Stem Cell Transcriptional Networks: Methods and Protocols (Methods in Molecular Biology, 2117)

Preface
Contents
Contributors
Part I: Next-Generation Sequencing Technologies and Data Analysis
	Chapter 1: Epitope Tagging ChIP-Seq of DNA Binding Proteins Using CETCh-Seq
		1 Introduction
		2 Materials
			2.1 CRISPR Design
			2.2 Donor Plasmid Construction
			2.3 gRNA Plasmid Construction
			2.4 Cell Growth and Passage
			2.5 Transfection
			2.6 Cross-Linking
			2.7 ChIP
			2.8 PCR
			2.9 Western Blot
		3 Methods
			3.1 CRISPR Design
			3.2 Donor Plasmid Construction
			3.3 gRNA Plasmid Construction
			3.4 Preparation of Cells for Transfection
			3.5 Nucleofection
			3.6 Growth and Maintenance of Cell Lines
			3.7 Cross-Linking
			3.8 Sonication (Diagenode, Model Pico Bioruptor Sonicator)
			3.9 Immunoprecipitation
			3.10 Reversing the Cross-Links
			3.11 Validation of Tag Integration
				3.11.1 PCR
				3.11.2 Western Blot
				3.11.3 Prepping Whole Cell Lysates for IP-Mass Spectrometry or IP-Western Blot
		4 Notes
		References
	Chapter 2: User-Friendly and Interactive Analysis of ChIP-Seq Data Using EaSeq
		1 Introduction
		2 Materials
			2.1 Installation and Computer Requirements
			2.2 Interface and Basic Concepts
				2.2.1 Data Types in EaSeq
				2.2.2 The Differences Between Datasets and Regionsets
				2.2.3 Datasets: Unmapped Reads vs. Mapped Reads vs. Coverage Vectors
				2.2.4 Formats
			2.3 Getting Data
				2.3.1 Acquiring and Processing Libraries for Import as Datasets
				2.3.2 Where to Get Data?
		3 Methods
			3.1 Example 1: Peak-Finding and -Annotation from Transcription Factor ChIP-Seq Data
				3.1.1 Optional Disabling of Hints
				3.1.2 Import of ChIP-Seq Data
				3.1.3 Peak-Finding
				3.1.4 Annotation of Peaks
				3.1.5 Saving Sessions
				3.1.6 Exporting Regionsets and Datasets
				3.1.7 Autogenerated Descriptions
				3.1.8 Making Tracks
				3.1.9 Using EaSeq to Browse the Genome
			3.2 Example 2: Quantifying, Calculating Ratios, and Visualizing Histone Mark ChIP-Seq Data
				3.2.1 Import of ChIP-Seq Data and Regions
				3.2.2 Make and Use Heatmaps for Genome Browsing and Inspection of Peaks
				3.2.3 Quantify ChIP-Seq at a Set of Peaks or Other Regions
				3.2.4 Calculate Ratios of Quantified ChIP-Seq Signal at Peaks
				3.2.5 Sort Peaks According to Quantified ChIP-Seq Ratios
				3.2.6 Making 1D- and 2D-Histograms
				3.2.7 Interactively Exploring Data in 2D-Histograms
				3.2.8 Adjusting Plot Settings and a Quick Way to Explore Subsets of Regions
				3.2.9 Making Subpopulations of Regions from Plots and by Using the Gate Tool
				3.2.10 Quick Ways to Make Identical Figures of Different Populations of Regions
		4 Notes
		References
	Chapter 3: Evaluation of 3D Chromatin Interactions Using Hi-C
		1 Introduction
		2 Materials
			2.1 Public Hi-C Data
			2.2 Public Software
			2.3 Online Web Servers
			2.4 The iHiC Package
		3 Methods
			3.1 Sequencing Data Extraction
			3.2 Sequence Alignment
			3.3 Quality Inspection
			3.4 Visualization of Interaction Matrix
			3.5 A/B Compartments Generation
			3.6 TAD Prediction
			3.7 Identification of Chromatin Interaction
		4 Notes
		References
	Chapter 4: MSTD for Detecting Topological Domains from 3D Genomic Maps
		1 Introduction
		2 Materials
		3 Methods
			3.1 MSTD
			3.2 Installation of MSTD
			3.3 Using MSTD to identify TADs and PADs
			3.4 Identifying PADs from Asymmetric Promoter Capture Hi-C Datasets
			3.5 Epigenetic Feature Is a Good Predictor of PADs
			3.6 Exploring PADs´ Biological Functions
		4 Notes
		References
	Chapter 5: Creating 2D Occupancy Plots Using plot2DO
		1 Introduction
		2 Materials
			2.1 Operating System
			2.2 Software
			2.3 Software Installation
			2.4 Input File Formats
			2.5 Example Data
		3 Methods
			3.1 Plot Using the Default Parameters
			3.2 Plot the Distribution of DNA Fragment Centers Near +1 Nucleosomes
			3.3 Compare DNA Distributions from Multiple Samples
			3.4 Plot the Distribution of 5′ or 3′ Ends of DNA Fragments
			3.5 Align Custom Lists of Genomic Loci Using User-Provided BED Files
			3.6 Simplify Figures by Plotting Only 2DO Heat Maps
			3.7 Multiple Organisms Are Supported
			3.8 Perform In-Silico Size Selection of DNA Fragments
			3.9 Customize the Displayed Windows Near the Reference Points
		4 Conclusions
		References
	Chapter 6: Detection of Epigenetic Field Defects Using a Weighted Epigenetic Distance-Based Method
		1 Introduction
		2 Methods
			2.1 The Proposed Distance-Based Method
				2.1.1 Step 1: Define Gene-Level Weighted Epigenetic Distance Matrix
				2.1.2 Step 2: Calculate Pseudo-F Statistic
				2.1.3 Step 3: Assess Statistical Significance Using Permutations
			2.2 Comparison Methods
			2.3 Simulation Study
				2.3.1 Simulation Setup
				2.3.2 Simulation Results
			2.4 Real Data Application
				2.4.1 Discovery Analysis Using the GEO BRCA Data
				2.4.2 Validation of the Identified Epigenetic Field Defects in the GEO BRCA Data
				2.4.3 Replication Analysis Using an Independent Data of Normal Tissues
			2.5 Discussion
		3 Notes
		References
Part II: Analysis and Visualization of Single-Cell and Bulk RNA-Seq Transcriptome Data
	Chapter 7: Profiling Cell Type Abundance and Expression in Bulk Tissues with CIBERSORTx
		1 Introduction
		2 Materials
		3 Methods
			3.1 Construction of a Custom Signature Matrix from scRNA-Seq Data
				3.1.1 Input File
				3.1.2 File Upload
				3.1.3 Building the scRNA-Seq Signature Matrix
				3.1.4 Validation of the scRNA-Seq Signature Matrix
			3.2 Impute Cell Fractions with CIBERSORTx
				3.2.1 Cross-Platform Deconvolution
					B-Mode Batch Correction
					S-Mode Batch Correction
				3.2.2 Configuring a CIBERSORTx Fractions Job
				3.2.3 Evaluation of Estimated Cell Fractions
			3.3 Impute Cell-Type-Specific Gene Expression with CIBERSORTx
				3.3.1 Group-Mode Expression Imputation
				3.3.2 High-Resolution Expression Imputation
				3.3.3 Input Files
				3.3.4 Configure a High-Resolution Job
				3.3.5 Output of High-Resolution Mode
				3.3.6 When to Run CIBERSORTx Group-Mode Versus High-Resolution Mode
			3.4 Conclusion
		4 Notes
		References
	Chapter 8: Visualization of Single Cell RNA-Seq Data Using t-SNE in R
		1 Introduction
		2 Materials
			2.1 R and R Packages
			2.2 Sample Data
		3 Methods
			3.1 Data Reading and Preprocessing
				3.1.1 Data Reading
				3.1.2 Quality Control (QC) and Filter Out of Cells
				3.1.3 Data Normalization
				3.1.4 Identification of Highly Variable Features
				3.1.5 Data Scaling
			3.2 Visualization of scRNA-seq Data Using t-SNE
				3.2.1 Principal Component Analysis (PCA)
				3.2.2 Cell Clustering
				3.2.3 Running t-SNE
				3.2.4 Visualization of Single Cell RNA-seq Data Using t-SNE or PCA
				3.2.5 Visualization of Feature Expression on t-SNE Projection
				3.2.6 Visualization of Other Features on t-SNE Projection
		4 Notes
		References
	Chapter 9: Use of SuperCT for Enhanced Characterization of Single-Cell Transcriptomic Profiles
		1 Introduction
			1.1 Samples of Heterogeneous Cellular Constituents
			1.2 Strategies of Characterizing Single-Cell Expression Profiles
			1.3 Supervised Classifiers and Model Selections
			1.4 Structure of SuperCT ANN
			1.5 Expansion of SuperCT Capability
		2 Materials
			2.1 Input File
			2.2 SuperCT Model and Cell-Type Description Sheet at GitHub
		3 Methods
			3.1 Online Prediction and Visualization Platform
			3.2 Stand-Alone Prediction Program
			3.3 Prepare Input DGE File from 10xGenomics Platform
		4 Notes
		References
	Chapter 10: Interactive Alternative Splicing Analysis of Human Stem Cells Using psichomics
		1 Introduction
		2 Materials
		3 Methods
			3.1 Loading Data
			3.2 Gene Expression Filtering and Normalization
			3.3 AS Quantification
			3.4 Creating Data Groups
				3.4.1 Grouping ESC and iPSC
				3.4.2 Grouping Isogenic Stem Cells and Isogenic Fibroblasts
			3.5 Principal Component Analysis (PCA)
				3.5.1 Performing PCA on Normalized Gene Expression
				3.5.2 PCA Plot
				3.5.3 PCA on AS Quantification
			3.6 Differential Gene Expression Analysis
				3.6.1 Highlight Differentially Expressed Genes
			3.7 Differential Splicing Analysis
				3.7.1 Highlight Differentially Spliced Events
				3.7.2 Label Specific AS Events
				3.7.3 Skipping of CD46 Penultimate Exon
			3.8 Correlation Between RNA-Binding Protein Gene Expression and AS Quantification
			3.9 Gene and Transcript Annotation
			3.10 Extending the Analyses to GTEx and TCGA
				3.10.1 Correlation of Gene Expression and AS Quantification Across GTEx Tissues
				3.10.2 Correlation of Gene Expression and AS Quantification Across TCGA Cancers
				3.10.3 Pancancer Prognostic Value of AS of CD46 Penultimate Exon
		4 Notes
		References
	Chapter 11: Gene Ontology Semantic Similarity Analysis Using GOSemSim
		1 Introduction
		2 Materials
		3 Methods
			3.1 Creating Semantic Data
			3.2 Measuring Semantic Similarity
			3.3 Visualizing Semantic Similarity Matrix
			3.4 Clustering Via Similarity of Biological Knowledge
		4 Notes
		References
Part III: Derivation of Stem Cells
	Chapter 12: Derivation of Maternal Epiblast Stem Cells from Haploid Embryos
		1 Introduction
		2 Materials
			2.1 Oocyte Collection, Oocyte Activation, and Embryo Culture
			2.2 Hormones
			2.3 Mouse Embryonic Fibroblast (MEF) Cell Culture
			2.4 Epiblast Stem Cell (EpiSC) Cell Culture
			2.5 Maternal (Parthenogenetic/Gynogenetic) EpiSCs (maEpiSC) Cell Culture
		3 Methods
			3.1 Superovulation of Female Mice
			3.2 Preparation of Culture Dishes for Oocyte Activation and Embryo Culture
			3.3 Collection of Mouse Oocytes from COCs, SrCl2 Induced Activation of Oocytes, and In Vitro Culture (IVC) of Embryos to the B...
			3.4 Preparation of Cell Culture Media
			3.5 Preparation of iMEF Feeder Layer
			3.6 Derivation of maEpiSCs
			3.7 Feeder-Free Culture of maEpiSCs
			3.8 Differentiation of maEpiSCs
		4 Notes
		References
	Chapter 13: Derivation of LIF-Independent Embryonic Stem Cells Using Inducible OCT4 Expression
		1 Introduction
		2 Materials
			2.1 Reagents for Cell Culture
			2.2 Equipment
			2.3 Culture Media
		3 Methods
			3.1 Prepare Feeder Layer of Mitotically Inactivated Mouse Embryonic Fibroblasts (iMEFs)
			3.2 Derivation of LIF-Independent iOCT4 ES Cells
			3.3 Differentiation of LIF-Independent iOCT4 Cells
		4 Notes
		References
	Chapter 14: Simultaneous Derivation of Embryonic and Trophoblast Stem Cells from Mouse Blastocysts
		1 Introduction
		2 Materials
			2.1 Cell Culture Reagents
			2.2 Equipment
			2.3 Culture Media
		3 Methods
			3.1 Preparation of Feeder Layer
			3.2 Blastocyst Isolation
			3.3 Simultaneous Derivation of ES Cells and TS Cells
		4 Notes
		References
Part IV: Transcriptional Programs that Promote Reprogramming, Transdifferentiation, and Cancer Formation
	Chapter 15: Reprogramming Fibroblasts to Neural Stem Cells by Overexpression of the Transcription Factor Ptf1a
		1 Introduction
		2 Materials
			2.1 Instruments
			2.2 Dissecting Tools
			2.3 Cell Culture for HEK293T, MEF and iNSC
				2.3.1 Materials and Reagents
				2.3.2 Reagent Setup
			2.4 Lentivirus Preparation and Transfection
				2.4.1 Materials and Reagents
				2.4.2 Reagent Setup
			2.5 iNSC Identification
				2.5.1 Materials and Reagents
				2.5.2 Reagent Setup
			2.6 In Vitro Differentiation of iNSC
				2.6.1 Materials and Reagents
				2.6.2 Reagent Setup
		3 Methods
			3.1 Lentivirus Preparation Using HEK293T Cells (See Note 4)
			3.2 MEF Preparation
			3.3 Infection of MEFs with Lentiviruses (See Note 7)
			3.4 iNSC Expansion, Passage, and Storage
			3.5 Identification of iNSCs with qRT-PCR, Cytoimmunofluorescence, and Karyotyping
			3.6 iNSC In Vitro Differentiation
		4 Notes
		References
	Chapter 16: Reprogramming of Fibroblasts to Neural Stem Cells by a Chemical Cocktail
		1 Introduction
		2 Materials
			2.1 Reagents for Isolating and Culturing Mouse Fibroblasts
			2.2 Reagents and Plasticware for NSC Induction
			2.3 Mice Strains
		3 Methods
			3.1 Isolation of Nestin-GFP-Negative Mouse Embryonic Fibroblasts
			3.2 Chemical Induction of Nestin-GFP-Positive Cells
			3.3 Neural Stem Cell Marker Characterization of Induced Nestin-GFP-Positive Cells
		4 Notes
		References
	Chapter 17: Efficient RNA-Based Reprogramming of Disease-Associated Primary Human Fibroblasts into Induced Pluripotent Stem Ce...
		Abbreviations
		1 Introduction
		2 Materials
			2.1 Fibroblast Culture
			2.2 Plating Fibroblasts for Reprogramming
			2.3 Reprogramming Fibroblasts
			2.4 Picking iPSC Colonies
			2.5 Equipment
		3 Methods
			3.1 Fibroblast Culture
			3.2 Plating Fibroblasts For Reprogramming
			3.3 Reprogramming Fibroblasts
			3.4 Picking Reprogrammed iPSC Colonies
		4 Notes
		References
	Chapter 18: Direct Reprogramming of Mouse Embryonic Fibroblasts to Induced Trophoblast Stem Cells
		1 Introduction
		2 Materials
			2.1 Reagents for Cell culture
			2.2 Equipment
			2.3 Lentivirus Production
			2.4 Culture Media
		3 Methods
			3.1 Prepare Mouse Embryonic Fibroblasts
			3.2 Lentiviral Particle Production
			3.3 Reprogramming of MEFs to iTS Cells
			3.4 Culture of iTS Cells in Feeder-Free Conditions
			3.5 Differentiation of iTS Cells
		4 Notes
		References
	Chapter 19: Characterization of Arsenic-Induced Cancer Stem-Like Cells
		1 Introduction
		2 Materials
			2.1 BEAS-2B Cells
			2.2 Sodium Arsenic
			2.3 Antibodies
		3 Methods
			3.1 Consecutive Arsenic Treatment of the BEAS-2B Cells (see Note 1)
			3.2 Flow Cytometry Analysis and Fluorescence-Activated Cell Sorting (FACS) (see Note 5)
			3.3 In Vitro Tumor Sphere Formation (see Note 4)
			3.4 Western Blot Analysis (see Notes 3 and 6)
			3.5 Gene Expression Profiling and Real-Time PCR (see Note 7)
			3.6 Tumorigenicity Assay in Nude Mice (see Note 2)
			3.7 Transplantation Assays Using CSCs in NOD/SCID Il2rγ-/- Mice (see Note 2)
			3.8 Cell Migration and Invasion (see Note 7)
			3.9 Apoptosis Analysis (see Note 7)
			3.10 Statistical Analysis
		4 Notes
		References
Index