TGF-Beta Signaling: Methods and Protocols (Methods in Molecular Biology, 2488)

Preface
Contents
Contributors
Chapter 1: Absolute Quantification of TGF-β Signaling Proteins Using Quantitative Western Blot
	1 Introduction
	2 Materials
		2.1 Cell Culture
		2.2 Protein Sample Preparation
		2.3 SDS Polyacrylamide Gel Electrophoresis (SDS-PAGE)
		2.4 Immunoblotting
		2.5 Data Analysis
	3 Methods
		3.1 Cell Culture
		3.2 Cell Lysis and Protein Extraction for Western Blotting
		3.3 Run SDS-PAGE
		3.4 Immunoblotting
		3.5 Data Analysis of Western Blot Images
	4 Notes
	References
Chapter 2: Fast Quantitation of TGF-β Signaling Using Adenoviral Reporter
	1 Introduction
	2 Materials
		2.1 Viruses
		2.2 Cell/Animal Treatment
		2.3 Luciferase Assay Reagents
		2.4 In Vivo Experiments
	3 Methods
		3.1 In Vitro Single Luciferase Assay to Assess SMAD3 Transcriptional Activity
		3.2 In Vivo Quantitation of SMAD3 Transcriptional Activity
	4 Notes
	References
Chapter 3: Complex Formation Among TGF-β Receptors in Live Cell Membranes Measured by Patch-FRAP
	1 Introduction
	2 Materials
		2.1 Reagents
		2.2 Tissue Culture
		2.3 Plasmids and Transfection Reagent
		2.4 Antibodies
		2.5 FRAP Instrumentation
		2.6 Other Supplies
	3 Methods
		3.1 Cell Culture and Transfection
		3.2 Preparation of Fab´ Fragments
		3.3 Fluorescent Labeling and Crosslinking of Epitope-Tagged TGF-β Receptors
		3.4 FRAP and Patch-FRAP Experiments
	4 Notes
	References
Chapter 4: Branched Proximity Hybridization Assay for the Quantification of Nanoscale Protein-Protein Proximity
	1 Introduction
	2 Materials
		2.1 Target Protein-Binding Reagents
		2.2 bPHA Reagents
		2.3 Oligo Pairs
		2.4 Z-DNA
		2.5 Coupling Reagents
	3 Methods
		3.1 Preparation of the Oligo-Coupled Target Protein-Binding Reagents
		3.2 Detect the Protein Proximity
		3.3 Z-DNA Probes Hybridization
		3.4 bDNA Amplification
	4 Notes
	References
Chapter 5: Visualizing Dynamic Changes During TGF-β-Induced Epithelial to Mesenchymal Transition
	1 Introduction
	2 Materials
		2.1 Cell Lines
		2.2 Cell Culture
		2.3 Reporter and Constructs
		2.4 Western Blotting
		2.5 Real-Time Polymerase Chain Reaction (Q-PCR)
		2.6 Cell Staining
		2.7 Wound Healing/Scratch Assay
	3 Methods
		3.1 TGF-β-Induced SMAD Activation
			3.1.1 Analysis of Activation of SMAD2 Phosphorylation by Western Blotting
			3.1.2 TGF-β/SMAD-Induced Activation of CAGA12-Luciferase Transcriptional Reporter Activity
			3.1.3 TGF-β/SMAD-Induced Activation of CAGA12-eGFP Transcriptional Reporter Activity
		3.2 Real-Time Reverse Transcriptase PCR for TGF-β Target Genes and EMT Markers
		3.3 TGF-β-Induced Changes in EMT Markers Using Western Blot Analysis
		3.4 TGF-β-Induced Cell Migration Analyzed Using Wound Healing/Scratch Assay
		3.5 TGF-β-Induced Induction of Vimentin in A549 VIM-RFP Cells
		3.6 TGF-β-Induced Changes in EMT Marker Localization Using Indirect Immunofluorescence and Cytoskeleton Reorganization by Dire...
	4 Notes
	References
Chapter 6: Establishment of Embryonic Zebrafish Xenograft Assays to Investigate TGF-β Family Signaling in Human Breast Cancer ...
	1 Introduction
	2 Materials
		2.1 Cell Culture
		2.2 Zebrafish Injection
	3 Methods
		3.1 Preparation of Injection Needles
		3.2 Zebrafish Embryos Preparation
		3.3 Preparation of the Fluorescent Labeled Cancer Cells
		3.4 Injection Preparation
		3.5 Doc Injection
		3.6 Perivitelline Space Injection
		3.7 Statistical Analysis
	4 Notes
	References
Chapter 7: Generating Somatic Knockout Cell Lines with CRISPR-Cas9 Technology to Investigate SMAD Signaling
	1 Introduction
	2 Materials
		2.1 Construct Vectors for sgRNA Expression
		2.2 Construct Vector for Repair Template
		2.3 Generate SMAD2 Knockout Cell  Line
		2.4 Screen for SMAD2 Knockout Cell  Line
	3 Methods
		3.1 Designing the Knockout Strategy
		3.2 Construct Vectors for sgRNA Expression
		3.3 Construct Vector for Repair Template
		3.4 Generate Knockout Cell  Line
		3.5 Screen for Successful Knockout Cell Lines
	4 Notes
	References
Chapter 8: CRISPR-Based Screening in Three-Dimensional Organoid Cultures to Identify TGF-β Pathway Regulators
	1 Introduction
	2 Materials
		2.1 HIO Culture
		2.2 CRISPR Library Production
		2.3 NGS Library Preparation and Sequencing
		2.4 Software
	3 Methods
		3.1 Lentivirus Production of Genome-Wide Library
		3.2 CRISPR Screening in  HIOs
		3.3 TGF-β1 Selection Assay, Organoid Picking, and DNA Extraction
		3.4 PCR for NGS Library Preparation of sgRNA Cassette
		3.5 Analysis of sgRNA Reads and Candidate Identification
		3.6 Validation Experiments
	4 Notes
	References
Chapter 9: Optogenetic Control of TGF-β Signaling
	1 Introduction
	2 Materials
		2.1 Plasmids
		2.2 Cell Culture
		2.3 Transfection, Transduction
		2.4 Microscope
		2.5 Miscellaneous
	3 Methods
		3.1 Generation of HeLa Cells Stably Expressing optoTβRI and optoTβRII
		3.2 Single Colony Isolation of the Infected HeLa Cells
		3.3 Generation of optoHeLa-TGFBRs Cells with the Expression of iRFP-Smad2 Protein
		3.4 Measuring Light-Induced TGF-β Signaling Dynamics in optoHeLa-TGFBRs Cells
		3.5 Spatiotemporal Control of the TGF-β Signaling in the optoTGFBRs System
		3.6 Image Analysis
	4 Notes
	References
Chapter 10: Using Microfluidics and Live Cell Reporters to Dissect the Dynamics of TGF-β Signaling in Mouse Embryonic Stem Cel...
	1 Introduction
	2 Materials
		2.1 PDMS Multilayer Cell Culture Chip
		2.2 Automated Operation of the Chip
		2.3 Cell Lines
		2.4 mESC Cell Culture and Stimulation
		2.5 Cell Culture Chip Preparation
		2.6 Microfluidic Cell Culture Accessories
		2.7 Microscopes
			2.7.1 Epifluorescence Live Cell Imaging
			2.7.2 Confocal Live Cell Imaging
		2.8 Plasmids
	3 Methods
		3.1 Setting up an Experiment
			3.1.1 Connect a New Chip to the Pressure Controller
			3.1.2 Passivate Chip Channels with Antifouling Agent to Prevent Adhesion of Cells or Proteins in the Chip outside the Culture ...
			3.1.3 Loading Cells in the Culture Chambers
		3.2 Automated Long-Term Culture and Stimulation of mESC with ACTIVIN
		3.3 Measuring Single Cell Response to TGF-β Stimulation in Real Time
			3.3.1 Generation of a RFP-Smad2 mESC Reporter Cell Line
			3.3.2 Cell Line with Stable Constitutive Expression of a Nuclear Marker for Nuclear Segmentation
			3.3.3 Image Acquisition: Recording ASE:YFP Signal
			3.3.4 Image Acquisition: Recording RFP-Smad2 Signal
			3.3.5 Image Analysis: Segmentation of Nuclei
			3.3.6 Image Analysis: Extraction of ASE-YFP Fluorescence or RFP-Smad2 Nuclear to Cytoplasmic Ratio
			3.3.7 Image Analysis: Plot Results
	4 Notes
	References
11: Energy Landscape Analysis of the Epithelial-Mesenchymal Transition Network
	1 Introduction
	2 Material
	3 Methods
		3.1 Ordinary Differential Equations (ODEs) Model of the EMT Gene Regulatory Network
		3.2 The Stochastic Descriptions of EMT Dynamics
		3.3 Solving the Fokker-Planck Equation in Low- and High-Dimensional System
		3.4 Identify Minimum Action Path from Path Integral
		3.5 Detailed Steps to Obtain the Energy Landscape and MAPs
	4 Notes
	References
Chapter 12: Discrete Logic Modeling of Cell Signaling Pathways
	1 Introduction
	2 Materials
	3 Methods
		3.1 Boolean Network Models: A Brief Mathematical Description
		3.2 Constructing a Boolean Network Model of Cell Signaling Pathways Based on Literature
			3.2.1 Literature Collection and Evaluation
			3.2.2 From Literature to Boolean Functions
			3.2.3 Practical Examples on How to Solve Modeling-Related Issues
		3.3 Studying the Model Dynamics: Attractors and Biological Interpretation
		3.4 From Matching Phenotypes to Molecular Mechanisms: How to Approach Study of Cascades
		3.5 Applying the Established Model to Predict Behaviors Under Perturbations
		3.6 Stability Assessment
			3.6.1 Stability of Phenotypes: Can Noise Induce Shift of Attractors?
			3.6.2 Comparing the Network Stability to Randomly Generated Networks
	4 Notes
	References
Chapter 13: Mining of Single-Cell Signaling Time-Series for Dynamic Phenotypes with Clustering
	1 Introduction
	2 Materials
		2.1 A Desktop or Laptop Computer
		2.2 R Installation
		2.3 TCI Installation
		2.4 Time-Series  Data
		2.5 Optional  Data
		2.6 Demo  Data
	3 Methods
		3.1 Starting the  App
		3.2 The Interface
		3.3 Loading  Data
		3.4 Plotting  Data
		3.5 Outliers
		3.6 Trimming
		3.7 Missing  Data
		3.8 Normalization
		3.9 Distributions
		3.10 Clustering
		3.11 Cluster Validation
		3.12 Conclusions
	4 Notes
	References
Chapter 14: Automated Classification of Cellular Phenotypes Using Machine Learning in Cellprofiler and CellProfiler Analyst
	1 Introduction
	2 Materials
		2.1 Immunofluorescence Images
		2.2 Software
	3 Methods
		3.1 Fiji/ImageJ: Image Pre-processing and File Conversion
		3.2 CellProfiler: Cell Segmentation and Feature Extraction
			3.2.1 Getting Started with CellProfiler
			3.2.2 Using the Input Modules
			3.2.3 Using the Analysis Modules
			3.2.4 Cell Segmentation and Generation of Segmentation Masks
			3.2.5 Feature Extraction
			3.2.6 Export of Data
		3.3 CellProfiler Analyst: Supervised Training and Cell Classification
			3.3.1 Getting Started with CellProfiler Analyst
			3.3.2 Using the Classifier Tool
			3.3.3 Supervised Training
			3.3.4 Classification
	4 Notes
	References
Chapter 15: Live Cell Imaging of Spatiotemporal Ca2+ Fluctuation Responses to Anticancer Drugs
	1 Introduction
	2 Materials
		2.1 Cell Culture
		2.2 Development of HeLa Cell Lines Stably Expressing Cytosolic or ER Targeted Ca2+ FRET Biosensor
		2.3 Live Cell Imaging
	3 Methods
		3.1 Cell Culture and Ca2+ Sensors Stable Transgenic Cell Lines Production
		3.2 Preparation of 96-Well Imaging Plates with Ca2+ Sensor Expressing Cells
		3.3 Tracking the Drug Dose-Dependent Ca2+ FRET Sensor Responses in Real Time
		3.4 Imaging Analysis and Visualization with MATLAB
	4 Notes
	References
Index