Modern Molecular Biology: (eBook)

Preface 6
Contents 8
Contributors 10
Chapter 1: Genome-Scale Analysis of Data from High-Throughput Technologies 13
1.1 The Genomic Scale 13
1.2 Different Experimental Designs Focus on Different Biological Processes 16
1.3 Analytic Approaches Fall into Three Categories 17
1.4 Databases and Sequence Repositories Play a Key Role in Modern Genomics Research 17
1.5 Sequencing 18
1.6 Alignment, Mapping, and Assembly 19
1.7 Microarrays 20
1.8 Experimental Design Considerations 21
1.9 Conclusions 22
References 22
Chapter 2: Analysis of Inherited and Acquired Genetic Variation 24
2.1 Introduction 24
2.1.1 Oncogenes and Tumor Suppressor Genes 25
2.1.2 Types of Genetic Variation and Alteration in Human Cancer 25
2.1.3 Familial Cancer Syndromes and Link to Sporadic Cancers Affecting the Same Organ Sites 26
2.1.4 Inherited Susceptibility to Common Sporadic Cancers and Role of Environmental/Lifestyle Factors in Modifying Risks 27
2.2 Use of Microarrays for Genome-Wide Analysis of Genetic Variation/Mutation 28
2.2.1 Comparative Genomic Hybridization 28
2.2.2 Single Nucleotide Polymorphism Microarrays 29
2.2.3 Sequencing Microarrays 30
2.2.4 Genome-Wide Association Studies 31
2.3 Use of Conventional and Next Generation Sequencing for Genome-Wide Analysis of Genetic Variation/Mutation 32
2.3.1 High-Throughput Sanger Sequencing 32
2.3.2 Next Generation Sequencing 33
2.3.3 Overview of Commercialized Next Generation Sequencing Platforms 33
2.3.3.1 Library Choice and Construction 34
2.3.3.2 Preparation of Libraries for Sequencing on NGS Platforms 36
2.3.3.3 Massively Parallel Sequencing of Libraries on NGS Instruments 36
2.3.4 The Near and Long Term Horizon 37
References 38
Chapter 3: Examining DNA–Protein Interactions with Genome-Wide Chromatin Immunoprecipitation Analysis 43
3.1 Introduction 43
3.2 Experimental Design for a Successful ChIP-Chip or ChIP-Seq Experiment 45
3.2.1 ChIP Assays 45
3.2.2 Obtaining Material for Microarray Hybridization or HTS 49
3.2.3 Labeling and Hybridizing the DNA for ChIP-Chip 50
3.2.4 Choosing the Right Microarray 50
3.2.5 ChIP-Seq 51
3.2.6 Validating ChIP-Chip and ChIP-Seq Results 52
3.3 ChIP-Chip and ChIP-Seq: When Structural and Functional Information About Chromatin Goes Genome-Wide 52
3.4 Summary 53
References 53
Chapter 4: Genome-Wide DNA Methylation Analysis in Cancer Research 56
4.1 Introduction, Background and Significance 56
4.1.1 DNA Methylation in Physiology and Cancer Pathophysiology 57
4.1.2 Clinical Translational Potential of Cancer-Associated Somatic DNA Methylation Alterations 59
4.1.3 Overview of Approaches for Detection of DNA Methylation 59
4.2 Sodium Bisulfite Conversion Based Methods for DNA Methylation Analysis 60
4.2.1 Sodium Bisulfite Conversion Coupled with Microarrays for Genome-Wide DNA Methylation Analysis 61
4.2.2 Sodium Bisulfite Conversion Coupled with Conventional or Next-Generation Sequencing for Genome-Wide DNA Methylation Analysis 62
4.2.3 Analytical Considerations for Bisulfite Sequencing Based Approaches 63
4.3 Methylation-Sensitive and -Specific Restriction Endonuclease (MSRE) Based Methods for DNA Methylation Analysis 64
4.3.1 MSRE Fractionation Coupled with Microarrays or NGS for Genome-Wide DNA Methylation Analysis 66
4.4 Affinity Enrichment Based Methods for Genome-Wide DNA Methylation Analysis 66
4.4.1 Affinity Reagents for Recognition of Methylated DNA 66
4.4.2 Affinity-Enrichment of Methylated DNA Coupled with Microarrays or NGS for Genome-Wide DNA Methylation Analysis 67
4.5 Strengths and Weaknesses of the Various Approaches for Genome-Wide DNA Methylation Analysis 69
4.6 Detection of Methylated DNA by Physical Properties: The Horizon for Massively Parallel, Genome-Wide DNA Methylation Analysis 70
References 70
Chapter 5: Use of Expression Microarrays in Cancer Research 76
5.1 Overall Goal of Expression Microrarray Analysis in Cancer Research 76
5.2 An Overview of Major Components of an Expression Microarray Study 77
5.3 Diversity of Array Platforms 78
5.4 Considerations Related to Platform Choices 80
5.5 Sample Type and Size 81
5.6 Generation of Expression Microarray Data 82
5.7 Microarray Data Normalization 84
5.8 Expression Microarray Data Analysis 85
5.9 Clinical Translation of Microarray Studies 88
5.10 Future Outlook 89
References 89
Chapter 6: Signal Sequencing for Gene Expression Profiling 95
6.1 Introduction 96
6.2 Technologies for Generating Signal Sequences for Expression Profiling 97
6.2.1 Serial Analysis of Gene Expression 97
6.2.2 Massively Parallel Signature Sequencing 101
6.3 Data Analysis for Signal Sequencing Based Expression Profiling 103
6.3.1 Mapping Tag to Gene 103
6.3.2 Statistical Analysis of Signal Sequencing Data 109
6.4 Application of Signal Sequencing to Cancer Research 110
6.4.1 Application of SAGE to Prostate and Ovarian Cancer Studies 110
6.4.2 Application of MPSS to Prostate and Ovarian Cancer Studies 112
6.5 The Future of Signal Sequencing Based Expression Profiling 118
References 120
Chapter 7: Mass Spectrometry Based Proteomics in Cancer Research 124
7.1 Introduction 125
7.2 Sample Preparation 126
7.2.1 Proteome Analysis Challenged by the Large Concentration Range 126
7.2.2 Fractionation Using Chromatographic Techniques 128
7.2.2.1 Gel Filtration 129
7.2.2.2 Ion Exchange Chromatography 129
7.2.2.3 Chromatofocusing 129
7.2.2.4 Reversed Phase Chromatography 130
7.2.2.5 Metal Chelate Chromatography 130
7.2.2.6 Affinity Chromatography 130
7.2.3 Fractionation by Gel Electrophoresis 131
7.2.4 Quantitative Proteomics 132
7.2.4.1 Pre-biosynthetic Labeling 133
7.2.4.2 Post-biosynthetic Labeling 135
7.2.4.3 The Absolute Quantification Strategy 136
7.2.4.4 Label-Free Quantification 137
7.3 Mass Spectrometric Analysis 138
7.3.1 Ionization 139
7.3.1.1 MALDI 139
7.3.1.2 ESI 140
7.3.2 Mass Analyzers 141
7.3.2.1 Quadrupole Mass Analyzer 141
7.3.2.2 Time of Flight 142
7.3.2.3 Ion Trap 142
7.3.2.4 Ion Cyclotron Resonance 143
7.3.2.5 Orbitrap 143
7.3.3 Using a Mass Spectrometer to Identify Species in a Mixture 144
7.3.3.1 Collision-Induced Dissociation 144
7.3.3.2 Electron Capture Dissociation 145
7.3.3.3 Electron Transfer Dissociation 146
7.3.3.4 Infrared Multiphoton Dissociation 146
7.4 Data Analysis 146
7.4.1 Identification 146
7.4.2 Quantification 148
7.5 Applications 149
7.5.1 Post-translational Modifications 149
7.5.1.1 Isolation of Modified Proteins 151
7.5.1.2 PTM Mapping of a Purified Protein 151
7.5.1.3 PTM Mapping of Protein Populations 153
7.5.2 Identification of Protein Complexes 154
7.5.2.1 Affinity Purification 154
7.5.2.2 Biochemical Fractionation in Protein Complex Analysis 155
7.5.2.3 Crosslinking of Protein Complexes 155
7.5.2.4 Complex Stoichiometry 156
7.6 Summary 156
References 157
Chapter 8: Tissue Microarrays in Cancer Research 164
8.1 Background 164
8.2 Collection, Fixation, and Processing of Tissues 165
8.3 TMA Design 167
8.4 TMA Construction 169
8.5 Microtomy and Slide Storage 171
8.6 Commercial Slides 172
8.7 Immunodetection 173
8.7.1 Immunodetection: Signal Amplification 173
8.7.2 Immunodetection: Antigen Retrieval 175
8.7.3 Immunodetection: Validation and Controls 176
8.8 TMA Imaging Systems 177
8.9 Image Analysis 178
8.9.1 Manual Scoring 178
8.9.2 Image Analysis: Segmentation of Images 180
8.9.3 Measurement of Staining 182
8.10 TMA Data Management 183
8.11 DNA In Situ Hybridization 184
8.12 Summary 185
Box 8.1 Steps in Constructing a TMA Using a Manual Arrayer 171
References 186
Index 192