Description
Book Synopsis
Using a collaborative and interdisciplinary author base with experience in the pharmaceutical industry and academia, this book is a practical resource for high content (HC) techniques.
Instructs readers on the fundamentals of high content screening (HCS) techniques
Focuses on practical and widely-used techniques like image processing and multiparametric assays
Breaks down HCS into individual modules for training and connects them at the end
Includes a tutorial chapter that works through sample HCS assays, glossary, and detailed appendices
Table of ContentsPREFACE xvii
CONTRIBUTORS xix
1 Introduction 1
Steven A. Haney
1.1 The Beginning of High Content Screening, 1
1.2 Six Skill Sets Essential for Running HCS Experiments, 4
1.3 Integrating Skill Sets into a Team, 7
1.4 A Few Words on Experimental Design, 8
1.5 Conclusions, 9
Key Points, 9
Further Reading, 10
References, 10
SECTION I FIRST PRINCIPLES 11
2 Fluorescence and Cell Labeling 13
Anthony Davies and Steven A. Haney
2.1 Introduction, 13
2.2 Anatomy of Fluorescent Probes, Labels, and Dyes, 14
2.3 Stokes’ Shift and Biological Fluorophores, 15
2.4 Fluorophore Properties, 16
2.5 Localization of Fluorophores Within Cells, 18
2.6 Multiplexing Fluorescent Reagents, 26
2.7 Specialized Imaging Applications Derived from Complex Properties of Fluorescence, 27
2.8 Conclusions, 30
Key Points, 31
Further Reading, 31
References, 31
3 Microscopy Fundamentals 33
Steven A. Haney, Anthony Davies, and Douglas Bowman
3.1 Introducing HCS Hardware, 33
3.2 Deconstructing Light Microscopy, 37
3.3 Using the Imager to Collect Data, 43
3.4 Conclusions, 45
Key Points, 45
Further Reading, 46
References, 46
4 Image Processing 47
John Bradley, Douglas Bowman, and Arijit Chakravarty
4.1 Overview of Image Processing and Image Analysis in HCS, 47
4.2 What is a Digital Image?, 48
4.3 “Addressing” Pixel Values in Image Analysis Algorithms, 48
4.4 Image Analysis Workflow, 49
4.5 Conclusions, 60
Key Points, 60
Further Reading, 60
References, 60
SECTION II GETTING STARTED 63
5 A General Guide to Selecting and Setting Up a High Content Imaging Platform 65
Craig Furman, Douglas Bowman, Anthony Davies, Caroline Shamu, and Steven A. Haney
5.1 Determining Expectations of the HCS System, 65
5.2 Establishing an HC Platform Acquisition Team, 66
5.3 Basic Hardware Decisions, 67
5.4 Data Generation, Analysis, and Retention, 72
5.5 Installation, 73
5.6 Managing the System, 75
5.7 Setting Up Workflows for Researchers, 77
5.8 Conclusions, 78
Key Points, 79
Further Reading, 79
6 Informatics Considerations 81
Jay Copeland and Caroline Shamu
6.1 Informatics Infrastructure for High Content Screening, 81
6.2 Using Databases to Store HCS Data, 86
6.3 Mechanics of an Informatics Solution, 89
6.4 Developing Image Analysis Pipelines: Data Management Considerations, 95
6.5 Compliance With Emerging Data Standards, 99
6.6 Conclusions, 101
Key Points, 102
Further Reading, 102
References, 102
7 Basic High Content Assay Development 103
Steven A. Haney and Douglas Bowman
7.1 Introduction, 103
7.2 Initial Technical Considerations for Developing a High Content Assay, 103
7.3 A Simple Protocol to Fix and Stain Cells, 107
7.4 Image Capture and Examining Images, 109
7.5 Conclusions, 111
Key Points, 112
Further Reading, 112
Reference, 112
SECTION III ANALYZING DATA 113
8 Designing Metrics for High Content Assays 115
Arijit Chakravarty, Steven A. Haney, and Douglas Bowman
8.1 Introduction: Features, Metrics, Results, 115
8.2 Looking at Features, 116
8.3 Metrics and Results: The Metric is the Message, 120
8.4 Types of High Content Assays and Their Metrics, 121
8.5 Metrics to Results: Putting it all Together, 126
8.6 Conclusions, 128
Key Points, 128
Further Reading, 129
References, 129
9 Analyzing Well-Level Data 131
Steven A Haney and John Ringeling
9.1 Introduction, 131
9.2 Reviewing Data, 132
9.3 Plate and Control Normalizations of Data, 134
9.4 Calculation of Assay Statistics, 135
9.5 Data Analysis: Hit Selection, 138
9.6 IC 50 Determinations, 139
9.7 Conclusions, 143
Key Points, 143
Further Reading, 143
References, 144
10 Analyzing Cell-Level Data 145
Steven A. Haney, Lin Guey, and Arijit Chakravarty
10.1 Introduction, 145
10.2 Understanding General Statistical Terms and Concepts, 146
10.3 Examining Data, 149
10.4 Developing a Data Analysis Plan, 155
10.5 Cell-Level Data Analysis: Comparing Distributions Through Inferential Statistics, 158
10.6 Analyzing Normal (or Transformed) Data, 159
10.7 Analyzing Non-Normal Data, 160
10.8 When to Call For Help, 162
10.9 Conclusions, 162
Key Points, 162
Further Reading, 163
References, 163
SECTION IV ADVANCED WORK 165
11 Designing Robust Assays 167
Arijit Chakravarty, Douglas Bowman, Anthony Davies, Steven A. Haney, and Caroline Shamu
11.1 Introduction, 167
11.2 Common Technical Issues in High Content Assays, 167
11.3 Designing Assays to Minimize Trouble, 172
11.4 Looking for Trouble: Building in Quality Control, 177
11.5 Conclusions, 179
Key Points, 180
Further Reading, 180
References, 180
12 Automation and Screening 181
John Ringeling, John Donovan, Arijit Chakravarty, Anthony Davies, Steven A Haney, Douglas Bowman, and Ben Knight
12.1 Introduction, 181
12.2 Some Preliminary Considerations, 181
12.3 Laboratory Options, 183
12.4 The Automated HCS Laboratory, 186
12.5 Conclusions, 192
Key Points, 192
Further Reading, 193
13 High Content Analysis for Tissue Samples 195
Kristine Burke, Vaishali Shinde, Alice McDonald, Douglas Bowman, and Arijit Chakravarty
13.1 Introduction, 195
13.2 Design Choices in Setting Up a High Content Assay in Tissue, 196
13.3 System Configuration: Aspects Unique to Tissue-Based HCS, 199
13.4 Data Analysis, 203
13.5 Conclusions, 207
Key Points, 207
Further Reading, 207
References, 208
SECTION V HIGH CONTENT ANALYTICS 209
14 Factoring and Clustering High Content Data 211
Steven A. Haney
14.1 Introduction, 211
14.2 Common Unsupervised Learning Methods, 212
14.3 Preparing for an Unsupervised Learning Study, 218
14.4 Conclusions, 228
Key Points, 228
Further Reading, 228
References, 229
15 Supervised Machine Learning 231
Jeff Palmer and Arijit Chakravarty
15.1 Introduction, 231
15.2 Foundational Concepts, 232
15.3 Choosing a Machine Learning Algorithm, 234
15.4 When Do You Need Machine Learning, and How Do You Use IT?, 243
15.5 Conclusions, 244
Key Points, 244
Further Reading, 244
Appendix A Websites and Additional Information on Instruments, Reagents, and Instruction 247
Appendix B A Few Words About One Letter: Using R to Quickly Analyze HCS Data 249
Steven A. Haney
B.1 Introduction, 249
B.2 Setting Up R, 250
B.3 Analyzing Data in R, 253
B.4 Where to Go Next, 261
Further Reading, 263
Appendix C Hypothesis Testing for High Content Data: A Refresher 265
Lin Guey and Arijit Chakravarty
C.1 Introduction, 265
C.2 Defining Simple Hypothesis Testing, 266
C.3 Simple Statistical Tests to Compare Two Groups, 269
C.4 Statistical Tests on Groups of Samples, 276
C.5 Introduction to Regression Models, 280
C.6 Conclusions, 285
Key Concepts, 286
Further Reading, 286
GLOSSARY 287
TUTORIAL 295
INDEX 323
