High Content Screening
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Preface xix
Contributors xxi
Section I Essentials of High Content Screening 1
1. Approaching High Content Screening and Analysis: Practical Advice for Users 3
Scott Keefer and Joseph Zock
1.1 Introduction 3
1.2 What is HCS and Why Should I Care? 4
1.3 How does HCS Compare with Current Assay Methods? 5
1.4 The Basic Requirements to Implement HCS 8
1.4.1 Cell Banking 9
1.4.2 Plating, Cell Density, and the Assay Environment 10
1.4.3 Compound Addition and Incubation 11
1.4.4 Post-Assay Processing 11
1.4.5 HCS Imaging Hardware 12
1.4.6 HCS Analysis Software 13
1.4.7 Informatics 13
1.5 The Process 15
1.6 An Example Approach 16
1.7 Six Considerations for HCS Assays 18
1.7.1 Garbage In, Garbage Out (GIGO) 18
1.7.2 This is Not a Plate Reader 19
1.7.3 Understand Your Biology 20
1.7.4 Subtle Changes Can Be Measured and are Significant 20
1.7.5 HCS Workflow - Flexibility is the Key 21
1.7.6 HCS is Hard - How Do I Learn It and Become Proficient at It? 21
References 22
2. Automated High Content Screening Microscopy 25
Paul A. Johnston
2.1 Introduction 25
2.2 Automated HCS Imaging Requirements 26
2.3 Components of Automated Imaging Platforms 26
2.3.1 Fluorescence Imaging and Multiplexing 26
2.3.2 Light Sources 28
2.3.3 Optical Designs: Confocal Versus Wide-Field 28
2.3.4 Objectives 29
2.3.5 Detectors 29
2.3.6 Autofocus 29
2.3.7 Environmental Controls and On-Board Liquid Handling Capabilities 30
2.4 Imaging Platform Software 31
2.5 Data Storage and Management 32
2.6 Selecting an HCS Platform 32
2.7 Comparison of a SAPK Activation HCS Assay Read on an ArrayScan¿ 3.1, an ArrayScan¿ VTi, and an IN Cell 3000 Automated Imaging Platform 33
References 40
3. A Primer on Image Informatics of High Content Screening 43
Xiaobo Zhou and Stephen T.C. Wong
3.1 Background 43
3.2 HCS Image Processing 46
3.2.1 Image Pre-Processing 46
3.2.2 Cell Detection, Segmentation, and Centerline Extraction 48
3.2.2.1 Cell Detection 48
3.2.2.2 Particle Detection 50
3.2.2.3 Cell Segmentation 52
3.2.2.4 Centerline/Neurite Extraction 57
3.2.3 Cell Tracking and Registration 60
3.2.3.1 Simple Matching Algorithm 60
3.2.3.2 Mean Shift 62
3.2.3.3 Kalman Filter 62
3.2.3.4 Mutual Information 63
3.2.3.5 Fuzzy-System-Based Tracking 64
3.2.3.6 Parallel Tracking 66
3.2.4 Feature Extraction 66
3.2.4.1 Features Extracted from Markov Chain Modeling of Time-Lapse Images 67
3.3 Validation 67
3.4 Information System Management 69
3.5 Data Modeling 70
3.5.1 Novel Phenotype Discovery Using Clustering 70
3.5.2 Gene Function Study Using Clustering 72
3.5.3 Screening Hits Selection and Gene Scoring for Effectors Discovery 74
3.5.3.1 Fuzzy Gene Score Regression Model 75
3.5.3.2 Experimental Results 76
3.5.4 Metabolic Networks Validated by Using Genomics, Proteomics, and HCS 76
3.5.5 Connecting HCS Analysis and Systems Biology 77
3.5.6 Metabolic Networks 78
3.6 Conclusions 79
3.7 Acknowledgments 79
References 80
4. Developing Robust High Content Assays 85
Arijit Chakravarty, Douglas Bowman, Jeffrey A. Ecsedy, Claudia Rabino, John Donovan, Natalie D'Amore, Ole Petter Veiby, Mark Rolfe, and Sudeshna Das
4.1 Introduction 85
4.2 Overview of a Typical Immunofluorescence-Based High Content Assay 86
4.2.1 Staining Protocol 87
4.2.2 Sources of Variability 87
4.3 Identifying Sources of Variability in a High Content Assay 88
4.3.1 Verifying the Accuracy and Precision of Liquid Handling Procedures 89
4.3.2 Deconstruction of Immunofluorescence and Cell Culture Protocols 90
4.3.3 Control Experiments 90
4.3.4 Protocol Optimization 92
4.3.5 Antibody Optimization Using a Design of Experiments Framework 94
4.3.6 Addressing Sources of Variability in Microscopy 96
4.3.7 Optimization of Image Processing Parameters in a High Content Assay 99
4.4 From Immunofluorescence to High Content: Selecting the Right Metric 101
4.5 Validation of High Content Assays 102
4.5.1 Establishing SOPs and Reagent Stocks for Cell Culture and Immunofluorescence Staining 103
4.5.2 Linking Assay Variability to Assay Performance 104
4.5.3 Design of Assay Quality Control Measures 105
4.6 Conclusion 107
4.7 Acknowledgments 108
References 108
Section II Applications of HCS In Basic Science and Early Drug Discovery 111
5. HCS in Cellular Oncology and Tumor Biology 113
Steven A. Haney, Jing Zhang, Jing Pan, and Peter LaPan
5.1 Cancer Cell Biology and HCS 113
5.1.1 Oncology Research and the Search for Effective Anticancer Therapeutics 113
5.1.2 A General Protocol for Establishing HCS Assays Within Oncology Research 115
5.1.2.1 What is the Underlying Biology to be Evaluated in an HCS Assay? 115
5.1.2.2 What Resources are Immediately Available for Characterizing the Target or its Activity? 116
5.1.2.3 How Do the Available Reagents Perform Quantitatively? 118
5.1.2.4 What Multiplexing is Required for the Assay? 119
5.2 The Cell Biology of Cell Death 120
5.2.1 Cell Death Stimuli and Response Pathways 120
5.2.2 Induction of Cell Death Signals 121
5.2.2.1 Activation of Cell Death Receptors 121
5.2.2.2 Mitochondrial Damage 122
5.2.2.3 Mitotic Arrest, Replication Stress, and DNA Damage 122
5.2.2.4 ER Stress 122
5.2.3 Propagation of Cell Death Signals into Specific Cell Death Responses 123
5.2.3.1 Apoptosis 126
5.2.3.2 Mitotic Catastrophe 127
5.2.3.3 Autophagy 127
5.2.3.4 Necrosis 128
5.2.3.5 Senescence 129
5.2.4 Cytological and High Content Assays for Cancer Cell Death 129
5.2.4.1 Detection of Moderate and Severe ER Stress in Cancer Cells 130
5.2.4.2 Effects of Cytotoxic Therapeutics on Apoptosis and Necrosis of Cancer Cells 130
5.3 Cell Signaling Pathways in Cancer 133
5.3.1 Signal Transduction in Cancer 133
5.3.2 A Multiparametric Assay for the PI3K/AKT Pathway as Representative of Quantitative Measures of Signal Transduction in Cancer Cells 135
5.4 HCS in Tumor Biology 137
5.4.1 The Biology of Tumor Growth 137
5.4.2 An HCS Assay to Study Tumor Biology in vitro 137
5.5 Conclusions 139
References 139
6. Exploring the Full Power of Combining High Throughput RNAi with High Content Readouts: From Target Discovery Screens to Drug Modifier Studies 145
Christoph 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. Echeverri
6.1 Background: The Convergence of High Content Analysis and RNAi 145
6.2 Integrating HT-RNAi and HCA in Drug Discovery: The Potential 146
6.2.1 Technology Platform, HCA, and HT-RNAi Methodologies 146
6.2.2 Key Applications of HT-RNAi Combined with HCA in Drug Discovery 148
6.2.2.1 Target Discovery Screens 148
6.2.2.2 Target Validation Studies 149
6.2.2.3 Drug Mechanism of Action Screens 149
6.3 Combining RNAi and HCA in One Assay - The Reality 150
6.3.1 General Considerations 150
6.3.1.1 Choice of the Right Cell Model 150
6.3.1.2 Establishment of an RNAi Delivery Protocol 150
6.3.1.3 Assay Optimization 151
6.3.2 Applications: Combining HCA with HT-RNAi to Integrate Functional Validation Directly Within Target Discovery Studies 151
6.3.2.1 Multipass Strategies for Systematic Screens 151
6.3.2.2 Hurdles and Caveats 152
6.3.2.3 Example: A Multiparametric Oncology Assay Platform 155
6.3.3 RNAi Target Validation Studies 159
6.3.3.1 Functional Profiling 160
6.3.3.2 Transcriptional Profiling 160
6.3.3.3 Cytological Profiling 160
6.3.3.4 Pathway Profiling 160
6.3.3.5 Target Titration 160
6.3.4 RNAi Drug Modifier Screens 161
6.4 HCA-Based RNAi Studies - The Future 164
6.5 Acknowledgments 166
References 166
7. Leveraging HCS in Neuroscience Drug Discovery 169
Myles Fennell, Beal McIlvain, Wendy Stewart, and John Dunlop
7.1 High Content Screening and Drug Discovery 169
7.2 The Neuron and Neuronal Morphology 169
7.2.1 What Does Morphology Tell Us About Neuronal Function? 170
7.3 Methods for Measuring Neuronal Morphology 172
7.3.1 Traditional Methods 172
7.3.2 Available HCS Systems for Neuronal Morphology Measurements and Evolution of Technology 173
7.3.3 Methods for Imaging Neurons and Types of Morphologic Measurement 176
7.4 Small Molecule Screening for Neurite Outgrowth 178
7.5 RNAi in Neuroscience and HCA 179
7.6 Measurement of Signal Transduction in Neurons 180
7.7 High Content Screening in Complex CNS Models 181
7.8 Methods Used in Neuronal HCS 182
7.8.1 Preparation of Neuronal Culture Samples for HCS Morphology Analysis 182
7.8.2 Culture Fixing 183
7.8.3 Immunocytochemistry 183
7.8.4 Neurite Morphology Measurement and Analysis 184
References 185
8. Live Brain Slice Imaging for Ultra High Content Screening: Automated Fluorescent Microscopy to Study Neurodegenerative Diseases 189
O. Joseph Trask, Jr., C. Todd DeMarco, Denise Dunn, Thomas G. Gainer, Joshua Eudailey, Linda Kaltenbach, and Donald C. Lo
8.1 Introduction and Background 189
8.2 Live Brain Slice Model to Study Huntington's Disease 191
8.3 Imaging Platforms 191
8.4 Center of Well (COW) for Image Processing 194
8.5 Generic Protocol for the Cellomics ArrayScan VTI 197
8.6 Data and Results 197
8.7 Discussion 201
References 203
9. High Content Analysis of Human Embryonic Stem Cell Growth and Differentiation 205
Paul J. Sammak, Vivek Abraham, Richik Ghosh, Jeff Haskins, Esther Jane, Patti Petrosko, Teresa M. Erb, Tia N. Kinney, Christopher Jefferys, Mukund Desai, and Rami Mangoubi
9.1 Introduction 205
9.2 Cell Culture Methods 206
9.2.1 Maintaining Pluripotency 206
9.2.2 Cardiomyocyte Differentiation 207
9.2.3 Neuronal Differentiation 207
9.3 Statistical Wavelet-Based Analysis of Images for Stem Cell Classification 207
9.3.1 Motivation for Algorithm Development 207
9.3.2 Measuring Amorphous Biological Shapes 209
9.3.3 Texture and Borders as Biological Features 210
9.3.4 Texture Analysis 210
9.4 Molecular Analysis of Pluripotency and Cell Proliferation in Undifferentiated Stem Cells 214
9.4.1 Methods 215
9.4.2 Analysis of Pluripotency and Cell Proliferation in Undifferentiated Stem Cells 215
9.5 Analysis of Cardiomyocyte Differentiation 218
9.6 Analysis of Neuronal Differentiation 219
9.6.1 Methods 219
9.6.2 Analysis of Neurectodermal Intermediates in Early Differentiated hESC 220
9.6.3 Analysis of Neuronal Processes 221
References 221
Section III HCS In Drug Development 225
10. HCS for HTS 227
Ann F. Hoffman and Ralph J. Garippa
10.1 Introduction 227
10.2 HCS for Orphan GPCRS and Transfluor 228
10.3 HCS for Multiparameter Cytotoxicity Screening 236
10.4 Discussion 243
10.5 Summary 246
References 246
11. The Roles of High Content Cellular Imaging in Lead Optimization 249
Jonathan A. Lee, Karen Cox, Aidas Kriauciunas, and Shaoyou Chu
11.1 Introduction 249
11.2 Statistical Validation of Assays 250
11.3 High Content Cellular Imaging is a Diverse Assay Platform 251
11.4 Use of High Content Cellular Imaging for Oncology Research at Eli Lilly 255
11.4.1 Cell Cycle and High Content Cellular Imaging 255
11.4.2 Advantages of High Content Cellular Imaging 256
11.4.2.1 Rare Cell Populations 256
11.4.2.2 End Point Multiplexing 257
11.4.2.3 Advantages of Multiplexing 259
11.5 The Future of High Content Cellular Imaging in Lead Optimization 261
11.6 Acknowledgments 264
References 264
12. Using High Content Analysis for Pharmacodynamic Assays in Tissue 269
Arijit Chakravarty, Douglas Bowman, Kristine Burke, Bradley Stringer, Barbara Hibner, and Katherine Galvin
12.1 Introduction 269
12.1.1 Preclinical Models 269
12.1.2 Pharmacokinetics/Pharmacodynamics (PK/PD) 270
12.1.3 PK/PD Approaches in Practice 271
12.2 Designing a High Content Assay for Use in Tissues 272
12.2.1 Preliminary Biomarker Characterization 272
12.2.2 Development and Validation of HC Assays in Tissue 273
12.3 Technical Challenges in Establishing High Content Assays for Tissue 274
12.3.1 Logistical Challenges in Tissue Staining and Acquisition 274
12.3.2 Plane-of-Focus and Plane-of-Section Issues 275
12.3.3 Heterogeneity in Tissue Samples 277
12.3.4 Automated Detection of Areas of Interest 279
12.3.5 Segmentation and Background Issues in High Content Assays 282
12.3.6 Variability in Staining 284
12.4 Case Study: Design and Validation of a High Content Assay for Biomarker X 286
12.5 Conclusions 289
12.6 Acknowledgments 290
References 290
13. High Content Analysis of Sublethal Cytotoxicity in Human HepG2 Hepatocytes for Assessing Potential and Mechanism for Chemicaland Drug-Induced Human Toxicity 293
Peter J. O'Brien
13.1 Introduction 293
13.1.1 Past Failure of Cytotoxicity Assessments 293
13.1.2 Development of a Novel Cellomic Cytotoxicity Model 295
13.1.3 Parameters Monitored in the Cellomic Cytotoxicity Model 296
13.1.4 Materials and Methods 300
13.2 Results from High Content Analysis of Human Toxicity Potential 301
13.3 Discussion 307
13.3.1 Applications of the Cellomic Cytotoxicity Model 307
13.3.2 Limitations of the Cellomic Cytotoxicity Model 308
13.3.3 Future Studies 309
13.4 Acknowledgments 309
13.5 Appendix: Detailed Methods 309
13.5.1 Materials 309
13.5.2 Methods: Cell Culture 310
13.5.3 Subculture of HepG2 Cells 310
13.5.4 Poly-D-Lysine Coating 311
13.5.5 Drug Treatment Protocol for Three-Day Plates 311
13.5.6 Drug Solubility 312
13.5.7 Preparing the Drug Plate 312
13.5.8 Indicator Dye Loading Procedure 312
13.5.9 KSR Protocol: Fluorescence Settings 313
13.5.9.1 Data Capture 314
13.5.9.2 Assay Protocol Settings 314
13.5.9.3 Plate Protocol Settings 315
13.5.9.4 Quality Control 315
References 315
Section IV Data Management, Data Analysis, and Systems Biology 317
14. Open File Formats for High Content Analysis 319
Jason R. Swedlow, Curtis Rueden, Jean-Marie Burel, Melissa Linkert, Brian Loranger, Chris Allan, and Kevin W. Eliceiri
14.1 Introduction 319
14.2 The Data Problem in Biology: Why is it so Hard? 319
14.3 High Content Data in Biology: A Definition 320
14.4 The Difference Between a File Format and a Minimum Specification 321
14.5 File Formats: Open vs Closed 321
14.6 File Formats: Balancing Flexibility with Standards 323
14.7 Supporting a Successful File Format 323
14.8 Commercial Realities: How Users and Developers Can Define File Formats 324
14.9 OME-XML and OME-TIFF: Moving Towards a Standard Format For High Content Biological Data 324
14.9.1 Metadata Support for High Throughput Assays 326
14.10 Data Model and File Format Integration: Towards Usable Tools 327
14.11 Conclusions 327
14.12 Acknowledgments 328
References 328
15. Analysis of Multiparametric HCS Data 329
Andrew A. Hill, Peter LaPan, Yizheng Li, and Steven A. Haney
15.1 Cytological Classification and Profiling 329
15.1.1 Multiparametric HCS Data and Cytological Profiling 329
15.1.2 Cytological Features 330
15.1.3 Using Cytological Features in Assays 331
15.2 Setting Up Cytological Profiling Studies 333
15.2.1 Planning for a Cytological Classification Experiment 333
15.2.2 Feature Extraction by Image Analysis and Export of Data for Analysis 335
15.2.3 Example Studies that Use Cytological Profiling to Study Small Molecule Inhibitors and siRNAs 336
15.3 Sources of Variability and Corrections 336
15.3.1 Detection and Elimination of Plate and Sample Outliers from a Data Set 336
15.3.2 Visualization of Plate-Level Features to Assess Data Quality 337
15.3.3 Normalization and Scaling of Data 340
15.3.4 Post-Normalization Analysis of Data Quality 341
15.4 General Analysis Considerations 341
15.4.1 Choosing the Appropriate Analysis Level: Well or Cell 342
15.4.1.1 Cell Cycle Analysis 342
15.4.1.2 Perturbations Where the Cell is an Effective Experimental Block 342
15.4.2 Statistical Summaries for Cell-Level Features 343
15.4.3 Feature Relationships, Redundancy, and Selection 343
15.5 Data Analysis Methods 346
15.5.1 Feature Transformation 346
15.5.2 Linear Modeling of Feature Responses 347
15.5.3 Unsupervised Clustering Methods 348
15.5.4 Supervised Classification Methods 351
15.6 Software for HCS Data Analysis 352
15.7 Conclusions 352
References 353
16. Quantitative and Qualitative Cellular Genomics: High Content Analysis as an End Point for HT-RNAi Phenotype Profiling Using GE's IN Cell Platform 355
David O. Azorsa, Christian Beaudry, Kandavel Shanmugam, and Spyro Mousses
16.1 Cellular Genomics 355
16.2 Enabling Technologies to Facilitate Cellular Genomics: RNA Interference 357
16.3 High Throughput RNAi (HT-RNAi) 358
16.3.1 Platforms and Screening Infrastructure 358
16.3.2 Establishing Methods for Successful HT-RNAi 358
16.4 High Content Analysis (HCA) for High Throughput Phenotype Profiling 362
16.4.1 IN Cell Analyzer 1000 363
16.4.2 IN Cell Analyzer 3000 363
16.4.3 HCA Assay Suites 363
16.4.4 Fixed-Cell Assays 366
16.4.5 Live-Cell Assays 366
16.5 Future Directions 368
References 368
17. Optimal Characteristics of Protein-Protein Interaction Biosensors for Cellular Systems Biology Profiling 371
Kenneth A. Giuliano, David Premkumar, and D. Lansing Taylor
17.1 Introduction 371
17.2 Challenge of Cellular Systems Biology (CSB) 372
17.3 Optimal Characteristics of Protein-Protein Interaction Biosensors (PPIBs) 373
17.4 Example of a PPIB and Cellular Systems Biology Profiling 375
17.4.1 Testing a First-Generation p53-HDM2 PPIB Based on Full Length and Protein Fragments 375
17.4.2 Overexpression of a Labeled p53 Fusion Protein Modulates Multiple Cellular Systems: Testing a Critical Potential Problem 379
17.4.3 An Optimized p53-HDM2 PPIB 380
17.5 Summary and Prospects 384
17.6 Acknowledgments 385
References 385
