isoamyl alcohol (25:24:1). 9. The DNA is precipitated again with 0.1 volume of 3 M sodium acetate and 0.8 volume of isopropyl alcohol. 10. The DNA is pelleted by centrifugation (16,000 g for 15 min), washed with 70% ethanol, dried in vacuum for 10 min, and dissolved in 100 μL TE. 3.2 Terminal Restriction Fragment Length Polymorphism 3.2.1 PCR Amplification of 16S rRNA Gene
1. Amplification reactions are performed in a total volume of 50 μL containing 5 μL of DNA extract (100 ng), 1.25 U Takara ExTaq (Takara Bio, Japan), 5 μL of 10 ExTaq buffer, 4 μL of dNTP mixture (2.5 mM each), and 10 pmol of each primer. 2. 16S rRNA genes are amplified in a Biometra Tgradient Thermocycler using the following program: 95 C for 3 min, followed by 30 cycles of 95 C for 30 s, 50 C for 30 s, and 72 C for 1.5 min, with a final extension at 72 C for 10 min. 3. Amplified DNA is verified by electrophoresis of aliquots of PCR mixture (2 μL) in 1.5% agarose in 1 TAE buffer. 4. A 50 μL aliquot of the 16S rRNA gene solution is mixed with 30 μL of a PEG solution and 12 μL of 3 M sodium acetate, gently shaken for 10 min at room temperature, and centrifuged at >16,000 g for 15 min. 5. The supernatant is removed carefully by pipetting, and then precipitated DNA is washed twice with 70% ethanol (see Note 7) and redissolved in 20 μL of sterile distilled water. Purified 16S rRNA genes are stored at 20 C until analysis.
3.2.2 T-RFLP Analysis for ABI PRISM 310 Genetic Analyser
The following protocol can be used in the ABI PRISM 310 Genetic Analyzer, ABI PRISM 3100 Genetic Analyzer, and ABI 3130xl Genetic Analyzer instruments. Any modification specific for each instrument is also noted. 1. Purified PCR product (2 μL) is digested with 20 U of restriction enzyme HhaI, MspI, AluI, HaeIII, or RsaI in a total volume of 10 μL at 37 C for 3 h. 2. The restriction digest product (1 μL) is mixed with 12 μL of Hi-Di Formamide and 1 μL of DNA fragment-length standard.
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The standard size marker is a 1:1 mixture of GS 500 ROX and GS 1000 ROX. In the case of ABI 3130xl Genetic Analyzer, GS 1200 LIZ is used as a standard size marker. 3. Each sample is denatured at 95 C for 2 min and then immediately placed on ice. 4. The length of T-RF is determined on an ABI PRISM 310 Genetic Analyser (Applied Biosystems) in GeneScan mode (15 kV, 8 μA, and 60 C for 48 min for each sample). 310 Capillary 47 cm and 310 POP-4 are used (see Note 8). 5. Fragment sizes are estimated by using the Local Southern Method in GeneScan 3.1 software (Applied Biosystems) (see Note 9). 6. T-RFs with a peak area of less than 25 fluorescence units are excluded from the analysis. In the case of ABI 3100 and 3130xl Genetic Analyzers, T-RFs with a peak area of % as.data.frame() %>% rownames_to_column() %>% dplyr::filter(padj% left_join(.,geneIdtogeneName, by ¼ c("rowname" ¼ "ensembl_gene_id")) %>% write.table (., filename, sep ¼ "\t", quote ¼ F) In total, there are 377 genes being differentially expressed using Kallisto and DESeq2 filtering for 5% False Discovery Rate. 6. Differential gene expression using StringTie and DESeq2 StringTie [60] assembles transcripts from RNA-Seq reads already mapped to the genome. It will first cluster the reads into loci and then assemble each locus to create isoforms. There will be as many isoforms as needed to explain the read/alignment structure. Interestingly, StringTie uses network flow allowing it to quantify and to assemble simultaneously. StringTie works on a mapping file in BAM format. StringTie can be used in two modes either for known transcripts/genes or for adding the de novo assembly. Here, we will examine the known transcripts. To do so, the BAM file produced by HISAT2 previously will be reused. $stringtie -p 8 -G ../Homo_sapiens.GRCh38.104.gtf -eB -o SRR3534841_stringTie.gtf SRR3534841_sorted.bam
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Note: -e : Estimates transcript abundance -G: annotation GTF file To perform the differential gene expression analysis, the file t_data.ctab, which is the transcript level abundance estimation, will be loaded in DESeq2. >files txi.stringtie dds dds res stringTie_res_file res %>% as.data.frame() %>% rownames_to_column() %>% dplyr::filter(padj% left_join(.,geneIdtoGeneName, by ¼ c("rowname" ¼ "ensembl_gene_id")) %>% write.table (., stringTie_res_file, sep ¼ "\t", quote ¼ F) Using StringTie and DESeq2, 411 genes are differentially expressed with a false discovery rate less than 5%. 7. Comparison of differentially expressed genes between stringTie-DESeq2, DESeq2, featureCount-DESeq2, Kallisto-DESeq2 for Kim et al.’s dataset [31]: The three different approaches: featureCounts-DESeq2, stringTie-DESeq2, and Kallisto-DESeq2 raised respectively 398, 377, and 411 genes differentially expressed using 5% FDR as a cut off. In total, they represent 544 unique genes. 49.4% of these genes are common to all three methods. 68.5% are found with at least two methods. Notably, 171 genes (31%) are found only by one method. Out of these, StringTie-DESeq2 is the combination raising the greatest number of genes (71) specific to one method. 8. Publicly available RNA-Seq workflows With the development and generalization of the use of NGS in research and especially RNA-Seq, researchers have developed standard approaches using workflow analysis. These workflows package the whole RNA-Seq step-by-step process into one wrapper, master
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script. The main advantages are modularity (part of the workflow can be used in different analyses such as mapping can be used in RNA-Seq, DNA-Seq, or ChIP-Seq), scalability (i.e., can support increasing number of samples), and reproducibility (i.e., can be reused since all the configuration and parameters will be stored). If there is a need of a graphical or point-and-click user interface (GUI), then the Galaxy workflow is to be preferred [61]. Otherwise, there are two main workflow engines: snakemake [62] and nextflow [63]. Both have their RNA-Seq workflows publicly available on https://github.com/snakemake-workflows/docs or https://nf-co.re/rnaseq, respectively. Finally, both are environmentally agnostic, that is, they will run efficiently on local computers, High Performance Computing (HPC), or on the cloud (Internet). More recently, very long read sequencing technologies have been developed and applied to transcriptomics, namely the Pacific Biosciences (PacBio) [64] or Oxford Nanopore Technology (ONT) platforms [65]. The main advantage is that transcripts can be full-length sequenced in a single read without going through a reconstruction stage. Early results from Byrne et al. [65] discovered thousands of new non-annotated transcripts and also new splicing events in murine B1a cells. Soneson et al. [66] analyzed and compared PCR-free (native) and cDNA using the ONT platform. They concluded that despite the long-read approach, transcripts are still truncated making their quantification complicated. One key advantage of naı¨ve RNA-Seq is the potential to retrieve direct posttranscription RNA modification.
4
Notes 1. Replication. As discussed earlier, replicates in RNA-Seq projects are mandatory to estimate the variance appropriately. There are two types of replicates: technical and biological. Technical replicates come from the same biological sample and different libraries are made from the same original sample. It measures the technical variance. In that case, the variation should be minimal because it will be induced by the library preparation and the sequencing. Biological replicates are samples coming from biologically different samples (e.g., a different animal, a different sampling of the same tumor type); it measures the biological variation between samples. In an RNA-Seq experiment, the total variation can be expressed as follows [67]: Total variation (Expression) ¼ Across Group Variability (interindividual variation) + Measurement Error (sequencing error or technical error) + Biological Variability
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The total variation then depends on both biological and technical variations. Very early on, technical variation for Illumina sequencing has been found to be very low [68]. 2. Sequencing strategy recommendations: Differential gene expression analysis: l
ENCODE recommends 30 million single end reads/sample. For lowly expressed genes, this needs to be increased.
l
Read length at least 50 bp
l
Number of replicates: >3 Isoform level expression:
l
Increasing sequencing depth and sequence length
l
Use paired end sequencing
3. Pooling samples. It is preferred to sequence biological replicates individually, but sometimes the quantity of RNA or the budget is limited. In such cases, samples from the same condition can be pooled together and read counts will then represent the average expression of the pool. The issue here is that the estimation of inter-replicates cannot be estimated accurately. Takele et al. [69] showed that an effective pooling strategy could have a better outcome than reducing sequencing depth or the number of samples [69]. 4. Trimming adaptor and quality. Removing bad quality or adaptors content can be performed using several tools. A good source of information about trimming can be found at: https://dnatech.genomecenter.ucdavis.edu/faqs/whenshould-i-trim-my-illumina-reads-and-how-should-i-do-it/. 5. Controlling for confounding factors by randomization. A confounding factor is an effect that cannot be distinguished from studied comparison. As an example, if an experimental design contains four biological replicates having treatment A in litter L1 and four biological replicates having treatment B in litter L2, it will then be impossible to distinguish between the treatment and the litter effect. To resolve this issue, the biological replicates should be split equally into the two different litters: L1 with two treated by A and two treated by B and the same for L2. If the RNA-Seq experiment is large and contains several different factors, it is important to randomize the different factors to avoid confounding effects [70]. 6. Correcting from batch effect. A batch effect is an artificial grouping of samples that is only due to a technical process (e.g., different library type, sequencing centers). If a batch effect is found after exploring the results of principal component analysis or correlation matrix, this needs to be corrected to get a meaningful interpretation [71]. There are several approaches to do so, for example, using a dedicated tool such
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as CombatSeq [72] or a dedicated function (e.g., limma::removeBatchEffect), which can perform correction by integrating the batch as a factor of the design matrix.
Acknowledgments We would like to thank the Rutherford Discovery Fellowship Program (Royal Society of New Zealand) for supporting AC’s current position and the Dunedin School of Medicine for supporting our work. References 1. Ozsolak F, Milos PM (2011) RNA sequencing: advances, challenges and opportunities. Nat Rev Genet 12(2):87–98. https://doi.org/10. 1038/nrg2934 2. Crick F (1970) Central dogma of molecular biology. Nature 227(5258):561–563 3. Crick FH (1958) On protein synthesis. Symp Soc Exp Biol 12:138–163 4. Statello L, Guo CJ, Chen LL, Huarte M (2021) Gene regulation by long non-coding RNAs and its biological functions. Nat Rev Mol Cell Biol 22(2):96–118. https://doi. org/10.1038/s41580-020-00315-9 5. Gebert LFR, MacRae IJ (2019) Regulation of microRNA function in animals. Nat Rev Mol Cell Biol 20(1):21–37. https://doi.org/10. 1038/s41580-018-0045-7 6. Schena M, Shalon D, Davis RW, Brown PO (1995) Quantitative monitoring of gene expression patterns with a complementary DNA microarray. Science 270(5235):467–470 7. Murphy D (2002) Gene expression studies using microarrays: principles, problems, and prospects. Adv Physiol Educ 26(1-4):256–270 8. Abdullah-Sayani A, Bueno-de-Mesquita JM, van de Vijver MJ (2006) Technology Insight: tuning into the genetic orchestra using microarrays–limitations of DNA microarrays in clinical practice. Nat Clin Pract Oncol 3(9): 5 0 1 – 5 1 6 . h t t p s : // d o i . o r g / 1 0 . 1 0 3 8 / ncponc0587 9. Wang Z, Gerstein M, Snyder M (2009) RNA-Seq: a revolutionary tool for transcriptomics. Nat Rev Genet 10(1):57–63. https:// doi.org/10.1038/nrg2484 10. Wilhelm BT, Landry JR (2009) RNA-Seqquantitative measurement of expression through massively parallel RNA-sequencing. Methods 48(3):249–257. https://doi.org/ 10.1016/j.ymeth.2009.03.016
11. Zhao S, Fung-Leung WP, Bittner A, Ngo K, Liu X (2014) Comparison of RNA-Seq and microarray in transcriptome profiling of activated T cells. PLoS One 9(1):e78644. https://doi.org/10.1371/journal.pone. 0078644 12. Leichter AL, Purcell RV, Sullivan MJ, Eccles MR, Chatterjee A (2015) Multi-platform microRNA profiling of hepatoblastoma patients using formalin fixed paraffin embedded archival samples. GigaScience 4:54. https://doi.org/10.1186/s13742-0150099-9 13. Chatterjee A, Leichter AL, Fan V, Tsai P, Purcell RV, Sullivan MJ, Eccles MR (2015) A cross comparison of technologies for the detection of microRNAs in clinical FFPE samples of hepatoblastoma patients. Sci Rep 5:10438. https://doi.org/10.1038/srep10438 14. Petrova OE, Garcia-Alcalde F, Zampaloni C, Sauer K (2017) Comparative evaluation of rRNA depletion procedures for the improved analysis of bacterial biofilm and mixed pathogen culture transcriptomes. Sci Rep 7:41114. https://doi.org/10.1038/srep41114 15. Wang C, Gong B, Bushel PR, Thierry-Mieg J, Thierry-Mieg D, Xu J, Fang H, Hong H, Shen J, Su Z, Meehan J, Li X, Yang L, Li H, Labaj PP, Kreil DP, Megherbi D, Gaj S, Caiment F, van Delft J, Kleinjans J, Scherer A, Devanarayan V, Wang J, Yang Y, Qian HR, Lancashire LJ, Bessarabova M, Nikolsky Y, Furlanello C, Chierici M, Albanese D, Jurman G, Riccadonna S, Filosi M, Visintainer R, Zhang KK, Li J, Hsieh JH, Svoboda DL, Fuscoe JC, Deng Y, Shi L, Paules RS, Auerbach SS, Tong W (2014) The concordance between RNA-seq and microarray data depends on chemical treatment and transcript abundance. Nat Biotechnol 32(9):926–932. https://doi.org/10.1038/nbt.3001
276
Gregory Gimenez et al.
16. Tang F, Barbacioru C, Wang Y, Nordman E, Lee C, Xu N, Wang X, Bodeau J, Tuch BB, Siddiqui A, Lao K, Surani MA (2009) mRNASeq whole-transcriptome analysis of a single cell. Nat Methods 6(5):377–382. https://doi. org/10.1038/nmeth.1315 17. Chatterjee A, Ahn A, Rodger EJ, Stockwell PA, Eccles MR (2018) A guide for designing and analyzing RNA-Seq data. Methods Mol Biol 1783:35–80. https://doi.org/10.1007/9781-4939-7834-2_3 18. Chen C-H, Pan C-Y, Lin W-c (2019) Overlapping protein-coding genes in human genome and their coincidental expression in tissues. Sci Rep 9(1):13377. https://doi.org/ 10.1038/s41598-019-49802-w 19. Zhao S, Zhang Y, Gordon W, Quan J, Xi H, Du S, von Schack D, Zhang B (2015) Comparison of stranded and non-stranded RNA-seq transcriptome profiling and investigation of gene overlap. BMC Genomics 16(1):675. https://doi.org/10.1186/s12864-0151876-7 20. Krzywinski M, Altman N (2013) Power and sample size. Nat Methods 10(12): 1139–1140. https://doi.org/10.1038/ nmeth.2738 21. Button KS, Ioannidis JPA, Mokrysz C, Nosek BA, Flint J, Robinson ESJ, Munafo` MR (2013) Power failure: why small sample size undermines the reliability of neuroscience. Nat Rev Neurosci 14(5):365–376. https://doi.org/10. 1038/nrn3475 22. Hart SN, Therneau TM, Zhang Y, Poland GA, Kocher J-P (2013) Calculating sample size estimates for RNA sequencing data. J Comput Biol 20(12):970–978. https://doi.org/10. 1089/cmb.2012.0283 23. Busby MA, Stewart C, Miller CA, Grzeda KR, Marth GT (2013) Scotty: a web tool for designing RNA-Seq experiments to measure differential gene expression. Bioinformatics 29(5):656–657. https://doi.org/10.1093/ bioinformatics/btt015 24. Tarazona S, Garcı´a-Alcalde F, Dopazo J, Ferrer A, Conesa A (2011) Differential expression in RNA-seq: a matter of depth. Genome Res 21(12):2213–2223. https://doi.org/10. 1101/gr.124321.111 25. Mortazavi A, Williams BA, McCue K, Schaeffer L, Wold B (2008) Mapping and quantifying mammalian transcriptomes by RNA-Seq. Nat Methods 5(7):621–628. https://doi.org/10.1038/nmeth.1226 26. Chhangawala S, Rudy G, Mason CE, Rosenfeld JA (2015) The impact of read length on quantification of differentially expressed genes and splice junction detection. Genome Biol
16(1):131. https://doi.org/10.1186/ s13059-015-0697-y 27. Uszczynska-Ratajczak B, Lagarde J, Frankish A, Guigo´ R, Johnson R (2018) Towards a complete map of the human long non-coding RNA transcriptome. Nat Rev Genet 19(9):535–548. https://doi.org/10. 1038/s41576-018-0017-y 28. Weirather JL, de Cesare M, Wang Y, Piazza P, Sebastiano V, Wang X-J, Buck D, Au KF (2017) Comprehensive comparison of Pacific Biosciences and Oxford Nanopore Technologies and their applications to transcriptome analysis. F1000Research 6:100. https://doi. org/10.12688/f1000research.10571.2 29. O’Neil D, Glowatz H, Schlumpberger M (2013) Ribosomal RNA depletion for efficient use of RNA-Seq capacity. Curr Protoc Mol Biol 103(1):4.19.11–4.19.18. https://doi.org/10. 1002/0471142727.mb0419s103 30. Zaghlool A, Ameur A, Nyberg L, Halvardson J, Grabherr M, Cavelier L, Feuk L (2013) Efficient cellular fractionation improves RNA sequencing analysis of mature and nascent transcripts from human tissues. BMC Biotechnol 13(1):99. https://doi.org/10.1186/14726750-13-99 31. Kim SH, Das A, Chai JC, Binas B, Choi MR, Park KS, Lee YS, Jung KH, Chai YG (2016) Transcriptome sequencing wide functional analysis of human mesenchymal stem cells in response to TLR4 ligand. Sci Rep 6: 30311–30311. https://doi.org/10.1038/ srep30311 32. Ewels P, Magnusson M, Lundin S, K€aller M (2016) MultiQC: summarize analysis results for multiple tools and samples in a single report. Bioinformatics 32(19):3047–3048. https://doi.org/10.1093/bioinformatics/ btw354 33. Liao Y, Shi W (2020) Read trimming is not required for mapping and quantification of RNA-seq reads at the gene level. NAR Genomics Bioinformatics 2(3):lqaa068. https://doi. org/10.1093/nargab/lqaa068 34. Meyers BC, Scalabrin S, Morgante M (2004) Mapping and sequencing complex genomes: let’s get physical! Nat Rev Genet 5(8): 578–588. https://doi.org/10.1038/nrg1404 35. Hatem A, Bozdag˘ D, Toland AE, C ¸ atalyu¨rek € UV (2013) Benchmarking short sequence mapping tools. BMC Bioinformatics 14(1): 184. https://doi.org/10.1186/1471-210514-184 36. Dobin A, Davis CA, Schlesinger F, Drenkow J, Zaleski C, Jha S, Batut P, Chaisson M, Gingeras TR (2013) STAR: ultrafast universal RNA-seq aligner. Bioinformatics 29(1):15–21. https:// doi.org/10.1093/bioinformatics/bts635
RNA-Sequencing Design and Analysis 37. Kim D, Langmead B, Salzberg SL (2015) HISAT: a fast spliced aligner with low memory requirements. Nat Methods 12(4):357–360. https://doi.org/10.1038/nmeth.3317 38. Bray NL, Pimentel H, Melsted P, Pachter L (2016) Near-optimal probabilistic RNA-seq quantification. Nat Biotechnol 34(5): 525–527. https://doi.org/10.1038/nbt. 3519 39. Patro R, Duggal G, Love MI, Irizarry RA, Kingsford C (2017) Salmon provides fast and bias-aware quantification of transcript expression. Nat Methods 14(4):417–419. https:// doi.org/10.1038/nmeth.4197 40. Li B, Dewey CN (2011) RSEM: accurate transcript quantification from RNA-Seq data with or without a reference genome. BMC Bioinformatics 12:323–323. https://doi.org/10. 1186/1471-2105-12-323 41. Wagner GP, Kin K, Lynch VJ (2012) Measurement of mRNA abundance using RNA-seq data: RPKM measure is inconsistent among samples. Theory Biosci 131(4): 281–285. https://doi.org/10.1007/s12064012-0162-3 42. Pachter L (2011) Models for transcript quantification from RNA-Seq. https://doi.org/10. 48550/arXiv.1104.3889 43. Zhao S, Ye Z, Stanton R (2020) Misuse of RPKM or TPM normalization when comparing across samples and sequencing protocols. RNA 26(8):903–909. https://doi.org/10. 1261/rna.074922.120 44. Conesa A, Madrigal P, Tarazona S, GomezCabrero D, Cervera A, McPherson A, Szczes´niak MW, Gaffney DJ, Elo LL, Zhang X, Mortazavi A (2016) A survey of best practices for RNA-seq data analysis. Genome Biol 17(1):13. https://doi.org/10.1186/s13059-0160881-8 45. Costa-Silva J, Domingues D, Lopes FM (2017) RNA-Seq differential expression analysis: an extended review and a software tool. PLoS One 12(12):e0190152. https://doi.org/10. 1371/journal.pone.0190152 46. Quinn TP, Crowley TM, Richardson MF (2018) Benchmarking differential expression analysis tools for RNA-Seq: normalizationbased vs. log-ratio transformation-based methods. BMC Bioinformatics 19(1):274. https:// doi.org/10.1186/s12859-018-2261-8 47. Everaert C, Luypaert M, Maag JLV, Cheng QX, Dinger ME, Hellemans J, Mestdagh P (2017) Benchmarking of RNA-sequencing analysis workflows using whole-transcriptome RT-qPCR expression data. Sci Rep 7(1):1559. https://doi.org/10.1038/s41598-01701617-3
277
48. Li H, Handsaker B, Wysoker A, Fennell T, Ruan J, Homer N, Marth G, Abecasis G, Durbin R, Genome Project Data Processing S (2009) The sequence alignment/map format and SAMtools. Bioinformatics 25(16): 2078–2079. https://doi.org/10.1093/bioin formatics/btp352 49. Anders S, Pyl PT, Huber W (2015) HTSeq–a Python framework to work with highthroughput sequencing data. Bioinformatics 31(2):166–169. https://doi.org/10.1093/ bioinformatics/btu638 50. Liao Y, Smyth GK, Shi W (2013) The Subread aligner: fast, accurate and scalable read mapping by seed-and-vote. Nucleic Acids Res 41(10):e108–e108. https://doi.org/10. 1093/nar/gkt214 51. O’Leary NA, Wright MW, Brister JR, Ciufo S, Haddad D, McVeigh R, Rajput B, Robbertse B, Smith-White B, Ako-Adjei D, Astashyn A, Badretdin A, Bao Y, Blinkova O, Brover V, Chetvernin V, Choi J, Cox E, Ermolaeva O, Farrell CM, Goldfarb T, Gupta T, Haft D, Hatcher E, Hlavina W, Joardar VS, Kodali VK, Li W, Maglott D, Masterson P, McGarvey KM, Murphy MR, O’Neill K, Pujar S, Rangwala SH, Rausch D, Riddick LD, Schoch C, Shkeda A, Storz SS, Sun H, Thibaud-Nissen F, Tolstoy I, Tully RE, Vatsan AR, Wallin C, Webb D, Wu W, Landrum MJ, Kimchi A, Tatusova T, DiCuccio M, Kitts P, Murphy TD, Pruitt KD (2016) Reference sequence (RefSeq) database at NCBI: current status, taxonomic expansion, and functional annotation. Nucleic Acids Res 44(D1):D733–D745. https://doi.org/10. 1093/nar/gkv1189 52. Aken BL, Ayling S, Barrell D, Clarke L, Curwen V, Fairley S, Fernandez Banet J, Billis K, Garcı´a Giro´n C, Hourlier T, Howe K, K€ah€ari A, Kokocinski F, Martin FJ, Murphy DN, Nag R, Ruffier M, Schuster M, Tang YA, Vogel J-H, White S, Zadissa A, Flicek P, Searle SMJ (2016) The ensembl gene annotation system. Database (Oxford) 2016:baw093. https://doi.org/10.1093/database/baw093 53. Frankish A, Diekhans M, Ferreira A-M, Johnson R, Jungreis I, Loveland J, Mudge JM, Sisu C, Wright J, Armstrong J, Barnes I, Berry A, Bignell A, Carbonell Sala S, Chrast J, Cunningham F, Di Domenico T, Donaldson S, Fiddes IT, Garcı´a Giro´n C, Gonzalez JM, Grego T, Hardy M, Hourlier T, Hunt T, Izuogu OG, Lagarde J, Martin FJ, Martı´nez L, Mohanan S, Muir P, Navarro FCP, Parker A, Pei B, Pozo F, Ruffier M, Schmitt BM, Stapleton E, Suner M-M, Sycheva I, Uszczynska-Ratajczak B, Xu J, Yates A, Zerbino D, Zhang Y, Aken B, Choudhary JS, Gerstein M, Guigo´ R, Hubbard
278
Gregory Gimenez et al.
TJP, Kellis M, Paten B, Reymond A, Tress ML, Flicek P (2019) GENCODE reference annotation for the human and mouse genomes. Nucleic Acids Res 47(D1):D766–D773. https://doi.org/10.1093/nar/gky955 54. Hamaguchi Y, Zeng C, Hamada M (2021) Impact of human gene annotations on RNA-seq differential expression analysis. BMC Genomics 22(1):730. https://doi.org/ 10.1186/s12864-021-08038-7 55. Wu PY, Phan JH, Wang MD (2012) The effect of human genome annotation complexity on RNA-Seq gene expression quantification. In: 2012 IEEE international conference on bioinformatics and biomedicine workshops, 4–7 October 2012, pp 712–717. https://doi. org/10.1109/BIBMW.2012.6470224 56. Wang L, Wang S, Li W (2012) RSeQC: quality control of RNA-seq experiments. Bioinformatics 28(16):2184–2185. https://doi.org/10. 1093/bioinformatics/bts356 57. Love MI, Huber W, Anders S (2014) Moderated estimation of fold change and dispersion for RNA-seq data with DESeq2. Genome Biol 15(12):550. https://doi.org/10.1186/ s13059-014-0550-8 58. Soneson C, Love MI, Robinson MD (2105) Differential analyses for RNA-seq: transcriptlevel estimates improve gene-level inferences. F1000Research 4:1521 59. Patro R, Mount SM, Kingsford C (2014) Sailfish enables alignment-free isoform quantification from RNA-seq reads using lightweight algorithms. Nat Biotechnol 32(5):462–464. https://doi.org/10.1038/nbt.2862 60. Pertea M, Pertea GM, Antonescu CM, Chang T-C, Mendell JT, Salzberg SL (2015) StringTie enables improved reconstruction of a transcriptome from RNA-seq reads. Nat Biotechnol 33(3):290–295. https://doi.org/10.1038/ nbt.3122 61. Afgan E, Baker D, Batut B, van den Beek M, ˇ ech M, Chilton J, Clements D, Bouvier D, C Coraor N, Gru¨ning BA, Guerler A, HillmanJackson J, Hiltemann S, Jalili V, Rasche H, Soranzo N, Goecks J, Taylor J, Nekrutenko A, Blankenberg D (2018) The Galaxy platform for accessible, reproducible and collaborative biomedical analyses: 2018 update. Nucleic Acids Res 46(W1): W537–W544. https://doi.org/10.1093/ nar/gky379 62. Ko¨ster J, Rahmann S (2012) Snakemake—a scalable bioinformatics workflow engine. Bioinformatics 28(19):2520–2522. https://doi. org/10.1093/bioinformatics/bts480
63. Ewels PA, Peltzer A, Fillinger S, Patel H, Alneberg J, Wilm A, Garcia MU, Di Tommaso P, Nahnsen S (2020) The nf-core framework for community-curated bioinformatics pipelines. Nat Biotechnol 38(3): 276–278. https://doi.org/10.1038/s41587020-0439-x 64. Sharon D, Tilgner H, Grubert F, Snyder M (2013) A single-molecule long-read survey of the human transcriptome. Nat Biotechnol 31(11):1009–1014. https://doi.org/10. 1038/nbt.2705 65. Byrne A, Beaudin AE, Olsen HE, Jain M, Cole C, Palmer T, DuBois RM, Forsberg EC, Akeson M, Vollmers C (2017) Nanopore longread RNAseq reveals widespread transcriptional variation among the surface receptors of individual B cells. Nat Commun 8(1): 1 6 0 2 7 . h t t p s : // d o i . o r g / 1 0 . 1 0 3 8 / ncomms16027 66. Soneson C, Yao Y, Bratus-Neuenschwander A, Patrignani A, Robinson MD, Hussain S (2019) A comprehensive examination of Nanopore native RNA sequencing for characterization of complex transcriptomes. Nat Commun 10(1): 3359. https://doi.org/10.1038/s41467019-11272-z 67. Hansen KD, Wu Z, Irizarry RA, Leek JT (2011) Sequencing technology does not eliminate biological variability. Nat Biotechnol 29(7):572–573. https://doi.org/10.1038/ nbt.1910 68. Marioni JC, Mason CE, Mane SM, Stephens M, Gilad Y (2008) RNA-seq: an assessment of technical reproducibility and comparison with gene expression arrays. Genome Res 18(9):1509–1517. https://doi. org/10.1101/gr.079558.108 69. Takele Assefa A, Vandesompele J, Thas O (2020) On the utility of RNA sample pooling to optimize cost and statistical power in RNA sequencing experiments. BMC Genomics 21(1):312. https://doi.org/10.1186/ s12864-020-6721-y 70. Klaus B (2015) Statistical relevance—relevant statistics, part I. EMBO J 34(22):2727–2730. https://doi.org/10.15252/embj.201592958 71. Goh WWB, Wang W, Wong L (2017) Why batch effects matter in omics data, and how to avoid them. Trends Biotechnol 35(6): 498–507. https://doi.org/10.1016/j.tibtech. 2017.02.012 72. Zhang Y, Parmigiani G, Johnson WE (2020) ComBat-seq: batch effect adjustment for RNA-seq count data. NAR Genomics Bioinformatics 2(3):lqaa078. https://doi.org/10. 1093/nargab/lqaa078
Chapter 17 Characterization of the Expression and Role of Histone Acetylation and Deacetylation in Dental Pulp Cells Yukako Yamauchi and Henry F. Duncan Abstract Histone acetylation and deacetylation of DNA-associated proteins have been shown to alter the architecture of chromatin, affecting gene expression and controlling a wide range of biological events. These events are balanced by two sets of cellular enzymes, histone-deacetylases (HDACs) and histone acetyl-transferases (HATs). Pharmacological inhibition of histone-deacetylases (HDACs) using HDAC-inhibitors (HDACis) has been shown to promote dental pulp cell reparative processes with therapeutic implications in various fields including regenerative dentistry. To date, pan-HDACi have generally been used rather than isoformspecific HDACi targeting, despite the fact that HDAC-specific inhibitors have been developed to target HDACs in several tissues. To identify potential therapeutic targets in the tooth, the expression and distribution of HDAC-isoforms need to be analyzed. This chapter focuses on techniques to analyze expression, location, and distribution of individual HDAC-isoforms under mineralizing conditions using both histology and cell biology, along with a description of basic techniques for culturing and mineralization of rodent dental pulp cells. Key words Histone acetylation/deacetylation, Dental pulp cells, Dental pulp regeneration, Immunohistochemistry, Mineralization, Protein expression, Epigenetics
1
Introduction The balance of acetylation and deacetylation of DNA-associated histone (and also nonhistone) proteins regulates gene expression through alteration of chromatin architecture [1]. In general, acetylation leads to a relaxed open transcriptionally active state, while deacetylation leads to a condensed transcriptionally repressive chromatin structure [2]. Promotion of histone acetylation by the use of pharmacological inhibitors has been linked to pro-mineralization effects and enhances reparative processes in various oral tissues [3, 4]. An attraction of targeting acetylation, compared with other epigenetic modifications (such as DNA-methylation), is that these modifications are considered labile and relatively easily reversed, thus they are regarded as attractive therapeutic targets [2, 5].
Gregory J. Seymour et al. (eds.), Oral Biology: Molecular Techniques and Applications, Methods in Molecular Biology, vol. 2588, https://doi.org/10.1007/978-1-0716-2780-8_17, © The Author(s), under exclusive license to Springer Science+Business Media, LLC, part of Springer Nature 2023
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The processes of histone acetylation and deacetylation are mediated by two sets of balancing enzymes; namely histonedeacetylases (HDACs) and histone-acetyl-transferases (HATs). HDACs have recently developed as a promising target for promoting regenerative strategies within dentistry as they have shown to play important roles in controlling dental stem cell (DSC) fate as well as tissue mineralization processes [3, 6, 7]. HATs, however, present more challenging therapeutic targets as their inhibition affects multiple cellular function including some undesirable side effects [8]. There are eighteen human HDACs categorized into four classes, which are well-conserved throughout mammalian species [9]. Class I, II, and IV are zinc-dependent enzymes with class IV being a unique group comprising only of HDAC11. Class III enzymes are dependent on nicotinamide dinucleotide and are also called ‘sirtuins’. Among HDACs, classes I and II have been the principal focus for dental researchers [10–13]. Class I HDACs show ubiquitous expression, while class II HDACs are more tissue-specific and localized to cell nuclei [14]. Consequently, class II HDAC tissue distribution has been investigated with links to mineralization processes being demonstrated [3, 15]. Pan-HDAC inhibitors (HDACis), such as trichostatin A (TSA), valproic acid (VPA), and suberoylanilide hydroxamic acid (SAHA), have been studied in wide range of medical fields, including cancer and inflammatory diseases, and some are already being clinically applied with FDA approval for the treatment of neoplasia [16, 17]. They have also been investigated for potential therapeutic benefit in dental regenerative processes, as reports have highlighted beneficial effects on DSC proliferation, differentiation, and mineralization [7, 18–20]. They also have been shown to interact with osteogenesis and odontogenesis-regulating genes and proteins that play important roles in regenerative process in teeth [21–23]. However, these HDACis are mostly pan-inhibitors that affect multiple HDAC-isoforms, therefore the detailed mechanisms and the role of each individual isoform remain unknown. This chapter provides protocols to enable the analysis of the expression of HDAC isoforms in rodent dental tissue as well as the application of HDACis in mineralizing cell culture.
2
Materials
2.1 Histochemical Analysis
1. Wistar Hannover rats (male, aged 25–30 days and weighing between 120 and 140 g) 2. Equipment: Scalpel, Forceps, Perforated biopsy bag (30 45 mm, depending on the tissue/sample size), cotton string (Fig. 1a).
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Fig. 1 (a) Experimental set up for rat skull fixation, demineralization, and dehydration. Skull can be placed in a labelled biopsy bag and hung with cotton thread to suspend near the base of the beaker. Fill in reagents for fixation, demineralization, and dehydration above the bag and cover with parafilm to avoid evaporation. Gently agitate on a rocking table at 4 C. (b) Dissection of the rat skull for sagittal analysis. A cut is made between the incisors and through the middle line of the palate (indicated with white line). Arrow head; upper incisor
3. 10% buffered formalin 4. 10% EDTA solution (pH 7.2–7.6); dilute EDTA powder in distilled water and then adjust pH using NaOH pellets. 10% formic acid alternative for demineralization. 5. Orbital shaker. 6. Phosphate-buffered saline (PBS). 7. PBS-T: 0.1% Tween added to PBS. 8. Ethanol. 9. Xylene. 10. Paraffin. 11. Embedding mold (Peel-A-Way Embedding Rectangular-R30, Polysciences, USA) (Fig. 2a)
Mold
12. Thermostatically controlled oven for softening of paraffin, set to 60 C. 13. Sectioning; Microtome, Blades, Water bath, Slide heater, Slides (Fig. 3). 14. Histological slide basket. 15. Slide tray for staining (Fig. 4). 16. PAP pen. 17. Secondary staining system (e.g., Dako Envision + System [Dako] or equivalent) (Fig. 5).
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Fig. 2 (a) Paraffin setting in Peel-A-Way Embedding Mold with sample placed initially on base of mold. (b) Paraffin block removed from mold to reveal tissue sample with sagittal surface exposed and embedded. (c) Trimmed block ready for microtome sectioning
Fig. 3 Equipment for sectioning. (a) Microtome. (b) Water bath at 40 C containing 5 μm sections (following cutting on a microtome) ‘floated’ in the water prior to being captured on a microscope slide for histological analysis
18. Blocking solution; 10 mL TBS, 5 μL Tween, 200 μL Goat serum, 0.1 g Bovine serum albumin (BSA). 19. Primary antibody solution; 10 mL TBS, 0.1 g BSA. Specific anti-HDAC antibodies. 20. Slides, cover slips, mounting medium, light microscope.
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Fig. 4 Slide tray used for staining. Distilled water is in the bottom of the tray to create a moist environment for overnight storage at 4–8 C
Fig. 5 An example of secondary staining system. Dako Envision+ System (Dako) 2.2 Dental Pulp Cell (DPC) Mineralization Culture
1. Equipment for dissection: Scalpel, Forceps, Slide glass, 50 mL centrifuge tubes, 70 μm cell strainer. 2. Centrifuge accommodating 50 mL Falcon tubes. 3. Medium: α-MEM is supplemented with 1% penicillin/streptomycin. For culture medium, 20% fetal bovine serum (FBS) is added for primary cells and 10% after first passage. 0.05% Trypsin-EDTA. 4. Tissue tube rotator (MACSmix Tube Rotator, Miltenyi Biotec). 5. 25 mL and 75 mL flasks for cell culturing. 6. Incubator with 37 C, 5% CO2, and 90–95% humidity for tissue culture. 7. Cell culturing plate (e.g., 6-well plate)
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8. Mineralization medium: α-MEM supplemented with 10% FBS, 50 mg/mL ascorbic acid, 10 mM β-glycerophosphate, and 0.1 mM dexamethasone. 9. Suberoylanilide Hydroxamic Acid (SAHA). Stock solution: 5 mM SAHA in DMSO (store at 20 C, stable for 6 months). Add 1 μM or other suitable culture to mineralization medium for culturing HDACi-exposed group. 10. PBS. 11. 10% formaldehyde. 12. Alizarin red solution (2%, pH 4.1–4.3): 2 g Alizarin Red S, 100 mL distilled water. Mix well and adjust pH with 10% ammonium hydroxide. 2.3 Western Blotting Analysis
1. Extraction buffer (for 3 wells): 900 μL RIPA buffer, 9 μL phenylmethyl sulfonyl fluoride (PMSF), 9 μL halt protease inhibitor (all from ThermoFisher Scientific, Waltham, MA, USA). Mix in a small tube and keep on ice. 2. PBS, keep on ice. 3. Cell-scraper. 4. 500 μL microfuge tube, keep on ice. 5. Vortex. 6. Centrifuge. 7. Bradford assay; Bovine serum albumin (BSA) standard, Dye reagent, Cuvette, spectrophotometer. 8. Laemmli sample buffer; mix 9:1 add 2-mercaptoethanol immediately prior to use. 9. Water bath. 10. Parafilm. 11. Gel for sodium dodecyl sulphate–polyacrylamide gel electrophoresis (SDS-PAGE) (Fig. 6), Running buffer (Tris/Glycine/SDS), Molecular weight marker (Western C Standard). 12. Polyvinylidene difluoride (PDVF) transfer membrane, blotting transfer system. 13. TBS: 10xTris Buffer Saline (TBS) diluted 1:9 with distilled water. 14. TBS-T: 0.1% Tween20 added to TBS. 15. 5% blocking solution: 100 mL TBS-T, 5 g blotting-grade skimmed milk. Mix in a tube and gently rock on an orbital shaker until the milk powder is dissolved. 16. Primary antibody (Ab) blocking solution (per membrane): 10 mL 2.5% blocking solution, anti-HDAC antibody of interest (dilution is indicated on product datasheet). Prepare immediately prior to use. For loading control, replace anti-HDAC antibody with anti-β-actin antibody.
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Fig. 6 An example of the readymade gel for sodium dodecyl sulphate– polyacrylamide gel electrophoresis (SDS-PAGE). Mini-PROTEAN TGX Precast Gels (Bio-Rad)
Fig. 7 An example of enhanced chemoluminescence (ECL) detection kit, ECL imaging system. SuperSignal West Dura Kit (Thermo Scientific)
17. Secondary Ab blocking solution (per membrane): 20 mL TBS, 0.6 g skimmed milk, 2 μL goat anti-rabbit IgG- horse radish peroxidase (HRP), 2 μL Strep-Tactin HRP conjugate. The type of IgG antibody is dependent on primary antibody. Prepare immediately prior to use. 18. Enhanced chemoluminescence (ECL) detection kit (Fig. 7), ECL imaging system. 19. Densitometric analysis software (e.g., BioRad Image Lab)
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Methods
3.1 Immunohistochemical Analysis 3.1.1 Preparation of the Tissue Section
1. Carefully dissect the rat maxillary bones from the skull taking care to remove soft tissue around the molar teeth. For mice and young Wistar rats ( 4 GB, about 1 TB free hard drive space.
l
For next-generation sequencing data analysis: A computer with x86-64 compatible processor(s) running Linux with as many processor cores as possible (see Notes 1 and 13), RAM > 32GB, and several TBs of free hard drive space.
l
For machine learning: A computer with x86-64 compatible processor(s) running either Linux, Mac OS X, or Windows (see Note 2). RAM 16GB. 1. The R statistical environment, including the Bioconductor framework, and the following libraries: (a)
Minfi
(b)
shinyMethyl
(c)
knitr
(d)
illuminaio
(e)
IlluminaHumanMethylationEPICanno.ilm10b4. hg19
(f)
IlluminaHumanMethylationEPICmanifest
(g)
Affy
(h)
missMethyl
(i)
Rsubreads
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Gviz
(k)
DMRcate
(l) (m)
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edgeR limma [15]
(n)
sva
(o)
statmod
(p)
CMA [17]
(q)
reshape [18]
(r)
ConsensusClusterPlus [19]
(s)
flexmix [16]
(t)
gplots [20]
(u)
mclust [21]
2. (Optional, but highly recommended) An integrated programming environment (IDE) for R, e.g., RStudio, or a programming editor, e.g., GNU Emacs/ESS 3. (Optional, but highly recommended) A version control system, e.g., git 4. FastQC software http://www.bioinformatics.babraham.ac.uk/ projects/fastqc/ 5. STAR aligner software [22, 23] https://github.com/ale xdobin/STAR 6. Trimmomatic software [24] http://www.usadellab.org/cms/? page¼trimmomatic 7. GSEA software [25] http://software.broadinstitute.org/gsea/ index.jsp 8. Cytoscape [26] http://www.cytoscape.org/ 9. Enrichment Map (Cytoscape plugin) [27] http://www. baderlab.org/Software/EnrichmentMap 10. ErmineJ software [28] http://erminej.chibi.ubc.ca/ 2.3 Manifests, Annotations, Genome Files
1. The manifest for the methylation arrays is part of the IlluminaHumanMethylationEPICmanifest R package 2. Genome files, e.g., from Ensembl
http://ftp.ensembl.
org/pub/release-84/fasta/homo_sapiens/dna/ Homo_sapiens.GRCh38.dna.primary_assembly.fa.gz
3. Matching annotation files, e.g., from Ensembl
ftp://ftp.
ensembl.org/pub/release-84/gtf/homo_sapiens/ Homo_sapiens.GRCh38.84.gtf.gz
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Targets File
l
Tab-delimited text (*.txt) or comma-separated text (*.csv) file
l
One row per sample
l
Has all technical information – Lab identifier – Array number (for microarray data) – Position on bead array (for microarray data) – Batch information – Possibly also quality information (yield, RIN, etc.)
l
And all phenotypic information of possible value, e.g., (in case of gingival tissue biopsies) – Demographics (age, gender, race, and ethnicity of study subject) – Diagnosis – Systemic conditions – Local measures of disease at the biopsy site (periodontal probing depth, clinical attachment level, and subgingival levels of periodontal bacteria associated with the tissue biopsy)
2.5
Raw Data
l
From microarray experiment 1. *.idat files for all arrays run
l
From next-generation sequencing experiment 1. *.fastq files for all sequenced samples, de-multiplexed and adaptor-trimmed by core facility
l
For machine learning 1. Quality-controlled, preprocessed mRNA expression profiling data from microarray or RNASeq experiments. For this case study, we utilize a hypothetical dataset: l
l
mRNA expression profiles generated using microarrays from clinically ”diseased” gingival tissue biopsies. 200 subjects with periodontitis, one sample per subject ¼ 200 samples in total (see Note 3).
l
For each subject, a diagnosis of chronic or aggressive periodontitis [29, 30] (as per previous classification) was assigned by consensus.
l
For each tissue biopsy, there exist clinical and microbiological data.
l
Expression data were quality-controlled, normalized and batch-corrected (see Note 4).
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Methods
3.1 Pre-processing of Array Data
l
EPIC methylation arrays 1. Gather the *.idat source files for all samples, and place a targets file (as *.csv) in the source directory 2. To load the data into R using the minfi library [31] # load libraries > library(minfi) >
library(IlluminaHumanMethylationEPI-
Canno.ilm10b4.hg19) >
library
(IlluminaHumanMethylationEPICmanifest) # set working directory and load targets file > setwd("~/projects/methylation") > workDir targets
RGset
detP failed 0.01 > pData(RGset) -> pDataRGSet > names(failed)
write.table(failed,
file¼"failed.txt",
sep¼"\t")
4. Generate an extensive quality control report for all samples > qcReport(RGset, pdf ¼ "qcReport.pdf")
5. Generate beanplots for all samples (see Note 6) > pdf(file¼"Beanplot.pdf", 5, 20) > densityBeanPlot(RGset, sampNames ¼ pDataRGSet$Sample_Name) > dev.off()
6. Pre-process and additional quality control using the minfiQC function (see Note 7) # normalize raw data > MSet.Ill QCout pdf("QC.pdf") > plotQC(QCout$qc) > dev.off()
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7. Convert to beta > ratioSet MSet.Ill.genome beta.Ill colnames(beta.Ill) fastqc sample1_read1.fq.gz
2. Preprocessing (a) Filtering removes entire reads below a certain quality threshold (see Note 8). We recommend the trimmomatic program http://www.usadellab.org/cms/? page¼trimmomatic , a standalone Java application, because it can filter paired end reads and is multithreaded, i.e., fast [24]. The following command runs trimmomatic on a paired end sample, and produces four output files, two paired ones where the initial pairs are still intact after filtering, and two unpaired files containing the data from broken pairs. > java --jar trimmomatic-0.36.jar PE -threads 24 --phred33 sample1_read1.fq.gz sample1_read2.fq.gz fq.gz
output_paired_read1.
output_unpaired_read1.fq.gz
put_paired_read2.fq.gz read2.fq.gz AVGQUAL:25
out-
output_unpaired_-
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Fig. 1 FastQC examples (a) Per base sequence quality. Note how the quality of the base calls decreases towards the end of the reads. (b) Per base sequence content. For each position in the read, the percentage of the four bases is plotted. Note the bias in the beginning of the read, a typical phenomenon for Illumina RNA Seq data caused by random hexamer priming
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(b) Trimming removes bases from the end of the reads, based on a given length and/or based on a quality threshold. The following command trims bases from the 30 end of the reads that are below 25, and eventually filters the whole read when it gets too short by the trimming. > java --jar trimmomatic-0.36.jar PE -threads 24 --phred33 sample1_read1.fq.gz sample1_read2.fq.gz
paired1.fq.gz
unpaired1.fq.gz paired2.fq.gz unpaired2. fq.gz TRAILING:25 MINLEN:75
In addition, in case FastQC reports adapter contaminations, trimmomatic can remove those using the following option (see Note 10) ILLUMINACLIP:TruSeq3-PE.fa:2:30:10
(c) Repeat FastQC evaluation to assess whether the preprocessing steps were successful. 3. Alignment to reference genome (a) The reads are aligned to the genome using the spliceaware and very fast STAR aligner [22]. needs at least 32GB of memory for human genome alignments (see Notes 1, 11, and 12).
(b)
STAR
(c)
is able to take advantage of multiple processing cores of the computer´s processor(s). The number of cores to use is up to 100% of all present physical cores, or – on more recent machines that allow hyperthreading – up to 200%. Select the number of parallel processes using the --runThreadN option (see Note 13). STAR
(d) The alignment workflow consists of two steps, (i) the generation of genome index files and (ii) the mapping of the user´s reads to the genome. (e) Generation of index files (i) Create directory ./genome in STAR directory, and place the latest ENSEMBL genome sequence in this directory > mkdir genome > cd genome >
wget
http://ftp.ensembl.org/pub/
release-84/fasta/homo_sapiens/dna/ Homo_sapiens.GRCh38.dna.primary_assembly.fa.gz >
gunzip
Homo_sapiens.GRCh38.dna.
primary_assembly.fa.gz
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(ii) Create index using 30GB)
STAR
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(space requirements ~
> STAR --runThreadN 24 --runMode genomeGenerate --genomeDir ./ --genomeFastaFiles
./Homo_sapiens.GRCh38.dna.
primary_assembly.fa
(f) Mapping of reads (i) Download annotation GTF file from the Ensembl ftp server and place it in the ./genome folder >
wget
ftp://ftp.ensembl.org/pub/
release-84/gtf/homo_sapiens/Homo_sapiens.GRCh38.84.gtf.gz
(ii) Change to the source data directory containing the FASTQ files and map using STAR and the previously generated index. Specify where your genome index is located, how many cores to use, and (for compressed source files) to use zcat instead of cat to decompress on the fly (see Notes 12, 14–17). > STAR ---runThreadN 24 --genomeDir ~/ bin/STAR/genome STAR/genome/ gtf.gz fq.gz
--sjdbGTFfile
~/bin/
Homo_sapiens.GRCh38.84.
--readFilesIn
./sample1-read1.
./sample1-read2.fq.gz
--readFi-
lesCommand zcat
(iii) The alignment produces the following files: 1.
Log.final.out
2.
Log.out -- detailed log of the run, can be used for troubleshooting
3.
Aligned.out.sam --
4.
SJ.out.tab --
– summary mapping statistics.
main results file, all aligned reads in SAM format. splice junctions
4. Quantification and normalization (a) The SAM file that was produced by STAR is analyzed using the featureCounts function (included in the Rsubreads R library) [32] to assign reads to genes. > library(Rsubreads) > counts library(edgeR) > dge norm library(sva) # read information about batches and the class difference of interest from the targets file > batch target
mod
data_combat library(limma) > condition design colnames(design) contr healthDisease
write.table(healthDisease,
file¼"results_disease_health.txt", sep¼"\t")
There are several open-source software packages that, based on a differential expression analysis as describe above, generate lists and/or networks of functional groups enriched in the experimental conditions. Here, we outline how to format the results from the differential expression analysis to run basic functional analyses in ermine [28] or GSEA [25] coupled to visualization using the EnrichmentMap plugin [27] in Cytoscape [26]. An example of the comparison of enriched functional groups in different clinical conditions is in Fig. 2.
3.4 Functional Analysis
Response to other organism
immune response
Regulation of apoptosis
Immune System Process
Induction of apoptosis by extracellular signals
Cell development Signal transduction
Response to virus
Regulation of programmed cell death
Apoptosis
Defense response Response to biotic stimulus
G protein signaling
Immune response
Programmed cell death
apoptosis Electron transport
Muscle development
Ectodermal ntt development
Response to oxidative stress
Negative regulation of cellular process
Organic acid metabolic process Regulation of transport
epithelial integrity
Digestion
Epidermis developmentt
Steroid metabolic process
Cellular lipid metabolic process
Negative regulation of transcription from RNA polymerase II promoter
Regulation of developmental process
Biopolymer metabolic process
RNA metabolic process
Transcription from RNA polymerase II promoter
Carboxylytic metabolic process Golgi vesicle transport
Functional map of chronic & aggressive periodontitis
Negative regulation of transcription (DNA dependent)
System process
Negative regulation of biological process
Regulation of transcription from RNA polymerase II
Lipid metabolic process
metabolism
Nucleic acid metabolic process
signal transduction & transcription
Heart development
enrichment in aggressive
chronic
Fig. 2 Visualization of GSEA results using the Enrichment Map plugin in Cytoscape. Visualization of gene sets significantly enriched in diseased gingival tissues from patients with chronic or aggressive periodontitis. Gene sets are depicted as nodes in a network. Color describes the disease entity (red for AP and blue for CP), and the color intensity represents the degree of enrichment. The size of the node represents the size of the enriched gene set, and the thickness of the connectors stands for the degree of overlap between the nodes [27]. (Reprinted from Ref. [3] with permission from Sage)
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1. Prepare a ranked list of features. Rank all genes by t-value. 2. Use these data for Gene Set Enrichment Analysis (GSEA) using the GSEAPreRanked function (when using the GSEA graphical user interface, this function can be found in the “tools” pull-down menu). 3. Import the results into Cytoscape following this tutorial http://www.baderlab.org/Software/EnrichmentMap/ Tutorial 4. (alternatively) use ranked list in ErmineJ. 3.5 Upload to Repositories
Most journals require the submission of the raw and/or processed data from high-throughput experiments to online repositories. Repositories exist in the US as well as in Europe, with differences in the accepted data formats. l
Array repositories 1. The Gene Expression Omnibus (GEO, nlm.nih.gov/geo) at NIH.
http://ncbi.
2. ArrayExpress (http://www.ebi.ac.uk/arrayexpress) at the European Bioinformatics Institute. l
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3.6 Use of Supervised Learning Algorithms for the Distinction of Formerly Classified Aggressive and Chronic Periodontitis Based on mRNA Expression
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Use batch-corrected, pre-processed, and normalized data from microarray or sequencing experiments. The data are usually in the form of a large array with thousands of rows for the different features (i.e., genes, transcripts, CpG islands, etc.) and columns for the individual samples. In this example, we assume that only samples with periodontal disease are present (edata_aff, a subset of the edata expression
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data matrix generated in the previous steps, with only affected samples remaining). In R, we format the data according to the specifications of the CMA R package we intent to use for the supervised analysis. # label with diagnosis > colnames(edata_aff) rows, features -> columns) > edata_aff_rot diseased diseasedY diseasedX labels rownames(diseasedX) library(CMA) #set a random seed -- important to keep constant for reproducible results > set.seed(651) # generate learning sets of the same size comprising on average about 2/3 of the different available samples by bootstrapping (sampling with replacement) or other methods (See Note 25) for a high number of iterations, e.g. 1,000 different sets (See Notes 3 and 25). This step needs to be adjusted in cases of multiple samples per subject (See Note 26). > datboot varsel class_svm class_lda [. . .] more classifiers l
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The performance of the classifiers can be assessed by different measures (see Note 30), including the Area under the Receptor Operating Curve (AUC) that plots the false positive by the true positive rates. The performance data can then be plotted, either for all 1000 iterations (Figs. 3 and 4a), or for a random iteration (Fig 4b).
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Fig. 3 Microarray-based classification of AP and CP gingival lesions. Four different microarray classifier algorithms were trained to distinguish gingival lesions from AP or CP patients based on their wholetranscriptome expression profiles. For each of the 1000 splittings into training/evaluation sets that accounted for multiple tissue samples per participant, variable selection was performed based on the training set using a mixed-effects linear model. Subsequently, four different classifier algorithms [diagonal linear discriminant analysis (DLDA), partial least square analysis combined with linear discriminant analysis (PLS-LDA), shrunken centroids discriminant analysis (scDA), or a support vector machines (SVM)] were trained on the training set to distinguish between AP and CP gingival lesions based on 250 genes (DLDA, PLS-LDA, and SVM). Performance of the algorithms in the classification of the corresponding evaluation sets was then assessed using the sensitivity and specificity of AP detection, as well as (ROC) area-under-the-curve. (With permission from Sage Publishing, reprinted from Ref. [3])
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Fig. 4 Microarray classifier distinction of gingival lesions from AP and CP – SVM algorithm using different feature set sizes. For each of the 1000 splittings into training/evaluation sets, a support vector machine (SVM) classifier algorithm was trained based on the training set to distinguish AP from CP gingival lesions using either 5, 10, 50, 100, 250, 500, 750, 1000, 2500, or 5000 genes. Performance of the algorithms in the classification of the corresponding evaluation datasets was then assessed using the sensitivity and specificity of AP detection, as well as (ROC) area-under-the-curve (a). In addition, for each number of features, a ROC curve was generated for a representative iteration (b). The SVM algorithm showed improving performance with increasing signature size. (With permission from Sage Publishing, reprinted from Ref. [3])
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3.7 Identification of Novel Classes of Periodontitis Based on mRNA Expression Profiles Using Unsupervised Clustering
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As in Subheading 3.6, we use a dataset of expression data from periodontally affected subjects (edata_aff, a matrix of > 50,000 features [rows] 200 samples [columns], with a correspondent data frame with phenotypical information, pheno_aff). In this unsupervised analysis, the data are not labeled. # set a random seed -- important to keep constant for reproducible results > set.seed(651) # take top genes used for clustering and number of bootstrap iterations (See Note 31) > numbertop x mapped_probes xx is.X is.y sexChr sexChr rownames(edata_aff) -> allGenes > overlap edata_aff mads edata_aff ¼ edata_aff[order(mads, decreasing ¼ TRUE)[1:numbertop], ] # scale data > edata_aff ¼ sweep(edata_aff, 1, apply(edata_aff, 1, median, na.rm ¼ TRUE)) # combine top genes with probing depth information for each sample > data data data library(reshape) > data.long ppd colors colors[colors¼¼4] colors[colors¼¼5] colors[colors¼¼6] colors[colors¼¼7] colors[colors¼¼8] colors[colors¼¼9] colors[colors¼¼10] colors[colors¼¼11] colors[colors¼¼12] library(ConsensusClusterPlus) > results library(flexmix) > model2_ppd clusters(model2_ppd) -> cluster2_ppd l
Phenotypic analysis of the new clusters 1. Differences in expression of features and the corresponding biological groups between the novel clusters can be identified using limma [15] for a differential expression analysis and ermineJ [28] for a subsequent ontology analysis. 2. Often, the separation of the obtained clusters is illustrated using a heatmap (Fig. 7), e.g., utilizing heatmap.2 in the gplots R package [20].
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3. To compare cluster assignments, e.g., the similarity of the novel classes identified by the unsupervised analysis and ”traditional” groupings, measures like the Hubert-Arabie adjusted Rand index [36] (with 0 indicating entirely random overlaps, and 1 indicating perfect agreement) can be used (Fig. 8). # load the mclust [21] package and compare the novel, mixture model clustering based classes and the 1999 classification > library(mclust) > clusterComp Post-Alignment QC > Run QC Check(s). Follow the on-screen instructions to carry out the desired QC checks. 9. Before continuing with creation of the miRNA experiment, ensure that the appropriate annotations have been downloaded via Annotations Manager. These include the following:
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(a) Build (b) Genes and transcripts (c) Small RNA Genes (d) miRNA Targets 10. Under Utilities, select Create Small RNA Analysis Experiment. Enter the experiment name. 11. On the following screen, select the desired transcript and small RNA annotations. 12. On the following screen, the previously aligned read files should be visible. Click Finish to complete the experiment creation. 3.10.2 Experimental Design
1. At this stage, experimental and control groups can be defined within Strand-NGS. In the Experiment Setup workflow section, choose Experiment Grouping. 2. Enter the experiment parameters as desired (e.g., Non-Mineralizing, Mineralizing, Mineralizing + SAHA etc.), assigning the parameters to the appropriate samples.
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Interpretations
1. In the Experiment Setup workflow section, select Create Interpretation. 2. Select the desired experiment parameter (e.g., Mineralizing) and proceed with creation of the interpretation.
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1. Under Workflow, go to Quantification > Quantification to open the Small RNA Quantification Wizard. Follow the on-screen instructions regarding desired input list, normalization parameters, and baseline options. 2. Following quantification, the Navigator on the left-hand side will display data for “All Genes” (containing miRNA precursor genes, tRNAs, snoRNAs, etc.) and “Active Regions” (containing mature miRNA sequences). Carry out subsequent analyses on the “Active Regions” Entity List.
3.10.5 Differential Expression Analysis
1. Under Workflow, go to Expression Analysis > Replicate Analysis. 2. Select the desired Entity List and Interpretation. 3. For comparison of an experimental group compared with a control, select the moderated T-test and apply Storey and Bootstrapping to correct for multiple testing (see Note 10). These post-hoc tests such as Bootstrapping or Bonferroni correction reduce false discovery rate and increase the reliability of the data. 4. Follow the on-screen instructions to generate a list of differentially expressed miRNAs for the selected experimental group compared to a control.
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3.10.6 Target Gene Prediction (see Note 11)
1. Before beginning target gene prediction, go to Annotations > Annotations Manager and ensure the desired target prediction databases are available. 2. Under Results Interpretation, select Find Targeted Genes. 3. Follow the on-screen instructions to generate a list of target genes for the selected list of differentially expressed miRNAs (see Note 12).
3.10.7 GO Enrichment and Pathway Analysis (see Note 11)
1. In the Navigator panel, right click on each list of target genes and click Export List. Follow the on-screen instructions to export and save the list as a *.txt file. 2. GO enrichment and pathway analysis can be carried out using the Database for Annotation, Visualization, and Integrated Discovery (DAVID Bioinfirmatics Resources) (Laboratory of Human Retrovirology and Immunoinformatics (LHRI), MD, USA), which is available online (https://david.ncifcrf.gov/). 3. Select Start Analysis at the top of the screen. 4. Follow the instructions on the left-hand side of the screen to upload a list of target genes. 5. Once submitted, select Functional Annotation Tool. 6. On the following screen, select the desired categories (e.g. GOTERM_BP_FAT, KEGG_PATHWAY) (see Note 13). 7. Select Functional Annotation Clustering to view a clustered or non-redundant chart of annotation terms (see Note 14). 8. Select Functional Annotation Chart to view a linear or redundant chart of annotation terms (see Note 15). 9. In both Functional Annotation Clustering and Functional Annotation Chart view, the blue bar can be selected to view the genes involved in a given term (see Note 16). Clicking on a given GO term or KEGG pathway will take users to a new page providing more detail on that term (Fig. 3).
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Notes 1. All solutions should be sterile, preferably by autoclaving where possible. All equipment should be nuclease-free as certified by the supplier. 2. All procedures should be carried out under aseptic conditions in a laminar flow hood. 3. Cell cultures should be incubated and equilibrated at 37 C, in 5% CO2 in air. 4. Ribonucleases are very stable and difficult to inactivate. For this reason, the RNA extraction should be carried out as quickly
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Fig. 3 Example of a generated overview of the bone morphogenic (BMP) signalling pathway, which is involved in odontoblast differentiation and dentine formation [10] and was highlighted as dysregulated by pathway analysis in mineralizing DPCs + SAHA and 5-AZA-CdR, compared with mineralizing cultures. A number of genes involved in the BMP pathway are targeted by miRNAs and were discovered by target gene analysis (see Subheading 3.10.6) and are highlighted in this overview. Note, green or red shading does not highlight up or down regulation as this is not possible to ascertain using target gene analysis. P phosphorylation, ACVR1 activin receptor type-1 (ACVR1) protein a member of the BMP family, BMPRII BMP receptor 2
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and carefully as possible, while taking care to maintain an aseptic working environment. 5. Before beginning RNA extraction, ensure the centrifuge used in step 4 above, is set to 4 C. 6. Take care not to disturb the resulting white precipitate, in order to avoid DNA contamination. 7. DNase digestion is considered optional, but recommended to remove DNA contamination which may interfere with downstream analysis. 8. High-quality input RNA is essential for successful RNA sequencing. The 260/280 and 260/230 ratios give an estimate of RNA purity, and are generally accepted to be ~2.0 for pure RNA. A low 260/230 ratio may be indicative of a contaminant absorbing at 230 nm or less (e.g., phenol), while a low 260/280 ratio may be indicative of a DNA contaminant absorbing at 280 nm or less. 9. In general, mineralization is initially evident using Alizarin Red S staining by day 11–14, but in primary human cultures, it is more robust by 21 days. 10. By default, a fold change cut-off of >2.0 and q-value of +/ 2.0-fold are labeled.
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Fig. 2 qRT2-PCR expression of the 8 genes significantly down-regulated genes with 30 μM ZA treatment (Genes 1–8a) and their response to 30 μM ZA + 50 μM GGOH (Genes 1–8b). RNA was isolated from the HOBs (n ¼ 3) at 72 h. Results show the significantly dis-regulated Genes 1a–8a ( p-value < 0.05 and fold regulation > +/ 2.0) exerted some reversal in the presence of GGOH (Genes 1b–8b) 3.6.2 Volcano Plot of Selected Regulation
A modified volcano plot (Fig. 2) can also be used to focus on specific genes of interest. Here, eight genes that were significantly down-regulated in the presence of 30 μM ZA as compared to controls are plotted. The effect of the addition of 30 μM ZA + 50 μM GGOH on the expression of these genes is also shown. 1. The Y-axis is a log10 scale with the p-values. 2. The X-axis shows the gene expression as fold regulation (2ΔΔCq) values on a log2 scale. 3. The vertical black dotted lines represent a fold regulation of +/ 2.0. 4. The horizontal dotted line represents a p-value of 0.05. 5. Only those genes significantly regulated > +/ 2.0-fold in the presence of 30 μM ZA as compared to controls are presented as Genes 1–8a. 6. The correction when 30 μM ZA + 50 μM GGOH is compared to 30 μM ZA is given by Genes 1–8b. 7. Individual genes are colored and linked for clarity.
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Fig. 3 Relative qRT2-PCR expression of the CCL2 gene with 30 μM ZA alone and in combination with 50 μM GGOH or control conditions. HOBs (n ¼ 3) at 72 h of treatment/control conditions. Results expressed as mean SD, D1 ¼ ZA/Control; D2 ¼ (ZA+GGOH)/ZA; *p-value 0.05; **p-value 0.005 3.6.3 Graph Demonstrating Expression Levels of an Individual Gene (2ΔCq)
A scatter plot (Fig. 3) demonstrates a way of presenting the gene expression levels of one gene of interest after it has been normalized with a selected HKG. 1. The Y-axis is the relative gene expression and plots the 2ΔCq values. 2. The X-axis is the different group. 3. The standard deviation (SD) is presented for the data with the lines drawn in later to present the significantly different groups. 4. Neither fold change nor fold regulation is presented; however, this is a clear way of presenting the relative expression levels.
3.6.4 Graph Demonstrating the Fold Regulation of an Individual Gene (2ΔΔCq)
This graph (Fig. 4) presents the fold regulation of an individual gene under different treatments as compared to control levels. Asterisks denote the statistical significance. 1. The Y-axis is fold regulation with the 2ΔΔCq values. 2. The X-axis is the different group. 3. The SD is presented for the data with asterisks to denote statistical significance. 4. The horizontal solid line is no change in the fold regulation and the +/ 2 thresholds are given as dotted lines.
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Fig. 4 Relative qRT2-PCR expression, of the CCL2 gene, by HOB cells (n ¼ 3) after treatment with 30 μM ZA alone and in combination with 50 μM GGOH as compared to control. The horizontal solid line is no change in fold regulation and the +/ 2-fold regulation thresholds are given as dotted lines. Results expressed as mean SD; *p-value 0.05; **p-value 0.005
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Notes 1. Stock solutions (1000) are made in sterile deionized H2O then filtered through a 0.22 μm filter, aliquoted, and frozen at 20 C. 2. Only in phenotyping experiments where matrix deposition is desired is 5 mM β-Glycerophosphate added to the osteoblast medium. 3. After 10–20 days of culture, cells are evident migrating out from the explants and after 3–4 weeks, the cultures reached 80% confluence ready for sub-plating. 4. The longer time, and incubation at 37 C, is necessary to detach osteoblasts. 5. Cells are seeded at 8000 cells/cm2. Well plates make recovery with TRIzol® easier than in the T-25/T-75 cell culture flasks. Good-quality RNA (A260/A280 ratio >1.8) with a recommended sample nucleic acid level of 1 pg to 100 ng is required. It is however important to assess the effects of your treatment (s) on RNA recovery, prior to initiation of the experiments.
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References 1. Zafar S, Coates DE, Cullinan MP, Drummond BK, Milne T, Seymour GJ (2014) Zoledronic acid and geranylgeraniol regulate cellular behaviour and angiogenic gene expression in human gingival fibroblasts. J Oral Pathol Med 43: 711–721 2. Dillon JP, Waring-Green VJ, Taylor AM, Wilson PJ, Birch M, Gartland A, Gallagher JA (2012) Primary human osteoblast cultures. Method Mol Biol 816:3–18 3. Ruggiero SL (2011) Bisphosphonate-related osteonecrosis of the jaw: an overview. Ann N Y Acad Sci 1218:38–46 4. Chim SM, Tickner J, Chow ST, Kuek V, Guo B, Zhang G, Rosen V, Erber W, Xu J (2013) Angiogenic factors in bone local environment. Cytokine Growth Factor Rev 24:297–310 5. Uchida S, Sakai A, Kudo H, Otomo H, Watanuki M, Tanaka M, Nagashima M,
Nakamura T (2003) Vascular endothelial growth factor is expressed along with its receptors during the healing process of bone and bone marrow after drill-hole injury in rats. Bone 32: 491–501 6. Ramasamy SK, Kusumbe AP, Wang L, Adams RH (2014) Endothelial Notch activity promotes angiogenesis and osteogenesis in bone. Nature 507:376–380 7. Kusumbe AP, Ramasamy SK, Adams RH (2014) Coupling of angiogenesis and osteogenesis by a specific vessel subtype in bone. Nature 507: 323–328 8. Czekanska EM, Stoddart MJ, Ralphs JR, Richards RG, Hayes JS (2014) A phenotypic comparison of osteoblast cell lines versus human primary osteoblasts for biomaterials testing. J Biomed Mater Res A 102:2636–2643
Chapter 25 Fabrication and Characterization of Decellularized Periodontal Ligament Cell Sheet Constructs Amro Farag, Cedryck Vaquette, Dietmar W. Hutmacher, P. Mark Bartold, and Sasˇo Ivanovski Abstract Decellularized tissue engineered constructs have the potential to promote regeneration by providing a biomimetic extracellular matrix that directs tissue specific regeneration when implanted in situ. Recently, the use of cell sheets has shown promising results in promoting periodontal regeneration. Here, we describe the fabrication of decellularized periodontal cell sheets with intact extracellular matrix structural and biological properties. Melt electro-spun polycaprolactone (PCL) scaffolds are used as a carrier for the inherently fragile cell sheets, in order to provide support during the processes of decellularization. An optimized decellularization method is outlined using perfusion with a combination of NH4OH and Triton X-100 together with a DNase treatment step for DNA removal. The maintenance of extracellular matrix structural and biological integrity is important, and here, we describe the assessment of these properties using immunostaining for extracellular matrix proteins and ELISA for growth factor quantification. Key words Cell sheet, Decellularization, Tissue engineering, Scaffolds, Constructs, Perfusion, Polycaprolactone, Periodontal regeneration
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Introduction A novel approach to tissue engineering using “cell sheet” technology has been proposed whereby contiguous cell monolayers complete with extracellular matrix could be produced from various cell types including periodontal ligament cells [1]. Periodontal ligament cell sheets have been shown to promote periodontal tissue regeneration in vivo [2, 3]. Recently, periodontal ligament cell sheets prepared in vitro and delivered using a novel biphasic polycaprolactone (PCL) scaffold were shown to be capable of simultaneous regeneration of bone and periodontal ligament [4]. Furthermore, periodontal ligament cells were shown to be superior to other periodontal tissue sources (alveolar bone and gingiva) in promoting new functional periodontal ligament,
Gregory J. Seymour et al. (eds.), Oral Biology: Molecular Techniques and Applications, Methods in Molecular Biology, vol. 2588, https://doi.org/10.1007/978-1-0716-2780-8_25, © The Author(s), under exclusive license to Springer Science+Business Media, LLC, part of Springer Nature 2023
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alveolar bone, and new cementum formation in a surgically created rat periodontal defect model [5]. While a tissue engineered cell sheet approach is very promising, there are several underlying limitations hindering this technology from becoming translated to clinical practice. One such limitation is the reliance on the patient’s own cells, resulting in problems associated with reproducibility and safety. Another limitation is that cell sheet technology requires cell culture facilities, technical expertise, and thus considerable expense to bring this technology into daily use in the clinic. To overcome these limitations which are common to many cellbased tissue engineering strategies, the use of decellularized matrices is gaining attention for tissue engineering applications. Indeed, cell-derived tissue engineered decellularized matrices prepared in vitro have also been shown to retain their structural integrity and maintain their molecular functionality [6, 7]. We have recently reported that decellularized periodontal ligament cell sheets can retain their structural and biological integrity and be readily recellularized by allogenic cells [8]. The aim of this chapter is to describe the method of fabrication and characterization of these tissue engineered decellularized periodontal ligament cell sheet constructs.
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Materials All fluids used in the cell culture and decellularization processes have to be sterile and warmed to 37 C. Aseptic techniques should be used at times. All cell culture incubation must be performed in a 37 C, 10% CO2 humidified incubator.
2.1 Primary Human Periodontal Ligament Cell (hPDLC) Harvesting and Expansion
1. Human periodontal ligament tissue harvested from freshly extracted teeth (see details in Subheading 3.1). 2. 25 and 175 cm2 culture flasks. 3. Dulbecco’s Modification of Eagle’s medium (DMEM). 4. 10% foetal calf serum (FCS). 5. Penicillin (50 units/mL, Invitrogen). 6. Streptomycin (50 μg/mL, Invitrogen). 7. 24 well culture plates. 8. Ascorbic acid (100 & 1000 μg/mL). 9. Cell culture incubator. 10. Centrifuge machine. 11. 50 mL Falcon tubes (sterile).
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2.2 Melt Electrospun PCL Carrier Membrane Fabrication
1. Direct writing melt electrospinner with suitable G-coding software (see Note 1, [8]).
2.3 Cell Sheet Fabrication and Harvesting
1. Fine pointed curved tweezers (sterile).
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2. Medical grade PCL (PC12, Corbion).
2. 5 mm sterile dermal Biopsy punch (Kai Medical®). 3. PCL scaffolds with 0–90 fibre orientations. 1. Phosphate buffered saline (PBS). 2. 0.05% Trypsin. 3. 20 mM NH4OH solution. 4. 0.5% v/v Triton X-100. 5. Bi-directional perfusion system (explained in Subheading 3.4, Fig. 1). 6. DNase I solution (100 U/mL, Invitrogen, Cat. No. 18047019). 7. CaCl2 (0.9 mM) and MgCl2 (0.5 mM) in sterile PBS. 8. Sterile double distilled water. 9. Petri dishes (sterile).
Fig. 1 Perfusion bioreactor system for cell decellularization. (a) Perfusion pump with attached chambers. (b) 3D printed chambers used to house the cell sheet constructs during the decellularization procedure
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2.5 Cell Sheet Fixation and Preparation for Immunostaining and Confocal Imaging
1. 4% paraformaldehyde solution in PBS at pH 7.4 (SigmaAldrich). 2. Triton X-100 (0.2%) in PBS. 3. Bovine serum albumin 1% (Sigma-Aldrich) in PBS. 4. Antibodies against human Collagen I and Fibronectin (Life Technologies, Invitrogen). 5. 40 ,6-diamidino-2-phenylindole (DAPI, 5 μg/mL). 6. Phalloidin–tetramethylrhodamine B isothiocyanate conjugate (Phalloidin-TRITC, 0.8 U/mL, Life Technologies, Invitrogen). 7. Fluorescently labelled secondary antibody Alexa 633 goat antimouse antibody (5 μg/mL, Alexa Fluor, Catalog #A-21126, Invitrogen).
2.6 Growth Factor Extraction
1. NaCl (2 M). 2. 20 mM HEPES. 3. EDTA protease inhibitor cocktail (Roche complete mini, Roche Applied Science, Indianapolis, IN).
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3.1 Primary Human Periodontal Ligament Cell (hPDLC) Harvesting and Expansion
1. Place redundant freshly extracted teeth immediately in warm DMEM. 2. Hold the extracted tooth by the crown in order to avoid any damage to the periodontal tissues. 3. Obtain periodontal ligament (PDL) tissues by gently scraping the tissues attached to the middle third of the roots. 4. Dice the tissues into smaller portions (approximately 1 1 mm). 5. Using a plastic pipette, transport the diced PDL tissues to a 25 cm2 flask and wet them with one or two drops of DMEM. Place the flask in the incubator upright for 5 min, then re-wet the diced tissues again and incubate for further 5 min until the PDL tissues are firmly attached to the flask inner surface. Add 5 mL of DMEM supplemented with 10% foetal calf serum (FCS), Penicillin (50 units/mL), and Streptomycin (50 μg/ mL), with culture medium changes every 48 h until the cells reach 80% confluency (see Note 2, Fig. 2a). 6. Passage the cells by discarding the culture medium, rinsing with warm PBS twice, and then adding sufficient 0.25% Trypsin
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Fig. 2 Periodontal ligament cell cultures as observed under inverted microscopy. (a) Establishment of primary periodontal ligament cell cultures from explanted tissues. (b) Confluent periodontal ligament cultures forming a cell sheet. (c) Decellularized cell sheet constructs
to just cover the cell layer. Place the flask in the incubator for 1 min, and then check under an inverted microscope for cell detachment. Collect the cells in 10 mL of DMEM and then split the cells in a 1:3 ratio. 7. Use cells between the 3rd and 4th passages for optimal cell growth and expansion (see Note 3). 3.2 Melt Electrospun PCL Carrier Membrane Fabrication
1. Load medical grade PCL granules into a syringe, set the temperature of the water tank to 100 C until the PCL melts completely (see Note 4). 2. Adjust the spinneret collector distance to 2 cm. 3. Set the feed rate to 20 μL/h and the voltage to 10 kV. 4. Set the transitional speed of the collector at 250 mm/min in order to obtain straight fibers. 5. Set the spinner to deposit alternating series of layers with 90 degree orientation. 6. Collect the deposited scaffolds using clean fine tipped tweezers into a clean Petri dish and seal the dish with parafilm. 7. PCL scaffolds must be cut into 5 mm diameter using a biopsy punch and sterilized before cell sheet harvesting (see Note 5). Use UV in a biosafety cabinet to perform scaffold sterilization overnight where scaffolds should be embedded in ethanol 100% (Fig. 3a, b).
3.3 Cell Sheet Fabrication and Harvesting
1. Discard culture medium from the cell culture flask/s, rinse gently twice with warm PBS then detach the cells by adding Trypsin as described in Subheading 3.1, step 6. 2. Re-suspend the cells using a master mixture of 50 mL into a 24 well cell culture plate, with seeding density of 50,000 cells/ well.
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Fig. 3 Fabrication of decellularized cell sheet constructs. (a) and (b) Melt electrospun polycaprolactone (PCL) scaffolds. (c) and (d) Periodontal ligament cell sheets placed on PCL scaffolds. (e) Inverted microscope image of periphery of PCL scaffold with attached cell sheet. (f) Scanning electron microscopy (SEM) image of decellularized cell sheet construct
3. Leave the cells in high glucose DMEM for the first 24 h. 4. After 1-day post seeding, discard the old medium and add 200 μL of DMEM supplemented with 1000 mg/mL ascorbic acid for a further 72 h. 5. Discard the medium and add the same volume of DMEM but only with 100 mg/mL ascorbic acid and change this medium every 48 h for the following 15 days until the cell sheets can be visibly detected (Fig. 2b). 6. Wet the 5 mm diameter PCL scaffolds with DMEM. 7. Place the PCL scaffold exactly in the centre of each well after discarding most of the medium from each well (see Note 6). 8. Make sure that the scaffold is in contact with the cell sheet and avoid excessive forces that may damage the cell sheet. 9. Using fine curved tweezers, start detaching the cell sheets from the boundaries of the wells in a circumferential manner (see Note 7). 10. Pull the cell sheet edges upwards and towards the centre of each well, fold it over the scaffold. 11. Gently use the tweezers to flip the scaffold with the cell sheet facing upwards.
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12. Wet the detached cell sheet with 10–20 μL of medium every 5 min for 20 min total, with the culture plate placed back into the incubator after each wetting cycle. 13. Transfer the cell sheet constructs (scaffold + cell sheet/ CSC) to a new 6 well cell culture plate, then add 300 μL of DMEM/ well. The cell sheet constructs are shown in Fig. 3c–e (see Note 8). 14. Leave the constructs overnight so the cell sheets adhere to the PCL scaffolds. 3.4 Cell Sheet Construct Decellularization
1. Use a perfusion system for the cell sheet construct (CSCs) decellularization. 2. We designed a perfusion bioreactor system composed of an infusion/withdrawal syringe pump, 30 mL plastic syringes, silicone tubes, and decellularization chambers fabricated from photo-curable material. The chambers and its components were designed with CAD software and additive manufactured using an inkjet 3D printer (Objet30 Pro Desktop, Stratasys) using an acrylic resin (Verowhite Plus 835, Stratasys) as shown in Fig. 3. 3. All components must be placed in a biosafety cabinet and exposed to UV radiation overnight for sterilization. 4. Rinse the CSCs once with warm PBS at 37 C and place them in the decellularization chambers with a maximum of 11 constructs per chamber. Constructs need to be placed with horizontal distribution, and vertical stacking should be avoided to prevent cell sheet detachment (see Notes 9 and 10). 5. Perfuse the CSCs in 30 mL of 20 mM NH4OH solution with 0.5% v/v Triton X-100 at room temperature (see Note 11). 6. Bi-directional perfusion of the constructs needs to be performed for 60 min at a rate of 1000 mL/h, with a flow inversion every 50 s (see Note 12). 7. Discard the liquids from the decellularization chambers by simply detaching the silicone tubes from the perfusion chambers, then immediately connect another loaded syringe for the DNase perfusion step to the perfusion chambers. 8. Perfuse with 30 mL of DNase I solution (100 U/mL, Invitrogen) in CaCl2 (0.9 mM) and MgCl2 (0.5 mM) in sterile PBS at 37 C for 60 min (see Note 13). 9. Finally, perfuse the constructs with sterile water at 37 C for another 60 min. 10. Carefully disconnect the perfusion chambers from the syringe pump after discarding all fluids.
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11. Collect the CSCs by opening the chambers inside the cabinet using sterile tweezers. Then, place them in a culture Petri dish, add sterile PBS to completely cover the scaffolds, seal the dishes completely with parafilm, and then place them in a refrigerator overnight at 4 C. The appearance of the cell sheet constructs using an inverted microscope and SEM is shown in Figs. 1c and 2f, respectively. 3.5 Immunostaining of Cell Sheet Constructs
1. Place constructs in a clean multi-well culture plate and rinse samples carefully twice with PBS at room temperature. 2. Fix constructs in 4% paraformaldehyde (PFA) for 20–30 min. 3. Discard PFA and then wash once with PBS. 4. If necessary, permeabilize cells for 5 min in 0.2% Triton X-100 in PBS. 5. Discard solutions and then wash samples twice gently with PBS. 6. Transfer samples into 1% BSA in PBS and incubate for 10 min. 7. Prepare solutions of antibodies diluted in 1% BSA in PBS, 1: 200 for collagen type I and 1:300 for fibronectin. 8. Add 100–200 μL of antibody solution for each well and then incubate for 45 min. 9. Rinse constructs three times with PBS. 10. Incubate samples in 1% BSA in PBS containing fluorescently labeled secondary antibody (Alexa 633 goat anti-mouse, 5 μg/ mL), 0.8 U/mL TRITC-conjugated phalloidin, and 5 μg/mL DAPI for 45 min protected from light. 11. Rinse twice with PBS at room temperature and then proceed with confocal imaging.
3.6 Growth Factor Extraction
1. Dissolve 1 tablet of the protease inhibitor in 10 mL of the extraction buffer solution (2 M NaCl in 20 mM HEPES). 2. Rinse constructs twice with PBS at room temperature. 3. Add 300 μL of extraction buffer solution with protease inhibitor for each well/construct. 4. Seal the plate with parafilm and incubate for 60 min on an orbital shaker at room temperature. 5. Collect the supernatant for each construct into a sterile Eppendorf tube and store at 80 C.
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Notes 1. We used a custom-made melt electrospinning writer fabricated in-house at the Institute of Health and Biomedical Innovation, Queensland, University of Technology [9]. 2. Avoid cell over-confluency during the cell propagation (expansion) phase as it affects their future survival, growth, and ability to form extracellular matrix. 3. When seeding in large 175 cm2, start with at least 500,000 cells as lower seeding densities are much slower to reach semiconfluency. 4. Multiple biomaterial options exist for the carrier component of the construct. We utilized electro-spun PCL because it is a medical grade material with excellent biocompatibility. It has the key properties of being able to be fabricated in a highly porous structure that allows perfusion of the decellularization reagents, while still retaining appropriate mechanical properties to provide sufficient support during the perfusion process. It is also a chemically stable polymer that retains its structural integrity during the decellularization process. Other biomaterials with similar properties could also be utilized as the cell sheet carrier component of the construct. 5. If using PCL scaffolds as a carrier for the cell sheets, it is advisable to enhance the scaffold hydrophilicity by immersion into 2 M NaOH for 30 min followed by 5 rinses of ultrapure water. 6. Avoid excessive pressure when placing a scaffold on top of a cell sheet during the cell sheet harvesting step. 7. When using fine tweezers to detach a cell sheet, always aim the tip to the boundaries of the well, to be able to harvest an intact cell sheet. 8. When relocating or transferring the constructs from one plate to another or to perfusion chambers, always handle the constructs with extra care holding each one separately at a time. Use broad-tipped tweezers for this procedure while holding the constructs from the sides, also avoid squeezing the construct in order not to damage the cell sheet and/or the scaffold. 9. Use spacers between the scaffolds inside the perfusion chamber. 10. Arrange the scaffolds with enough distance between each, so they do not adhere to each other. Avoid overfilling of the decellularization chambers with fluids. However, please note that scaffolds must be immersed completely in fluids all the time.
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11. Avoid rapid fluid perfusion as this will disrupt the constructs. 12. CaCl2 (0.9 mM) and MgCl2 (0.5 mM) concentrations in PBS are essential to activate the DNase enzyme. However, if these concentrations are exceeded, calcium ions will chelate with phosphate forming a turbid white solution. If this occurs during the DNase perfusion step, the solution should be discarded and a fresh one prepared. 13. DNase enzyme concentration and the period of perfusion should not exceed what is outlined in Subheading 3.4, step 8, as this will damage the extracellular matrix components.
Acknowledgments This work was supported by the Australia’s National Health and Medical Research Council project grant 1086181. References 1. Ishikawa I, Iwata T, Washio K et al (2009) Cell sheet engineering and other novel cell-based approaches to periodontal regeneration. Periodontol 2000 51:220–238 2. Flores MG, Yashiro R, Washio K et al (2008) Periodontal ligament cell sheet promotes periodontal regeneration in athymic rats. J Clin Periodontol 35:1066–1072 3. Akizuki T, Oda S, Komaki M et al (2005) Application of periodontal ligament cell sheet for periodontal regeneration: a pilot study in beagle dogs. J Periodontal Res 40:245–251 4. Vaquette C, Fan W, Xiao Y et al (2012) A biphasic scaffold design combined with cell sheet technology for simultaneous regeneration of alveolar bone/periodontal ligament complex. Biomaterials 33:5560–5573 5. Dan H, Vaquette C, Fisher AG et al (2014) The influence of cellular source on periodontal regeneration using calcium phosphate coated
polycaprolactone scaffold supported cell sheets. Biomaterials 35:113–122 6. Gawlitta D, Benders KE, Visser J et al (2015) Decellularized cartilage-derived matrix as substrate for endochondral bone regeneration. Tissue Eng Part A 21:694–703 7. Sadr N, Pippenger BE, Scherberich A et al (2012) Enhancing the biological performance of synthetic polymeric materials by decoration with engineered, decellularized extracellular matrix. Biomaterials 33:5085–5093 8. Farag A, Vaquette C, Theodoropoulos C, Hamlet SM, Hutmacher DW, Ivanovski S (2014) Decellularized periodontal ligament cell sheets with recellularization potential. J Dent Res 93: 1313–1319 9. Brown TD, Dalton PD, Hutmacher DW (2011) Direct writing by way of melt electrospinning. Adv Mater 23:5651–5657
Chapter 26 Immunohistochemistry and Immunofluorescence Haizal Mohd Hussaini, Benedict Seo, and Alison M. Rich Abstract Immunohistochemistry (IHC) is one of the most widely used protein detection techniques. The principle of this technique is based on the binding of a specific antibody to a matching specific antigen in tissue. The bound antigen-antibody complex then is visualized using a range of detection techniques. IHC uses a number of different enzymatic labels, such as peroxidase and alkaline phosphatase, for the detection of the antigens of interest whereas immunofluorescence (IF) uses a fluorescent signal. In this chapter, IHC will be described using the peroxidase label. Both IHC and IF can be used on formalin-fixed paraffin-embedded (FFPE) or appropriately processed fresh tissues. IHC/IF can be multiplexed to detect more than one antigen at a time, or may be sequentially stained to detect multiple targets. These techniques are routinely used in diagnostic pathology laboratories, not just for diagnostic purposes but many biomarkers are used for patient staging, treatment allocation, and prognostication. Immunofluorescence is routinely used for the detection of antibodies and antigens in freshly biopsied tissues, particularly for immune-mediated and vesiculobullous lesions. In this chapter, the principles of IHC are reviewed followed by examples of IHC and IF staining using readily available antibodies. Steps and processes involved in IHC/IF double staining are also described. Key words Immunohistochemistry (IHC), Immunofluorescence (IF), Antigen-antibody complex, Multiplex, Double staining
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Introduction Immunohistochemistry can be defined as a technique that can identify or localize an antigen within a tissue or cell using a labeled antibody against the specific antigen. It has become an important tool to determine tissue distribution of an antigen of interest in diagnostic pathology as well as in research [1]. The technique of labeling an antigen for microscopic visualization was first described by Coons and Kaplan [2, 3]. Initially, a fluorescent dye, fluorescein isothiocyanate (FITC), was used to identify and label the specific antibody. As the technique developed and expanded, enzymatic
Gregory J. Seymour et al. (eds.), Oral Biology: Molecular Techniques and Applications, Methods in Molecular Biology, vol. 2588, https://doi.org/10.1007/978-1-0716-2780-8_26, © The Author(s), under exclusive license to Springer Science+Business Media, LLC, part of Springer Nature 2023
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labels such as horse-radish peroxidase and alkaline phosphatase were introduced in the early 1960s and 1970s [4]. With enzymatic labels, the specific antigen-antibody complex can be visualized using a conventional light microscope over a period of time without fading of the signal, as will happen with IF. The complex can be visualized when labeled to primary, secondary, or tertiary antibodies of a detection system. The detection systems may use a “direct” or “indirect” method. The “direct” detection method is the original method described by Coons and Kaplan, which is a one-step process with the primary antibody conjugated directly to the label. This method is efficient but lacks sensitivity. Following this, an “indirect” method was introduced whereby the first antibody is unlabeled, but the second layer is labeled [4]. This method allows detection of smaller amount of the antigen epitope, with greater sensitivity. This method, utilizing the strong attraction between avidin and biotin (the avidin-biotin complex [ABC] method), has tremendously improved the specificity and the intensity of the final reaction and thus enhanced the visualization of the antigen. The ABC detection system has expanded the use of IHC in diagnostic pathology, including cancer diagnosis, so much so that IHC has become a routine application in clinical diagnostic pathology [5]. Polymer-based detection systems have now superseded the ABC system. A problem with the ABC technique was that endogenous tissue avidin and biotin caused background staining which had to be blocked. The polymer-based system utilizes a polymer backbone which allows multiple antibodies and antigens to be conjugated. This gives the polymer-based system higher specificity and specificity as well as essentially eliminating background staining. Detection systems such as EnVision™ [6, 7] are relatively new two-step indirect dextran polymer systems which use less labor and time compared with the three-step ABC method. Less application time is required (30 min incubation time or less). These systems are more efficient and sensitive compared with earlier methods with improved standardization. Immunohistochemistry techniques do have limitations. Multiple variables may impact the sensitivity and specificity of antigen detection and may yield false-positive or false-negative results. These variables include tissue fixation, tissue processing, antigen retrieval methods, antigen concentration, staining, and visualization techniques [8]. Nevertheless, IHC is still the most widely used application in tissue and cellular antigen detection, and in this chapter, we provide the methods for conducting simple IHC staining using enzymatic and fluorescent labels.
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Materials
2.1 Tissue and Slide Preparation
1. Appropriately fixed tissue (including tissue for positive and negative controls). 2. Positively charged microscope slides (e.g., Histobond® slides, Marienfeld Laboratory Glassware, Germany). 3. Microtome set at 4 μm thickness. 4. Materials for deparaffinization and rehydration; xylene, 100% alcohol, and phosphate buffered saline (PBS) (see Note 1).
2.2 Antigen Retrieval (Heat-Induced Method)
1. Coplin jar. 2. Conventional laboratory oven. 3. Sodium citrate buffer (see Note 2). 4. Scientific microwave or a water bath unit.
2.3 Antigen Retrieval (Enzyme Digestion Method)
1. Conventional laboratory oven.
2.4 Immunohistochemistry
1. 3% hydrogen peroxide (H2O2) in methanol
2. Proteinase K, ready-to-use (RTU) (S3020, Agilent Dako). 3. PBS.
2. Antibody diluent (S3022, Agilent Dako). 3. Humidity chamber, e.g., commercially available plastic tray/ humidity chamber. 4. IHC PAP pen for forming a liquid barrier. 5. Set of laboratory micropipettes (e.g., Eppendorf) with relevant tips. 6. Refrigerator (0–4 C). 7. Primary antibody (mouse or rabbit/monoclonal or polyclonal) from commercial manufacturer. 8. Secondary antibody EnVision+ Dual Link System-HRP antimouse/rabbit. 9. Diaminobenzidine (DAB) + Substrate Chromogen System. 10. Gill’s hematoxylin solution. 11. Scott’s Tap Water substitute working solution concentrate bluing reagent. 12. Materials for dehydration and mounting (Scott’s tap water, absolute alcohol (100%) xylene, cover slips, and xylene-based embedding agent).
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2.4.1 Double Immunohistochemistry
1. Similar materials to 2.4, with additional color chromogen, e.g., alkaline phosphatase red. 2. Glycerine.
2.5 Immunofluorescence
1. Similar materials to 2.4 with darker humidity chamber. 2. Secondary fluorochrome either (a) anti-rabbit green fluorochrome Alexa Fluor® 488 or (b) anti-mouse red fluorochrome Alexa Fluor® 594, or both. 3. Mounting medium, Laboratories).
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Vectashield®
(H1500,
Vector
Methods
3.1 Tissue and Slide Preparation
To deparaffinize and rehydrate tissue in preparation for IHC, immerse slide in xylene for 5 min twice, immerse in absolute alcohol (100%) for 2 min three times, immerse in running tap water for 2 min once, and immerse in PBS for 3 min once.
3.2 Manual Antigen Retrieval (HeatInduced) Method
Most archival tissue samples that have been stored in a tissue bank or in a pathology laboratory would have been formalin-fixed and embedded in paraffin (FFPE). Formalin-fixed tissue will contain cross-linked proteins that could potentially mask the antigen site. In order to unmask the tissue antigens and make them available for binding to the primary antibody, a heat-induced antigen retrieval (HIAR) technique is commonly used. This is a relatively easy and cost-effective way to retrieve the antigen. This method can be used with most commercially available antibodies. 1. Sections from specimens are cut using a microtome set at 4 μm thickness and embedded on adhesive positively charged Histobond® microscope slides. 2. Tissue sections are kept overnight at 37 C (conventional laboratory oven) to secure the tissue onto the slides. 3. Sections are deparaffinized and rehydrated prior to antigen retrieval. Following rehydration, the sections are placed into a Coplin jar containing 0.01 M pre-prepared sodium citrate buffer (pH 6.0). 4. The Coplin jar is covered with kitchenware plastic wrap and ventilated to allow evaporation during the heating process which takes place in a scientific microwave oven set at ~80 C. The heating is done in two cycles of 5 min each with a 1 min interval between to allow for topping up of any buffer solution which has evaporated. Alternatively, this process can be done in a water bath with the temperature set at ~80 C. Make sure that the Coplin jar is securely resting in the water bath and the heated water does not go into the Coplin jar.
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5. After completion of the heating phase, the Coplin jar is taken out of the microwave or water bath and cooled off at room temperature (RT) for approximately 30 min. 6. The slides can now be taken out of the buffer and washed twice in PBS for 10 min each. 3.3 Antigen Retrieval (Enzyme Digestion Method)
Another method to unmask tissue antigens and make them available for binding to the primary antibody is to use proteolytic digestion of FFPE tissue prior to IHC procedures. A commercially available protein digestion enzyme can be used, such as RTU Proteinase K. This method can be used on any commercially available research antibodies. 1. Sections from specimens are cut using a microtome set at 4 μm thickness and embedded on adhesive positively charged Histobond® microscope slides as above. 2. Tissue sections are kept overnight at 37 C (conventional laboratory oven) to secure the tissue onto the slides. 3. Sections are deparaffinized and rehydrated prior to antigen retrieval. Following rehydration, place the slides in a humidity chamber or an enclosed plastic container. The slides will need to rest on a raised block to facilitate washing and pipetting of solutions. 4. Pipette 150 μL of Proteinase K enzyme and drop it onto the tissue. Leave the enzyme to incubate for 6 min. 5. To stop the enzymatic reaction, wash the slides with 150 μL PBS (pipetted on the slide in two 5-min intervals).
3.4 Preparation of Primary Antibody (Optimization of Antibody)
Commercially obtained primary antibody will normally have a range of optimum dilutions recommended by the manufacturer to get the best staining outcome. This dilution range has been obtained from company testing and/or various published research work using a similar antibody. Some antibodies will come pre-diluted and are known as RTU antibodies. This means that the primary antibody is ready to be applied to the slides without needing any optimization. For a primary antibody that is not RTU, it is wise to start the optimization process using a working sheet (example shown in Table 1), beginning with one dilution at each end of the range and one in the middle. Calculation of the correct dilution is important to obtain the correct concentration. The antibody is usually diluted with a commercially available diluent. Primary antibody incubation times are often not specified by the manufacturer and therefore should also be tested to determine the most suitable incubation period. The optimization procedures should be done on positive control tissue that has been recommended by the antibody’s manufacturer, or on tissues known to
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Table 1 Example of a working sheet for antibody optimization CD3
CD4
CD8
Positive control tissue
Spleen
Human tonsil
Human tonsil
Dilution
Commercially RTU
1:50
Equivalent concentration μg/mL
Unspecified
5 μg/mL 2.5 μg/mL 1.25 μg/mL 4 μg/mL 2 μg/mL 1 μg/mL
Staining intensity
++
Too +++ strong
+++
Too +++ strong
+++
Incubation time (min)
30*
30
45
60*
30
45
60*
Staining intensity
Good Good Too Weak strong
Good
Good
Weak
Good
Good
HIAR
0.01 M sodium citrate buffer (pH 6.0) 2 cycles of 5 min each
0.01 M sodium citrate buffer (pH 6.0) 2 cycles of 5 min each
0.01 M sodium citrate buffer (pH 6.0) 2 cycles of 5 min each
Final protocol
RTU at 30 min
1:200 at 1 h
1:200 at 1 h
Isotype
No staining detected (anti-rabbit IgG)
No staining detected (anti-mouse IgG)
No staining detected (anti-mouse IgG)
Staining chromogen
DAB (DAKO)
DAB (DAKO)
DAB (DAKO)
Counterstain
Hematoxylin
Hematoxylin
Hematoxylin
45
60
1:100
1:200
1:50
1:100
1:200
express that particular antigen. The optimum dilution and incubation time should be selected based on the preferred staining intensity. Example of Antibody Calculation for Dilution Manufacturer’s recommended concentration for staining: 1 μg/mL. Antibody concentration received: 100 μg mL 100 1 A ¼ 1000 1000 A ¼ 1=100 The starting dilution is 1/100. Depending on how many slides to be stained, each slide will have 1 μL of antibody in 99 μL of antibody diluent. Each slide will need between 100 and 150μL of antibody solution, so it is wise to calculate and prepare 10–20% more than the needed amount of working antibody solution. Example: 2 slides need to be stained. 2 ul of primary antibody + 198 ul diluent ¼ 200 μL.
Immunohistochemistry and Immunofluorescence Add 100ul of diluent Take 100ul
1:100
Add 100ul of diluent Take 100ul
1:200
1:400
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Add 100ul of diluent
Take 100ul
1:800
Fig. 1 Showing serial dilution
Serial Dilutions In order to prepare the antibody for a range of dilution, e.g., 1/100, 1/200, and 1/400, stepwise dilutions can be done after preparing the first solution (Fig. 1). 3.5 Primary Antibody Incubation and Secondary Antibody Detection (Single Immunostaining)
Following antigen retrieval, the slides are ready to be incubated with the primary antibody. The presence of endogenous peroxidase in the tissue may react with the chromogen solution (e.g., DAB) applied to the tissue and produce a false-positive result. Therefore, endogenous peroxidase enzyme activity in the tissue sections should be blocked to prevent non-specific background staining. It is advisable to run the experiment together with an isotype control antibody, which is an IgG-matched rabbit or mouse isotype antibody used at the same concentration as the primary antibody. The current preferred method to detect antigen is the indirect method. The direct detection method is a one-step process with the primary antibody conjugated directly to the label, whereas in the indirect method, the primary antibody is unlabeled, but the secondary antibody is labeled. The indirect method is more sensitive and can detect smaller amounts of antigen. Newer technology using polymer-based systems (e.g., Envision+, Agilent Dako) allows multiple antigens and antibodies to be conjugated, and this method has superseded the avidin-biotin complex (ABC) method. The antigenantibody complex can be visualized with light microscopy by adding a chromogen such as DAB (Fig. 2). The methods described below can be used for staining with pre-optimized antibody or RTU as well as in the process of optimizing an antibody itself. 1. Slides that have gone through antigen retrieval now ready for primary antibody incubation.
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Fig. 2 (a) Showing magnification 20, visualization of lymphocytes with anti-CD25 single immunostaining positivity with DAB (brown). The cell surface and cytoplasmic staining is visible without any positive staining of the nucleus (no nuclear staining). (b) Showing 40 magnification of double immunostaining with anti-TLR2 (Alkaline phosphatase: red) and anti-FoxP3 (DAB: brown). FoxP3 can be visualized with prominent nuclear staining as shown with the yellow arrow, whereas TLR2 has a cytoplasmic staining as shown by the black arrow. (c) Showing 100 magnification. The circle is showing a co-expression (double-stained) of TLR2 and FoxP3 which can be visualized with a cell showing a brown nuclear staining and red cytoplasmic stain. This double-stained cell is surrounded by three single-stained of red cytoplasmic staining of TLR2. (d) Showing fluorescence magnification 100. The yellow box shows co-expression of TLR2 and FoxP3 while the white arrow shows co-localization between a FoxP3 positive cell and a TLR2 positive cell
2. Place the slides in a Coplin jar and submerge the slides in 3% H2O2 in methanol for 15 min, followed by two 10-min washes in PBS (blocking endogenous peroxidase). 3. Place the slides in a humidity chamber or an enclosed plastic container. The slides will need to rest on a raised block to facilitate washing and pipetting of solutions. 4. Draw a circle around the tissues using the IHC PAP pen. The pen will provide a thin water repellent barrier and surface
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tension to hold the antibody solution and prevent it from running off the slides. 5. The primary antibody that has been prepared to the desirable/ optimum concentration using an antibody diluent, can now be applied using a pipette. Draw up 100 μL of the primary antibody and drop it onto the tissue gently within the drawn circle. Simultaneously, the isotype control antibodies (mouse/rabbit) that serve as negative controls for the IHC experiments should be processed in parallel with the experimental sections, along with the positive control. 6. Incubate the primary antibody with the tissue for the pre-determined desirable period of time, usually 1–2 h at RT. Incubation time varies and can be up to 24 h. If it is longer than 2 h, it is best to keep it at 4 C in the refrigerator. During the incubation period, make sure that the chamber is humid with a layer of PBS at the base of the chamber to prevent the antibody from evaporating. 7. After the incubation period has ended, wash the slides with 150 μL PBS (pipetted on the slide in two 5-min intervals). It is important to always have a layer of PBS solution so that the tissue will not dry out. 8. Draw up 100 μL of the secondary antibody (e.g., EnVision+ anti-mouse/rabbit) and drop it on the tissue gently within the drawn circle. 9. Incubate for 30 min at RT in the humidity chamber. 10. After the secondary antibody incubation period has ended, wash the slides with 150 μL PBS (pipetted on the slide in two 5-min intervals). 11. Pipette 100 μL of DAB solution onto each slide and leave for 1–3 min. The DAB solution should be pre-prepared by mixing one drop of DAB substrate with 1 mL of supplied buffered solution (DAB+ Substrate Chromogen System, Dako Agilent). 12. The sections can be now counterstained with Gill’s hematoxylin for 30 s, followed by dipping into Scott’s tap water for 30 s and washing under running water for 1 min. The sections should then be rehydrated with a series of graded alcohols followed by xylene. 13. Finally, the slides can be cover-slipped individually in a fume hood using a xylene based non-aqueous mounting media. 3.6 Qualitative and Quantification Analysis of Immunostaining
In qualitative analysis, usually the location of staining, type of staining (membranous, intracellular, or nucleus), and intensity are described. For quantification analysis, a semi-quantitative method such as the immunoreactive score described by Koo et al. [9] and Lee et al. [10] is commonly used.
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3.7 Double Immunostaining Immunohistochemistry
Double immunostaining can be performed to detect two types of antigens (e.g., S100 and smooth muscle actin [SMA]) in the same tissue section. This will allow the user to investigate co-localization of both antibodies. It is preferable that the two antibodies be raised in different animal, (e.g., S100 is raised in mouse and SMA in rabbit) in order to prevent cross-reactions between the individual detection methods. In this method, two protocols for single immunostaining need to be performed for each antibody sequentially and the visualization chromogens needs to be of two different colors (e.g., DAB ¼ brown and alkaline phosphatase ¼ red) (Fig. 2). Below is the example of how to run a double immunostaining protocol. 1. Prepare the slides for antigen retrieval using protocols already described. 2. Apply the first primary antibody using the protocol described in Subheading 3.5 followed by application of the polymer secondary antibody. 3. Add the DAB solution to the slides, and wash with 150 μL PBS (pipetted on the slide in two 5-min intervals). 4. After completing the washing cycle, the first layer of the antigen-antibody complex needs to be denatured using the antigen retrieval techniques already described. 5. The second primary antibody is then applied, followed by the polymer secondary antibody as per step 2. 6. For the second antibody, a different type of chromogen (e.g., Alkaline Phosphatase Red, Vector Red) needs to be applied. 7. Once this is completed, the sections can be now counterstained with Gill’s hematoxylin for 30 s, followed by dipping into Scott’s tap water for 30 s. The sections will then be rehydrated with a series of graded alcohols followed by xylene (use a very short dip of less than 30 s in xylene). 8. Finally, the slides can be cover-slipped individually in a fume hood. It is advisable to use a water-based media such as glycerine as Alkaline Phosphatase chromogen is not compatible with xylene-based medium.
3.8 Immunofluorescence Staining
The IF technique uses a fluorochrome label instead of an enzymebased reagent such as DAB or alkaline phosphatase. The IF technique is often used to demonstrate co-localization; therefore, double-labeling IF is usually preferred to single labeling. When working with double IF, similar considerations should apply as with double chromogenic staining. In order to prevent cross-reaction(s) between individual detection methods, antibodies from different species (mouse-rabbit monoclonal combination) with color combinations that provide sufficient contrast between the two antibodies are needed to enhance visualization. This
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consideration, plus the different localization of the antigens of interest (e.g., cell membrane or nuclear), allows double IF to be a specific and reliable method. The fluorochromes (e.g., Alexa Fluor®) are light sensitive and need to be in a completely dark humidity chamber for incubation. Also required is a fluorescence microscope with a similar wavelength filter as the fluorochromes (Fig. 2). Below is the protocol for double IF immunostaining on FFPE prepared slides. For those who want only single labeling, similar steps apply but without the second antibody. The example given here is anti-human rabbit polyclonal antibody for CD4 (cell membrane staining) and anti-human mouse monoclonal antibody for FoxP3 (nuclear staining). 1. The sectioned tissue samples are placed onto adhesive positively charged Histobond® microscope slides (Marienfeld Laboratory Glassware, Germany). 2. Undertake antigen retrieval using protocols already described. 3. Optimized working dilutions of the primary antibodies (in this case, anti-CD4 and anti-FoxP3) can be pre-mixed into a cocktail solution (without changing their optimum dilutions) and incubated simultaneously. 4. Experimental and control slides incubated overnight at 4 C in a refrigerator. 5. On day 2, the secondary labeling antibodies (anti-rabbit green fluorochrome [Alexa Fluor® 488] and anti-mouse red fluorochrome [Alexa Fluor® 594]), which are also pre-mixed are placed on the slides and incubated simultaneously for 1 h at 25 C (RT) in a light-tight box. 6. The sections can now be mounted manually within a fume cupboard using Vectashield® (Vector Laboratories) which is an aqueous hard-set mounting media with DAPI (40 , 6-diamidino-2-phenylindole), a blue fluorescence counterstain. Supplemental Information: Immunohistochemistry Using an Autostainer All busy contemporary diagnostic histopathology laboratories have an autostainer for their routine IHC diagnostic work, as do many research laboratories. Commercial companies that manufacture IHC autostainers produce machines with different specifications for either diagnostic work or purely research work, since diagnostic IHC autostainers need to adhere to strict unvarying protocols required for quality assurance programs and diagnostic accreditation agencies. On the other hand, it is desirable that a research IHC autostainer offers more flexibility in their testing parameters, e.g., a research IHC autostainer would be able to change the temperature range of HIAR steps, whereas a diagnostic IHC autostainer would have to be fixed at a specific temperature for a specific test. Nevertheless, all fully automated IHC
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autostainers are able to de-paraffinize, retrieve the antigen using HIAR or enzymatic methods, incubate primary and secondary antibody, enhance the antibody binding, label the antibody complex, and counterstain with hematoxylin. Most IHC autostainers would also be able to undertake in situ hybridization with binding to nucleic acids within the nucleus.
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Notes 1. Use commercially purchased PBS (e.g., Sigma-Aldrich P5493, pH 7.4) or prepare, thus mix 8 g sodium chloride (NaCl), 0.2 g of potassium chloride (KCl), 1.44 g disodium hydrogen phosphate (Na2HPO4), and 0.24 g of potassium dihydrogen phosphate (KH2PO4) in 1 L distilled water until dissolved, adjust to pH 7.4 by using phosphoric acid or sodium hydroxide with a pH meter probe. 2. Sodium citrate buffer (0.01 M): Mix 2.94 g trisodium citrate with 1 L distilled water, adjust the pH to 6.0 by using phosphoric acid or sodium hydroxide with a pH meter probe.
References 1. Duraiyan J, Govindarajan R, Kaliyappan K, Palanisamy M (2012) Applications of immunohistochemistry. J Pharm Bioallied Sci 4:S307 2. Coons AH, Leduc EH, Kaplan MH (1951) Localization of antigen in tissue cells. VI. The fate of injected foreign proteins in the mouse. J Exp Med 93:173–188 3. Coons AH, Kaplan MH (1950) Localization of antigen in tissue cells; improvements in a method for the detection of antigen by means of fluorescent antibody. J Exp Med 91:1–13 4. Nakane PK, Pierce GB (1966) Enzyme-labeled antibodies: preparation and application for the localization of antigens. J Histochem Cytochem 14:929–931 5. Idikio HA (2010) Immunohistochemistry in diagnostic surgical pathology: contributions of protein life-cycle, use of evidence-based methods and data normalization on interpretation of immunohistochemical stains. Int J Clin Exp Pathol 3:169–176 6. Vyberg M, Nielsen S (1998) Dextran polymer conjugate two-step visualization system for immunohistochemistry: a comparison of EnVision+ with two three-step avidin- biotin techniques. Appl Immunohistochem 6:3–10 7. Sabattini E, Bisgaard K, Ascani S, Poggi S, Piccioli M, Ceccarelli C, Ieri F, FraternaliOrcioni G, Pileri SA (1998) The EnVision
(TM)+ system: a new immunohistochemical method for diagnostics and research. Critical comparison with the APAAP, ChemMate (TM), CSA, LABC, and SABC techniques. J Clin Pathol 51:506–511 8. Engel KB, Moore HM (2011) Effects of preanalytical variables on the detection of proteins by immunohistochemistry in formalin-fixed, paraffin-embedded tissue. Arch Pathol Lab Med 135:537–543 9. Lee C-H, Ali RH, Rouzbahman M, MarinoEnriquez A, Zhu M, Guo X, Brunner AL, Chiang S, Leung S, Nelnyk N, Huntsman DG, Gilks CB, Nielsen O, Cin PD, van de Rijn M, Oliva E, Fletcher JA, Nucci MR (2012) Cyclin D1 as a diagnostic immunomarker for endometrial stromal sarcoma with YWHAE-FAM22 rearrangement. Am J Surg Pathol 36:1562–1570 10. Koo CL, Kok LF, Lee MY, Wu TS, Cheng YW, Hsu JD, Ruan A, Chao KC, Han CP (2009) Scoring mechanisms of p16INK4a immunohistochemistry based on either independent nucleic stain or mixed cytoplasmic with nucleic expression can significantly signal to distinguish between endocervical and endometrial adenocarcinomas in a tissue microarray study. J Transl Med 7:25. https://doi.org/10.1186/ 1479-5876-7-25
Chapter 27 Characterization, Quantification, and Visualization of Neutrophil Extracellular Traps Josefine Hirschfeld, Ilaria J. Chicca, Carolyn G. J. Moonen, Phillipa C. White, Martin R. Ling, Helen J. Wright, Paul R. Cooper, Mike R. Milward, and Iain L. C. Chapple Abstract Following the discovery of neutrophil extracellular traps (NETs) in 2004 by Brinkmann and colleagues, there has been extensive research into the role of NETs in a number of inflammatory diseases, including periodontitis. This chapter describes the current methods for the isolation of peripheral blood neutrophils as well as of oral neutrophils for subsequent NET experiments, including approaches to quantify and visualize NET production, the ability of NETs to entrap and kill bacteria, and the removal of NETs by nuclease-containing plasma. Key words Neutrophil extracellular traps, Reactive oxygen species, Fluorescence microscopy, SEM, Chemiluminescence, DNA, Elastase, Myeloperoxidase, Cathepsin G, Immunostaining
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Introduction Neutrophils are terminally differentiated effector cells of the hematopoietic myeloid lineage, which are critical to both the innate and acquired (humoral) immune response. In order to eliminate invading pathogens, neutrophils have a comprehensive cytotoxic arsenal at their disposal, which includes reactive oxygen species (ROS) and granule-derived proteins [1]. In 2004, a new paradigm in neutrophil biology was described by Brinkmann and colleagues, referred to as neutrophil extracellular traps (NETs) [2]. NETs are highly conserved extracellular mesh-like structures of decondensed
The original version of this chapter was revised. The correction to this chapter is available at https://doi.org/ 10.1007/978-1-0716-2780-8_32 Authors Josefine Hirschfeld and Ilaria J. Chicca have equally contributed to this chapter. Gregory J. Seymour et al. (eds.), Oral Biology: Molecular Techniques and Applications, Methods in Molecular Biology, vol. 2588, https://doi.org/10.1007/978-1-0716-2780-8_27, © The Author(s), under exclusive license to Springer Science+Business Media, LLC, part of Springer Nature 2023, Corrected Publication 2023
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nuclear chromatin and associated antimicrobial peptides (AMPs), whose putative role is to immobilize and kill invading pathogens. The expulsion of nuclear NETs is believed to arise during an active form of programmed cell death, often termed NETosis, that is dependent upon ROS production and the citrullination of arginine and methylarginine residues of the nuclear chromatin by peptidyl arginine deiminase 4 (PAD4) [3, 4]. However, there is emerging evidence to suggest that NETs are also induced by other intricate molecular pathways, such as the release of mitochondrial derived NETs from cells that remain viable following NET expulsion [5] and a rapid form of ROS-independent NET release [6]. To determine the role of NETs in various disease states, neutrophils are often isolated from peripheral venous blood and investigated ex vivo. Neutrophils circulate in the blood stream, from which they may extravasate into tissues exposed to a microbial challenge, including the oral cavity, which they enter through the gingival crevice adjacent to teeth. Neutrophils are the predominant immune effector cells present in the mouth. Around 30,000 neutrophils extravasate per minute into the oral cavity from the periodontal tissues via the gingival crevicular fluid [7]. Once in the oral cavity, neutrophils perform several immune defense functions against microorganisms [8, 9], including NET release [10]. Oral neutrophil counts fluctuate and have been proven to increase with a higher severity of periodontitis [11]. Hitherto, peripheral blood neutrophils have been extensively studied in relation to oral health and disease; however, blood-derived neutrophils do not necessarily reflect the state of oral neutrophils. Neutrophil isolation requires an efficient, aseptic, and reproducible method to obtain non-activated and viable cells. The most frequently used approaches utilize density gradient centrifugation and other techniques that are widely employed include Percoll™ gradients, Histopaque™ gradients, and dextran sedimentation. In addition, negative or positive cell selection using antibody/protein-coated magnetic beads (magnetic-activated cell sorting [MACS]), or fluorescently labeled antibodies (fluorescence-activated cell sorting [FACS]) are used. These antibodies are directed against surface markers of those cells to be included (neutrophils) or excluded (such as other leukocytes). Neutrophils can also be isolated from the oral cavity using mouth rinses and filtration steps. Isolating oral neutrophils is an easy and non-invasive way of obtaining cells that are reflective of the oral health status. Our own comparison of neutrophil isolation techniques indicates that neutrophils isolated using discontinuous Percoll gradients produce non-activated cells that are more responsive to exogenous stimulation [12]. To induce NET release, in vitro stimuli such as phorbol 12-myristate 13-acetate (PMA) [13] and hypochlorous acid (HOCl) [14] are routinely used. Gram-positive and Gram-negative bacteria [15, 16], protozoan parasites [17], fungi (cellular and hyphal forms) [18], and viruses [19] have all been reported to elicit
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NET release. Interestingly, host-derived inflammatory mediators, such as pro-inflammatory cytokines [20], can also stimulate NET generation. The quantification of NET release frequently utilizes fluorometric detection of DNA [21]. To ensure NET-DNA is being measured, and not DNA produced by other cellular processes, such as necrosis, the AMPs associated with the NET-DNA backbone are also routinely co-labeled to demonstrate their close association with NET-derived DNA. Other methods to determine NET release include the quantification of citrullinated histones, as well as the use of polymerase chain reaction (PCR) to analyze genomic NET-DNA sequences [5]. In addition, fluorescence microscopy, confocal microscopy, and scanning electron microscopy (SEM) are all routinely employed to visualize NET structures, but quantification of NET release by microscopy alone is problematic. In addition, high content analysis (HCA), an automated highresolution fluorescence imaging approach with computational analysis, has been employed to investigate NETs. HCA is extensively applied by the pharmaceutical industry for drug screening and lead drug identification [22], although it is also used for the characterization of complex cellular activities including NET production [23, 24]. The main advantages of using HCA are its ability to quantify protein staining intensity, to determine molecular subcellular localizations, and for rapid acquisition information on spatial and temporal variables [25]. To study the interactions between NETs and bacterial species, NET entrapment and microbial killing can be determined using ex vivo assays. Microscopy provides qualitative results that enable the visualization of bacteria associating with NET lattices. The quantification of bacteria entrapped within NET structures can be determined by the fluorometric detection of labeled bacteria within NET structures [26]. Following the incubation of bacteria with NETs, the bacteria-NET suspension can be diluted and inoculated onto agar media and bacterial colonies can be enumerated to determine bacterial NET killing after incubation [27–29]. Despite the discovery of NETs in 2004, interest in the process of NET degradation and NET removal has only more recently arisen. The ability of individuals to degrade NETs and the nature of the mechanisms involved has been linked to disease pathogenesis and is of particular interest in diseases characterized by a destructive host response, such as chronic periodontitis. NETs are believed to be disassembled by nucleases (e.g., DNases) transported in circulating plasma [30, 31]; therefore, assays of DNase-containing plasma to measure NET degradation may provide useful insights into disease mechanisms and identify novel therapeutic approaches. Detailed below are the materials, methods, and notes used for the isolation of neutrophils from human peripheral venous blood
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and from the human oral cavity, neutrophil activation, ROS and NET quantification, NET visualization, characterization of NETs by HCA, the quantification of NET entrapment and NET killing of bacteria, as well as NET degradation by human plasma.
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Materials
2.1 Isolation of Neutrophils from Human Peripheral Blood by Density Gradient Centrifugation
1. 1.079 g/mL Percoll solution: Combine 19.708 mL Percoll, 11.792 mL distilled water (dH2O), and 3.5 mL of 1.5 M NaCl. This will equate to 35 mL of 1.079 g/mL Percoll solution, which is sufficient for four gradients. 2. 1.098 g/mL Percoll solution: Combine 24.823 mL Percoll, 6.677 mL dH2O, and 3.5 mL of 1.5 M NaCl. This equates to 35 mL of 1.098 g/mL Percoll solution, which is sufficient for four gradients. 3. Erythrocyte lysis buffer: Dissolve 8.3 g NH4Cl, 1 g KHCO3, 0.04 g Na2EDTAl2H2O, and 2.5 g bovine serum albumin (BSA) in 1 L dH2O. Store at 4 C. 4. Phosphate buffered saline (PBS): Dissolve 7.75 g NaCl, 0.2 g KH2PO4, and 1.5 g K2HPO4 in 1 L dH2O. Store at 4 C. 5. Sterile 3-mL Pasteur pipettes. 6. Centrifuge (preferably with ramp/brake and temperature settings). 7. 25- and 50-mL sterile centrifuge tubes. 8. Hemocytometer, coverslips, and a light microscope (with 20 objective).
2.2 Isolation of Neutrophils from Human Peripheral Blood by Negative Selection
1. One vial, Buffer B and Buffer A of a MACSxpress® Whole Blood Neutrophil Isolation Kit (Miltenyi Biotec, Bergisch Gladbach, Germany). 2. MACSxpress® Separator. 3. 15 mL sterile centrifuge tubes. 4. 3 mL sterile Pasteur pipettes. 5. Erythrocyte lysis buffer: 8.3 g NH4Cl, 1 g KHCO3, 0.04 g Na2EDTAl2H2O, and 2.5 g bovine serum albumin (BSA) dissolved in 1 L dH2O (stored at 4 C). 6. Phosphate buffered saline (PBS): 7.75 g NaCl, 0.2 g KH2PO4, and 1.5 g K2HPO4 in 1 L dH2O (stored at 4 C). 7. Centrifuge with ramp/brake and temperature settings. 8. Hemocytometer, coverslips. 9. Light microscope with 20 objective.
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1. 50-mL sterile centrifuge tubes. 2. 15-mL plastic cups. 3. 0.9% sterile sodium chloride (NaCl) solution: 9 g NaCl in 1 L sterile water. 4. RPMI-1640 medium: Containing sodium bicarbonate, no L-glutamine or phenol red. 5. Nylon mesh of 70.0, 40.0, 31.5, and 10.0 μm. 6. Cold PBS (4 C). 7. Ice. 8. Vortexer. 9. Centrifuge (preferably with acceleration/brake and temperature settings).
2.4 Neutrophil ROS Assays
1. PBS supplemented with glucose and cations (gPBS): Weigh 7.75 g NaCl, 0.2 g KH2PO4, 1.5 g K2HPO4, 1.8 g glucose, and 0.15 g CaCl2 and dissolve in 1 L of dH2O, and then add 1.5 mL of MgCl2. Add and dissolve, filter-sterilize (pore size 0.22 μm) and store at 4 C or at 20 C for longer-term storage. 2. PBS with 1% BSA: Add 10 g of BSA to 1 L of previously made PBS. Syringe-filter (pore size 0.22 μm), aliquot (20 mL), and store at 20 C. 3. Luminol: Dissolve 0.5 g luminol in 94.05 mL 0.1 M NaOH to obtain a 30 mM stock solution and store at 4 C in the dark until needed. Dilute 1 mL of stock solution in 9 mL PBS for a 3 mM working solution and adjust pH to 7.3. 4. Isoluminol: Dissolve 0.5 g isoluminol in 94.05 mL 0.1 M NaOH to obtain a 30 mM stock solution and store at 4 C in the dark until needed. Dilute 1 mL of stock solution in 9 mL PBS for a 3 mM working solution and adjust pH to 7.3. 5. Horseradish peroxidase (HRP): Dissolve 5000 units of HRP in 5 mL PBS. Dilute 105 μL of stock solution in 945 μL PBS to obtain a 1.5 units/15 μL solution. 6. Lucigenin: Dissolve 0.01 g lucigenin in 10 mL PBS to produce a 1 mg/mL stock solution, store at 4 C in the dark. Dilute 4 in PBS immediately prior to use. 7. 96-well reading luminometer set to 37 C. 8. White tissue culture grade 96-well plates.
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NET Assays
1. RPMI-1640 medium: Containing sodium bicarbonate, no L-glutamine or phenol red. 2. Dimethyl sulfoxide (DMSO). 3. Black tissue culture grade 96-microwell plates.
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4. Clear tissue culture grade 6-, 24-, and 96-microwell plates. 5. Micrococcal nuclease (MNase): Add 1 mL dH2O to MNase (lyophilized powder, 21.363 units) to produce a stock solution. Dilute 311.3 μL of the stock solution in 50 mL PBS for a working solution of 13.3 units/mL and a final well concentration of 1 unit/mL. Aliquot (1.5 mL) and store at 20 C. 6. SYTOX™ Green (Thermo Fisher Scientific, Waltham, MA, USA): Dilute vial (5 mM DMSO) 1:500 in RPMI to obtain a 10 μM working solution. Store at 20 C in the dark. 7. 96-well reading fluorometer. 8. 4-(2-hydroxyethyl) piperazine-1-ethanesulfonic acid (HEPES): Prepare 0.1 M HEPES by adding 2.38 g HEPES to 100 mL dH2O, adjust to pH 7.5, and store at 4 C. 9. Cytochalasin B: Use at a final concentration of 10 μg/mL by adding 166.5 μL of cytochalasin B (2 mg/mL stock in DMSO) to 4.833 mL of PBS, store at 20 C. 10. 1 M sodium phosphate. 11. N-Methoxysuccinyl-Ala-Ala-Pro-Val p-nitroanilide: Add 5.64 mL of DMSO to 50 mg vial to obtain a 15 mM stock. Use at 0.5 mM by diluting stock solution in PBS and store at 20 C. 12. 3,30 ,5,50 -Tetramethylbenzidine (TMB): Store at 4 C. 13. N-Succinyl-Ala-Ala-Pro-Phe p-nitroanilide: Add 2 mL DMSO to 25 mg vial to obtain a 20 mM stock and use at 1 mM by diluting stock solution in 0.1 M HEPES. Store at 20 C. 14. Neutrophil elastase (NE): To produce a standard curve, serially dilute 9 units/mL human NE in PBS and store at 20 C. 15. Myeloperoxidase (MPO): To produce a standard curve, serially dilute 1 unit/mL MPO derived from human leukocytes and store at 20 C. 16. Cathepsin G (CG): To produce a standard curve, serially dilute 1 unit/mL cathepsin G from human leukocytes and store at 20 C. 17. Fluorescein-5-isothiocyanate (FITC): Use at a final concentration of 0.3 mg/mL by adding 50 μL of 6 mg/mL FITC to 1 mL of bacteria. Store at 20 C in the dark. 18. 96-well plate reader capable of reading optical densities at wavelengths of 405 and 450 nm. 2.6 Visualization of NETs by SEM
1. 2.5% glutaraldehyde in 0.1 M sodium cacodylate buffer: Prepare 0.1 M sodium cacodylate by adding 4.28 g sodium cacodylate trihydrate to 100 mL of dH2O, and adjust to pH 7.4. Make 2.5% glutaraldehyde by combining 7.5 mL of the sodium cacodylate buffer, 6 mL of dH2O, and 1.5 mL of glutaraldehyde solution.
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2. 11-mm round cover slips. 3. Graded ethanol series (20, 30, 40, 50, 60, 70, 90, and 100%) produced by diluting 100% ethanol in dH2O accordingly. 4. Sputter coater to cover specimen with layer of gold. 5. Hexamethyldisilazane (HMDS). 6. 25-mm aluminum stubs with carbon conductive tabs. 7. Scanning electron microscope. 2.7 Visualization of NETs by Fluorescence Microscopy and HCA
1. Black, clear, flat bottom 96- or 384-well plate. 2. RPMI-1640 medium containing sodium bicarbonate and no L-glutamine or phenol red. 3. PBS with 1% BSA: Add 10 g of BSA to 1 L of previously made PBS. Syringe-filter (pore size 0.22 μm), aliquot at 20 mL, and store at 20 C. 4. 4% paraformaldehyde (PFA): In a chemical fume hood, dissolve 2 g PFA in 50 mL PBS on a mixer plate heated to 60 C. Allow the solution to cool down prior to filter sterilizing (pore size 0.22 μm). Store in aliquots at 20 C. 5. 0.05% digitonin in PBS: Dissolve 15 mg digitonin in 30 mL PBS to produce a 500 μg/mL solution, heat the solution for a maximum of 20 s, and leave to cool at room temperature for 1 h. 6. Rabbit anti-human myeloperoxidase (MPO) monoclonal antibody conjugated to Alexa Fluor® 488 (Abcam, Cambridge, UK), and mouse anti-human neutrophil elastase (NE) monoclonal antibody conjugated to Alexa Fluor® 594 (R&D Systems, Abingdon, UK). 7. SYTOX™ Green fluorescent nucleic acid stain: Dilute stock concentration (5 mM) 500 in RPMI to obtain a 10 μM working solution. Store at 20 C and in the dark. 8. Hoechst 33342 fluorescent nucleic acid stain (Merck/SigmaAldrich, Darmstadt, Germany): Dilute stock concentration (20 mM) 600 in PBS to obtain a working solution of 20 μg/mL. Store at 4 C in the dark. 9. A fluorescence microscope that covers excitation and emissions ranges (in nm) for the fluorescent dyes: Alexa Fluor® 594: 591/614 nm; Alexa Fluor® 488: 493/519; SYTOX™ Green: 504/523; Hoechst 33342: 350/461. For HCA, a highresolution, automated microscope with multi-channeled fluorescent filters is required.
2.8 Neutrophil Activation
1. Phorbol 12-myristate 13-acetate (PMA): Add 1 mL DMSO to 1 mg vial to obtain a 1.62 mM PMA stock solution. Dilute stock solution (4000-fold dilution) in PBS to a 405 nM
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working solution. Aliquot the solution and store at 80 C. A final well concentration of 25 and 50 nM is used for ROS and NETs, respectively, by diluting further in PBS. 2. Hypochlorous acid (HOCl): Dilute 10 μL of sodium hypochlorite stock solution in 1 mL PBS (foil wrapped 1.5-mL centrifuge tube) and use immediately. 3. Periodontal bacteria American Type Culture Collection (ATCC). 4. Disposable presterilized loops. 5. Aerobic and anaerobic chamber. 6. Brain heart infusion (BHI) broth. 7. For growth of Porphyromonas gingivalis: Menadione (Vitamin K3) and hemin. Prepare a hemin stock solution (100) by dissolving 50 mg of hemin powder in 100 ml of 0.01 M NaOH with heat and stirring until the powder is thoroughly dissolved. Autoclave for 20 min and store at 4 C. Prepare a menadione stock solution (5000) by dissolving 50 mg menadione powder in 10 mL of 95% ethanol. Avoiding evaporation, filter-sterilize, and store in aliquots below 20 C. 8. Horse blood agar plates. 9. Cling film. 10. Microbiology oven to heat-kill bacteria.
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3.1 Isolation of Neutrophils from Human Peripheral Blood by Density Gradient Centrifugation
1. To prepare Percoll gradients, layer 8 mL of 1.098 g/mL Percoll solution underneath 8 mL of 1.079 g/mL Percoll in a 25 mL centrifuge tube using a Pasteur pipette. Carefully layer peripheral blood (max. 16 mL) onto discontinuous Percoll density gradients (see Notes 1–3). 2. Centrifuge for 8 min at 150 g, followed by 10 min at 1200 g (both set to 4 C with brake turned on and acceleration/deceleration ramps at the lowest settings). 3. Remove the plasma, lymphocyte, and monocyte layers at the top of the centrifuge tube using a Pasteur pipette. 4. Carefully collect the neutrophil cell layer located at the top of the erythrocyte cell layer (see Note 4) and transfer to a 50 mL centrifuge tube containing ~20 mL erythrocyte lysis buffer. Add additional erythrocyte lysis buffer to a final volume of ~50 mL per centrifuge tube and invert the centrifuge tube to mix. 5. Incubate at room temperature for 5–10 min or until the erythrocytes have lysed (see Notes 5 and 6).
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6. Centrifuge the lysed cell suspension for 6 min at 500 g to pellet the neutrophils (see Note 7). 7. Carefully remove the supernatant and discard. 8. Resuspend neutrophils in 3 mL erythrocyte lysis buffer, incubate for 5 min at room temperature, and re-centrifuge for 6 min at 500 g. 9. Carefully remove the supernatant (i.e., repeat step no. 7) and resuspend neutrophil cell pellet in 3 mL sterile PBS. 10. Count the cells on a hemocytometer by light microscopy (see Notes 8 and 9). 3.2 Isolation of Neutrophils from Human Peripheral Blood by Negative Selection
1. Reconstitute the lyophilized MACSxpress® Whole Blood Neutrophil Isolation Cocktail by adding 2 mL of Buffer A to one vial (see Notes 10 and 11). 2. To prepare the antibody cocktail solution, mix 1 volume of reconstituted pellet with 1 volume of buffer B in a 15-mL sterile centrifuge tube. 3. The ratio of blood, reconstituted pellet, and buffer B must be 1:0.25:0.25. For the isolation of neutrophils from 8 mL of blood, mix 2 mL of reconstituted pellet and 2 mL of Buffer B (see Notes 12 and 13). 4. Add EDTA blood to the antibody cocktail solution, invert gently three times, and incubate sample for 5 min at room temperature (see Note 14). 5. Place the tube with loose cap in the magnetic field of the MACSxpress Separator for 15 min at room temperature (see Note 15). 6. While the tube is still inside the MACSxpress® Separator, carefully collect the supernatant (see Note 16). 7. Centrifuge the supernatant at 300 g for 10 min to pellet the neutrophils. 8. Carefully remove the supernatant and discard. 9. Resuspend neutrophils in 10 mL erythrocyte lysis buffer, incubate for 5 min at room temperature, and re-centrifuge at 300 g for 10 min. 10. Carefully remove the supernatant and resuspend the neutrophil pellet in 5 mL sterile PBS. 11. Count the cells on a hemocytometer under a light microscope (see Note 17).
3.3 Isolation of Neutrophils from the Human Oral Cavity
1. Oral rinse samples are preferably collected in the morning. The subject should refrain from eating, drinking, or oral hygiene procedures for 30 min prior to oral rinse collection.
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2. Instructions for oral rinse procedures are as follows: (a) Keep collection tubes on ice during the whole process. (b) Rinse the oral cavity thoroughly with 10–30 mL 0.9% NaCl for 30 s. (c) Expectorate into 50 mL centrifuge tubes. (d) Interval time is 4.5 min (see Note 18). (e) Samples should be processed immediately after collection. 3. Vortex the collected samples briefly to mix. 4. Centrifuge collection tubes for 10 min at 500 g at 4 C. 5. Carefully remove the supernatant and discard. 6. Suspend the pellet and pool samples in 10 mL cold PBS. 7. Filter the oral samples through 70.0-, 40.0-, 31.5-, and 10.0-μ m nylon meshes to exclude epithelial cells and cell debris. 8. Centrifuge for 10 min at 500 g at 4 C, and wash in 20 mL cold PBS. 9. Suspend the neutrophil pellet in culture medium (see Note 19). 10. Count the cells on a hemocytometer by light microscopy (see Notes 8 and 9). 11. For NET assays conducted in 96-well plates, a concentration of 105 cells per well is required [10]. 3.4 Chemiluminescence to Measure Neutrophil ROS
1. Add 200 μL syringe-filtered PBS with 1% BSA to each well of a white 96-well plate and store at 4 C overnight. Following neutrophil isolation the next day, remove the BSA solution from the plate and rinse with PBS (see Note 20). 2. Add 105 neutrophils in 100 μL gPBS to each well to measure neutrophil ROS: (a) For total ROS, add 30 μL luminol and 45 μL gPBS. (b) For extracellular ROS, add 60 μL isoluminol, 15 μL of 1.5 units HRP and 30 μL gPBS. (c) For extracellular superoxide, add 30 μL lucigenin and 45 μL gPBS. 3. Place the plate in the luminometer (37 C) and incubate for a 30-min baseline period (see Note 21). 4. Next, pause the luminometer to stimulate the cells with 25 μL of PBS (negative control), PMA (25 nM) or bacteria (5 107, multiplicity of infection [MOI] of 500). 5. Place the plate back in the luminometer and continue to measure light output for a further 120 min.
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1. Add 200 μL syringe-filtered PBS with 1% BSA to each well of a clear 96-well plate and store at 4 C overnight. Following neutrophil isolation the next day, remove the BSA solution from the plate (see Note 20). 2. Add 105 neutrophils in 175 μL RPMI-1640 to each well and incubate for a 30-min baseline period (37 C, 5% CO2) (see Note 21). 3. Stimulate neutrophils with 25 μL of PBS (negative control), PMA (50 nM), HOCl (0.75 mM), or bacteria (108, MOI of 1000), and incubate for 3 h (37 C, 5% CO2) (see Notes 22 and 23). 4. Post incubation, add 15 μL of MNase at 1 unit/mL to each well and incubate for 15 min at room temperature (see Note 24). 5. Centrifuge the plate for 10 min at 1800 g. Post centrifugation, transfer 150 μL of the supernatant to a black 96-well plate (see Notes 25 and 26). 6. Add 15 μL of 10 μM SYTOX™ Green to quantify any free DNA within the supernatant and read the fluorescence immediately in a fluorometer (excitation: 485 nm, emission: 535 nm) (see Notes 27 and 28).
3.6 Quantification of NET-Bound Components 3.6.1 Production of NETs and NET-Bound Components
1. Add 1 mL syringe-filtered PBS with 1% BSA to each well of a clear 24-well plate and store at 4 C. Following neutrophil isolation the next day, remove the BSA solution from the plate (see Note 20). 2. Add 106 neutrophils in 875 μL RPMI-1640 to each well and incubate for a 30 min baseline period (37 C, 5% CO2) (see Note 21). 3. Stimulate neutrophils with 125 μL of PBS (negative control), PMA (50 nM), HOCl (0.75 mM), or bacteria (108, MOI of 1000), and incubate for 3 h in the dark (37 C, 5% CO2) (see Notes 22 and 23). 4. Post incubation, wash the NETs by gently aspirating the supernatant and adding 1 mL of pre-warmed RPMI-1640. Repeat these steps once more (see Notes 29 and 30). 5. Add 75 μL of MNase at 1 unit/mL to each well and incubate for 15 min at room temperature. 6. Centrifuge the plate for 10 min at 1800 g (see Note 25). 7. Post centrifugation, NET bound components can be quantified immediately or transferred to cryotubes and stored at 4 C (for up to 1 week) or at 20 C (for up to 6 months) for quantification at a later time.
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3.6.2 Measuring NETBound Neutrophil Elastase (NE)
1. Add 100 μL of NET supernatant to each well of a clear 96-microwell plate in duplicate. 2. Add 100 μL of 0.5 M N-Methoxysuccinyl-Ala-Ala-Pro-Val p-nitroanilide to each well, and incubate for 2 h in the dark (37 C, 5% CO2). 3. Post incubation, measure the absorbance at 405 nm. 4. Generate a standard curve by serially diluting human NE in 100-μL aliquots and running the assay in duplicate on the same plate.
3.6.3 Measuring NETBound Myeloperoxidase (MPO)
1. Add 50 μL of NET supernatant to each well of a clear 96-well plate in duplicate. 2. Add 50 μL of TMB substrate solution to each well, and incubate in the dark for 20 min at room temperature. 3. Post incubation, add 50 μL of 1 M sodium phosphate to stop the reaction. 4. Measure the absorbance at 450 nm. 5. Generate a standard curve by serially diluting human MPO in 50-μL aliquots and running the assay in duplicate on the same plate.
3.6.4 Measuring NETBound Cathepsin G (CG)
1. Add 50 μL of NET supernatant to each well of a clear 96-well plate in duplicate. 2. Add 50 μL of 1 mM N-Succinyl-Ala-Ala-Pro-Phe p-nitroanilide in 0.1 M HEPES to each well, and incubate for 2 h in the dark (37 C, 5% CO2). 3. Post incubation, measure the absorbance at 405 nm. 4. Generate a standard curve by serially diluting human CG in 50-μL aliquots and running the assay in duplicate on the same plate.
3.7 Quantification of NET-Entrapped Bacteria
1. Add 200 μL syringe-filtered PBS with 1% BSA to each well of a black 96-well plate and store at 4 C overnight. Following neutrophil isolation the next day, remove the BSA solution from the plate (see Note 20). 2. Add 105 neutrophils in 175 μL RPMI-1640 to each well and incubate for a 30 min baseline period (37 C, 5% CO2) (see Note 21). 3. Stimulate neutrophils with 25 μL of HOCl (0.75 mM); also include a neutrophil-free well and cells treated with PBS as negative controls. Incubate for 3 h (37 C, 5% CO2) (see Notes 22 and 23). 4. Following the culture of planktonic bacteria (Table 1), measure the turbidity by spectroscopy and dilute to 107 per 50 μL (MOI of 100) with PBS in 1 mL aliquots.
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Table 1 Culture conditions for periodontal bacteria ATCC number
Growth condition (at 37 C)
Bacterial cells/mL if OD at 600 nm ¼ 1
43146
Anaerobic
8.3 108
Aggregatibacter 43718 actinomycetemcomitans serotype b
Anaerobic
6.8 109
Propionibacterium acnes
11827
Anaerobic
1.69 109
Selenomonas noxia
43541
Anaerobic
1.69 109
Veillonella parvula
10790
Anaerobic
6.8 109
Streptococcus sanguinis
10556
5% CO2
1.69 109
Streptococcus oralis
35037
5% CO2
1.69 109
Streptococcus intermedius
27335
5% CO2
1.69 109
Streptococcus anginosus
33397
5% CO2
1.69 109
Streptococcus gordonii
10558
5% CO2
1.69 109
Capnocytophaga gingivalis
33624 (27)
Anaerobic
1.62 109
Eikenella corrodens
23834
Anaerobic
6.8 109
Aggregatibacter actinomycetemcomitans serotype a
29523
Anaerobic
6.8 109
Capnocytophaga sputigena
33612 (4)
Anaerobic
1.62 109
Streptococcus constellatus
27823 (M32b)
5% CO2
1.69 109
Campylobacter rectus
33238 (371)
Anaerobic
6.8 109
Campylobacter showae
51146
Anaerobic
6.8 109
Fusobacterium nucleatum subsp. nucleatum
25586
Anaerobic
1.62 109
Fusobacterium nucleatum subsp. polymorphum
10953
Anaerobic
1.62 109
Porphyromonas gingivalis
W83
Anaerobic
1.69 109
Bacterial strain Actinomyces viscosus (naeslundii genospecies 2)
Note: Bacteria we have previously used in the above assays are listed alongside their respective American Type Culture Collection (ATCC) number. The solid and liquid culture media are horse blood agar, and brain heart infusion broth (supplemented with hemin [5 mg/L], and menadione [1 mg/L] for growth of Porphyromonas gingivalis). Following planktonic growth, bacterial concentrations are determined by spectrophotometry (optical density at 600 nm [OD600]), and our own experiments determined the number of bacteria per mL of broth if the OD600 is 1.0
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5. Fluorescently stain bacteria by incubating bacteria with 0.3 mg/mL FITC for 30 min on ice with continuous agitation. Next, centrifuge the bacteria (10 min at 5000 rpm) to precipitate FITC-stained bacteria, and resuspend in 1 mL PBS. 6. Prior to adding FITC-stained bacteria to the well, add 1 unit/ mL MNase to control wells containing NETs and incubate for 15 min at room temperature (see Note 31). 7. Add 50 μL of the live FITC-stained bacteria to all wells (neutrophil-free wells, PBS-treated cells, HOCl-treated cells, and MNase-degraded HOCl-NETs) and incubate for 1 h (37 C, 5% CO2). 8. Post incubation, carefully remove the supernatant and replace with 200 μL fresh, pre-warmed RPMI-1640. Repeat wash step (see Note 32). 9. Quantify entrapped bacteria by measuring the fluorescence using a fluorometer (excitation: 485 nm, emission: 535 nm). 3.8 Quantification of NET-Mediated Killing of Bacteria
1. Add 500 μL syringe-filtered PBS with 1% BSA to each well of a clear 24-well plate and store at 4 C overnight. Following neutrophil isolation the next day, remove the BSA solution from the plate (see Note 20). 2. Add 105 neutrophils in 440 μL RPMI-1640 to each well and incubate for a 30 min baseline period (37 C, 5% CO2) (see Note 21). 3. Stimulate neutrophils with 60 μL of HOCl (0.75 mM), cover with foil, and incubate for 3 h (37 C, 5% CO2) (see Notes 22 and 23). 4. Next, carefully remove the supernatant from each well and replace with 500 μL pre-warmed RPMI-1640, with or without 10 μg/mL cytochalasin B or 1 unit/mL MNase, and incubate for 15 min at room temperature (see Note 33). 5. Following the culture of planktonic bacteria (Table 1), measure the turbidity by spectroscopy and dilute to 107 per 50 μL (MOI of 100) with PBS in 1 mL aliquots. 6. Add 50 μL of the live bacteria to the selected wells and centrifuge the plate for 10 min at 700 g, then incubate the plate for 1 h (37 C, 5% CO2) (see Note 25). 7. Post incubation, add 25 μL of MNase (100 units/mL) and incubate for 15 min at room temperature (see Note 34). 8. Transfer the well contents to 1.5-mL centrifuge tubes and dilute with broth. 9. To determine bacterial viability, inoculate 50 μL of this suspension in triplicate on agar plates and count bacterial colonies after 24 h (see Note 35).
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3.9 Quantification of NET Degradation by Human Plasma
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1. Stimulate NET release with 0.75 mM HOCl in a 96-well plate, as previously described. 2. Defrost previously isolated plasma samples at room temperature and dilute to 10% in PBS. Add 10% plasma to the wells in 50 μL aliquots and incubate for 3 h (37 C, 5% CO2) (see Note 36). 3. Treat control wells with 1 unit/mL MNase for 15 min at room temperature, instead of plasma (see Note 37). 4. Following incubation with plasma, centrifuge the plate (10 min at 1800 g), and then transfer 150 μL of the supernatant to a black 96-well plate (see Notes 25 and 26). 5. Add 15 μL of 10 μM SYTOX™ Green to quantify any DNA within the supernatant in a fluorometer (excitation: 485 nm, emission: 535 nm).
3.10 Scanning Electron Microscopy (SEM) of NETs
1. Sterilize round 11 mm glass coverslips (see Note 38) in 0.2 M HCl, followed by two wash steps in dH2O. Once dry, add 100 μL of syringe-filtered PBS with 1% BSA and keep at room temperature for 1 h prior to use (see Note 20). 2. Add 105 neutrophils in 100 μL RPMI-1640 onto each coverslip, and after a 30-min baseline incubation period (37 C, 5% CO2) (see Note 21), stimulate cells with PBS (negative control), PMA (50 nM), or live bacteria (107, MOI of 100) (see Note 22). 3. Following a 3 h incubation (37 C, 5% CO2), fix samples with 2.5% glutaraldehyde in 0.1 M sodium cacodylate buffer (pH 7.3) for 30 min at room temperature. 4. Dehydrate samples by immersing in a graded ethanol series (20, 30, 40, 50, 60, 70, 90, 100, and 100% for 10 min each). 5. Dry samples by adding 100 μL of HMDS, and leave to evaporate in a fume hood overnight. 6. Mount samples onto 25 mm aluminum stubs with carbon conductive tabs and coat in gold for 90 s, prior to analysis by SEM.
3.11 Immunofluorescence and Fluorescence Microscopy of NETs
1. Add 300 μL of syringe-filtered 1% BSA to each well of a clear 24-well plate and store at 4 C overnight. Following neutrophil isolation the next day, remove the 1% BSA from the plate using an aspirator (see Note 20). 2. Add 105 neutrophils in 175 μL RPMI-1640 to each well, and incubate for a 30 min baseline period (37 C, 5% CO2) (see Note 21). 3. Stimulate neutrophils with 25 μL aliquots of PBS (negative control), PMA (50 nM) or HOCl (0.75 mM), and incubate for 3 h (37 C, 5% CO2) (see Notes 22 and 23).
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4. Following the production of NETs, centrifuge the plate for 10 min at 1800 g. 5. After centrifugation, carefully remove media and add 50 μL 4% PFA to each well. Incubate for 10 min at room temperature. Next, gently remove all supernatant, carefully wash twice with PBS. 6. For visualizing NET DNA, add 100 μL of PBS and 10 μL of 10 μM SYTOX™ Green. Observe immediately under a fluorescence microscope (excitation: 485 nm, emission: 535 nm). 7. For visualization of NET-bound proteins, add 50 μL of PBS with 1% BSA to each well. Incubate for 30 min at room temperature and wash with PBS. 8. Dilute rabbit anti-human MPO monoclonal antibody conjugated to Alexa Fluor® 488 and mouse anti-human NE monoclonal antibody conjugated to Alexa Fluor® 594 (100 in 0.05% digitonin). 9. Add 50 μL of this digitonin-antibody mix to each well and incubate in the dark for 1 h at room temperature, then wash with PBS. 10. Add 50 μL of Hoechst 33342 working solution and incubate for 15 min at room temperature in the dark, then wash with PBS. Add 50 μL of PBS to prevent the samples from drying. 11. Visualize NETs using a fluorescence microscope. 3.12 NET Quantification and Visualization with HCA
1. Add 200 μL or 50 μL syringe-filtered PBS 1% BSA to each well of a black, clear, flat bottom 96- or 384-well plate, respectively, and store at 4 C overnight. Following neutrophil isolation the next day, remove the PBS with 1% BSA from the plate (see Note 39). 2. Add 105 cells/well in 175 μL RPMI-1640 to each well of a 96-well plate or 5 103 cells/well in 35 μL RPMI-1640 to each well of a 384-well plate, and incubate for a 30 min baseline period (37 C, 5% CO2) (see Note 40). 3. Stimulate neutrophils with 25 μL or 5 μL of PBS (negative control) or PMA (50 nM) per well in a 96- or 384-well plate, respectively, and incubate for up to 4 h (37 C, 5% CO2) in the dark. 4. Add 50 μL or 10 μL of the 20% PFA solution to each well of a 96- or 384-well plate, respectively, and incubate a room temperature for 10 min (see Note 41). 5. Centrifuge the plate at 1800 g for 10 min (brake on, room temperature). 6. Carefully remove the supernatant from each well and wash twice with PBS. At the last wash, leave 180 μL or 45 μL PBS in each well of a 96- or 384-well plate, respectively (see Note 42).
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7. Add 20 μL or 5 μL of SYTOX™ Green solution (10 μM) (96or 384-well plate, respectively). 8. Add 20 μL or 5 μL of Hoechst 33342 solution (20 μg/mL) (96- or 384-well plate, respectively) (see Note 43). 9. Proceed to image acquisition with a fluorescent, highresolution, automated microscope (see Note 44). 3.13 Bacterial Culture
1. Store bacterial suspensions at 80 C in cryotubes containing BHI and 10% DMSO. When required, defrost at room temperature and inoculate agar plates by adding 100 μL of bacterial suspension and spreading with a disposable pre-sterilized loop (Table 1). 2. Loosely wrap plates in cling film, invert, and incubate in the appropriate conditions for at least 3 days (see Note 45). 3. Grow cultures planktonically by inoculating a single colony into broth, and incubate for at least 3 days on a shaker (see Notes 46 and 47). 4. Confirm planktonic growth by measuring the optical density in a cuvette (600 nm) using non-inoculated media to calibrate the spectrophotometer. 5. Centrifuge the bacterial suspension in broth (15 min, 1800 g, 4 C). 6. Discard the supernatant and resuspend the bacterial pellet in PBS. Repeat centrifugation and PBS wash steps twice more (see Note 48). 7. To heat-kill bacteria, incubate the bacterial suspension in PBS at 80 C in a microbiology oven for 30 min (see Note 49).
4
Notes 1. Collect blood in lithium-heparin anticoagulant tubes. Experiments from our own laboratory reveal greater cell retrieval and less inadvertent cell activation compared with other anticoagulants. Isolate cells as soon as possible. We typically isolate approximately 106 cells per mL of blood. 2. If possible, layer gradients immediately prior to neutrophil isolation to minimize the chance of gradient disruption; however, gradients may be carefully stored at 4 C for a day if needed. 3. The 1.098 g/mL Percoll solution can be layered underneath the 1.079 g/mL Percoll solution, or the latter carefully and slowly layered on top of the first. 4. Collect as few erythrocytes as possible to minimize the risk of erythrocyte contamination.
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5. Erythrocyte lysis is indicated by the increased transparency of the blood/lysis buffer solution. Do not incubate neutrophils in erythrocyte lysis buffer for longer than recommended due to the potential for cell activation. 6. One lysis step is preferred, but a second one can be carried out if erythrocytes have remained in the cell pellet. If erythrocytes are taking more time to lyse (solution clearing), this could be due to the age of the lysis buffer, which should be stored for a maximum of 3 weeks at 4 C. 7. During the lysis buffer/PBS washing stages and removal of supernatant, the operator must take care not to disturb the cell pellet to maximize cell retrieval. 8. We recommend duplicating the cell counting using a hemocytometer and calculation of the mean. 9. We routinely ensure neutrophil viability by trypan blue exclusion and CellTiter-Glo® assays, as well as neutrophil purity by flow cytometry using CD16 antibodies [32]. 10. Bring reagents to room temperature before use. 11. Prepare the antibody cocktail fresh before every use. 12. One vial of reconstituted pellet is sufficient for processing up to 8 mL of blood. The reconstituted pellet should be used within 1 day. 13. Buffer B tends to foam easily, handle with extra care to avoid bubbles. 14. Collect blood in an EDTA-coated vacutainer. 15. The magnetically labeled erythrocytes and other components will adhere to the wall of the tube, while unlabeled neutrophils remain in suspension (do not move the tube during the separation process). 16. For optimal recovery, use a Pasteur pipette and collect supernatant by moving the pipette tip top-to-bottom along the back wall of the tube. Recovered volume is variable, but is expected to be around 6 mL of solution from processing 8 mL of blood. 17. Approximately 2 106 cells per mL of blood can be isolated with this technique. 18. Four consecutive (4 30 s with intermission periods of 4.5 min) oral rinses from periodontally healthy donors result in the collection of approximately 106 oral neutrophils. 19. As oral neutrophil samples contain numerous oral microorganisms, isolated oral PMN samples can be pre-incubated with an antibiotic and antifungal cocktail (final concentration of 2.5 μg/mL amoxicillin, 25 μg/mL tetracycline, 25 μg/mL metronidazole, and 2.5 μg/mL amphotericin B) for 15 min at room temperature to inhibit these, followed by a final wash
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in 20 mL PBS (centrifugation 500 g for 10 min). Supernatants can be stored to test the possible presence of antibiotics in the oral PMN samples. Although the current evidence is weak, it is important to note that some antibiotics may influence neutrophil functions [33]. 20. Adding 1% BSA to plasticware prior to the addition of neutrophils coats the surface, which has been found to reduce inadvertent neutrophil activation. 21. A 30-min baseline period prior to neutrophil activation allows the cells to settle in the plasticware leading to more consistent results. 22. HOCl and PMA stimuli are light-sensitive. Make up/defrost at room temperature immediately prior to use and protect from light by foil wrapping. 23. NETs are produced in response to HOCl in as little as 30 min [14], PMA and bacteria typically induce NET production in 2–3 h. 24. Following their release, NETs are attached to the neutrophil they are derived from. The addition of MNase degrades the NET structures, which following centrifugation and the sedimentation of neutrophils, allows for the fluorometric quantification of NET-DNA in the supernatants. 25. Neutrophils are centrifuged in a plate spinner. If you do not have a plate spinner, the well contents can be transferred to 1.5mL centrifuge tubes for centrifugation. 26. Care needs to be taken when transferring NET supernatants to a new plate to ensure the operator is not disturbing the neutrophils at the bottom of the well; tipping the plate toward oneself helps. 27. Bacteria produce DNA [34] that can interfere with NET-DNA assays. If quantifying NET production in response to bacteria, control wells containing bacteria in PBS (and no neutrophils) need to be measured and subtracted from the final NET production values. We do not employ a bacterial NET stimulus during SYTOX™ Green fluorescence microscopy as it is very difficult to distinguish between NET-DNA and bacterial DNA; therefore, use PMA or HOCl for NET fluorescence visualization. 28. SYTOX™ Green does not permeate viable cells; therefore, very high readings in control wells are indicative of high numbers of non-viable neutrophils. 29. Neutrophils and the attached NET structures settle to the bottom of plasticware; however, they are not adherent cells and washing needs to be done carefully to minimize the disruption of NET structures and loss of cells.
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30. Washing removes components that are not NET-bound but are concurrently released during neutrophil activation. 31. The addition of MNase to NETs in selected wells produces degraded NET structures to serve as an additional control. 32. The duplicate wash steps ensure bacteria not entrapped within NET structures are removed, and therefore, only the fluorescence of entrapped bacteria is measured. 33. The addition of cytochalasin B to selected wells following NET release inhibits neutrophil phagocytosis, and the addition of MNase degrades NETs. 34. The addition of MNase post incubation disassembles the NETs and frees the bacteria prior to inoculation on agar plates. 35. The dilution factor required prior to inoculating agar plates with bacteria needs to be determined in preliminary experiments for each bacterial species. 36. Plasma is isolated from whole blood in 6 mL lithium heparin anticoagulant tubes by centrifuging for 30 min at 1000 g (4 C). Following centrifugation, plasma is transferred to cryotubes in 500 μL aliquots and stored at 80 C. 37. The treatment of some wells with MNase will serve as a positive control and the MNase treatment is considered 100% NET degradation. 38. It is easier to handle coverslips for SEM in 6-well plates. 39. The use of a robotic benchtop/workstation is recommended for accurate liquid dispensing in a 384-well plate. 40. If drugs/modulators are tested, add 105 cells/well in 165 μL RPMI-1640 to each well of a 96-well plate and 10 μL of 20 concentrated drug/modulator; or 5 103 cells/well in 33 μL RPMI-1640 to each well of a 384-well plate and 2 μL of 20 concentrated drug/modulator. 41. The final concentration of PFA in the well is 4%. 42. All steps of the washing must be performed slowly to minimize disruption of NETs. 43. SYTOX™ Green is a DNA-binding fluorescent dye non-permeant to the intact cell membrane. Hoechst 33342 is a fluorescent DNA dye which can penetrate cell membranes and stain nuclei of viable neutrophils. Hoechst is used for studying nuclear activation states and morphology in viable or NETosing cells, while SYTOX™ Green is used for studying NET structures and their release. With a HCA platform, both dyes can be used simultaneously for an accurate and more complete characterization of NETosis. 44. Image acquisition may be performed up to 24 h after PFA fixation. If image acquisition is delayed, seal the plate with adhesive film to minimize media evaporation.
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45. Wrapping agar plates in cling film helps to prevent the desiccation of agar following prolonged incubation. 46. Periodontal bacteria typically take at least 3 days to grow planktonically. However, growth can be determined at any time point following inoculation of broth by measuring the optical density at 600 nm. 47. Confirm the bacterial species by Gram staining and PCR. 48. Planktonic bacteria are washed in PBS multiple times to remove broth prior to neutrophil assays. 49. Killing of bacteria can be confirmed by inoculating a fresh agar plate with the heated bacterial suspension. References 1. Borregaard N, Cowland JB (1997) Granules of the human neutrophilic polymorphonuclear leukocyte. Blood 89:3503–3521 2. Brinkmann V, Reichard U, Goosmann C, Fauler B, Uhlemann Y, Weiss DS, Weinrauch Y, Zychlinsky A (2004) Neutrophil extracellular traps kill bacteria. Science 303: 1532–1535 3. Bianchi M, Niemiec MJ, Siler U, Urban CF, Reichenbach J (2011) Restoration of antiAspergillus defense by neutrophil extracellular traps in human chronic granulomatous disease after gene therapy is calprotectin-dependent. J Allergy Clin Immunol 127:1243–1252 4. Wang Y, Li M, Stadler S, Correll S, Li P, Wang D, Hayama R, Leonelli L, Han H, Grigoryev SA, Allis CD, Coonrod SA (2009) Histone hypercitrullination mediates chromatin decondensation and neutrophil extracellular trap formation. J Cell Biol 184:205–213 5. Yousefi S, Mihalache C, Kozlowski E, Schmid I, Simon HU (2009) Viable neutrophils release mitochondrial DNA to form neutrophil extracellular traps. Cell Death Differ 16:1438–1444 6. Pilsczek FH, Salina D, Poon KK, Fahey C, Yipp BG, Sibley CD, Robbins SM, Green FH, Surette MG, Sugai M, Bowden MG, Hussain M, Zhang K, Kubes P (2010) A novel mechanism of rapid nuclear neutrophil extracellular trap formation in response to Staphylococcus aureus. J Immunol 185:7413–7425 7. Fine N, Hassanpour S, Borenstein A, Sima C, Oveisi M, Scholey J, Cherney D, Glogauer M (2016) Distinct oral neutrophil subsets define health and periodontal disease states. J Dent Res 95(8):931–938 8. Rijkschroeff P, Loos BG, Nicu EA (2018) Oral polymorphonuclear neutrophil contributes to
oral health. Curr Oral Health Rep 5(4): 211–220 9. Nicu EA, Rijkschroeff P, Wartewig E, Nazmi K, Loos BG (2018) Characterization of oral polymorphonuclear neutrophils in periodontitis patients: a case-control study. BMC Oral Health 18:149 10. Moonen CGJ, Hirschfeld J, Cheng L, Chapple ILC, Loos BG, Nicu EA (2019) Oral neutrophils characterized: chemotactic, phagocytic, and neutrophil extracellular trap (NET) formation properties. Front Immunol 10:635 11. Landzberg M, Doering H, Aboodi GM, Tenenbaum HC, Glogauer M (2015) Quantifying oral inflammatory load: oral neutrophil counts in periodontal health and disease. J Periodontal Res 50(3):330–336 12. Harris PC (2012) Effect of density gradient material upon ex-vivo neutrophil behaviour, and effect of neutrophil extracellular traps upon the growth and survival of periodontopathogenic bacteria. MRes thesis, University of Birmingham 13. Fuchs TA, Abed U, Goosmann C, Hurwitz R, Schulze I, Wahn V, Weinrauch Y, Brinkmann V, Zychlinsky A (2007) Novel cell death program leads to neutrophil extracellular traps. J Cell Biol 176:231–241 14. Palmer L, Cooper PR, Ling MR, Wright HJ, Huissoon A, Chapple IL (2012) Hypochlorous acid regulates neutrophil extracellular trap release in humans. Clin Exp Immunol 167: 261–268 15. Beiter K, Wartha F, Albiger B, Normark S, Zychlinsky A, Henriques-Normark B (2006) An endonuclease allows Streptococcus pneumoniae to escape from neutrophil extracellular traps. Curr Biol 16:401–407
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16. Delbosc S, Alsac JM, Journe C, Louedec L, Castier Y, Bonnaure-Mallet M, Ruimy R, Rossignol P, Bouchard P, Michel JB, Meilhac O (2011) Porphyromonas gingivalis participates in pathogenesis of human abdominal aortic aneurysm by neutrophil activation. Proof of concept in rats. PLoS One 6:e18679 17. Behrendt JH, Ruiz A, Zahner H, Taubert A, Hermosilla C (2010) Neutrophil extracellular trap formation as innate immune reactions against the apicomplexan parasite Eimeria bovis. Vet Immunol Immunopathol 133:1–8 18. Byrd AS, O’Brien XM, Johnson CM, Lavigne LM, Reichner JS (2013) An extracellular matrix–based mechanism of rapid neutrophil extracellular trap formation in response to Candida albicans. J Immunol 190:4136–4148 19. Saitoh T, Komano J, Saitoh Y, Misawa T, Takahama M, Kozaki T, Uehata T, Iwasaki H, Omori H, Yamaoka S, Yamamoto N, Akira S (2012) Neutrophil extracellular traps mediate a host defense response to human immunodeficiency virus-1. Cell Host Microbe 12:109–116 20. Keshari RS, Jyoti A, Dubey M, Kothari N, Kohli M, Bogra J, Barthwal MK, Dikshit M (2012) Cytokines induced neutrophil extracellular traps formation: implication for the inflammatory disease condition. PLoS One 7: e48111 21. Palmer LJ (2010) Neutrophil extracellular traps in periodontitis. Ph.D. thesis, University of Birmingham 22. Shariff A, Kangas J, Coelho LP, Quinn S, Murphy RF (2010) Automated image analysis for high-content screening and analysis. J Biomol Screen 15(7):726–734 23. Chicca IJ, Milward MR, Chapple ILC, Griffiths G, Benson R, Dietrich T, Cooper PR (2018) Development and application of highcontent biological screening for modulators of NET production. Front Immunol 9:337 24. Zhao W, Fogg DK, Kaplan MJ (2015) A novel image-based quantitative method for the characterization of NETosis. J Immunol Methods 423:104–110 25. Drake PJM, Griffiths GJ, Shaw L, Benson RP, Corfe BM (2009) Application of high-content analysis to the study of post-translational modifications of the cytoskeleton. J Proteome Res 8(1):28–34
26. Berends ET, Horswill AR, Haste NM, Monestier M, Nizet V, von Ko¨ckritz-Blickwede M (2010) Nuclease expression by Staphylococcus aureus facilitates escape from neutrophil extracellular traps. J Innate Immun 2:576–586 27. Wartha F, Beiter K, Albiger B, Fernebro J, Zychlinsky A, Normark S, Henriques-Normark B (2007) Capsule and D-alanylated lipoteichoic acids protect Streptococcus pneumoniae against neutrophil extracellular traps. Cell Microbiol 9:1162–1171 28. Lauth X, von Ko¨ckritz-Blickwede M, McNamara CW, Myskowski S, Zinkernagel AS, Beall B, Ghosh P, Gallo RL, Nizet V (2009) M1 protein allows Group A streptococcal survival in phagocyte extracellular traps through cathelicidin inhibition. J Innate Immun 1: 202–214 29. Urban CF, Reichard U, Brinkmann V, Zychlinsky A (2006) Neutrophil extracellular traps capture and kill Candida albicans yeast and hyphal forms. Cell Microbiol 8:668–676 30. Hakkim A, Fu¨rnrohr BG, Amann K, Laube B, Abed UA, Brinkmann V, Herrmann M, Voll RE, Zychlinsky A (2010) Impairment of neutrophil extracellular trap degradation is associated with lupus nephritis. Proc Natl Acad Sci U S A 107:9813–9818 31. Leffler J, Martin M, Gullstrand B, Tyde´n H, Lood C, Truedsson L, Bengtsson AA, Blom AM (2012) Neutrophil extracellular traps that are not degraded in systemic lupus erythematosus activate complement exacerbating the disease. J Immunol 188:3522–3531 32. Zhou L, Somasundaram R, Nederhof RF, Dijkstra G, Faber KN, Peppelenbosch MP, Fuhler GM (2012) Impact of human granulocyte and monocyte isolation procedures on functional studies. Clin Vaccine Immunol 19: 1065–1074 33. Bongers S, Hellebrekers P, Leenen LPH, Koenderman L, Hietbrink F (2019) Intracellular penetration and effects of antibiotics on Staphylococcus aureus inside human neutrophils: a comprehensive review. Antibiotics (Basel) 8(2):54 34. Das T, Sehar S, Manefield M (2013) The roles of extracellular DNA in the structural integrity of extracellular polymeric substance and bacterial biofilm development. Environ Microbiol Rep 5:778–786
Chapter 28 Cell Seeding on 3D Scaffolds for Tissue Engineering and Disease Modeling Applications Fanny Blaudez, Cedryck Vaquette, and Sasˇo Ivanovski Abstract Scaffold cell seeding is a crucial step for the standardization and homogeneous maturation of tissue engineered constructs. This is particularly critical in the context of additively manufactured scaffolds whereby large pore size and high porosity usually impedes the retention of the seeding solution resulting in poor seeding efficacy and heterogeneous cell distribution. To circumvent this limitation, a simple yet efficient cell seeding technique is described in this chapter consisting of preincubating the scaffold in 100% serum for 1 h leading to reproducible seeding. A proof of concept is demonstrated using highly porous melt electrowritten polycaprolactone scaffolds as the cell carrier. As cell density, cell distribution, and differentiation within the scaffold are important parameters, various assays are proposed to validate the seeding and perform quality control of the cellularized construct using techniques such as alizarin red, Sirius red, and immunostaining. Key words Cell seeding, Melt electrospinning writing, 3D-scaffolds, Cell-scaffold constructs, Tissue engineering, Polycaprolactone
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Introduction The development of constructs for tissue regeneration generally involves cell seeding, either for the in vitro characterization of biomaterials or for in vitro maturation of cell-scaffold constructs before implantation [1]. Static cell seeding, the most common method, encompasses the deposition of a cell suspension droplet onto the construct surface and allowing cells to adhere to the scaffold surface before culturing the construct [2]. Besides its simplicity, it is generally associated with poor and heterogeneous cell attachment and lack of reproducibility [3, 4]. These parameters are detrimental to the reproducibility of in vitro experiments as well as the regenerative outcomes of tissue engineered constructs once implanted [5]. High cellularity of seeded scaffolds allows for increased cell-to-cell communication, resulting in enhanced extracellular matrix production
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[3, 6, 7]. Moreover, a uniform cell distribution is necessary for homogeneous tissue maturation by avoiding nutrient competition in areas with higher cell density [8, 9]. Several techniques have been developed to enhance both cellular distribution and seeding efficacy. These methods generally involve external mechanical forces such as rotation [6], centrifugation [10], application of vacuum or suction [11, 12], or fluid perfusion [3, 13–15]. Even though the aforementioned methods have displayed promising results in the context of large dimension scaffolds, dynamic seeding has not proven as efficient in the context of scaffolds with a macroscopic pore size and high pore interconnectivity, such as those fabricated by additive manufacturing [16– 18]. Surface modification of porous scaffolds is commonly employed for enhancing cell seeding of highly porous constructs [19, 20]. This is achieved by chemical treatments [21] or the grafting of proteins [22] to increase hydrophilicity and higher cell affinity of the modified surfaces. Although improving cellbiomaterial interactions, these methods have minimal impact upon cell retention and cell distribution within a highly organized and porous 3D scaffold [23]. We systematically investigated several parameters which impact cell seeding and developed an optimized method for highly porous scaffolds [24]. Efficiency of the technique was validated on scaffolds of various structures and biodegradable polymers. This chapter describes the method we developed for efficient and reproducible cell seeding on highly porous scaffolds.
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Materials Every process, material, and solution involved in the handling of cells must be sterile and pre-warmed at 37 C. Cell culture incubation must be performed in a 37 C, 5% CO2 humidified incubator.
2.1 Primary Human Osteoblasts Cell (hOB) Harvesting and Expansion
1. Human alveolar bone tissue remaining on freshly extracted teeth (see details in Subheading 3.1). 2. Culture flasks (25 and 75 cm2). 3. Dulbecco’s Modification of Eagle’s medium (DMEM, Invitrogen). 4. 10% Foetal Bovine Serum (FBS) 5. 4% Penicillin (50 U/mL, Invitrogen) (dropped to 1% after 1 week) 6. 4% Streptomycin (50 μg/mL, Invitrogen) (dropped to 1% after 1 week).
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Fig. 1 Schematic of the melt electrospinner machine and typical morphology of the melt electrowritten scaffold 2.2 Melt Electrowritten Scaffold Fabrication
1. Melt electrospinning writing (MEW) device (see Note 1, [25]; Fig. 1). 2. Medical grade polycaprolactone (mPCL, 120 kDa Corbion). 3. 2 N NaOH 4. Distilled water. 5. pH strips, 6. 80% Ethanol 7. 100% FBS.
2.3
Cell Seeding
1. Fine-pointed curved tweezers. 2. 90 mm Petri dish 3. PCL scaffolds. 4. 5 mm sterile dermal Biopsy punch (Kai Medical ®) 5. Cell culture incubator (37 C; 5% CO2). 6. Phosphate-buffered saline (PBS). 7. 1% Trypsin 8. Centrifuge. 9. 50 mL falcon tubes 10. 48-well plates.
2.4 Seeded-Scaffold Culture
1. 50 μg/mL Ascorbic acid (AA) 2. 0.1 μM Dexamethasone 3. 10 mM β-Glycerophosphate 4. 48-Well plates.
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2.5 3D Construct Imaging
1. Phosphate-buffered saline (PBS). 2. 4% Paraformaldehyde (PFA) solution in PBS at pH 7.4 (SigmaAldrich) 3. Microplate reader (POLARstar Omega, BMG Labtech, Germany). 4. 1% Alizarin Red S solution in osmosed water (pH adjusted to 4.2) 5. 10% acetic acid (Sigma) in PBS 6. 1% Sirius red in a saturated picric acid solution 7. 0.5% acetic acid 8. 0.1 N NaOH 9. Triton X-100 (0.1% v/v) in PBS. 10. 1% Bovine Serum Albumin 11. 10% goat serum 12. 0.05% tween-20 solution in PBS 13. 5 μg/mL 4,6-diamino-2-phenylindole (DAPI, Life Technologies, NY, USA) 14. 0.8 U/mL Alexa Fluor 568 Phalloidin (Life Technologies Grand Island, NY, USA) 15. Collagen I mouse primary antibody (1:400, ab6308, Abcam). 16. Goat anti-mouse IgG Alexa Fluor® 488-conjugated secondary antibody (1:500, ab150113, Abcam).
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3.1 Primary Human Osteoblasts Cell (hOB) Harvesting and Expansion
1. Place redundant freshly extracted teeth immediately in warm DMEM supplemented with 10% FBS, 4% PenicillinStreptomycin (PS). 2. Extract remaining bone fragments present on the teeth roots. 3. Section bone fragments into small segments, spread them in a 25 cm2 flask and cover with 5 mL DMEM supplemented with 10% FBS and 4% PS (reduced to 1% after 24 h) and incubate in a cell culture incubator at 37 C with 5% CO2. 4. Change cell culture media gently to maintain the bone fragments in their location. 5. When cells reach approximately 80% confluence, remove the medium along with the bone fragments and wash twice with PBS. 6. Harvest cells by incubating with sufficient trypsin (1%) to cover the cell layer. Incubate the flask in the incubator and monitor cell detachment every 1 min using an inverted microscope.
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7. Collect the cells in complete medium (DMEM supplemented with 10% FBS, 1% PS), in twice the volume of trypsin used. Centrifuge and resuspend in 15 mL of complete medium. 8. Dispense the cell suspension in a 75 cm2 flask and expand the cells. Place the flask in an incubator, change culture media every 2–3 days to 80% confluence. 9. Use cells at passages 3–4 for optimal cell growth and expansion. 3.2 Melt Electrowritten Scaffold Fabrication
1. Load mPCL pellets into a 2 mL syringe with a 21 G needle and heat in an oven set at 80 C to melt the polymer (see Note 2). 2. Place the syringe in the heated chamber of the MEW device, set the pressure at 1.6 bar, the voltage at 9 kV and the spinneret collector distance at 8 mm (Fig. 1). 3. Set the temperatures of the first heater (placed near the syringe) and the second heater (placed near the needle) to 75 C and 85 C, respectively. 4. Set the translational speed of the collector to 850 mm/min to obtain straight fibres. A GCode was written to print a scaffold mesh with a square wave pattern, composed of alternating series of layers oriented at 0 and 90 , with 250 μm fiber inter-distance and 1 mm thick. 5. Collect the printed melt electrowritten scaffold mesh using clean fine-pointed tweezers. 6. Section the PCL scaffolds using a 5 mm diameter biopsy punch.
3.3 Surface Modification
1. Place the 5 mm diameter scaffolds in 2 N NaOH for 60 min at 37 C to increase hydrophilicity and roughness (Wang et al. 2016). 2. Rinse the scaffolds several times in distilled water to remove any excess NaOH. 3. Check the pH of the rinsing water using pH strips to ensure complete removal of NaOH which is highly toxic for cells. 4. Sterilize the scaffolds for further use by immersing them in 80% ethanol for 60 min. Remove the excess of ethanol and let dry overnight in a biosafety cabinet prior to a 30-min UV-exposure. 5. Immerse sterile scaffolds in 100% FBS for 1 h at 37 C to improve cell seeding efficiency [24]. 6. Remove the FBS solution and any excess liquid before depositing the scaffolds on a 9 mm Petri dish (see Note 3).
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Cell Seeding
1. Trypsinize cells (see Note 4) and resuspend in complete medium to a concentration of 1.5 106 cells/mL.
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Fig. 2 Morphology of the seeding solution deposited onto the melt electrowritten scaffold
2. Position the scaffolds on a Petri dish separately and dispense 30 μL of cell suspension on each scaffold [24]. Place the Petri dish in an incubator and allow cells to attach for 2 h prior to transferring the scaffolds into 48-well plates containing 500 μL of medium as shown in Fig. 2 (see Note 5). 3.5 Seeded-Scaffold Culture
1. Culture the scaffolds for 3 days in complete medium before supplementing with osteogenic compounds (AA, Dexamethasone and β-Glycerophosphate), referred to hereafter as osteogenic medium (see Note 6). 2. Change medium every 2–3 days. 3. Inevitably, some cells will migrate to the well plate surface and expand. When these cells reach about 80% confluence, place the scaffolds in a new 48-well plate (see Note 7).
3.6 Imaging of 3D Construct
3.6.1 Alizarin Red Staining
Scaffold staining and imaging provide information about cell seeding efficiency, cell proliferation and differentiation, along with scaffold maturation. Therefore, 3D construct imaging can be valuable in the context of in vitro characterization of biomaterials, as well as the characterization of cell-based models. 1. Wash scaffolds in PBS, then fix by adding 500 μL of 4% PFA, and incubate at room temperature for 30 min. Then wash three times in PBS (see Note 8). 2. Incubate scaffolds with 1% Alizarin Red solution for 10 min at room temperature (see Note 9). Gently and thoroughly wash stained scaffolds with distilled water. 3. Image scaffolds using an inverted phase-contrast microscope for qualitative analysis (Fig. 3a). 4. For quantitative analysis, place each scaffold in 400 μL of 10% acetic acid in PBS for 30 min on a rotational shaker (100 rpm) to dissolve the staining (see Note 10).
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Fig. 3 (a) Alizarin Red staining of the melt electrospun scaffolds at various timepoints of in vitro culture. (b) Semi-quantification of the staining
5. Plate 100 μL of each sample in triplicate in a clear 96-well plate and measure the absorbance at 405 nm using a microplate reader (Fig. 3b). 3.6.2 Picrosirius Red Staining
1. Use the scaffolds previously fixed with 4% PFA and remove the PBS in which they are stored. 2. Incubate scaffolds with 1% Sirius red in saturated picric acid solution for 60 min at room temperature (see Note 9). Wash several times with 0.5% acetic acid. 3. Image scaffolds using an inverted phase-contrast microscope for qualitative analysis (Fig. 4a). 4. For quantitative analysis, place each scaffold in 400 μL of 0.1 N NaOH for 30 min on a rotational shaker (100 rpm) to dissolve the staining (see Note 10). 5. Plate 100 μL of each sample in triplicate in a clear 96-well plate and measure the absorbance at 540 nm using a microplate reader (Fig. 4b).
3.6.3 Immunostaining for Confocal Microscopy
1. Use scaffolds previously fixed with 4% PFA and remove the PBS in which they are stored. 2. Incubate scaffolds for 30 min in blocking buffer (1% BSA; 10% goat serum; 0.05% tween-20 solution in PBS) (see Note 9). 3. Incubate with mouse anti-Collagen I primary antibody in blocking buffer overnight at 4 C. 4. Wash scaffolds 3 times in PBS and subsequently treat with 0.1% Triton-X100 for 5 min.
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Fig. 4 (a) Picrosirius Red staining of the melt electrospun scaffolds at various timepoints of in vitro culture. (b) Semi-quantification of the staining
Fig. 5 Collagen type 1 immunostaining of the melt electrowritten scaffold
5. Incubate scaffolds with 5 μg/mL DAPI, 0.8 U/mL Alexa Fluor 568 Phalloidin and goat anti-mouse IgG secondary antibody in blocking buffer. 6. Rinse scaffolds thoroughly with PBS. 7. Visualize and image scaffolds using excitation/emission wavelength of (blue), 488/500–550 nm for 561/570–1000 nm for Phalloidin 12) (Fig. 5).
a Confocal Microscope at 405/417–477 for DAPI collagen (green), and (red) (see Notes 11 and
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Notes 1. We used a custom-made melt electrospinning writer manufactured in-house at the Institute of Health and Biomedical Innovation, Queensland, University of Technology [25]. 2. Avoid the presence of air bubbles in the polymer syringe for consistent printing and accurate structure of the scaffold. This can be achieved by leaving the syringe in the oven at 80 C overnight. 3. Remove as much FBS solution as possible to limit the variability of volume in the scaffold during seeding. If the scaffolds are wet when being placed in the Petri dish, the wettened area around the scaffold will result in spreading of the seeding medium outside of the scaffold edges. Ensure the scaffolds are well placed apart from each other and away from the plate edges to allow the cell solution to stay confined to each individual scaffold. 4. The trypsin treatment must be performed within 5–7 min before complete medium is added to limit an adverse effect of prolonged trypsin exposure on cells. 5. Common static seeding methods involve the addition of medium regularly during cell attachment, which can be disruptive to the seeding process and can be avoided by optimizing the cell solution volume to incubation time ratio. 6. The addition of osteogenic compounds is relevant to developing cellularized scaffolds for bone regeneration using osteoblasts. Even though this may not be appropriate to different applications and cell types, cell seeding can be a stressful process for the cells by changing from a 2D to a 3D environment. A change in the culture medium being another source of stress, a short expansion before a medium change might improve overall scaffold colonization and maturation. 7. Confluent cells release inhibiting growth signals which might affect the growth of the cell of interest contained in the scaffold, hence it is recommended to move the scaffolds to new plates regularly. 8. After PFA fixation, the scaffolds can be stored in PBS at 4 C until further use. 9. Scaffolds which were coated with FBS but not seeded with cells should be prepared as controls to establish a baseline for staining. This is also relevant if the study includes other characterization such as mechanical testing. 10. The volume of solution used to dissolve the fixed dye on the scaffold should be adjusted for each sample to ensure dye recovery and accurate absorbance reading. If the samples
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present with marked differences in mineralization content, samples can be incubated in different volumes of acetic acid and the absorbance analyzed relative to the volume used (absorbance/volume). Always ensure complete coverage of the scaffolds. Place scaffolds in smaller wells if necessary. Ensure that the scaffolds always remain covered as drying of the scaffolds will compromise the structure of organic components. This can be done by adding water between the plates and sealing with parafilm. 11. Imaging of immunostained 3D constructs can be challenging. Firstly, observe the stained scaffold on the lowest magnification for an overall perspective of the amount and location of the target compound. It is subsequently easier to identify a representative area at a higher magnification. 12. Other antibodies can be used in this procedure depending on the protein of interest. Optimization of the immunostaining protocol is however required with different products (antibody type and supplier). Depending on the number of channels available on the confocal microscope used, more components can be targeted with appropriate secondary antibodies (host specie and fluorescence wavelength).
Acknowledgments This study has been financially supported by the Australian National Health Medical Research Council (grant number: APP1086181). References 1. O’Brien FJ (2011) Biomaterials & scaffolds for tissue engineering. Mater Today 14:88–95. https://doi.org/10.1016/S1369-7021(11) 70058-X 2. Tan L, Ren Y, Kuijer R (2012) A 1-min method for homogenous cell seeding in porous scaffolds. J Biomater Appl 26:877–889. h t t p s : // d o i . o r g / 1 0 . 1 1 7 7 / 0885328210389504 3. Bueno EM, Laevsky G, Barabino GA (2007) Enhancing cell seeding of scaffolds in tissue engineering through manipulation of hydrodynamic parameters. J Biotechnol 129:516–531. https://doi.org/10.1016/j.jbiotec.2007. 01.005 4. Thevenot P, Nair A, Dey J et al (2008) Method to analyze three-dimensional cell distribution and infiltration in degradable scaffolds. Tissue
Eng Part C Methods 14:319–331. https://doi. org/10.1089/ten.tec.2008.0221 5. Sikavitsas VI, van den Dolder J, Bancroft GN et al (2003) Influence of the in vitro culture period on the in vivo performance of cell/titanium bone tissue-engineered constructs using a rat cranial critical size defect model. J Biomed Mater Res A 67:944–951. https://doi.org/10. 1002/jbm.a.10126 6. Van den Dolder J, Spauwen PHM, Jansen JA (2003) Evaluation of various seeding techniques for culturing osteogenic cells on titanium fiber mesh. Tissue Eng 9:315–325. https:// doi.org/10.1089/107632703764664783 7. Saini S, Wick TM (2003) Concentric cylinder bioreactor for production of tissue engineered cartilage: effect of seeding density and hydrodynamic loading on construct development.
Cell Seeding on 3D Scaffolds for Tissue Engineering and Disease Modeling. . . Biotechnol Prog 19:510–521. https://doi. org/10.1021/bp0256519 8. Li Y, Ma T, Kniss DA et al (2001) Effects of filtration seeding on cell density, spatial distribution, and proliferation in nonwoven fibrous matrices. Biotechnol Prog 17:935–944. https://doi.org/10.1021/bp0100878 9. Soletti L, Nieponice A, Guan J et al (2006) A seeding device for tissue engineered tubular structures. Biomaterials 27:4863–4870. h t t p s : // d o i . o r g / 1 0 . 1 0 1 6 / J . BIOMATERIALS.2006.04.042 10. Godbey WT, Stacey Hindy BS, Sherman ME, Atala A (2004) A novel use of centrifugal force for cell seeding into porous scaffolds. Biomaterials 25:2799–2805. https://doi.org/10. 1016/J.BIOMATERIALS.2003.09.056 11. van Wachem PB, Stronck JWS, KoersZuideveld R et al (1990) Vacuum cell seeding: a new method for the fast application of an evenly distributed cell layer on porous vascular grafts. Biomaterials 11:602–606. https://doi. org/10.1016/0142-9612(90)90086-6 12. Nasrollahzadeh N, Applegate LA, Pioletti DP (2017) Development of an effective cell seeding technique: simulation, implementation, and analysis of contributing factors. Tissue Eng Part C Methods 23:485–496. https:// doi.org/10.1089/ten.tec.2017.0108 13. Alvarez-Barreto JF, Linehan SM, Shambaugh RL, Sikavitsas VI (2007) Flow perfusion improves seeding of tissue engineering scaffolds with different architectures. Ann Biomed Eng 35:429–442. https://doi.org/10.1007/ s10439-006-9244-z 14. Bes¸kardes¸ IG, Aydın G, Bektas¸ S¸ et al (2018) A systematic study for optimal cell seeding and culture conditions in a perfusion mode bonetissue bioreactor. Biochem Eng J 132: 100–111. https://doi.org/10.1016/J.BEJ. 2018.01.006 15. Campos Marı´n A, Brunelli M, Lacroix D (2018) Flow perfusion rate modulates cell deposition onto scaffold substrate during cell seeding. Biomech Model Mechanobiol 17: 675–687. https://doi.org/10.1007/s10237017-0985-4 16. Cao T, Ho K-H, Teoh S-H (2003) Scaffold design and in vitro study of osteochondral coculture in a three-dimensional porous polycaprolactone scaffold fabricated by fused deposition modeling. Tissue Eng 9:103. https:// doi.org/10.1089/10763270360697012
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17. Jukes JM, Moroni L, Van Blitterswijk CA, De Boer J (2008) Critical steps toward a tissueengineered cartilage implant using embryonic stem cells. Tissue Eng Part A 14:135–147. https://doi.org/10.1089/ten.a.2006.0397 18. Woodfield TBF, Malda J, de Wijn J et al (2004) Design of porous scaffolds for cartilage tissue engineering using a three-dimensional fiberdeposition technique. Biomaterials 25: 4149–4161. https://doi.org/10.1016/J. BIOMATERIALS.2003.10.056 19. Vaquette C, Ivanovski S, Hamlet SM, Hutmacher DW (2013) Effect of culture conditions and calcium phosphate coating on ectopic bone formation. Biomaterials 34:5538–5551. https://doi.org/10.1016/j.biomaterials. 2013.03.088 20. Jinyoon J, Song SH, Lee DS, Park TG (2004) Immobilization of cell adhesive RGD peptide onto the surface of highly porous biodegradable polymer scaffolds fabricated by a gas foaming/salt leaching method. Biomaterials 25: 5613–5620. https://doi.org/10.1016/j. biomaterials.2004.01.014 21. Wang W, Caetano G, Ambler WS et al (2016) Enhancing the hydrophilicity and cell attachment of 3D printed PCL/graphene scaffolds for bone tissue engineering. Materials (Basel) 9:992. https://doi.org/10.3390/ma9120992 22. Truong YB, Glattauer V, Briggs KL et al (2012) Collagen-based layer-by-layer coating on electrospun polymer scaffolds. Biomaterials 33:9198–9204. https://doi.org/10.1016/j. biomaterials.2012.09.012 23. Jordan AM, Viswanath V, Kim SE, Pokorski JK, Korley LTJ (2016) Processing and surface modification of polymer nanofibers for biological scaffolds: a review. J Mater Chem B 4:5958–5974. https://doi.org/10.1039/ c6tb01303a 24. Blaudez F, Ivanovski S, Ipe D, Vaquette C (2020) A comprehensive comparison of cell seeding methods using highly porous melt electrowriting scaffolds. Mater Sci Eng C 117: 111282. https://doi.org/10.1016/j.msec. 2020.111282 25. Brown TD, Dalton PD, Hutmacher DW (2011) Direct writing by way of melt electrospinning. Adv Mater 23:5651–5657. https:// doi.org/10.1002/adma.201103482
Chapter 29 Workflow for Fabricating 3D-Printed Resorbable Personalized Porous Scaffolds for Orofacial Bone Regeneration Cedryck Vaquette, Danilo Carluccio, Martin Batstone, and Sasˇo Ivanovski Abstract Resorption of alveolar bone following tooth extraction is a physiological process that can often prevent the placement of dental implants due to the limited bone remaining. In severe cases, vertical bone augmentation, which aims to restore bone in an extraskeletal dimension (outside of the skeletal envelope), is required prior to implant placement. While current treatment strategies rely on autologous grafts, or “Guided Bone Regeneration” involving the placement of particulate bone grafting biomaterials under a protective membrane, the field is shifting to patient-matched solutions. Herein, we describe the various steps required for modeling the patient data, creating the patient-matched scaffold geometry and 3D-printing using the biodegradable polymer polycaprolactone for application in the oro-dental and craniofacial areas. Key words Vertical bone augmentation, 3D-printing, Mimics, 3matics, Patient matched scaffold, Polycaprolactone
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Introduction Severe bone resorption and atrophy occur following multiple tooth extraction, surgical resection or trauma [1], often preventing the placement of prosthetic devices such as dental implants for the restoration of masticatory function and aesthetics. In order to enable the safe and efficient placement of dental implants, restoration of the lost bone volume outside the newly formed skeletal envelope is required [2, 3]. The regeneration of bone in a vertical manner in the orofacial region is one of the most challenging procedures in dentistry because of the requirement for growing bone outside the skeletal envelope [3] and the paucity of soft tissue for coverage. There exist multiple clinical strategies for achieving vertical bone regeneration, ranging from the placement of autologous bone grafts [4], distraction osteogenesis [5, 6] and guided bone regeneration whereby an occlusive membrane is placed over
Gregory J. Seymour et al. (eds.), Oral Biology: Molecular Techniques and Applications, Methods in Molecular Biology, vol. 2588, https://doi.org/10.1007/978-1-0716-2780-8_29, © The Author(s), under exclusive license to Springer Science+Business Media, LLC, part of Springer Nature 2023
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particulate bone grafting materials [7, 8]. These techniques possess significant limitations such as the necessity of harvesting autologous bone leading to pain and donor site morbidity, and the outcome of these approaches are clinician dependent and often unpredictable [9, 10]. In addition, issues related to long-term space maintenance, which is essential for osteogenesis and bone resorption, still remain. The advent of 3D-printing for vertical bone augmentation has demonstrated promising outcomes using both bioceramic [11–16] and polymeric-based 3D constructs [17– 22]. It was recently demonstrated that the use of a polycaprolactone scaffold functionalized with bone morphogenetic protein resulted in excellent and predictable bone formation, while also preventing early bone resorption upon surgical re-entry and implant placement [22]. This represents a significant advancement for the field. Indeed, the utilization of anatomically accurate scaffolds mimicking the patient’s natural bone contours is attractive from a clinical point of view [23] and a patient matched titanium scaffold is currently used clinically [24–26]. This chapter describes the various steps required for the manufacturing of a biodegradable patient-matched 3D-printed scaffold for vertical bone augmentation. It describes in detail the protocols for segmenting the patient’s data, computationally creating a scaffold with dimensions and shape resembling the previous bone anatomy, and finally the 3D printing.
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Materials The chapter is divided into two parts, the computational modeling of the patient matched scaffold which requires the utilization of several commercially available software and the 3D printing of polycaprolactone for fabrication of an anatomically accurate construct.
2.1 Computational Modeling of the Patient Matched Scaffold
Human ethics approval for the use of the de-identified computed tomography (CT) scans of patients described in this chapter was granted under number 2018001757. The materials required for this step are as follows: 1. Patient CT or CBCT data. 2. Materialise Mimics Research 21.0 or above. 3. Materialise 3matics Research 13.0 or above (Materialise NV).
2.2 3D Printing of the Patient Matched Scaffold
1. Medical grade polycaprolactone (PCL; product name: PC8, 0.82 dl/g Corbion) 2. SweetPearl® Maltitol (Roquette) 3. An extrusion 3D-printer (Bioplotter Developer series, Envisiontech)
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3.1 Computational Modeling of the Patient Matched Scaffold
This section requires a license for the software Materialise Mimics 21.0. 1. Load the de-identified computed tomography (CT) scans in Materialise Mimics 21.0. 2. Create a mask using the default contrast threshold tool to isolate the bony tissues of the patient, the threshold can be adjusted depending on the quality of the scan. 3. Extract the region of interest by using the region grow tool, and cropping the unwanted tissue. When a unilateral defect is present, the healthy bony region can be saved as a reference for designing the natural bone contour of the resorbed side (Fig. 1). 4. Clean the mask from any noise coming from the scanning and/or from the presence of metallic implants or fixation screws or pins. For this, utilize the multiple slice edit tool in combination with the region grow tool (6 pixels). 5. Parts may need to be separated (e.g., Mandible from rest of skull) using the split mask tool. 6. Convert the mask to a part using the calculate part tool. 7. Once the part is complete, it can be exported directly into 3-Matics Research for further processing and template model creation (Fig. 2).
Fig. 1 Segmenting Patient data CT scan using Materialise Mimics Research 21.0. This is achieved using the thresholding region grow tool. (a–c) coronal, axial, and sagittal views of the patient anatomy after segmentation. (d) Resulting mask which is then exported as STL file for modeling
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Fig. 2 Computational modeling of the 3D-scaffold at vertical bone regeneration using Materialise 3-Matic 13.0. (a) Creation of the base surface, (b) creation of the volume base surface, (c) vertical bulk shape, and (d) subsequently combined with the base volume
8. The parts can be smoothed using the smooth tool if appropriate, and subsequently re-meshed to minimize the presence of surface artifacts from the STL conversion process. If suitable, in order to compensate for potential dimensional errors in the subsequent processes and manufacturing step, the part is wrapped using the wrap tool. 9. The surface of the area to undergo vertical bone elevation is then traced using a curve tool in order to design a wider base for the scaffold. Here, two methods are used for designing the anatomically correct elevated volume. In the case where the patient has a healthy contralateral side, the healthy geometry is duplicated, mirrored in reference to its sagittal plane, and virtually placed over the defect side to aid the design of the anatomical template.
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Fig. 3 Gross morphology of the patient matched scaffold after removal of the support structure. The scaffold design accommodates holes to facilitate subsequent restorative implant placement
10. The template model is created by tracing contour surfaces over the remaining bone and converting it into a 3D model. 11. To this end, the curves are converted into surfaces and the surfaces are extruded into 3D volume. 12. If applicable, the mirrored/healthy patient model geometry is utilized to aid the cropping, trimming, and placement of the 3D template. 13. Once the template is validated by the clinician, the wider base (described in step 8) is then combined with the 3D template. This enables the placement of fixation screws or pins by the clinician (Fig. 3). 14. After modeling is finalized, the scaffold template is exported as a STL file. 3.2 3D Printing of the Patient Matched Scaffold
1. The patient matched STL model is loaded into the slicing software embedded with the Bioplotter (Perfactory RP, EnvisionTEC). 2. Support structures are added when required by using the automated support generation software function. 3. The resulting models are sliced at 160 μm and transferred to the main software VisualMachines of the 3D-Bioplotter. 4. The models are assigned to their corresponding printing cartridges previous filled with either mPCL or Maltitol. 5. Once the cartridges are allocated to their material parameter, the models are assigned an infill comprising 1 mm and 60 layer-to-layer rotation. 6. The PCL is printed at a temperature of 110 C at a pressure of 9 bars and at a speed of 2 mm/s through a 200 μm nozzle.
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7. The support structures are printed at a temperature of 135 C at a pressure of 7 bar and at a speed of 10 mm/s through a 200 μm nozzle. 8. Once the scaffold is printed, it is immersed in distilled water for 10 min with gentle stirring. This procedure is repeated twice prior to rinsing in ethanol and drying.
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Notes: 3D Printing of the Patient Matched Scaffold 1. Other polymers for both the scaffold and the support structure can be utilized. However, optimization of the printing conditions is required prior to manufacturing complex scaffold shapes. 2. Larger diameter nozzles can be utilized when less accuracy is acceptable. This only requires the adjustment of the layer height which should be at 80% of the nozzle diameter. 3. Heating the maltitol to 160 C for 20 min first and then decreasing the temperature to 135 C enhances the printability of the support material. 4. The quality of the printing and more particularly the printing resolution is enhanced by printing identical parts simultaneously. This is more relevant when printing small scaffolds whereby heat accumulation resulting from continuous printing on a reduced area may induce some further fusion of the polymer struts. Therefore, the printing of multiple parts ensures that the printing head moves to a different area between each layer allowing sufficient cooling of the previous layer. Alternatively, a pause (30 s or more) between layers can be implemented between layers by using the corresponding command in VisualMachines. 5. The dissolution of the support structure can be performed in warm water to accelerate the removal of the maltitol. It is recommended to immerse the scaffold at least three times in water followed with a rinse in 100% ethanol.
References 1. Teitelbaum SL (2000) Bone resorption by osteoclasts. Science 289(5484):1504–1508. https://doi.org/10.1126/science.289.5484. 1504 2. Esposito M, Grusovin M, Felice P et al (2009) The efficacy of horizontal and vertical bone augmentation procedures for dental implants – a Cochrane systematic review. Int J Oral Implantol (Berl) 2(3):167–184
3. Urban IA, Montero E, Monje A et al (2019) Effectiveness of vertical ridge augmentation interventions: a systematic review and metaanalysis. J Clin Periodontol 46(S21): 319–339. https://doi.org/10.1111/jcpe. 13061 4. Myeroff C, Archdeacon M (2011) Autogenous bone graft: donor sites and techniques. J Bone
Workflow for Fabricating 3D-Printed Resorbable Personalized Porous. . . Joint Surg Am 93(23):2227–2236. https:// doi.org/10.2106/jbjs.j.01513 5. Chiapasco M, Consolo U, Bianchi A et al (2004) Alveolar distraction osteogenesis for the correction of vertically deficient edentulous ridges: a multicenter prospective study on humans. Int J Oral Maxillofac Implants 19(3): 399–407 6. Chiapasco M, Zaniboni M, Rimondini L (2007) Autogenous onlay bone grafts vs. alveolar distraction osteogenesis for the correction of vertically deficient edentulous ridges: a 2–4-year prospective study on humans. Clin Oral Implants Res 18(4): 432–440. https://doi.org/10.1111/j. 1600-0501.2007.01351.x 7. Urban IA, Lozada JL, Wessing B et al (2016) Vertical bone grafting and periosteal vertical mattress suture for the fixation of resorbable membranes and stabilization of particulate grafts in horizontal guided bone regeneration to achieve more predictable results: a technical report. Int J Periodontics Restorative Dent 36(2):153–159. https://doi.org/10.11607/ prd.2627 8. Dahlin C, Linde A, Gottlow J et al (1988) Healing of bone defects by guided tissue regeneration. Plast Reconstr Surg 81: 6 7 2 – 6 7 6 . h t t p s : // d o i . o r g / 1 0 . 1 0 9 7 / 00006534-198805000-00004 9. Rocchietta I, Fontana F, Simion M (2008) Clinical outcomes of vertical bone augmentation to enable dental implant placement: a systematic review. J Clin Periodontol 35(s8): 203–215. https://doi.org/10.1111/j.1600051x.2008.01271.x 10. Rocchietta I, Simion M, Hoffmann M et al (2016) Vertical bone augmentation with an autogenous block or particles in combination with guided bone regeneration: a clinical and histological preliminary study in humans. Clin Implant Dent Relat Res 18(1):19–29. https:// doi.org/10.1111/cid.12267 11. Carrel J-P, Wiskott A, Moussa M et al (2016) A 3D printed TCP/HA structure as a new osteoconductive scaffold for vertical bone augmentation. Clin Oral Implants Res 27:55–62. https://doi.org/10.1111/clr.12503 12. Carrel JP, Wiskott A, Scherrer S et al (2016) Large bone vertical augmentation using a three-dimensional printed TCP/HA bone graft: a pilot study in dog mandible. Clin Implant Dent Relat Res 18:1183–1192. https://doi.org/10.1111/cid.12394 13. Gbureck U, Ho¨lzel T, Klammert U et al (2007) Resorbable dicalcium phosphate bone substitutes prepared by 3D powder printing.
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Adv Funct Mater 17(18):3940–3945. https://doi.org/10.1002/adfm.200700019 14. Moussa M, Carrel JP, Scherrer S et al (2015) Medium-term function of a 3D printed TCP/HA structure as a new osteoconductive scaffold for vertical bone augmentation: a simulation by BMP-2 activation. Materials 8(5): 2174–2190. https://doi.org/10.3390/ ma8052174 15. Tamimi F, Torres J, Al-Abedalla K et al (2014) Osseointegration of dental implants in 3D-printed synthetic onlay grafts customized according to bone metabolic activity in recipient site. Biomaterials 35(21):5436–5445. https://doi.org/10.1016/j.biomaterials. 2014.03.050 16. Tamimi F, Torres J, Gbureck U et al (2009) Craniofacial vertical bone augmentation: a comparison between 3D printed monolithic monetite blocks and autologous onlay grafts in the rabbit. Biomaterials 30(31): 6318–6326. https://doi.org/10.1016/j. biomaterials.2009.07.049 17. Kumar SPT, Hashimi S, Saifzadeh S et al (2018) Additively manufactured biphasic construct loaded with BMP-2 for vertical bone regeneration: a pilot study in rabbit. Mater Sci Eng C 92:554–564. https://doi.org/10. 1016/j.msec.2018.06.071 18. Shim J-H, Jeong J-h, Won J-Y et al (2017) Porosity effect of 3D-printed polycaprolactone membranes on calvarial defect model for guided bone regeneration. Biomed Mater 13(1):015014. https://doi.org/10.1088/ 1748-605X/aa9bbc 19. Shim J-H, Won J-Y, Park J-H et al (2017) Effects of 3D-printed polycaprolactone/β-tricalcium phosphate membranes on guided bone regeneration. Int J Mol Sci 18(5):899–915. https://doi.org/10.3390/ ijms18050899 20. Shim J-H, Won J-Y, Sung S-J et al (2015) Comparative efficacies of a 3D-printed PCL/PLGA/β-TCP membrane and a titanium membrane for guided bone regeneration in beagle dogs. Polymers 7(10):2061–2077. https://doi.org/10.3390/polym7101500 21. Shim JH, Yoon MC, Jeong CM et al (2014) Efficacy of rhBMP-2 loaded PCL/PLGA/β-TCP guided bone regeneration membrane fabricated by 3D printing technology for reconstruction of calvaria defects in rabbit. Biomed Mater (Bristol) 9(6):065006. https://doi.org/10.1088/1748-6041/9/6/ 065006 22. Vaquette C, Mitchell J, Fernandez-Medina T et al (2021) Resorbable additively
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manufactured scaffold imparts dimensional stability to extraskeletally regenerated bone. Biomaterials 269:120671. https://doi.org/10. 1016/j.biomaterials.2021.120671 23. Bartnikowski M, Vaquette C, Ivanovski S (2020) Workflow for highly porous resorbable custom 3D printed scaffolds using medical grade polymer for large volume alveolar bone regeneration. Clin Oral Implants Res 31(5): 431–441. https://doi.org/10.1111/clr. 13579 24. Chiapasco M, Casentini P, Tommasato G et al (2021) Customized CAD/CAM titanium meshes for the guided bone regeneration of severe alveolar ridge defects: preliminary results of a retrospective clinical study in humans. Clin
Oral Implants Res 32(4):498–510. https:// doi.org/10.1111/clr.13720 25. Dellavia C, Canciani E, Pellegrini G et al (2021) Histological assessment of mandibular bone tissue after guided bone regeneration with customized computer-aided design/computer-assisted manufacture titanium mesh in humans: a cohort study. Clin Implant Dent Relat Res 23(4):600–611. https://doi.org/ 10.1111/cid.13025 26. Li L, Wang C, Li X et al (2021) Research on the dimensional accuracy of customized bone augmentation combined with 3D-printing individualized titanium mesh: a retrospective case series study. Clin Implant Dent Relat Res 23(1):5–18. https://doi.org/10.1111/cid. 12966
Chapter 30 Methacrylated Gelatin as an On-Demand Injectable Vehicle for Drug Delivery in Dentistry W. Benton Swanson, Abdel Hameed Mahmoud, Seth Woodbury, and Marco C. Bottino Abstract Gelatin methacrylate (GelMA) is a biodegradable and biocompatible engineered material with significant promise for its applications in tissue engineering, drug delivery, and 3D bioprinting applications. Gelatin is functionalized with terminal methacrylate groups which allow for its photoinducible crosslinking, and thereby tunable properties. Photocrosslinking of GelMA solution in situ allows for fabrication of hydrogels to fit patient-specific defects. Given its favorable biologic properties, GelMA may be used as a carrier for bioactive substances necessary to induce regenerative phenotypes or augment healing, such as growth factors and biotherapeutics. Gelatin is cleaved by cell-secreted enzymes such that its degradation, and subsequently release of bioactive substances, is well-matched to tissue regeneration processes. GelMA may be mixed with a wide array of additives to enhance and improve the specificity of its biologic activity. Here, we present two protocols for novel fabrications and their uses as clinically relevant drug delivery systems. GelMA hydrogels provides a versatile platform for the development of injectable drug delivery therapeutics for broad applications in regenerative dental medicine. Key words Drug delivery, Tissue engineering, Regenerative medicine, 3D bioprinting, Scaffolds, Biofabrication
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Introduction Tissue engineering aims to restore, maintain, improve, or replace various types of biological tissues in the human body by combining cells, biomaterial matrices, and appropriate biochemical and physicochemical factors [1–3]. Biomaterial matrices are an important factor in the tissue engineering triad, providing mechanical support and organization to engineered tissues throughout the regenerative process [4, 5]. Significant advancements have been made in identifying favorable architectural features critical to their success in vivo, including extracellular matrix (ECM) mimicking nanofibers and porosity [6]. The highly porous nature of hydrogels and biomaterial matrices fabricated for tissue engineering applications facilitates
Gregory J. Seymour et al. (eds.), Oral Biology: Molecular Techniques and Applications, Methods in Molecular Biology, vol. 2588, https://doi.org/10.1007/978-1-0716-2780-8_30, © The Author(s), under exclusive license to Springer Science+Business Media, LLC, part of Springer Nature 2023
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a significantly increased surface area within its internal structure [7]. This high surface area provides attachment for cells as well as protein adsorption [8, 9], and moreover is capable of facilitating controlled release of inductive cues or biomolecules to augment tissue regeneration within the defect site [10, 11]. Gelatin methacrylate (GelMA) is a biodegradable and biocompatible engineered gelatin-based material that has proven to be versatile for tissue engineering, drug delivery, and 3D bioprinting applications. Gelatin is isolated from porcine skin through denaturation of collagen, a major ECM component, and functionalized with terminal methacrylate groups by nucleophilic addition, which are subsequently capable of photoinduced polymerization, to form GelMA [12–14]. Its utility has been demonstrated in a variety of tissue engineering applications including skin, tendon, bone, cartilage, and vascular regeneration, reviewed by Piao et al. [15]. Traditionally, gelatin-based materials have demonstrated poor mechanical properties and unpredictable degradation [16]. Their crosslinking results in significantly increased mechanical stability and predictable biodegradation profiles [17]. The stiffness and density of hydrogels may be adjusted by altering the polymer dry mass, degree of functionalization, photo-initiator concentration, UV intensity and exposure duration [13]. To further tailor its properties, GelMA may be mixed with other materials to create scaffolds that are suited for certain purposes, such as in bone regeneration [18]. Other modifications may include pH and temperature responsive properties, and inclusion of functional groups could enhance properties. Hydrogels have a high water content, tissue-like elastic properties and possess unique tissue engineering advantages [17]. Hydrogels from GelMA are synthesized by radical polymerization, catalyzed by a photo-inducible radical agent, such as lithium phenyl-2,4,6-trimethylbenzoylphosphinate (LAP, 405 nm excitation wavelength) [19–21]. A solution of GelMA and LAP may be injected into a mold or directly into a tissue defect and polymerized in situ using an LED light [19–22]. Scaffolds that can release growth factors or drugs are excellent prospects for tissue engineering [3, 23]. Modifications including the incorporations of nanomaterials increase the predictability of tissue-specific regenerative outcomes [24]. A GelMA macromonomer precursor solution may also be combined with a variety of other additives such as nanofibers, nanoparticles or other carriers, proteins, and/or small molecules. After crosslinking, these components become a part of the matrix and are released as the GelMA matrix degrades. This protocol includes recent advances in GelMA-based therapeutics for drug delivery and highlights its uses. We provide a step-by-step method for GelMA synthesis, purification, and lyophilization. This method facilitates the encapsulation, delivery, and sustained release of biologically active substances from the
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hydrogel. We also describe methods to fabricate and formulate hydrogel-based biomaterials for drug delivery applications. Finally, we highlight two clinically relevant applications to demonstrate the versatility and functionality of GelMA-based materials for drug delivery.
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Materials
2.1 Gelatin Methacrylation
1. Gelatin derived from porcine skin (Sigma, SLBM9945V), 10 g 2. Phosphate buffered saline (PBS, sterile), 100 mL 3. Methacrylic anhydride (Sigma, 276685), 8 mL 4. Dulbecco’s phosphate buffered saline (DPBS, sterile), 100 mL 5. Magnetic stir plate with heating element 6. Erlenmeyer flask (250 mL) 7. Magnetic stir bar 8. P1000 pipette
2.2 GelMA Purification
1. Dialysis membrane (Spectro/Por molecular porous membrane tubing, MWCO 12–14,000, Fisher Scientific), 2 20–30 cm length 2. Dialysis membrane clips 3. Funnel 4. Large plastic beaker (5 L) 5. Magnetic stir bar 6. Magnetic stir plate 7. 40 C oven
2.3
Lyophilization
1. 50 mL Falcon tubes 2. 0.22 μm filtration cup (Millipore) 3. Lyophilizer/freeze dryer
2.4 Hydrogel Fabrication
1. Lithium phenyl-2,4,6-trimethylbenzoylphosphinate Sigma: 900899)
(LAP,
2. Silicone mold 3. LED dental curing light (405 nm wavelength) 4. Syringe and 25G needle 5. Bioactive substances (a) Example 3.4.1: Halloysite aluminosilicate clay nanotubes (HNT) loaded with chlorohexidine (CHX) (b) Example 3.4.2: Ciprofloxacin inclusion complexes (CIP-IP) and polydioxanone (PDS)-based electrospun fibers
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Methods
3.1 Gelatin Methacrylation
1. Combine 10 g Gelatin Porcine Skin with 100 mL PBS (sterile) in a cleaned Erlenmeyer flask with a magnetic stir bar. 2. Set undissolved mixture on heating plate with gentle stirring (240 rpm) at 50 C until Gelatin is completely dissolved. Cover Erlenmeyer flask with aluminum foil (ca. 1 h). Check temperature using a thermometer (see Note 1). 3. When Gelatin is melted, add 8 mL Methacrylic Anhydride dropwise and allow emulsion to rotate (240 rpm) at 50 C for 2 h, covered (Fig. 1). 4. Preheat 100 mL of sterile PBS in Erlenmeyer flask to 50 C. Use this preheated DPBS to dilute Gel-MA solution (total final volume of the solution should be 200 mL). After mixing of DPBS with concentrated solution, stir gently for 10 min at 50 C.
3.2 GelMA Purification
1. Prepare dialysis membrane by cutting into appropriate sizes (45 mm flat diameter membrane, 2 20–30 cm) and immerse them into distilled water to soften them. Close one end by twisting the membrane and making a knot (see Note 2) (Fig. 2). 2. Transfer diluted GelMA using a funnel into the dialysis membranes (see Note 3). Close the second end of the membrane using the same approach. Place dialysis tubing into distilled water in 5 L plastic beaker.
Fig. 1 Images (a–c) show components and addition of methacrylic anhydride to the gelatin solution in the flask
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Fig. 2 Dialysis purification of GelMA to remove unreacted methacrylic anhydride. Images (a) and (b) show dialysis tubing within the distilled water of the 5 L plastic beaker which is stirred
3. Continue dialysis at 40 C for at least 5 days with magnetic stirrer (ca. 500 rpm) and covered with aluminum foil. Change water twice daily (see Note 4). 3.3
Lyophilization
1. Add 200 mL of ultrapure water (same amount as the gel within the dialysis membranes) in an empty conical flask and add GelMA. Heat the solution on a hot plate for 15 min covered at 40 C (see Note 5). 2. Prepare 50 mL Falcon tubes, each containing 25 mL of the solution. 3. Use sterile vacuum 0.22 μm filtration cup to filter the liquid rapidly (Fig. 3), 4. Transfer sterilized polymer into 50 mL Falcon tubes (no more than 25 mL in each). Store tubes at 80 C for at least 2 days. Store Falcon tubes horizontally when freezing (see Note 6). 5. The frozen GelMA is freeze-dried for 5 days. Remove caps and cover the Falcon tubes’ opening with Kimwipes secured with rubber band. Verify that the pressure is reduced to ~100–200 10 3 Mbar after ~30 min to ensure that the vessel containing the Falcon tubes is appropriately sealed (Fig. 4).
3.4 Hydrogel Fabrication
GelMA, a bifunctional polymer, may be used directly through radical-initiated crosslinking to fabricate hydrogels. These gels can be loaded with various nanoparticles or drug delivery modalities by dispersion in the matrix [25, 26]. A general protocol is presented along with two specific examples from the recent literature. 1. GelMA is solubilized in PBS at 50 C at a concentration of 15–20% w/v.
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Fig. 3 Filtration of GelMA (a and b) in 50 mL conical tubes (c) used for the lyophilization procedure
Fig. 4 Lyophilization of GelMA (a and b) resulting in the final product (c). Morphology is assessed by scanning electron microscopy (SEM, d)
2. Nanoparticles or other modalities of drug delivery are incorporated by mechanical stirring to disperse in the solution at a standardized weight-percent concentration. 3. LAP photo-initiator is added to give a final concentration of 0.05% w/v. 4. A small volume, for example, 100 μL, is pipetted into a silicone mold followed by UV crosslinking (405 nm wavelength) for 60 seconds using an LED device. l
3.4.1 Utilization of the Biodegradability of GelMA for Controlled Delivery of Chlorohexidine
As an alternative, the solution may be injected into a defect and photopolymerized in situ for on-demand hydrogel synthesis.
1. Load halloysite aluminosilicate clay nanotubes (HNT) with chlorohexidine (CHX), an antimicrobial agent commonly used in clinical dentistry (see Note 7). 2. Combine CHX-HNT in suspension with 15% w/v GelMA and LAP photoinitiator, as described in the general protocol above,
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Fig. 5 Schematic of the formulated GelMA hydrogels containing CHX-loaded HNT. (a and b) SEM micrographs of a cross section in (a) a GelMA hydrogel and (b) a GelMA hydrogel modified with CHX-loaded HNT (5% H–10%CHX). (c) Cumulative release percentage of CHX as a function of time from GelMA hydrogels modified with distinct HNT (1, 2, and 5%) concentrations, loaded with unique CHX solution concentrations (10% and 20%). (d–f) SEM micrographs of the bacterial biofilms on the dentin surfaces of (d) Unmodified GelMA, (e) GelMA-5%H–10%CHX, and (f) GelMA-5%H with no loaded CHX
and light-cure to induce photo-crosslinking. When incubated in solution in vitro, CHX is released over the course of 14 days and the amount released correlates to the weight-percent incorporation of CHX-HNT incorporated into the gel at the time of fabrication. 3. The amount of CHX-HNT incorporated, in addition to the weight-percent of GelMA, influences mechanical properties and degradation rate; therefore, it is critical to consider these factors when optimizing a construct for a specific application. 4. A schematic overview is provided in Fig. 5. 3.4.2 An AntibioticEluting Hydrogel for Oral Infection Ablation
1. Fabricate ciprofloxacin inclusion complexes (CIP-IP) to improve CIP solubility and bioavailability. The controlled release of small molecules is relatively straightforward for hydrophilic molecules; however, for hydrophobic molecules such as CIP, a carrier is necessary (see Note 8). 2. Combine CIP-IP with polydioxanone (PDS)-based electrospun fibers to disperse the CIP-IPs (see Note 9).
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Fig. 6 Schematic of the procedural overview for synthesizing the GelMA-CIP-SF and GelMA-CIP/IC-SF along with important experiments characterizing the release of the different systems and their biologic efficacies. (a–b) Electrospun PDS nanofibers embedded with (a) CIP and (b) CIP/IC. (c–d) Cryo-cut short fibers from the previous nanofibers in (a–b) containing (c) CIP and (d) CIP/IC. (e) Agar diffusion assay against E. faecalis using the short fibers from (c–d) demonstrating the effectiveness of the CIP/IC. (f) 2.5% or 10% GelMA solution containing one type of short fibers from (c–d) being injected from a needle onto a microscope slide as a model for clinical translation. (g) Enzymatic degradation of the GelMA hydrogels (2.5% or 10%) unloaded or loaded (CIP-SF or CIP/IC-SF) quantifying the degradation of the 2.5% GelMA hydrogels to be significantly faster than that of the 10% GelMA hydrogel group. (h) Release kinetics of CIP from the different GelMA hydrogels (2.5% or 10%) loaded with CIP-SF or CIP/IC-SF. (i–k) SEM micrographs of the infected detin biofilm models evaluating the antimicrobial efficacy of treatment by (i) the control, (j) 10% GelMA-CIP-SF, and (k) 10% GelMA-CIP/ IC-SF. Significantly fewer bacteria appear in (j–k) because of the CIP release
3. Cryo-cut PDS fibers to improve their dispersion and allow the solution to be injectable. These carriers add a degree of complexity to encapsulation and delivery strategies, which is wellaccommodated by GelMA. 4. Suspend CIP-IC in a GelMA macromonomer precursor solution with LAP photoinitiator. Use Tween80 as an adjunctive surfactant to prevent CIP-IP aggregation. 5. Light-cure GelMA gels as described in the protocol above. 6. A schematic overview is provided in Fig. 6.
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Notes 1. Gelatin melting is characterized by a uniform solution, i.e., no more visible granula. 2. It is important that the dialysis tubing is shorter than the dialysis vessel to avoid contacting the stir bar. It is similarly important to examine the membrane tubing to ensure there are no holes or defects.
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3. When loading GelMA solution into the dialysis tubing, remember to leave extra space inside the membrane (i.e., do not squeeze GelMA; this will allow some water in and allow enhanced dialysis). Check again the tubing for leakage. 4. During each water change during dialysis, turn/flip the membranes upside down 5–6 times to homogenize the content! (This step removes the toxic unreacted MA, so if more GelMA is dialyzed, consider changing the water more often and/or let the step run longer). 5. This step must be handled rapidly to maintain the liquid at 40 C. 6. The purpose of storing tubes horizontally is to ensure an optimal repartition in the tube when gel will be lyophilized. 7. CHX-HNT/GelMA constructs are assessed for their potential cytotoxicity; their degradation products showed no adverse effects on cellular proliferation [26]. The same constructs are implanted in an animal model to assess inflammation; GelMA constructs should demonstrate no signs of host inflammation. When tested for efficacy with oral pathogenic bacteria, significant inhibition is achieved by the controlled release of CHX. This method is not exclusive to the delivery of CHX, but may be adapted for other molecules including dexamethasone, as described by Bordini et al. [27] for the purpose of an injectable drug delivery system for hard tissue regeneration. 8. Of relevance to the clinical context, the authors have shown that a GelMA solution could be injected and cured in situ [28]. Both 2.5% w/v and 10% w/v solutions are injectable. 2.5% w/v gels are rapidly degraded within 24 h, while 10% gels degrade over the course of 1 week. As the gel degrades, CIP-IPs are released as the gel degrades, over the course of the first day of incubation, more quickly from a 2.5% w/v gel compared to 10% w/v gel. In an in vitro model of a dental pathogen, E. faecalis, CIP-IP-eluting GelMA hydrogels causes significant inhibition of bacterial growth in an agar diffusion assay and biofilm model. 9. Electrospinning should be undertaken in a vented fume hood due to the nature of the volatile organic solvents used.
Acknowledgments M.C.B. acknowledges the National Institutes of Health (NIH – National Institute of Dental and Craniofacial Research/NIDCR, R01DE026578). M.C.B. is grateful for funds received from the OsteoScience Foundation (Peter Geistlich Research Award) and the American Academy of Implant Dentistry Foundation
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(AAIDF). W.B.S. acknowledges the NIH/NIDCR (F30DE029359) and American Academy of Implant Dentistry Foundation Student Research Fellowship (AAIDF). The authors are indebted to Luiz E. Bertassoni (Oregon Health & Science University, School of Dentistry) for sharing the protocol involved in the synthesis of methacrylated gelatin hydrogels. The content is solely the responsibility of the authors and does not necessarily represent the official views of the NIH and NSF. We apologize to colleagues whose work we could not discuss due to the space limitations. References 1. Langer R, Vacanti JP (1993) Tissue engineering. Science 260:920–926. https://doi.org/ 10.1126/science.8493529 2. Langer R, Vacanti J (2016) Advances in tissue engineering. J Pediatr Surg 51:8–12. https:// doi.org/10.1016/j.jpedsurg.2015.10.022 3. Ma PX (2008) Biomimetic materials for tissue engineering. Adv Drug Deliv Rev 60:184–198 4. Chan BP, Leong KW (2008) Scaffolding in tissue engineering: general approaches and tissue-specific considerations. Eur Spine J 17 (Suppl 4):467–479. https://doi.org/10. 1007/s00586-008-0745-3 5. Kim Y, Ko H, Kwon IK, Shin K (2016) Extracellular matrix revisited: roles in tissue engineering. Int Neurourol J 20:S23–S29. https://doi.org/10.5213/inj.1632600.318 6. Swanson WB, Ma PX (2020) Nanofibrous and porous biomaterials. In: Zhang G (ed) Biomaterials science, an introduction to materials in medicine. Elsiever 7. Rasouli R, Barhoum A, Bechelany M, Dufresne A (2018) Nanofibers for biomedical and healthcare applications. Macromol Biosci 19. https://doi.org/10.1002/mabi.201800256 8. Zhang R, Ma PX (1999) Porous poly(L-lactic acid)/apatite composites created by biomimetic process. J Biomed Mater Res A 45: 285–293 9. Liu X, Ma PX (2010) The nanofibrous architecture of poly(L-lactic acid)-based functional copolymers. Biomaterials 31:259–269. https://doi.org/10.1016/j.biomaterials. 2009.09.046 10. Swanson WB, Gong T, Zhang Z, Eberle M, Niemann D, Dong R, Rambhia KJ, Ma PX (2020) Controlled release of odontogenic exosomes from a biodegradable vehicle mediates dentinogenesis as a novel biomimetic pulp capping therapy. J Control Release 324:679–694.
https://doi.org/10.1016/j.jconrel.2020. 06.006 11. Swanson WB, Zhang Z, Xiu K, Gong T, Eberle M, Wang Z, Ma PX (2020) Scaffolds with controlled release of pro-mineralization exosomes to promote craniofacial bone healing without cell transplantation. Acta Biomater 118:215–232. https://doi.org/10.1016/j. actbio.2020.09.052 12. Bello AB, Kim D, Kim D, Park H, Lee SH (2020) Engineering and functionalization of gelatin biomaterials: from cell culture to medical applications. Tissue Eng Part B Rev 26: 164–180. https://doi.org/10.1089/ten. TEB.2019.0256 13. Aldana AA, Malatto L, Rehman MAU, Boccaccini AR, Abraham GA (2019) Fabrication of gelatin methacrylate (GelMA) scaffolds with nano- and micro-topographical and morphological features, vol 9, Nanomaterials (Basel). https://doi.org/10.3390/nano9010120 14. Yue K, Trujillo-de Santiago G, Alvarez MM, Tamayol A, Annabi N, Khademhosseini A (2015) Synthesis, properties, and biomedical applications of gelatin methacryloyl (GelMA) hydrogels. Biomaterials 73:254–271. https:// doi.org/10.1016/j.biomaterials.2015.08.045 15. Piao Y, You H, Xu T, Bei H-P, Piwko IZ, Kwan YY, Zhao X (2021) Biomedical applications of gelatin methacryloyl hydrogels. Engineered Regeneration 2:47–56. https://doi.org/10. 1016/j.engreg.2021.03.002 16. Gorgieva S, Kokol V (2011) Collagenvs. gelatine-based biomaterials and their biocompatibility: review and perspectives. In: Biomaterials applications for nanomedicine. InTech Rijeka 17. Zhu M, Wang Y, Ferracci G, Zheng J, Cho N-J, Lee BH (2019) Gelatin methacryloyl and its hydrogels with an exceptional degree of controllability and batch-to-batch consistency.
Gelatin Methacrylate Hydrogel for On-Demand Drug Delivery Sci Rep 9. https://doi.org/10.1038/s41598019-42186-x 18. Lantigua D, Wu X, Suvarnapathaki S, Nguyen MA, Camci-Unal G (2021) Composite scaffolds from gelatin and bone meal powder for tissue engineering. Bioengineering 8. https:// doi.org/10.3390/bioengineering8110169 19. Monteiro N, He W, Franca CM, Athirasala A, Bertassoni LE (2018) Engineering microvascular networks in LED light-cured cell-laden hydrogels. ACS Biomater Sci Eng 4: 2563–2570. https://doi.org/10.1021/ acsbiomaterials.8b00502 20. Athirasala A, Lins F, Tahayeri A, Hinds M, Smith AJ, Sedgley C, Ferracane J, Bertassoni LE (2017) A novel strategy to engineer pre-vascularized full-length dental pulp-like tissue constructs. Sci Rep 7. https://doi.org/10. 1038/s41598-017-02532-3 21. Monteiro N, Thrivikraman G, Athirasala A, Tahayeri A, Franc¸a CM, Ferracane JL, Bertassoni LE (2018) Photopolymerization of cellladen gelatin methacryloyl hydrogels using a dental curing light for regenerative dentistry. Dent Mater 34:389–399. https://doi.org/10. 1016/j.dental.2017.11.020 22. Bertassoni LE, Cecconi M, Manoharan V, Nikkhah M, Hjortnaes J, Cristino AL, Barabaschi G, Demarchi D, Dokmeci MR, Yang Y et al (2014) Hydrogel bioprinted microchannel networks for vascularization of tissue engineering constructs. Lab Chip 14: 2202–2211. https://doi.org/10.1039/ c4lc00030g 23. Ren X, Zhao M, Lash B, Martino MM, Julier Z (2020) Growth factor engineering strategies for regenerative medicine applications. Front
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Bioeng Biotechnol 7. https://doi.org/10. 3389/fbioe.2019.00469 24. Scantlebury T, Ambruster J (2012) The development of guided regeneration: making the impossible possible and the unpredictable predictable. Journal of Evidence Based Dental Practice 12:101–117. https://doi.org/10. 1016/s1532-3382(12)70022-2 25. Bordini EAF, Ferreira JA, Dubey N, Ribeiro JS, de Souza Costa CA, Soares DG, Bottino MC (2021) Injectable multifunctional drug delivery system for hard tissue regeneration under inflammatory microenvironments. ACS Applied Bio Materials 4:6993–7006. https:// doi.org/10.1021/acsabm.1c00620 26. Ribeiro JS, Bordini EAF, Ferreira JA, Mei L, Dubey N, Fenno JC, Piva E, Lund RG, Schwendeman A, Bottino MC (2020) Injectable MMP-responsive nanotube-modified gelatin hydrogel for dental infection ablation. ACS Appl Mater Interfaces 12:16006–16017. https://doi.org/10.1021/acsami.9b22964 27. Bordini EAF, Ferreira JA, Dubey N, Ribeiro JS, de Souza Costa CA, Soares DG, Bottino MC (2021) Injectable multifunctional drug delivery system for hard tissue regeneration under inflammatory microenvironments. ACS Appl Bio Mater 4:6993–7006. https://doi.org/10. 1021/acsabm.1c00620 28. Ribeiro JS, Daghrery A, Dubey N, Li C, Mei L, Fenno JC, Schwendeman A, Aytac Z, Bottino MC (2020) Hybrid antimicrobial hydrogel as injectable therapeutics for oral infection ablation. Biomacromolecules 21:3945–3956. h t t p s : //d o i . o r g / 1 0. 10 2 1 / a c s . bi o m a c . 0c01131
Chapter 31 In Vitro Biological Testing of Dental Materials Jithendra Ratnayake, Josette Camilleri, T. Nethmini Haththotuwa, and Jeffrey Huang Abstract Dental materials are specially fabricated materials designed for use in dentistry. A variety of materials may be used, including cements, impression, lining, and dental restorative materials. Some of these dental materials provide temporary dressings while others are more permanent and are in contact with host tissue for prolonged periods of time. Consequently, newly developed dental materials not only require mechanical, chemical, and physical testing but also require in vitro analysis to ensure their safety and biocompatibility. The current chapter provides background on dental material characterization and a protocol for its in vitro biological testing. Key words Dental material, Material classification, Material characterization, Cell culture, MTS assay, Alkaline phosphatase assay
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Introduction A variety of materials are used in dentistry. Some materials come into contact only briefly with host oral tissues while others are in contact with the patient tissues for an extended period of time. For the context of this chapter, only materials which are placed in the body and interact with the host tissues are considered. The type of testing that needs to be undertaken on dental materials that are in contact with the host tissues varies depending on the material. The Food and Drug Administration (FDA) classifies medical devices depending on the risk they pose to the patient [1]. Most dental materials can be classified as Class II and III medical devices. Class II devices pose intermediate risk to the patient and require special testing. Class IIb devices are generally medium to high risk and will often be installed within the body for periods of 30 days or longer. Class III devices are high risk and require premarket approval.
Gregory J. Seymour et al. (eds.), Oral Biology: Molecular Techniques and Applications, Methods in Molecular Biology, vol. 2588, https://doi.org/10.1007/978-1-0716-2780-8_31, © The Author(s), under exclusive license to Springer Science+Business Media, LLC, part of Springer Nature 2023
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Fig. 1 Classification of dental materials based on composition
Prior to deciding what type of testing is to be undertaken, it is necessary to classify the material type and to chemically characterize it. Following this depending on where the material is to be located and its chemistry, the type of testing to be carried out should be determined. Below, we consider the classification of dental materials depending on use, propose appropriate chemical characterization methods and provide a biological testing protocol. 1.1 Classification of Dental Materials
Dental materials are classified by their chemistry. There are three categories of dental materials as indicated in Fig. 1 with examples of each subtypes shown. The classification based on material chemistry does not identify the material specific use and the environment in which it is placed. The latter is particularly important as the biological interactions are a function of both the material chemistry and also the material-host interaction. Thus, the level of testing carried out should reflect this. A classification of dental materials that distinguishes the clinical location and a sub-classification based on their specific application and the substrate receiving the device is proposed (Fig. 2). This classification has been used for device classification for antimicrobial testing of dental devices [2]. This classification approach is convenient as besides the material type, the location placement is also a key variable. This is important for biological testing of dental materials as implantable materials in direct contact with tissue require rigorous testing. Furthermore, material biocompatibility is not just a material
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Fig. 2 Classification of dental materials based on location with a sub-classification by specialty and the environment that the material is in contact with
property, but rather a measure of its interaction with its environment. Biocompatibility is defined as the ability of a biomaterial to perform its desired function with respect to a medical therapy. Furthermore, it should not elicit any undesirable local and systemic effects in the recipient, but ideally it should generate a beneficial cellular or tissue response for that specific situation [3]. 1.2 Material Characterization Methods
Characterization should be performed on set materials if the material requires mixing and has a setting reaction. The age of testing and any aging procedures should be specified. Characterization should include both bulk and surface characterization. If the material is degradable, elution and degradation tests should also be performed.
1.2.1 Chemical Characterization
Chemical characterization needs to be performed in bulk and on the material surface. Suggested characterization methods are provided in Table 1. Furthermore, the nature of the leachate and degraded material should be assessed. The appropriate methods for the chemical characterization depend on the chemical nature of the material. It is very important to differentiate between bulk characterization where the material chemistry is investigated and surface characterization. Surface characterization is necessary for coatings, thin films, and also to assess surface deposits and interactions on the surfaces. Most materials elute substances, and depending on the nature of the leachate, chemical analysis needs to be carried out.
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Table 1 Proposed chemical analyses for both bulk and surface testing of dental materials Metal
Material Type
Polymer
Ceramic
Bulk Coating Bulk Coating Bulk Coating
Scanning electron microscopy, energy dispersive spectroscopy – SEM/EDS
X-ray fluorescence – XRF
Nuclear magnetic resonance imaging – NMR
Inductively coupled plasma – ICP
X-ray diffraction – XRD
Fourier transform infrared spectroscopy-attenuated total reflectance – FTIR-ATR
RAMAN spectroscopy
X-ray photoelectron spectroscopy – XPS
Time-of-flight secondary ion mass Spectrometry – TOF SIMS
The ISO 10993-18; 2020 standard highlights why chemical characterization is necessary and when it should be undertaken. Chemical analysis will support the overall biological safety of the materials, determine the leachable substances and also verify if any processes such as manufacturing or sterilization required for the undertaking of in vitro biological assessment have caused any chemical change to the material. Chemical characterization of new dental materials can be used to identify their suitability for use and similarity to existing materials. Since most dental materials are placed in contact with the host for the longer term, specimen aging is important. This can be simulated use conditions or exaggerated conditions depending on the location of the material. The aging and how it is performed is important. Some materials may regress and reconvert back after a certain period of time [4], thus the specific details of the material preparation and aging need to be documented in the experimental protocol [5]. This information can then be fed into any downstream biological testing to ensure in vitro models are as relevant as possible. 1.3 In Vitro Biological Testing
Cell culture in vitro is a widely used in dental research to evaluate the biocompatibility of materials to predict its likely effect (e.g., toxicity) when placed in vivo. In adherent 2D cultures, cells grow as a monolayer in a culture flask attached to a plastic surface. Ideally, the cell lines and types used in the in vitro study should be
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Table 2 American Type Culture collection (ATCC) cell types which can be used to represent different oral and dental locations to assess a range of dental materials [23] Device
Procedure
Location/substrate
Tissue
Cell line
ATCC number
Implantable
Oral surgery
Bone
Bone
MG- 63
CRL-1427
Implantology
Bone Bone
Bone Bone
Saos-2 MG- 63
HTB-85 CRL-1427
Bone
Bone
Saos-2
HTB-85
Coronal/oral mucosa Coronal/tooth (dentine-crown-pulp) Radicular (dentine-root) Extra-radicular (dentin-root-bone) Oral mucosa Tooth (enamel)
Fibroblasts Pulpal cells
L929 N/A
CCL-1 N/A
N/A Bone
N/A MG- 63
N/A CRL-1427
Fibroblasts N/A
L929 N/A
CCl-1 N/A
Non-implantable
Restorative dentistry
Orthodontics
representative of, and derived from, the local target tissue relevant to the material’s clinical placement [6, 7]. Appropriate cell types that can be obtained via American Type Culture Collection (ATCC) are listed below (see Table 2). There are several limitations of 2D cell culture. These unicellular models are somewhat simplistic as the in vivo environment routinely comprises a mixture of cell types and also includes a blood flow which may affect the local response to the material [8]. Two-dimensional cell cultures do not mimic the natural structures of tissues, such as the disturbance of interactions between the cellular and extracellular environments [9, 10]. Another drawback of 2D culture is that the cells in the monolayer have unlimited access to the ingredients of the medium oxygen, nutrients, metabolites, and signaling molecules [11]. Furthermore, in vitro testing models may also be limited as there can be significant variability between individuals in their cellular responses. Nevertheless, the advantages of in vitro biological testing models include them being less time-consuming, more affordable, highly repeatable, and having less ethical concerns, compared with animal studies or human clinical trials [12]. Indeed, culturing of cells directly on the material surface or by indirectly exposing cells to material eluates provides valuable data as to how the material may affect local tissues in vivo. Subsequently, cellular responses can be characterized and quantified and results can demonstrate biocompatibility, cytotoxicity, proliferative, and differentiation responses. The following protocol describes the laboratory reagents, equipment, and procedures which can be used for direct in vitro biological testing of a dental material.
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Materials
2.1 Preparation of Growth Media
1. Growth media (GM): Dulbecco’s Modified Eagle’s Medium (DMEM) 2. Foetal bovine serum (FBS) 3. Antibiotics Antimycotic (10,000 units/mL of penicillin, 10,000 μg/mL of streptomycin, and 25 μg/mL Amphotericin B) 4. Water bath 5. Class II biohazard laminar flow hood 6. 50 mL Falcon tubes 7. Transfer pipettes 8. 10 mL and 50 mL serological pipettes 9. Pipette gun
2.2 Preparation of Differentiation Medium (DM)
1. Growth media (GM): DMEM 2. Dexamethasone 3. β-glycerophosphate (β-GP) 4. L-ascorbic acid 2–phosphate 5. Transfer pipettes 6. Falcon tubes 7. 2 μm, 20 μm, 100 μm, 200 μm, and 1000 μm pipettes and pipette tips
2.3 Cell Seeding and Cell Growth
1. Growth media (GM): DMEM 2. 25 cm2 culture flasks 3. Transfer pipettes 4. Falcon tubes 5. 1000 μm pipettes and pipette tips 6. Water bath 7. CO2 incubator 8. Inverted microscope
2.4 Subculturing (Passaging) Cells
1. Growth media (GM): DMEM 2. Dulbecco’s phosphate buffered saline (DPBS): without CaCl2 and MgCl2 3. Trypsin EDTA 4. Transfer pipettes 5. 25 cm2 culture flasks 6. 15 mL falcon tubes 7. Centrifuge
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1. Automated cell counter 2. Counting chambers 3. Trypan blue 4. Transfer pipettes 5. 10 μL pipette tips and micropipette
2.6 Cryopreserving Cells
1. Growth media: DMEM 2. Dulbecco’s phosphate buffered saline (DPBS): without CaCl2 and MgCl2 3. Dimethyl sulfoxide (DMSO) 4. Foetal bovine serum (FBS) 5. Trypsin EDTA 6. Transfer pipettes 7. 15 mL Falcon tubes 8. Nalgene® cryofreezing container 9. Isopropanol 10. Cryotubes 11. Centrifuge
2.7 Reviving Frozen Cells and Material Sterilization
1. 70% ethanol 2. Testing material 3. Growth media 4. Forceps 5. Dulbecco phosphate buffered saline (DPBS) 6. 24-Well plates
2.8 Cell Seeding on to the Test Material
1. 70% ethanol 2. Sterilized testing material 3. 48 well or 24 well plates 4. Culture flasks 5. Transfer pipettes 6. Cell counting chambers 7. Trypan blue 8. 2 μm, 20 μm, 100 μm, 200 μm, and 1000 μm pipettes and pipette tips
2.9 Cell Viability/ Cytotoxicity Assay (LIVE/DEAD Assay)
1. LIVE/DEAD Cytotoxicity Assay Kit: 4 mM calcein AM and 2 mM ethidium homodimer-1 2. Dulbecco Phosphate Buffered Saline (DPBS) 3. Transfer pipettes
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4. Glass slides 5. Glass cover slips 6. 2 μm, 20 μm, 100 μm, 200 μm, and 1000 μm pipettes and pipette tips 7. Confocal laser-scanning microscope 2.10 The MTS Cell Proliferation Assay
microscope
or
fluorescence
1. MTS cell proliferation Assay Kit 2. Dulbecco Phosphate Buffered Saline (DPBS) 3. Growth medium 4. 2 μm, 20 μm, 100 μm, 200 μm, and 1000 μm pipettes and pipette tips 5. Transfer pipettes 6. 96 well plates 7. Multi-Mode Microplate Reader 8. Spectrophotometer
2.11 Alkaline Phosphatase (ALP) Assay
1. SensoLyte® pNPP Alkaline Phosphatase Assay kit 2. Dulbecco Phosphate Buffered Saline (DPBS) 3. Transfer pipettes 4. 96 well plates 5. Centrifuge tubes 6. Vortex 7. Centrifuge 8. Multi-Mode Microplate Reader 9. Fluorometer
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Methods
3.1 Preparation of Growth Media (GM) (See Note 1)
1. Spray all equipment with 70% alcohol and treated under ultraviolet (UV) radiation for 30 min in a class II biohazard laminar flow hood (Fig. 3) 2. Pre-warm GM in a 37 C water bath 3. Remove 55 mL of culture medium from a ready to use 500 mL GM bottle in a class 2 laminar floor hood 4. Add 50 mL of heat-inactivated foetal bovine serum and 5 mL of Antibiotic-Antimycotic to the culture media bottle 5. Aliquot 45 mL aliquots in 50 mL Falcon tubes 6. Store aliquots in a 4 C refrigerator prior to use
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Fig. 3 Sterilization of equipment in a class II laminar flow hood under ultraviolet (UV) radiation 3.2 Preparation of Differentiation Medium (DM)
1. Add 10 nM dexamethasone, 5 mM β-glycerophosphate (β-GP), and 100 μM L-ascorbic acid 2–phosphate supplements to the growth medium (see Note 2).
3.3 Reviving Frozen Cells, Cell Seeding, and Cell Growth
1. Label the 25 cm2 flask with passage number, name of the cell line, date, and name of the user. 2. Add pre-warmed media (~5–6 mL) into 25 cm2 vented cell culture flasks. 3. Retrieve the cryotubes from the liquid nitrogen Dewar and thaw rapidly in a water bath at 37 C. 4. Transfer the content of the cryotube to the prepared 25 cm2 flasks using a transfer pipette under sterile conditions in a laminar flow hood. 5. Incubate the flasks at 37 C, in a 5% CO2 incubator. 6. Replace the culture media with fresh media on the following day to remove residual dimethyl sulfoxide (DMSO).
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Fig. 4 (a) Cells at 70–80% confluence ready for passaging. (b) Detached cells floating in culture medium due to trypsin treatment
7. Examine the cells under an inverted microscope to visualize cell growth and observe typical cell morphology. 8. Remove old growth media once every three days and replace with 5–6 mL of fresh preheated (37 C) GM. 9. Culture cells until they reach 70–80% confluence (Fig. 4a). 3.4 Subculturing (Passaging) Cells
1. Passage/subculture once the cells reach 70–80% confluence. 2. Discard the old media from the flasks. 3. Rinse the flasks with 2 mL of Dulbecco’s phosphate-buffered saline (DPBS) to remove the remaining media and unattached cells. 4. Add 2 mL of trypsin/EDTA to detach the cells from the adherent culture surface (see Note 3). 5. Incubate the flasks in an incubator at 37 C for 2–3 min to optimize the trypsin activity. 6. Confirm cell detachment by observing the flasks under an inverted microscope. Detached cells will appear as floating (balling up) in the media (Fig. 4b). 7. Neutralize the trypsin activity by adding 4 mL of growth media. 8. Transfer the content of the flask to a 15 mL Falcon tube and centrifuge at 220 g for 3 min. 9. Discard the supernatant, ensuring the cell pellet is not dislodged (Fig. 5). 10. Re-suspend the cell pellet in 1 mL of pre-warmed (37 C) GM media. 11. Add 5–6 mL of fresh GM growth media into new flasks.
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Fig. 5 Cell pellet and supernatant after centrifuging at 220 g for 3 min
12. Add 3–4 drops of the cell suspension to the prepared culture flasks and place the flasks in a 37 C incubator. 13. Once the cells reach the third passage, the expanded cells are used in experiments or are cryopreserved, as is described later (see Note 4). 3.5
Cell Counting
1. Detach the cells from the flask using trypsin (as described above) and obtain cell a suspension following the method described in Subheading 3.4 and up to the step 10. 2. Determine the live cell count using an automated cell counter. 3. Pipette 10 μL of cell suspension and 10 μL of trypan blue on to the disposable counting chamber (Fig. 6a) and mix by aspirating with a pipette several times. 4. Pipette 10 μL of the cells-trypan blue suspension to the counting chamber (Fig. 6a) and obtain the number of total cells, live cells, dead cells, and percentage cell viability using the automated cell counter (Fig. 6b).
3.6 Cryopreserving/ Freezing Cells
1. Detach the cells in the culture flasks using trypsin EDTA, centrifuge to obtain cell suspension, and re-suspend with ~1–2 mL media (see Subheading 3.4 up to step 10).
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Fig. 6 (a) Cell suspension and trypan blue placed in the disposable glass slide. (b) Countess Automated cell counter for determining cell viability, by calculating numbers of total cells, live cells, and dead cells
2. Prepare 5 mL of freezing media by adding 0.5 mL of DMSO and 0.5 mL of FBS in 4 mL of growth media to a 15 mL Falcon tube (see Note 5). 3. Add 800 μL of the freezing media into a 1.2 mL cryotube. 4. Add cell suspension dropwise (3–4 drops) until the cryovial is filled up to 1 mL. 5. Seal the cryotubes, label, and place in a Nalgene® cryofreezing container with isopropanol (Fig. 7a). 6. Place the Nalgene container in 20 C freezer overnight. 7. Transfer the cryotubes into aluminum canes and store in a liquid nitrogen dewar for long-term storage (Fig. 7b). 3.7 Dental Material Preparation and Sterilization
1. Prepare the test dental material to specific dimensions to fit snugly into a well of a 24 or 48 well plate. This is routinely undertaken by casting the material into a mold with dimensions to enable the test material to fit within an appropriate cell cultureware well.
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Fig. 7 (a) Nalgene® cryofreezing container containing cryovials. (b) Liquid nitrogen dewar used for storage
2. Sterilize the test material stepwise by immersing in 70% ethanol for 30 min until dry, exposing to ultraviolet radiation, followed by rinsing with sterile PBS for 2 min to remove any remaining debris (see Note 6). 3. Place the test material in 1 mL of growth medium in the selected well plate and equilibrated overnight at 37 C in a humidified atmosphere of 5% CO2 prior to cell seeding. 3.8 Cell Seeding on to the Dental Material Surface
1. Sterilized materials (n ¼ 3) are used for each experiment to evaluate cell viability, proliferation, and differentiation. Place the sterilized material into the wells of a 48 well or 24 well plate. The well size is dependent on the testing materials dimensions. 2. Seed the cells from the third to –eighth passage directly onto the surface of each testing material at a cell density of 6 103 cells/well for the cell proliferation and cell viability assays (see Note 7). 3. Incubate the cell-seeded materials for 1 h at 37 C to allow the cells to adhere to the test material surface. 4. Add growth media (~1 mL) until test material fully cover in each well. For both the cell viability and cell proliferation assays, fresh GM is replaced daily for the cultivation period (72 h). 5. To induce cell differentiation in mineralizing cells, e.g., bonederived Saos-2 or MG- 63 cells (see Table 2), cells cultured on
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the test material are fed with a differentiation medium (DM) after three days of culture with media change every three days for up to 14 days. 3.9 Cell Viability/ Cytotoxicity Assay (LIVE/DEAD Assay)
1. The cell viability (the number of live cells with respect to the total number of cells) of the respective testing materials are assessed using the LIVE/DEAD cytotoxicity assay. Prepare LIVE/DEAD assay solution by mixing 1.5 μL calcein AM and 3 μL ethidium homodimer-1 with 1 mL of DPBS (see Note 8). 2. Transfer the cell-seeded material on to a glass slide. 3. Pipette the assay solution (100 μL onto each material surface) and incubate at 37 C for 30 min. 4. Cover the test material with a glass coverslip before fluorescent visualization under a confocal laser-scanning microscope or fluorescence microscope. 5. Discriminate the live cells from dead cells by the green fluorescence. Simultaneously red fluorescence indicates dead cells (Fig. 8). 6. Obtain live cell count on three random fields per material for each time point (n ¼ 9) for quantitative analysis. 7. Calculate the cell viability for each image manually according to the following equation. Cell viability ð%Þ ¼
3.10 Cell Proliferation (MTS Assay)
Number of Live Cells 100 Total Cell Number
1. Remove the media from the well plates after 24-, 48-, and 72-h incubation. 2. Add a mixture containing 40 μL of MTS and 160 μL of growth media to each well. 3. Incubate the plates for a further 2 h to allow the proliferating cells to metabolize the MTS reagent (Fig. 9). 4. Transfer 100 μL of the supernatant into a well of a 96 well plate. 5. Measure the absorbance (i.e., the purple color of the formazan dye produced by the proliferating cells) using a spectrophotometer at a wavelength of 490 nm. 6. Perform the experiment in triplicate and repeat three times (n ¼ 9) (see Note 9).
3.11 Alkaline Phosphatase (ALP) Assay
1. Alkaline phosphatase activity of the cells seeded on to the test dental material is detected using the SensoLyte® pNPP Alkaline Phosphatase Assay Kit (AnaSpec/EGT Group, California, USA) according to the manufacturer’s instructions. The cell
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Fig. 8 Fluorescent images showing cell viability of Saos-2 cells seeded on hydroxyapatite scaffolds at 24 h, 48 h, and 72 h. Green ¼ live cells (calcein), Red ¼ dead cells (ethidium homodimer-1). Scale bar ¼ 100 μm
Fig. 9 Example of a 96 well plate after incubation with the MTS reagent. Spectrophotometric analysis at a wavelength of 490 nm will indicate degree of MTS proliferation
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seeded materials are assayed (20 103 cells/material) at 1, 3, 7, and 14 days. The ALP assay is used to indicate the comparative degree of cell differentiation of mineralizing cells in response to culture on the dental material. 2. Remove the spent culture media and wash the cell-seeded materials twice with 1X assay buffer at the end of each incubation period. 3. Add 500 μL of the cell lysis solution (1X assay buffer, 0.5% v/v Triton X-100) to each well containing the cell-seeded material. 4. Transfer the cell lysate solution to 2 mL centrifuge tubes along with the respective test material. 5. Vortex the centrifuge tubes for 30 s to lyse any adherent cells. 6. Remove each testing material from the tubes and discard. 7. Incubate the cell suspension for 10 min at 4 C under agitation. 8. Centrifuge the cell suspension at 2500 g for 10 min at 4 C. 9. Dilute the standard ALP solution (10 μg/mL) to 0.2 μg/mL (1:50) using two-fold serial dilutions to enable generation of a standard curve. The supernatant is then assayed for the transformation of p-nitrophenyl phosphate (pNPP) into p-nitrophenol. 10. Collect the lysed cell supernatant (50 μL) and incubate with 50 μL of the alkaline phosphatase reagent (pNPP solution) at room temperature for 1 h in a 96 well plate. 11. Measure the ALP concentration at 410 nm using a spectrophotometer. 12. Calculate the ALP activity from the standard curve. 13. Determine the total protein content using a fluorometer, and normalize the ALP activity to the corresponding value. ALP activity is expressed as units per micrograms (μg) of protein.
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Notes 1. Cell culture media preparation can significantly impact mammalian cell growth and experimental outcomes. Culture media provide a source of energy and nutrients for cell growth. Media selection depends on the culture type, purpose of cultivation, and cell density requirements. For mammalian cell culture, the growth medium is typically complex and may contain up to one hundred ingredients, which include amino acids, inorganic salts, carbohydrates, vitamins, fatty acids, trace metal elements, and other constituents, such as buffering agents and surfactants [13]. Foetal bovine serum (FBS) is the most common supplement in animal cell culture media and is routinely at 10% supplementation. It is used as a low-cost supplement to provide
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an optimal culture medium for cell growth. Serum provides carriers or chelators for labile or water-insoluble nutrients, hormones and growth factors, protease inhibitors, and binds and neutralizes toxic moieties [13, 14]. Although not required for cell growth, antibiotics (routinely used at 1% concentration) are frequently used to control the growth of bacterial and fungal contaminants. Select the appropriate growth media depending on the cell type for the purpose of the culture as different cell types have specific growth requirements. The ATCC website as indicated above (Table 2) can be used to source this information, and this suitable media for each cell type can also be determined experimentally [15, 16]. 2. Osteogenic medium is prepared freshly prior to each experiment. 3. When subculturing cells, if cells have still adhered to the flask after 2–3 mins of incubation, leave the flask for an additional 1 min to optimize the trypsin activity to detach the cells. 4. Cells between the third and eighth passage are routinely used for experiments/material testing. Beyond this passage number, cells may exhibit an altered phenotype. 5. A cell line provides a valuable resource which is susceptible to microbial contamination and replacement is expensive and time consuming. Therefore, freezing cells after each passage is important to keep stocks available for future use. Cells are frozen down in sterile cryotubes in freezing media, which consists of their respective cell growth media, 10% FBS, and 10% dimethyl sulfoxide (DMSO). DMSO is a cryoprotectant that protects cells from the detrimental side effects of freezing, such as damaging ice crystal formation within their cytoplasmic compartment. 6. Inadequately sterilized specimens will lead to inaccurate testing and give false-positive/negative results. Appropriate sterilization procedures depending on the material type need to be selected. Some sterilization procedures have been shown to induce chemical changes to the material [17]. The most frequently used methods include stepwise immersion in 70% ethanol for 30 min until dry, exposure to ultraviolet radiation, followed by rinsing with PBS for 2 min to remove any remaining debris. If there is uncertainty or the material is new, chemical characterization following material sterilization should be undertaken and compared chemically with the material prior to sterilization. Microbiological infection testing should be undertaken if there is any doubt of material sterility. This can be undertaken by simply incubating the material at 37 C overnight in the culture medium and observing under light microscopy to determine the presence of any microbial infection.
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7. We have identified that for Saos-2 osteoblastic cells that a seeding density of 6 103 cells/material reaches an appropriate level of confluence after 72 h incubation. For differentiation studies, cells are seeded at a density of 20 103 cells/scaffold and 80 103 cells/scaffold for the ALP assay and immunohistochemical analysis, respectively, due to the longer culture and exposure periods required for the assay [18, 19]. 8. For the LIVE/DEAD assay, live cells are discriminated from dead cells due to the green fluorescence resulting from the enzymatic conversion of “calcein AM” to calcein (excitation 494 nm, emission 517 nm). Simultaneously red fluorescence indicates dead cells as the ethidium homodimer-1 can enter the cell and bind to nucleic acids due to the damaged cell membranes (excitation 528 nm, emission 617 nm) [20–22]. 9. The MTS proliferation assay is a colorimetric method that measures the amount of succinate dehydrogenase activity in metabolically active proliferating cells. Tetrazolium salt 3-(4,5-dimethylthiazol-2-yl)-5-(3-carboxymethoxyphenyl)-2(4-sulfophenyl)-2H-tetrazolium or MTS is reduced into a formazan dye by dehydrogenases present in living cells. The reduction of tetrazolium salts is widely accepted as a reliable way to examine cell proliferation and is a quantitative test. Trypan blue is qualitative and indicates only if a cell is alive. There is a direct linear relationship between the number of proliferating cells and the amount of formazan produced. A standard curve (3750–60,000 cells) is produced for the selected cell type for each separate trial (Fig. 10).
Fig. 10 Standard curve for the cell proliferation assay
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References 1. Administration USFaD. (2020). Classify your medical device. https://www.fda.gov/medicaldevices/overview-device-regulation/classifyyour-medical-device. Accessed 15 Dec 2021 2. Camilleri J, Arias Moliz T, Bettencourt A, Costa J, Martins F, Rabadijeva D, Rodriguez D, Visai L, Combes C, Farrugia C, Koidis P, Neves C (2020) Standardization of antimicrobial testing of dental devices. Dent Mater 36(3):e59–e73. https://doi.org/10. 1016/j.dental.2019.12.006 3. Williams DF (2008) On the mechanisms of biocompatibility. Biomaterials 29(20): 2941–2953. https://doi.org/10.1016/j. biomaterials.2008.04.023 4. Minamikawa H, Ikeda T, Att W, Hagiwara Y, Hirota M, Tabuchi M, Aita H, Park W, Ogawa T (2014) Photofunctionalization increases the bioactivity and osteoconductivity of the titanium alloy Ti6Al4V. J Biomed Mater Res A 102(10):3618–3630. https://doi.org/10. 1002/jbm.a.35030 5. Mattioli-Belmonte M, Natali D, Tosi G, Torricelli P, Totaro I, Zizzi A, Fini M, Sabbatini S, Giavaresi G, Biagini G (2006) Resin-based dentin restorative materials under accelerated ageing: bio-functional behavior. Int J Artif Organs 29(10):1000–1011. https:// doi.org/10.1177/039139880602901011 6. Khampang S, Cho IK, Punyawai K, Gill B, Langmo JN, Nath S, Greeson KW, Symosko KM, Fowler KL, Tian S, Statz JP, Steves AN, Parnpai R, White MA, Hennebold JD, Orwig KE, Simerly CR, Schatten G, Easley C (2021) Blastocyst development after fertilization with in vitro spermatids derived from nonhuman primate embryonic stem cells. F S Sci 2(4): 365–375. https://doi.org/10.1016/j.xfss. 2021.09.001 7. Shaw SW, David AL, De Coppi P (2011) Clinical applications of prenatal and postnatal therapy using stem cells retrieved from amniotic fluid. Curr Opin Obstet Gynecol 23(2): 109–116. https://doi.org/10.1097/GCO. 0b013e32834457b1 8. Jaroch K, Taczynska P, Czechowska M, Bogusiewicz J, Luczykowski K, Burlikowska K, Bojko B (2021) One extraction tool for in vitro-in vivo extrapolation? SPMEbased metabolomics of in vitro 2D, 3D, and in vivo mouse melanoma models. J Pharm Anal 11(5):667–674. https://doi.org/10.1016/j. jpha.2021.03.005 9. Baker BM, Chen CS (2012) Deconstructing the third dimension: how 3D culture microenvironments alter cellular cues. J Cell Sci 125
(Pt 13):3015–3024. https://doi.org/10. 1242/jcs.079509 10. Pampaloni F, Reynaud EG, Stelzer EH (2007) The third dimension bridges the gap between cell culture and live tissue. Nat Rev Mol Cell Biol 8(10):839–845. https://doi.org/10. 1038/nrm2236 11. Kapalczynska M, Kolenda T, Przybyla W, Zajaczkowska M, Teresiak A, Filas V, Ibbs M, Blizniak R, Luczewski L, Lamperska K (2018) 2D and 3D cell cultures – a comparison of different types of cancer cell cultures. Arch Med Sci 14(4):910–919. https://doi.org/10. 5114/aoms.2016.63743 12. Millennium WHOSGotBoMCatSotN (2003) The burden of musculoskeletal conditions at the start of the new millenium: report of a WHO scientific group. World Health Organization, Geneva 13. Arora M (2013) Cell culture media: a review. Mater methods 3(175):24 14. Yao T, Asayama Y (2017) Animal-cell culture media: history, characteristics, and current issues. Reprod Med Biol 16(2):99–117. https://doi.org/10.1002/rmb2.12024 15. Yang H (1991) Selection of culture media for human and rabbit corneal epithelia. Zhonghua Yan Ke Za Zhi 27(6):351–353 16. Clifford WJ, Anellis A, Ross EW (1974) Evaluation of media, time and temperature of incubation, and method of enumeration of several strains of clostridium-perfringens spores. Appl Microbiol 27(4):784–792. https://doi.org/ 10.1128/Aem.27.4.784-792.1974 17. Andre CB, Dos Santos A, Pfeifer CS, Giannini M, Girotto EM, Ferracane JL (2018) Evaluation of three different decontamination techniques on biofilm formation, and on physical and chemical properties of resin composites. J Biomed Mater Res B Appl Biomater 106(3):945–953. https://doi.org/10. 1002/jbm.b.33907 18. Prideaux M, Wijenayaka AR, Kumarasinghe DD, Ormsby RT, Evdokiou A, Findlay DM, Atkins GJ (2014) SaOS2 osteosarcoma cells as an in vitro model for studying the transition of human osteoblasts to osteocytes. Calcif Tissue Int 95(2):183–193. https://doi.org/10. 1007/s00223-014-9879-y 19. Raman H (2005) An organic bovine hydroxyapatite-PLGA composites for bone tissue engineering. Masters thesis. University of Kentucky, Kentucky 20. Ratnayake JT, Ross ED, Dias GJ, Shanafelt KM, Taylor SS, Gould ML, Guan GZ, Cathro
524
Jithendra Ratnayake et al.
PR (2020) Preparation, characterisation and in-vitro biocompatibility study of a bone graft developed from waste bovine teeth for bone regeneration. Materials Today Communications 22:100732. https://doi.org/10.1016/j. mtcomm.2019.100732. ARTN 100732 21. Ratnayake JTB, Gould ML, Shavandi A, Mucalo M, Dias GJ (2017) Development and characterization of a xenograft material from N ew Z ealand sourced bovine cancellous bone. J
Biomed Mater Res B Appl Biomater 105(5): 1054–1062 22. Tredwin CJ (2009) Sol-gel derived hydroxyapatite. Doctor of Philosophy, University College London, Fluorhydroxyapatite and Fluorapatite Coatings for Titanium Implants 23. ATCC (1925) Cell products. https://www. atcc.org/. Accessed 23 Mar 2022
Correction to: Characterization, Quantification, and Visualization of Neutrophil Extracellular Traps Josefine Hirschfeld, Ilaria J. Chicca, Carolyn G. J. Moonen, Phillipa C. White, Martin R. Ling, Helen J. Wright, Paul R. Cooper, Mike R. Milward, and Iain L. C. Chapple
Correction to: Chapter 27 in: Gregory J. Seymour et al. (eds.), Oral Biology: Molecular Techniques and Applications, Methods in Molecular Biology, vol. 2588, https://doi.org/10.1007/978-1-0716-2780-8_27 Owing to the oversight by the typesetters, two affiliations of Professor Carolyn G.J. Moonen were incorrectly published in the online version of the chapter. The two affiliations published are Bruker Corporation, Leiderdorp, and Academic Centre for Dentistry Amsterdam (ACTA), University of Amsterdam, Amsterdam, the Netherlands. ACTA is the correct one and Bruker is incorrect. It is important to this author to have Bruker removed, as the company has not specifically agreed on being mentioned in his chapter.
The updated original version of this chapter can be found at https://doi.org/10.1007/978-1-0716-2780-8_27 Gregory J. Seymour et al. (eds.), Oral Biology: Molecular Techniques and Applications, Methods in Molecular Biology, vol. 2588, https://doi.org/10.1007/978-1-0716-2780-8_32, © The Author(s), under exclusive license to Springer Science+Business Media, LLC, part of Springer Nature 2023
C1
INDEX A Adhesion ...........131–149, 154, 155, 191, 371, 372, 408 Adipose-derived stem cells (ADSC).................... 407–414 Aggregatibacter actinomycetemcomitans (A. actinomycetemcomitans)............................... 68, 157–161, 164, 193, 195, 463 Alignment .......................................... 4, 8, 19, 20, 34, 96, 98, 223, 253, 258, 261–263, 269, 271, 326–328, 331, 342, 344, 345, 361 Alkaline phosphatase assay................................... 512, 518 Antigen-antibody complex ......................... 440, 445, 448 Antioxidants ....................................42, 44, 51–53, 55–57 Ascorbic acid (AA) ............................................42, 44, 47, 54, 55, 284, 355, 430, 434, 475, 478
B Bacterial communities.........................64, 68, 69, 92, 105 Bacteriocins ......................................................... 171–173, 175, 177–180, 182–185 Biofilm models ....................................187–198, 500, 501 Biofilms .......................................... 64, 68, 131, 133, 138, 139, 149–152, 155–157, 172, 173, 175, 188, 189, 191–197, 372, 373, 499 Bioinformatic analysis ................................................... 7, 8 Bioinformatics ........................................4, 14, 22, 32, 77, 79, 86, 87, 107, 223, 251, 257, 331, 342, 356, 361, 362, 367
C Campylobacter rectus (C. rectus) .........159–162, 164, 463 Candida albicans (C. albicans)......... 132–138, 140–147, 149–152, 154, 192–194 Cas9 ............................................................. 217, 218, 221 Cathepsin G (CG)................................................ 456, 462 Cell culture ................................ 133, 134, 136, 139–140, 183, 219, 223, 227, 232, 236, 244, 280, 287–290, 297, 310, 348, 353–367, 371–391, 393–405, 408–412, 418–421, 426, 430, 432, 433, 435, 474–476, 508, 509, 513, 520 Cell seeding ........................................400, 473–475, 477, 478, 481, 510, 511, 517 Cell sheets............................................................. 429–438 Cell-scaffold constructs................................................. 473
CellSearch ............................................................. 232, 233 Chemiluminescence ............................................... 44, 292 Circulating tumor cell (CTC) .............. 93, 161, 231–246 Clustered regularly interspaced short palindromic repeat (CRISPR) ............ 217, 218, 220, 224–226 Co-culture model.......................................................... 197 Colonization................................. 66, 131, 132, 172, 191 Comet assay ........................................... 42, 46, 49–51, 56 Competence ................................................ 202, 204, 205 Competence-stimulating peptide (CSP).....................202, 208, 209, 214 Constructs ......................................... 154, 181, 201, 221, 265, 429–438, 473, 474, 478, 482, 486, 499, 501 Culture media.................................... 134, 136, 190, 197, 226, 234, 283, 288, 302, 355, 357, 358, 360, 376, 377, 381, 395, 397–403, 421, 432, 433, 460, 463, 476, 477, 481, 512–514, 520, 521
D DADA2.............................. 107, 109, 117, 118, 120–128 Decellularization .................................430, 431, 434, 437 Denaturing gradient gel electrophoresis (DGGE) ......................................... 62, 63, 91–103 Dental caries ............... 64, 131, 172, 191, 193, 195, 353 Dental material ....................................146, 393, 505–522 Dental pulp................................................... 67, 288, 353, 354, 393, 394, 397–399, 403, 407 Dental pulp cell (DPC)....................................... 279–292, 353–367, 393–405 Dental pulp stem cell (DPSC).................... 353, 354, 394 Denture stomatitis ..............................188, 191, 193, 195 Differential expression analyses ............................ 32, 251, 254, 263–268, 318, 324, 329, 330, 339, 348, 354, 362 DNA ................................................... 4–8, 25–29, 31–34, 36, 37, 42–44, 49–51, 56, 62, 63, 76–80, 82, 85, 86, 92–97, 99, 102, 105–107, 128, 158–168, 174, 179–182, 184, 185, 201–208, 212, 214, 215, 218, 219, 221–223, 227, 233, 239, 249, 252, 257, 298, 299, 301, 303–308, 311–313, 348, 354, 366, 375, 379, 386–389, 422, 453, 461, 465, 466, 469, 470 DNA methylation ............... 26, 279, 295, 296, 300, 307 DNMT inhibitors (DNMTis) ...................................... 354
Gregory J. Seymour et al. (eds.), Oral Biology: Molecular Techniques and Applications, Methods in Molecular Biology, vol. 2588, https://doi.org/10.1007/978-1-0716-2780-8, © The Editor(s) (if applicable) and The Author(s), under exclusive license to Springer Science+Business Media, LLC, part of Springer Nature 2023
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526 Index
Double staining............................................................. 446 Drug deliveries ..................................................... 493–501
E Elastase......................................................... 456, 457, 462 Electrophoresis ........................................... 29, 38, 46, 51, 94–97, 99, 134, 135, 153, 158, 204, 206, 207, 225, 284, 285, 291, 375, 380, 388–391 Epigenetics .................................279, 296, 354, 355, 358 Epithelial cell adhesion molecule (EpCAM) ..............232, 233, 235, 236, 238, 239 Epithelial cells...................................................... 133, 136, 143–145, 371–391, 460 Epithelium ...........................................371–373, 377, 391
F Fetal bovine serum (FBS) ................................... 234–237, 240, 241, 244–246, 283, 284, 288, 299, 310, 395, 403, 407, 408, 418, 420, 421, 474–477, 481, 510, 511, 516, 520, 521 Fluorescence microscopy .............................................. 457 Functional group............................................20, 329, 494 Fusobacterium spp. ............................................... 159, 161
G Gene editing ......................................................... 217, 224 Gene expression ..............................................3, 201, 250, 251, 254, 258, 261, 264, 267, 269, 271, 279, 296, 297, 309, 313, 319, 331, 337, 339, 341, 372, 373, 375, 376, 379, 380, 386–389, 418, 423–425 Genomic DNA purification .................... 78, 85, 220, 223 Geranylgeraniol ............................................................. 419 Gingiva.................................................................. 348, 429 Gingival crevicular fluid (GCF) ........................ 13–17, 20, 25, 44, 47, 52, 53, 56, 57, 157–160, 162, 163, 167, 452 Gingivitis.................................13, 68, 191–195, 296, 318
H H400 oral epithelial cells (OEC) ................................. 377 HDAC-inhibitors (HDACis) .............................. 280, 354 Histone acetylation and deacetylation ................ 279–292 Human oral microbiota .................................................. 75 Hydrogen peroxide (H2O2) .......... 45, 49, 172, 287, 441 Hydroxyapatite (HA)..........................133, 135, 136, 142
I Illumina........................................ 6, 7, 27–29, 32, 34, 37, 63, 76, 77, 81, 88, 106, 256, 259, 264, 300, 307, 309, 313, 325, 331, 343, 344, 361
Immunocytochemistry (ICC) ............................ 373, 379, 394–396, 402, 403, 405 Immunofluorescence (IF)................................... 233, 417, 439, 440, 448, 449 Immunohistochemistry (IHC)....................................240, 439–443, 446, 447 Immunostaining......................................... 233, 235–240, 244, 245, 287, 292, 436, 446, 448, 449, 479, 480, 482 In vitro ........................................... 55, 62, 188, 217, 225, 233, 354, 372, 373, 393, 394, 418, 429, 430, 452, 473, 478–480, 499, 501, 508, 509 Interspecies competition............................. 175, 176, 180
L Label-free quantification...........................................19, 20 Linux.................................. 79, 81, 86, 87, 107, 257, 320
M Machine learning..........................................297, 317–348 Mass spectrometry ..................... 13–22, 26, 35, 178, 508 Melt electrospinning writing (MEW) ................. 475, 477 Mesenchymal stem cell (MSC)............................ 407–410 MetaCell ..............................................233, 234, 236–246 Metastasis.............................................................. 231, 232 Microarrays .............................................3, 4, 62, 63, 250, 297, 300, 307, 317, 318, 320, 322, 324, 331, 333, 334, 347, 348 Microbial diversity.................................................. 63, 105 Micronutrients ..........................................................41–57 MicroRNA (miRNA) ............................................ 5–7, 26, 250, 304, 305, 309, 311, 312, 345, 354, 356, 358, 359, 361–366 Mimics .......................................137, 144, 146, 149, 297, 393, 394, 486, 487, 509 Mineralisation............................. 280, 283, 284, 287–290 MTS assay ...................................................................... 518 Multiplex .............................................6, 7, 14, 27, 63, 79 Multiplex qPCR (m-qPCR) ....................... 159, 162, 164 Myeloperoxidase (MPO) ....................456, 457, 462, 466
N Nanopore sequencing ..................................................... 76 Natural transformation ........................................ 182, 203 Neutrophil extracellular trap (NET)...........................346, 451–458, 460–462, 464–466, 469, 470 Next generation sequencing (NGS) ...................... 26, 27, 34, 63, 64, 67–69, 76, 77, 81–84, 250, 256, 258, 272, 297, 300, 307, 313, 317, 320, 344, 361 Non-coding RNA (ncRNA) ...........................6, 250, 252, 256, 261, 354
ORAL BIOLOGY: MOLECULAR TECHNIQUES
AND
APPLICATIONS Index 527
O
Q
Oral biology .................................................................. 297 Oral diseases ........................................36, 64, 66–70, 131 Oral health................................................... 188, 191, 452 Oral keratinocytes ................................................ 217–228 Oral microbiota .......................................... 61, 62, 64, 69, 70, 75, 105–129, 196 Oral streptococci .................................................... 78, 172 Ovine .................................................................... 408, 411 Oxidative stress..........................................................41–57
QIIME2............................. 107, 108, 114–117, 128, 129 Quantitative PCR (qPCR)..............................7, 158–162, 164, 166, 167, 196, 197, 313, 419, 422
P Passage ............................................... 132, 149, 183, 283, 288, 357, 377, 380–382, 391, 394, 399, 402, 410–412, 432, 433, 477, 513–515, 517, 521 Patient matched scaffold...................................... 486, 489 Perfusion.....................................431, 434, 437, 438, 474 Peri-implant disease ...................................................... 297 Periodontal diseases ..........................................13, 26, 36, 68, 187, 319, 331, 373 Periodontitis ............................................... 13, 25, 26, 36, 64, 68, 69, 97, 131, 157, 191–196, 296, 297, 319, 322, 330, 332, 337–340, 342, 346, 372, 391, 452, 453 Pheromones................................................................... 202 Plasma ............................... 25, 42, 44, 46, 47, 52, 55–57, 410, 412, 453, 454, 458, 465, 470, 508 Polycaprolactone (PCL) ..................................... 429, 431, 433–435, 437, 475, 477, 486, 488 Polymerase chain reaction (PCR) ....................29, 62, 63, 86, 88, 92, 93, 95–97, 99, 103, 106, 107, 109, 157–168, 179–182, 184, 202, 204, 207, 208, 210–212, 214, 215, 220, 223–225, 227, 298, 299, 311, 313, 375, 376, 380, 386, 388–390, 419, 422, 453, 471 Polymethyl methacrylate ....................137, 139, 146–148 Polymicrobial............................................... 173, 188, 196 Porphyromonas gingivalis (P. gingivalis)..........65, 68, 85, 157–162, 164, 187, 193, 195, 198, 371, 373, 381, 391, 458, 463 Primary cell cultures............................................. 143, 227 Primers.......................................................... 7, 27, 29, 32, 37, 92, 93, 95–97, 99, 107, 109, 128, 159, 161, 164, 166, 167, 179, 180, 182, 184, 202–204, 206–212, 214, 215, 223, 227, 256, 379, 380, 387–389 Probes ................................................... 99, 158–160, 162, 164, 166, 167, 173, 250, 332, 337–339, 343, 347, 449 Protein carbonyls ............................................... 42, 45, 48 Protein expression ......................................................... 372 Proteomics...................................... 14, 16, 22, 26, 38, 76
R Reactive oxygen species (ROS) .......................... 451, 452, 454, 455, 458, 460 RNA ............................................. 3–9, 25, 27, 31, 32, 36, 37, 77, 80, 106, 218, 219, 233, 239, 249, 250, 252, 255–257, 260, 261, 264, 273, 274, 295, 297–301, 304, 305, 307–313, 345, 354, 356, 359, 361–364, 366, 375, 379, 386–388, 418, 419, 422, 424, 426 RNA seq .........................................3–9, 27, 32, 249–274, 300, 307, 309, 313, 314, 324, 325, 328, 344, 345, 348, 354, 362, 366
S Saliva ............................................ 3–9, 13–19, 25, 26, 28, 29, 31, 33, 36–38, 44, 47, 52, 53, 56, 57, 61, 94, 105, 133, 136–144, 146, 147, 149, 151, 154, 177 Salivaomics.................................................................25–38 Salivary RNAs.......................................... 9, 31, 32, 36, 37 Salivary small extracellular vesicles (sEVs) .................... 26, 28–30, 36 Scaffolds................................................16, 20, 80, 83, 88, 376, 429, 431, 433–437, 473–475, 477–482, 485–490, 494, 522 Scanning electron microscopy (SEM) ........................205, 434, 436, 453, 456, 465, 470, 498–500, 508 Sequence reads ........................................... 77, 82, 83, 88, 106, 109, 113, 114, 120, 128 Sequencing ............................................... 3–9, 27, 29, 31, 32, 34, 36, 62, 63, 75–84, 86–88, 92, 106, 107, 128, 178, 179, 184, 185, 203, 208, 212, 214, 223, 225, 228, 250–253, 255, 259–262, 264–267, 269, 273, 274, 297, 300, 307, 313, 318, 320, 324, 331, 344, 348, 361, BNF–88 Serum-free media .......................................................... 408 Silicone............................................... 133, 137, 138, 146, 153, 155, 434, 495, 498 16S rRNA gene .......................................... 29, 31, 63, 86, 92, 95, 97, 106, 109, 112 16S rRNA sequencing .................................................. 297 Size exclusion chromatography (SEC) ............. 26, 28–30 SPAdes genome assembler........................................81–83 Staphylococcus epidermidis (S. epidermidis) ........ 132–134, 137, 138, 140, 146–149 Streptococcus ...................................................8, 64–69, 82, 85–87, 97, 172, 191–194, 218, 463
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528 Index
Streptococcus mutans (S. mutans) ........................... 65, 67, 172, 175, 176, 182, 185, 187, 191, 193, 194, 202, 205, 209–212 Streptococcus oralis (S. oralis) ................................ 65, 160, 161, 164, 191–194, 463 Streptococcus pneumoniae (S. pneumoniae) .................202, 204, 208, 209, 211, 212, 214
T Tannerella forsythia (T. forsythia) .......................... 66, 68, 157–162, 164 TaqMan ......................................158, 160, 165, 419, 422 TaqMan gene expression assays........................... 419, 422 Target gene analysis ...................................................... 365 Taxonomy...................... 31, 62–64, 66–69, 87, 106, 122 Terminal restriction fragment length polymorphism (T-RFLP) ....................................... 62, 63, 91–103 3d bioprinting ...................................................... 376, 494
3D printing.................................................................... 486 3d scaffold ....................................................473–482, 488 3matics........................................................................... 486 Tissue engineering ..................................... 394, 429, 430, 473–482, 493, 494 Transcriptomes ........................................... 3, 8, 9, 26, 81, 249, 250, 252, 253, 256, 257, 260, 261, 269, 296, 297, 319, 344 Transcripts .............................. 3, 4, 9, 32, 249, 250, 252, 256, 261–263, 269–271, 273, 331, 344, 363
V Vertical bone augmentation ......................................... 486
X XIP .......................................................202, 205–208, 214