Introduction To High Content Screening

Content: 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  Jonathan 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