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 Contents:
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