Single Cell Transcriptomics: Methods and Protocols

Preface
Contents
Contributors
Chapter 1: Guidance on Processing the 10x Genomics Single Cell Gene Expression Assay
1 Introduction
2 Materials
2.1 Plasticware
2.2 Kits and Reagents
2.3 Equipment
3 Methods
3.1 Cell Preparation
3.1.1 Cell Viability
3.1.2 Cell Counting
3.1.3 Fully Dissociated Cell Suspension
3.1.4 Clean Cell Suspension
3.1.5 How to Keep the Cells Alive and Happy
3.1.6 Limited Cell Numbers
3.1.7 Using Nuclei as Suspension Input
3.2 GEM Generation and Barcoding
3.3 Post-GEM-RT Cleanup and cDNA Amplification
3.4 Libraries Preparation and Sequencing
3.5 Bioinformatic and Quality Control of the Data
4 Notes
References
Chapter 2: BD Rhapsody Single-Cell Analysis System Workflow: From Sample to Multimodal Single-Cell Sequencing Data
1 Introduction
2 Materials
2.1 The BD Rhapsody Single-Cell Analysis System
2.2 BD Rhapsody Express Single-Cell Analysis System
2.3 BD Rhapsody Scanner
3 Methods
3.1 Sample Quality Control (Cell Viability Assessment and Cell Counting)
3.2 BD Rhapsody Cartridge Workflow Quality Control
3.3 BD Rhapsody Single-Cell Analysis System Workflow
3.4 Cell Counting
3.5 Antibody Staining (Cell Sorting, Multiplexing, and Protein Profiling)
3.6 Identification of Antigen-Specific T Cells and B Cells
3.7 Preparation of a Nuclei Suspension
3.8 BD Rhapsody Cartridge Workflow
3.9 Preparation of the BD Rhapsody Cartridge
3.10 Imaging Cells in a Cartridge
3.11 Cell Capture Beads
3.12 NGS Library Preparation
3.13 Cell Capture Bead Preparation for cDNA Synthesis and Reverse Transcription
3.14 Template Switch Oligo
3.15 Denaturation and Self-Hybridization
3.16 TCR/BCR Extension
3.17 Exonuclease I Treatment
3.18 Random Priming and Extension (RPE) on Cell Capture Beads with cDNA
3.19 BD AbSeq/Sample Tag and TCR/BCR Amplification
3.20 Quantifying BD AbSeq/Sample Tag PCR Products
3.21 Performing Random Priming and Extension (RPE) on TCR/BCR Amplicons from the Second PCR
3.22 Performing TCR/BCR and BD AbSeq/Sample Tag Index PCR
3.23 Sequencing
3.24 Sequencing Flow Cell Loading and PhiX Concentrations
3.25 BD AbSeq/Sample Tag Libraries
3.26 WTA and TCR/BCR Libraries (With or Without BD AbSeq/Sample Tag)
3.27 Bioinformatics
3.28 Filter by Read Quality
3.29 Annotate R1 Reads
3.30 Annotate R2 Reads
3.31 Annotate Molecules
3.32 Determine Putative Cells
3.33 Determine the Sample of Origin (Sample Multiplexing Only)
3.34 Generate Expression Matrices (Reporting RSEC and DBEC Metrics)
3.35 Annotate BAM
3.36 TCR and BCR Analysis (If Applicable)
3.37 Sequencing Analysis Output Files
4 Notes
Glossary
References
Chapter 3: Profiling Transcriptional Heterogeneity with Seq-Well S3: A Low-Cost, Portable, High-Fidelity Platform for Massivel...
1 Introduction
2 Materials
2.1 Array Processing Prior to Reverse Transcription
2.2 Array Storage
2.3 Reverse Transcription
2.4 Exonuclease Treatment and Second Strand Synthesis
2.5 WTA, Library Preparation and Sequencing
2.6 Primers
3 Methods
3.1 Membrane Functionalization
3.2 Bead Loading
3.3 Cell Loading
3.4 Membrane Sealing
3.5 Lysis and Hybridization
3.6 Bead Recovery
3.7 Reverse Transcription
3.8 Exonuclease I Treatment
3.9 Second Strand Synthesis
3.10 Whole Transcriptome Amplification (WTA)
3.11 Purification of WTA Products
3.12 Nextera Library Preparation
3.13 Quality Control and Library Prep
3.14 Sequencing
4 Preprocessing of Sequencing Data
4.1 Alignment and Preprocessing
4.2 Cell Quality Control
4.3 Doublets and Ambient RNA Detection and Removal
5 Computational Analysis
5.1 Variable Genes
5.2 Dimensionality Reduction
5.3 Clustering
6 Notes
References
Chapter 4: A MATQ-seq-Based Protocol for Single-Cell RNA-seq in Bacteria
1 Introduction
2 Materials
2.1 Single Cell Isolation and Lysis
2.2 Reverse Transcription
2.3 Primer and RNA Digestion
2.4 Tailing
2.5 Second Strand Synthesis
2.6 PCR Amplification
2.7 Cleanup with Magnetic Beads
2.8 Quality Control of cDNA
2.9 Library Preparation and Pooling
2.10 Sequencing
3 Methods
3.1 Single Cell Isolation and Lysis
3.2 Reverse Transcription
3.3 Primer and RNA Digestion
3.4 Tailing
3.5 Second-Strand Synthesis
3.6 PCR Amplification
3.7 Cleanup with Magnetic Beads
3.8 Quality Control of cDNA
3.9 Library Preparation and Pooling
3.10 Sequencing and Demultiplexing
4 Note
References
Chapter 5: Full-Length Single-Cell RNA-Sequencing with FLASH-seq
1 Introduction
2 Materials
2.1 Cell Lysis Mix
2.2 RT-PCR Mix
2.3 Magnetic Beads Preparation
2.4 Library Preparation with Commercial Illumina Reagents
2.5 Library Preparation with In-House Tn5 Transposase
2.6 Sample QC and Sequencing
2.7 Instruments and Consumables
3 Methods
3.1 Cell Lysis Mix Preparation
3.2 RT-PCR Mix Preparation and RT-PCR Reaction
3.3 Magnetic Beads Cleanup After Preamplification
3.4 cDNA Quantification and Sample Normalization
3.5 Library Preparation
3.5.1 Library Preparation with the Nextera XT Kit
3.5.2 Library Preparation with In-House Tn5 Transposase
3.6 Pooling and Bead Cleanup of the Final Library
3.7 Sequencing Library QC and Sequencing
4 Data Preprocessing
4.1 Prerequisites
4.2 Optional: Demultiplexing
4.3 Optional: Remove Sequencing Adapter Leftovers
4.4 Data Alignment
4.4.1 Genome Indexing
4.4.2 Align Reads
4.4.3 Optional: Filter Reads
4.5 Assign Reads to Feature Using Featurecounts
4.6 Recommended: Quality Checkups
4.6.1 Gene-Body Coverage (ReSQC)
4.6.2 Read Distribution (ReSQC)
4.6.3 STAR Mapping Statistics (STAR)
4.7 Recommended: Processing Multiple Samples
5 Data Postprocessing
6 FLASH-seq Low-Amplification (FS-LA)
6.1 Cell Lysis Mix
6.2 RT-PCR Mix
6.3 Magnetic Beads Preparation
6.4 Library Preparation with Commercial Illumina Reagents
6.5 Library Preparation with In-House Tn5 Transposase
6.6 Sample QC and Sequencing
6.7 Instruments and Consumables
7 Methods
7.1 Cell Lysis Mix Preparation
7.2 RT-PCR Mix Preparation and RT-PCR Reaction
7.3 Library Preparation
7.3.1 Library Preparation with In-House Tn5 Transposase
7.3.2 Library Preparation with the Nextera XT Kit
7.4 Pooling and Bead Cleanup of the Final Library
7.5 Sequencing Library QC and Sequencing
8 Data Processing
9 FLASH-seq with Unique Molecular Identifiers (FS-UMI)
9.1 Cell Lysis Mix
9.2 RT-PCR Mix
9.3 Magnetic Beads Preparation
9.4 Library Preparation with Commercial Illumina Reagents
9.5 Sample QC and Sequencing
9.6 Instruments and Consumables
10 Methods
10.1 Cell Lysis Mix Preparation
10.2 RT-PCR Mix Preparation and RT-PCR Reaction
10.3 Magnetic Beads Cleanup After Preamplification
10.4 cDNA Quantification and Sample Normalization
10.5 Library Preparation with the Nextera XT Kit
10.6 Pooling and Bead Cleanup of the Final Library
10.7 Sequencing Library QC and Sequencing
11 Data Preprocessing
11.1 Prerequisites
11.2 Demultiplexing
11.3 Separate UMI/Internal Reads and Extract UMI Sequences
11.4 Optional: Remove Sequencing Adapter Leftovers
11.5 Recommended: Discarding Too Short Reads
11.6 Data Mapping
11.6.1 Genome Indexing
11.6.2 Align Reads
11.7 Optional: Filter Reads
11.8 Optional: Remove UMI Invasion Events
11.9 Assign Reads to Feature Using Featurecounts
11.10 Recommended: Quality Checkups
12 Data Postprocessing
13 Notes
References
Chapter 6: Plant Single-Cell/Nucleus RNA-seq Workflow
1 Introduction
2 Materials
3 Methods
3.1 Preparation of Protoplast Suspensions
3.2 Isolation of Plant Nuclei
3.3 Bioinformatics Analysis of Plant Single-Cell Transcriptomes
3.3.1 Bioinformatics Analysis of Protoplast- and Nuclei-Based Single-Cell Transcriptomes
3.3.2 Considering Introns When Mapping Single-Nuclei RNA-seq Transcripts
3.3.3 Cross-Species Integration of Single-Cell Transcriptome
3.3.4 Future Directions in the Field of Plant Single-Cell Transcriptomic
Spatial Transcriptomic Analysis Using Visium and Probe-Based Technologies
Nanopore Sequencing for More In-Depth Discoveries of Plant Gene Regulatory Mechanisms
4 Notes
References
Chapter 7: Ensuring Quality Cell Input for Single Cell Sequencing Experiments by Viability and Singlet Enrichment Using Cell S...
1 Introduction
2 Materials
2.1 Reagent and Buffers
2.2 Cell Sorter
3 Methods
3.1 Caspase-Based Viability Enrichment
3.2 Calcein Metabolic Labeling Enrichment
4 Notes
References
Chapter 8: Tissue RNA Integrity in Visium Spatial Protocol (Fresh Frozen Samples)
1 Introduction
2 Materials
2.1 Tissue Preparation and Section Placement
2.2 RNA Quality Assessment
2.3 Tissue Fixation, H&E Staining, and Tissue Imaging
2.4 Permeabilization and Reverse Transcription
2.5 Second Strand Synthesis and Denaturation
2.6 cDNA Amplification and Quality Control
2.7 Libraries Preparation and Sequencing
3 Methods
3.1 Tissue Preparation, RNA Quality Assessment, and Section Placement on Visium Slide
3.2 Tissue Fixation, H&E Staining, and Tissue Imaging
3.3 Permeabilization, Reverse Transcription, Second Strand Synthesis and Denaturation
3.4 cDNA Amplification and Quality Control
3.5 Libraries Preparation and Sequencing
3.6 Sequencing Quality Control
4 Notes
References
Chapter 9: Single-Cell RNAseq Data QC and Preprocessing
1 Introduction
2 Materials
2.1 Minimal Hardware/Software Requirements
3 Methods
3.1 rCASC Installation
3.2 Converting a Sparse Matrix in a Dense Matrix
3.3 Plotting Cells Based on Their Ribosomal/Mitochondrial Content
3.4 Filtering the Count Matrix Based on the Information Retrieved from the Inspection of mitoRiboUmi Output
3.5 Imputing Missing Values
3.6 Estimating the Cell Cycle State
4 Notes
References
Chapter 10: Single-Cell RNAseq Complexity Reduction
1 Introduction
2 Materials
2.1 Minimal Hardware/Software Requirements
2.2 Exemplary Dataset
3 Methods
3.1 Data Normalization
3.2 Dimensionality Reduction with UMAP
3.3 Dimensionality Reduction with t-SNE
3.4 Dimensionality Reduction with EDGE
3.5 General Considerations on Dimensionality Reduction
4 Notes
References
Chapter 11: Functional-Feature-Based Data Reduction Using Sparsely Connected Autoencoders
1 Introduction
2 Materials
2.1 Minimal Hardware/Software Requirements
2.2 Exemplary Datasets
3 Methods
3.1 rCASC Installation
3.2 Whole Transcriptome Clustering
3.3 Biology-Driven SCA Clustering
3.3.1 TFs-Driven SCA Clustering
3.3.2 Cytobands-Driven SCA Clustering
4 Notes
References
Chapter 12: Single-Cell RNAseq Clustering
1 Introduction
2 Materials
2.1 Minimal Hardware/Software Requirements
2.2 Exemplary Dataset
3 Methods
3.1 rCASC Installation
3.2 Griph Clustering
3.3 SHARP Clustering
3.4 Seurat Clustering
4 Notes
References
Chapter 13: Identifying Gene Markers Associated with Cell Subpopulations
1 Introduction
2 Materials
2.1 Minimal Hardware/Software Requirements
2.2 Exemplary Dataset
3 Methods
3.1 rCASC Installation
3.2 Data Cleanup, Filtering, and Clustering
3.3 Sparsely Connected Autoencoders for Pseudo-Bulk Generation
3.4 ANOVA-Like on RNA-5c Pseudo-Bulks Data
3.5 DESeq2 on Pseudo-Bulk Data from Gut Immune Atlas Sample 390c
3.6 COMET-A Tool to Identify Cluster-Specific Genes
4 Notes
References
Chapter 14: A Guide to Trajectory Inference and RNA Velocity
1 Introduction
2 Materials
2.1 Trajectory Inference Data Preprocessing
2.2 RNA Velocity Data Preprocessing
3 Methods
3.1 Trajectory Inference
3.1.1 Single Lineage
3.1.2 Diverging Lineages
3.1.3 Converging Lineages
3.1.4 Cycles
3.1.5 Challenges and Limitations
3.2 RNA Velocity
3.2.1 Splicing Dynamics
3.2.2 Parameter Inference
Steady-State Model
Dynamical Model
3.2.3 From Velocities to Transition Probabilities
3.2.4 Gene-Shared Latent Time
3.2.5 Challenges and Limitations
3.3 Combining Trajectory Inference and RNA Velocity
3.4 Hands-On Real Data Analyses
3.4.1 Trajectory Inference Analysis Pipeline
3.4.2 RNA Velocity Analysis Pipeline
4 Notes
References
Chapter 15: Integration of scATAC-Seq with scRNA-Seq Data
1 Introduction
2 Materials
2.1 Exemplary Datasets
2.2 Computational Hardware
2.3 Computational Software
2.4 Data Availability
3 Methods
3.1 Integration of Multiple scRNA-Seq Datasets
3.2 Integration of scRNA-Seq and scATAC-Seq Datasets
3.2.1 Quality Control Analysis and scATAC-Seq Datasets Merging
3.2.2 Annotating scATAC Data Based on the Shared scRNA-Seq
References
Chapter 16: Using ``Galaxy-rCASC´´: A Public Galaxy Instance for Single-Cell RNA-Seq Data Analysis
1 Introduction
1.1 Single-Cell RNA-Seq and Galaxy-rCASC
2 Materials
2.1 Example Dataset
3 Methods
3.1 The rCASC Workflow
3.2 Using Galaxy-rCASC
3.2.1 Using Galaxy
3.2.2 Loading Data
3.2.3 Generating the Count Matrix
3.2.4 Count Matrix Quality Control
3.2.5 Count Matrix Annotation and Cell Filtering
3.2.6 Count Matrix Gene Filtering
3.2.7 Clustering
3.2.8 Feature Selection
3.2.9 Using the rCASC Workflow
4 Final Considerations
References
Chapter 17: Bringing Cell Subpopulation Discovery on a Cloud-HPC Using rCASC and StreamFlow
1 Introduction
2 Materials
2.1 Minimal Hardware/Software Requirements
2.2 Exemplary Dataset
2.3 Used Hardware
2.4 StreamFlow
2.4.1 Architecture
2.4.2 How to Install
2.5 Clustering Algorithms
2.5.1 Hierarchical Clustering
Griph
2.5.2 Partitional Clustering
tSne + K-means
SIMLR
3 Methods
3.1 Executing Command
3.2 StreamFlow´s File
3.3 Analysis
References
Chapter 18: Profiling RNA Editing in Single Cells
1 Introduction
2 Materials
2.1 Prerequisites
2.1.1 Hardware
2.1.2 Software
2.1.3 Annotations
2.1.4 RNAseq Data
3 Methods
3.1 Downloading scRNAseq Data
3.2 Preprocessing and Alignment of Reads
3.3 Calling Known RNA Editing Candidates
3.4 Calculating the Alu Editing Index
3.5 Calculating the Recoding Index
3.6 Expression of ADAR Genes
4 Notes
References
Chapter 19: Practical Considerations for Complex Tissue Dissociation for Single-Cell Transcriptomics
1 Introduction
2 Materials and Equipment
3 Methods
4 Notes
References
Index