An Introduction To High Content Screening Imaging Technology, Assay Development, and Data Analysis in Biology and Drug Discovery

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

PREFACE 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

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