High Content Screening

STEVEN A. HANEY, PHD, is a Principal Scientist in the Department of Biological Technologies at Wyeth Research, where he has developed programs for oncology drug development, built a HCS program for use in target validation and drug discovery, and prepared gene family-based target validation strategies. Dr. Haney has authored many peer-reviewed articles and has spoken at numerous conferences on HCS.

• Preface xixContributors xxiSection I Essentials of High Content Screening 11. Approaching High Content Screening and Analysis: Practical Advice for Users 3Scott Keefer and Joseph Zock1.1 Introduction 31.2 What is HCS and Why Should I Care? 41.3 How does HCS Compare with Current Assay Methods? 51.4 The Basic Requirements to Implement HCS 81.4.1 Cell Banking 91.4.2 Plating, Cell Density, and the Assay Environment 101.4.3 Compound Addition and Incubation 111.4.4 Post-Assay Processing 111.4.5 HCS Imaging Hardware 121.4.6 HCS Analysis Software 131.4.7 Informatics 131.5 The Process 151.6 An Example Approach 161.7 Six Considerations for HCS Assays 181.7.1 Garbage In, Garbage Out (GIGO) 181.7.2 This is Not a Plate Reader 191.7.3 Understand Your Biology 201.7.4 Subtle Changes Can Be Measured and are Significant 201.7.5 HCS Workflow — Flexibility is the Key 211.7.6 HCS is Hard — How Do I Learn It and Become Proficient at It? 21References 222. Automated High Content Screening Microscopy 25Paul A. Johnston2.1 Introduction 252.2 Automated HCS Imaging Requirements 262.3 Components of Automated Imaging Platforms 262.3.1 Fluorescence Imaging and Multiplexing 262.3.2 Light Sources 282.3.3 Optical Designs: Confocal Versus Wide-Field 282.3.4 Objectives 292.3.5 Detectors 292.3.6 Autofocus 292.3.7 Environmental Controls and On-Board Liquid Handling Capabilities 302.4 Imaging Platform Software 312.5 Data Storage and Management 322.6 Selecting an HCS Platform 322.7 Comparison of a SAPK Activation HCS Assay Read on an ArrayScanⓇ 3.1, an ArrayScanⓇ VTi, and an IN Cell 3000 Automated Imaging Platform 33References 403. A Primer on Image Informatics of High Content Screening 43Xiaobo Zhou and Stephen T.C. Wong3.1 Background 433.2 HCS Image Processing 463.2.1 Image Pre-Processing 463.2.2 Cell Detection, Segmentation, and Centerline Extraction 483.2.2.1 Cell Detection 483.2.2.2 Particle Detection 503.2.2.3 Cell Segmentation 523.2.2.4 Centerline/Neurite Extraction 573.2.3 Cell Tracking and Registration 603.2.3.1 Simple Matching Algorithm 603.2.3.2 Mean Shift 623.2.3.3 Kalman Filter 623.2.3.4 Mutual Information 633.2.3.5 Fuzzy-System-Based Tracking 643.2.3.6 Parallel Tracking 663.2.4 Feature Extraction 663.2.4.1 Features Extracted from Markov Chain Modeling of Time-Lapse Images 673.3 Validation 673.4 Information System Management 693.5 Data Modeling 703.5.1 Novel Phenotype Discovery Using Clustering 703.5.2 Gene Function Study Using Clustering 723.5.3 Screening Hits Selection and Gene Scoring for Effectors Discovery 743.5.3.1 Fuzzy Gene Score Regression Model 753.5.3.2 Experimental Results 763.5.4 Metabolic Networks Validated by Using Genomics, Proteomics, and HCS 763.5.5 Connecting HCS Analysis and Systems Biology 773.5.6 Metabolic Networks 783.6 Conclusions 793.7 Acknowledgments 79References 804. Developing Robust High Content Assays 85Arijit Chakravarty, Douglas Bowman, Jeffrey A. Ecsedy, Claudia Rabino, John Donovan, Natalie D’Amore, Ole Petter Veiby, Mark Rolfe, and Sudeshna Das4.1 Introduction 854.2 Overview of a Typical Immunofluorescence-Based High Content Assay 864.2.1 Staining Protocol 874.2.2 Sources of Variability 874.3 Identifying Sources of Variability in a High Content Assay 884.3.1 Verifying the Accuracy and Precision of Liquid Handling Procedures 894.3.2 Deconstruction of Immunofluorescence and Cell Culture Protocols 904.3.3 Control Experiments 904.3.4 Protocol Optimization 924.3.5 Antibody Optimization Using a Design of Experiments Framework 944.3.6 Addressing Sources of Variability in Microscopy 964.3.7 Optimization of Image Processing Parameters in a High Content Assay 994.4 From Immunofluorescence to High Content: Selecting the Right Metric 1014.5 Validation of High Content Assays 1024.5.1 Establishing SOPs and Reagent Stocks for Cell Culture and Immunofluorescence Staining 1034.5.2 Linking Assay Variability to Assay Performance 1044.5.3 Design of Assay Quality Control Measures 1054.6 Conclusion 1074.7 Acknowledgments 108References 108Section II Applications of HCS In Basic Science and Early Drug Discovery 1115. HCS in Cellular Oncology and Tumor Biology 113Steven A. Haney, Jing Zhang, Jing Pan, and Peter LaPan5.1 Cancer Cell Biology and HCS 1135.1.1 Oncology Research and the Search for Effective Anticancer Therapeutics 1135.1.2 A General Protocol for Establishing HCS Assays Within Oncology Research 1155.1.2.1 What is the Underlying Biology to be Evaluated in an HCS Assay? 1155.1.2.2 What Resources are Immediately Available for Characterizing the Target or its Activity? 1165.1.2.3 How Do the Available Reagents Perform Quantitatively? 1185.1.2.4 What Multiplexing is Required for the Assay? 1195.2 The Cell Biology of Cell Death 1205.2.1 Cell Death Stimuli and Response Pathways 1205.2.2 Induction of Cell Death Signals 1215.2.2.1 Activation of Cell Death Receptors 1215.2.2.2 Mitochondrial Damage 1225.2.2.3 Mitotic Arrest, Replication Stress, and DNA Damage 1225.2.2.4 ER Stress 1225.2.3 Propagation of Cell Death Signals into Specific Cell Death Responses 1235.2.3.1 Apoptosis 1265.2.3.2 Mitotic Catastrophe 1275.2.3.3 Autophagy 1275.2.3.4 Necrosis 1285.2.3.5 Senescence 1295.2.4 Cytological and High Content Assays for Cancer Cell Death 1295.2.4.1 Detection of Moderate and Severe ER Stress in Cancer Cells 1305.2.4.2 Effects of Cytotoxic Therapeutics on Apoptosis and Necrosis of Cancer Cells 1305.3 Cell Signaling Pathways in Cancer 1335.3.1 Signal Transduction in Cancer 1335.3.2 A Multiparametric Assay for the PI3K/AKT Pathway as Representative of Quantitative Measures of Signal Transduction in Cancer Cells 1355.4 HCS in Tumor Biology 1375.4.1 The Biology of Tumor Growth 1375.4.2 An HCS Assay to Study Tumor Biology in vitro 1375.5 Conclusions 139References 1396. Exploring the Full Power of Combining High Throughput RNAi with High Content Readouts: From Target Discovery Screens to Drug Modifier Studies 145Christoph Sachse, Cornelia Weiss-Haljiti, Christian Holz, Kathrin Regener, Francoise Halley, Michael Hannus, Corina Frenzel, Sindy Kluge, Mark Hewitson, Benjamin Bader, Amy Burd, Louise Perkins, Alexander Szewczak, Stefan Prechtl, Claudia Merz, Peter Rae, Dominik Mumberg, and Christophe J. Echeverri6.1 Background: The Convergence of High Content Analysis and RNAi 1456.2 Integrating HT-RNAi and HCA in Drug Discovery: The Potential 1466.2.1 Technology Platform, HCA, and HT-RNAi Methodologies 1466.2.2 Key Applications of HT-RNAi Combined with HCA in Drug Discovery 1486.2.2.1 Target Discovery Screens 1486.2.2.2 Target Validation Studies 1496.2.2.3 Drug Mechanism of Action Screens 1496.3 Combining RNAi and HCA in One Assay — The Reality 1506.3.1 General Considerations 1506.3.1.1 Choice of the Right Cell Model 1506.3.1.2 Establishment of an RNAi Delivery Protocol 1506.3.1.3 Assay Optimization 1516.3.2 Applications: Combining HCA with HT-RNAi to Integrate Functional Validation Directly Within Target Discovery Studies 1516.3.2.1 Multipass Strategies for Systematic Screens 1516.3.2.2 Hurdles and Caveats 1526.3.2.3 Example: A Multiparametric Oncology Assay Platform 1556.3.3 RNAi Target Validation Studies 1596.3.3.1 Functional Profiling 1606.3.3.2 Transcriptional Profiling 1606.3.3.3 Cytological Profiling 1606.3.3.4 Pathway Profiling 1606.3.3.5 Target Titration 1606.3.4 RNAi Drug Modifier Screens 1616.4 HCA-Based RNAi Studies — The Future 1646.5 Acknowledgments 166References 1667. Leveraging HCS in Neuroscience Drug Discovery 169Myles Fennell, Beal McIlvain, Wendy Stewart, and John Dunlop7.1 High Content Screening and Drug Discovery 1697.2 The Neuron and Neuronal Morphology 1697.2.1 What Does Morphology Tell Us About Neuronal Function? 1707.3 Methods for Measuring Neuronal Morphology 1727.3.1 Traditional Methods 1727.3.2 Available HCS Systems for Neuronal Morphology Measurements and Evolution of Technology 1737.3.3 Methods for Imaging Neurons and Types of Morphologic Measurement 1767.4 Small Molecule Screening for Neurite Outgrowth 1787.5 RNAi in Neuroscience and HCA 1797.6 Measurement of Signal Transduction in Neurons 1807.7 High Content Screening in Complex CNS Models 1817.8 Methods Used in Neuronal HCS 1827.8.1 Preparation of Neuronal Culture Samples for HCS Morphology Analysis 1827.8.2 Culture Fixing 1837.8.3 Immunocytochemistry 1837.8.4 Neurite Morphology Measurement and Analysis 184References 1858. Live Brain Slice Imaging for Ultra High Content Screening: Automated Fluorescent Microscopy to Study Neurodegenerative Diseases 189O. Joseph Trask, Jr., C. Todd DeMarco, Denise Dunn, Thomas G. Gainer, Joshua Eudailey, Linda Kaltenbach, and Donald C. Lo8.1 Introduction and Background 1898.2 Live Brain Slice Model to Study Huntington’s Disease 1918.3 Imaging Platforms 1918.4 Center of Well (COW) for Image Processing 1948.5 Generic Protocol for the Cellomics ArrayScan VTI 1978.6 Data and Results 1978.7 Discussion 201References 2039. High Content Analysis of Human Embryonic Stem Cell Growth and Differentiation 205Paul J. Sammak, Vivek Abraham, Richik Ghosh, Jeff Haskins, Esther Jane, Patti Petrosko, Teresa M. Erb, Tia N. Kinney, Christopher Jefferys, Mukund Desai, and Rami Mangoubi9.1 Introduction 2059.2 Cell Culture Methods 2069.2.1 Maintaining Pluripotency 2069.2.2 Cardiomyocyte Differentiation 2079.2.3 Neuronal Differentiation 2079.3 Statistical Wavelet-Based Analysis of Images for Stem Cell Classification 2079.3.1 Motivation for Algorithm Development 2079.3.2 Measuring Amorphous Biological Shapes 2099.3.3 Texture and Borders as Biological Features 2109.3.4 Texture Analysis 2109.4 Molecular Analysis of Pluripotency and Cell Proliferation in Undifferentiated Stem Cells 2149.4.1 Methods 2159.4.2 Analysis of Pluripotency and Cell Proliferation in Undifferentiated Stem Cells 2159.5 Analysis of Cardiomyocyte Differentiation 2189.6 Analysis of Neuronal Differentiation 2199.6.1 Methods 2199.6.2 Analysis of Neurectodermal Intermediates in Early Differentiated hESC 2209.6.3 Analysis of Neuronal Processes 221References 221Section III HCS In Drug Development 22510. HCS for HTS 227Ann F. Hoffman and Ralph J. Garippa10.1 Introduction 22710.2 HCS for Orphan GPCRS and Transfluor 22810.3 HCS for Multiparameter Cytotoxicity Screening 23610.4 Discussion 24310.5 Summary 246References 24611. The Roles of High Content Cellular Imaging in Lead Optimization 249Jonathan A. Lee, Karen Cox, Aidas Kriauciunas, and Shaoyou Chu11.1 Introduction 24911.2 Statistical Validation of Assays 25011.3 High Content Cellular Imaging is a Diverse Assay Platform 25111.4 Use of High Content Cellular Imaging for Oncology Research at Eli Lilly 25511.4.1 Cell Cycle and High Content Cellular Imaging 25511.4.2 Advantages of High Content Cellular Imaging 25611.4.2.1 Rare Cell Populations 25611.4.2.2 End Point Multiplexing 25711.4.2.3 Advantages of Multiplexing 25911.5 The Future of High Content Cellular Imaging in Lead Optimization 26111.6 Acknowledgments 264References 26412. Using High Content Analysis for Pharmacodynamic Assays in Tissue 269Arijit Chakravarty, Douglas Bowman, Kristine Burke, Bradley Stringer, Barbara Hibner, and Katherine Galvin12.1 Introduction 26912.1.1 Preclinical Models 26912.1.2 Pharmacokinetics/Pharmacodynamics (PK/PD) 27012.1.3 PK/PD Approaches in Practice 27112.2 Designing a High Content Assay for Use in Tissues 27212.2.1 Preliminary Biomarker Characterization 27212.2.2 Development and Validation of HC Assays in Tissue 27312.3 Technical Challenges in Establishing High Content Assays for Tissue 27412.3.1 Logistical Challenges in Tissue Staining and Acquisition 27412.3.2 Plane-of-Focus and Plane-of-Section Issues 27512.3.3 Heterogeneity in Tissue Samples 27712.3.4 Automated Detection of Areas of Interest 27912.3.5 Segmentation and Background Issues in High Content Assays 28212.3.6 Variability in Staining 28412.4 Case Study: Design and Validation of a High Content Assay for Biomarker X 28612.5 Conclusions 28912.6 Acknowledgments 290References 29013. High Content Analysis of Sublethal Cytotoxicity in Human HepG2 Hepatocytes for Assessing Potential and Mechanism for Chemicaland Drug-Induced Human Toxicity 293Peter J. O’Brien13.1 Introduction 29313.1.1 Past Failure of Cytotoxicity Assessments 29313.1.2 Development of a Novel Cellomic Cytotoxicity Model 29513.1.3 Parameters Monitored in the Cellomic Cytotoxicity Model 29613.1.4 Materials and Methods 30013.2 Results from High Content Analysis of Human Toxicity Potential 30113.3 Discussion 30713.3.1 Applications of the Cellomic Cytotoxicity Model 30713.3.2 Limitations of the Cellomic Cytotoxicity Model 30813.3.3 Future Studies 30913.4 Acknowledgments 30913.5 Appendix: Detailed Methods 30913.5.1 Materials 30913.5.2 Methods: Cell Culture 31013.5.3 Subculture of HepG2 Cells 31013.5.4 Poly-D-Lysine Coating 31113.5.5 Drug Treatment Protocol for Three-Day Plates 31113.5.6 Drug Solubility 31213.5.7 Preparing the Drug Plate 31213.5.8 Indicator Dye Loading Procedure 31213.5.9 KSR Protocol: Fluorescence Settings 31313.5.9.1 Data Capture 31413.5.9.2 Assay Protocol Settings 31413.5.9.3 Plate Protocol Settings 31513.5.9.4 Quality Control 315References 315Section IV Data Management, Data Analysis, and Systems Biology 31714. Open File Formats for High Content Analysis 319Jason R. Swedlow, Curtis Rueden, Jean-Marie Burel, Melissa Linkert, Brian Loranger, Chris Allan, and Kevin W. Eliceiri14.1 Introduction 31914.2 The Data Problem in Biology: Why is it so Hard? 31914.3 High Content Data in Biology: A Definition 32014.4 The Difference Between a File Format and a Minimum Specification 32114.5 File Formats: Open vs Closed 32114.6 File Formats: Balancing Flexibility with Standards 32314.7 Supporting a Successful File Format 32314.8 Commercial Realities: How Users and Developers Can Define File Formats 32414.9 OME-XML and OME-TIFF: Moving Towards a Standard Format For High Content Biological Data 32414.9.1 Metadata Support for High Throughput Assays 32614.10 Data Model and File Format Integration: Towards Usable Tools 32714.11 Conclusions 32714.12 Acknowledgments 328References 32815. Analysis of Multiparametric HCS Data 329Andrew A. Hill, Peter LaPan, Yizheng Li, and Steven A. Haney15.1 Cytological Classification and Profiling 32915.1.1 Multiparametric HCS Data and Cytological Profiling 32915.1.2 Cytological Features 33015.1.3 Using Cytological Features in Assays 33115.2 Setting Up Cytological Profiling Studies 33315.2.1 Planning for a Cytological Classification Experiment 33315.2.2 Feature Extraction by Image Analysis and Export of Data for Analysis 33515.2.3 Example Studies that Use Cytological Profiling to Study Small Molecule Inhibitors and siRNAs 33615.3 Sources of Variability and Corrections 33615.3.1 Detection and Elimination of Plate and Sample Outliers from a Data Set 33615.3.2 Visualization of Plate-Level Features to Assess Data Quality 33715.3.3 Normalization and Scaling of Data 34015.3.4 Post-Normalization Analysis of Data Quality 34115.4 General Analysis Considerations 34115.4.1 Choosing the Appropriate Analysis Level: Well or Cell 34215.4.1.1 Cell Cycle Analysis 34215.4.1.2 Perturbations Where the Cell is an Effective Experimental Block 34215.4.2 Statistical Summaries for Cell-Level Features 34315.4.3 Feature Relationships, Redundancy, and Selection 34315.5 Data Analysis Methods 34615.5.1 Feature Transformation 34615.5.2 Linear Modeling of Feature Responses 34715.5.3 Unsupervised Clustering Methods 34815.5.4 Supervised Classification Methods 35115.6 Software for HCS Data Analysis 35215.7 Conclusions 352References 35316. Quantitative and Qualitative Cellular Genomics: High Content Analysis as an End Point for HT-RNAi Phenotype Profiling Using GE’s IN Cell Platform 355David O. Azorsa, Christian Beaudry, Kandavel Shanmugam, and Spyro Mousses16.1 Cellular Genomics 35516.2 Enabling Technologies to Facilitate Cellular Genomics: RNA Interference 35716.3 High Throughput RNAi (HT-RNAi) 35816.3.1 Platforms and Screening Infrastructure 35816.3.2 Establishing Methods for Successful HT-RNAi 35816.4 High Content Analysis (HCA) for High Throughput Phenotype Profiling 36216.4.1 IN Cell Analyzer 1000 36316.4.2 IN Cell Analyzer 3000 36316.4.3 HCA Assay Suites 36316.4.4 Fixed-Cell Assays 36616.4.5 Live-Cell Assays 36616.5 Future Directions 368References 36817. Optimal Characteristics of Protein–Protein Interaction Biosensors for Cellular Systems Biology Profiling 371Kenneth A. Giuliano, David Premkumar, and D. Lansing Taylor17.1 Introduction 37117.2 Challenge of Cellular Systems Biology (CSB) 37217.3 Optimal Characteristics of Protein–Protein Interaction Biosensors (PPIBs) 37317.4 Example of a PPIB and Cellular Systems Biology Profiling 37517.4.1 Testing a First-Generation p53–HDM2 PPIB Based on Full Length and Protein Fragments 37517.4.2 Overexpression of a Labeled p53 Fusion Protein Modulates Multiple Cellular Systems: Testing a Critical Potential Problem 37917.4.3 An Optimized p53–HDM2 PPIB 38017.5 Summary and Prospects 38417.6 Acknowledgments 385References 385