Yegnasubramanian / Isaacs Modern Molecular Biology:

Approaches for Unbiased Discovery in Cancer Research
1. Auflage 2010
ISBN: 978-0-387-69745-1
Verlag: Springer
Format: PDF
Kopierschutz: 1 - PDF Watermark

E-Book, Englisch, 192 Seiten

Reihe: Applied Bioinformatics and Biostatistics in Cancer Research

Molecular biology has rapidly advanced since the discovery of the basic flow of information in life, from DNA to RNA to proteins. While there are several important and interesting exceptions to this general flow of information, the importance of these biological macromolecules in dictating the phenotypic nature of living creatures in health and disease is paramount. In the last one and a half decades, and particularly after the completion of the Human Genome Project, there has been an explosion of technologies that allow the broad characterization of these macromolecules in physiology, and the perturbations to these macromolecules that occur in diseases such as cancer. In this volume, we will explore the modern approaches used to characterize these macromolecules in an unbiased, systematic way. Such technologies are rapidly advancing our knowledge of the coordinated and complicated changes that occur during carcinogenesis, and are providing vital information that, when correctly interpreted by biostatistical/bioinformatics analyses, can be exploited for the prevention, diagnosis, and treatment of human cancers. The purpose of this volume is to provide an overview of modern molecular biological approaches to unbiased discovery in cancer research. Advances in molecular biology allowing unbiased analysis of changes in cancer initiation and progression will be overviewed. These include the strategies employed in modern genomics, gene expression analysis, and proteomics.

Autoren/Hrsg.

Yegnasubramanian, Srinivasan

Isaacs, William

Isaacs, William B.

Inhaltsverzeichnis

1;Preface;6
2;Contents;8
3;Contributors;10
4;Chapter 1: Genome-Scale Analysis of Data from High-Throughput Technologies;13
4.1;1.1 The Genomic Scale;13
4.2;1.2 Different Experimental Designs Focus on Different Biological Processes;16
4.3;1.3 Analytic Approaches Fall into Three Categories;17
4.4;1.4 Databases and Sequence Repositories Play a Key Role in Modern Genomics Research;17
4.5;1.5 Sequencing;18
4.6;1.6 Alignment, Mapping, and Assembly;19
4.7;1.7 Microarrays;20
4.8;1.8 Experimental Design Considerations;21
4.9;1.9 Conclusions;22
4.10;References;22
5;Chapter 2: Analysis of Inherited and Acquired Genetic Variation;24
5.1;2.1 Introduction;24
5.1.1;2.1.1 Oncogenes and Tumor Suppressor Genes;25
5.1.2;2.1.2 Types of Genetic Variation and Alteration in Human Cancer;25
5.1.3;2.1.3 Familial Cancer Syndromes and Link to Sporadic Cancers Affecting the Same Organ Sites;26
5.1.4;2.1.4 Inherited Susceptibility to Common Sporadic Cancers and Role of Environmental/Lifestyle Factors in Modifying Risks;27
5.2;2.2 Use of Microarrays for Genome-Wide Analysis of Genetic Variation/Mutation;28
5.2.1;2.2.1 Comparative Genomic Hybridization;28
5.2.2;2.2.2 Single Nucleotide Polymorphism Microarrays;29
5.2.3;2.2.3 Sequencing Microarrays;30
5.2.4;2.2.4 Genome-Wide Association Studies;31
5.3;2.3 Use of Conventional and Next Generation Sequencing for Genome-Wide Analysis of Genetic Variation/Mutation;32
5.3.1;2.3.1 High-Throughput Sanger Sequencing;32
5.3.2;2.3.2 Next Generation Sequencing;33
5.3.3;2.3.3 Overview of Commercialized Next Generation Sequencing Platforms;33
5.3.3.1;2.3.3.1 Library Choice and Construction;34
5.3.3.2;2.3.3.2 Preparation of Libraries for Sequencing on NGS Platforms;36
5.3.3.3;2.3.3.3 Massively Parallel Sequencing of Libraries on NGS Instruments;36
5.3.4;2.3.4 The Near and Long Term Horizon;37
5.4;References;38
6;Chapter 3: Examining DNA–Protein Interactions with Genome-Wide Chromatin Immunoprecipitation Analysis;43
6.1;3.1 Introduction;43
6.2;3.2 Experimental Design for a Successful ChIP-Chip or ChIP-Seq Experiment;45
6.2.1;3.2.1 ChIP Assays;45
6.2.2;3.2.2 Obtaining Material for Microarray Hybridization or HTS;49
6.2.3;3.2.3 Labeling and Hybridizing the DNA for ChIP-Chip;50
6.2.4;3.2.4 Choosing the Right Microarray;50
6.2.5;3.2.5 ChIP-Seq;51
6.2.6;3.2.6 Validating ChIP-Chip and ChIP-Seq Results;52
6.3;3.3 ChIP-Chip and ChIP-Seq: When Structural and Functional Information About Chromatin Goes Genome-Wide;52
6.4;3.4 Summary;53
6.5;References;53
7;Chapter 4: Genome-Wide DNA Methylation Analysis in Cancer Research;56
7.1;4.1 Introduction, Background and Significance;56
7.1.1;4.1.1 DNA Methylation in Physiology and Cancer Pathophysiology;57
7.1.2;4.1.2 Clinical Translational Potential of Cancer-Associated Somatic DNA Methylation Alterations;59
7.1.3;4.1.3 Overview of Approaches for Detection of DNA Methylation;59
7.2;4.2 Sodium Bisulfite Conversion Based Methods for DNA Methylation Analysis;60
7.2.1;4.2.1 Sodium Bisulfite Conversion Coupled with Microarrays for Genome-Wide DNA Methylation Analysis;61
7.2.2;4.2.2 Sodium Bisulfite Conversion Coupled with Conventional or Next-Generation Sequencing for Genome-Wide DNA Methylation Analysis;62
7.2.3;4.2.3 Analytical Considerations for Bisulfite Sequencing Based Approaches;63
7.3;4.3 Methylation-Sensitive and -Specific Restriction Endonuclease (MSRE) Based Methods for DNA Methylation Analysis;64
7.3.1;4.3.1 MSRE Fractionation Coupled with Microarrays or NGS for Genome-Wide DNA Methylation Analysis;66
7.4;4.4 Affinity Enrichment Based Methods for Genome-Wide DNA Methylation Analysis;66
7.4.1;4.4.1 Affinity Reagents for Recognition of Methylated DNA;66
7.4.2;4.4.2 Affinity-Enrichment of Methylated DNA Coupled with Microarrays or NGS for Genome-Wide DNA Methylation Analysis;67
7.5;4.5 Strengths and Weaknesses of the Various Approaches for Genome-Wide DNA Methylation Analysis;69
7.6;4.6 Detection of Methylated DNA by Physical Properties: The Horizon for Massively Parallel, Genome-Wide DNA Methylation Analysis;70
7.7;References;70
8;Chapter 5: Use of Expression Microarrays in Cancer Research;76
8.1;5.1 Overall Goal of Expression Microrarray Analysis in Cancer Research;76
8.2;5.2 An Overview of Major Components of an Expression Microarray Study;77
8.3;5.3 Diversity of Array Platforms;78
8.4;5.4 Considerations Related to Platform Choices;80
8.5;5.5 Sample Type and Size;81
8.6;5.6 Generation of Expression Microarray Data;82
8.7;5.7 Microarray Data Normalization;84
8.8;5.8 Expression Microarray Data Analysis;85
8.9;5.9 Clinical Translation of Microarray Studies;88
8.10;5.10 Future Outlook;89
8.11;References;89
9;Chapter 6: Signal Sequencing for Gene Expression Profiling;95
9.1;6.1 Introduction;96
9.2;6.2 Technologies for Generating Signal Sequences for Expression Profiling;97
9.2.1;6.2.1 Serial Analysis of Gene Expression;97
9.2.2;6.2.2 Massively Parallel Signature Sequencing;101
9.3;6.3 Data Analysis for Signal Sequencing Based Expression Profiling;103
9.3.1;6.3.1 Mapping Tag to Gene;103
9.3.2;6.3.2 Statistical Analysis of Signal Sequencing Data;109
9.4;6.4 Application of Signal Sequencing to Cancer Research;110
9.4.1;6.4.1 Application of SAGE to Prostate and Ovarian Cancer Studies;110
9.4.2;6.4.2 Application of MPSS to Prostate and Ovarian Cancer Studies;112
9.5;6.5 The Future of Signal Sequencing Based Expression Profiling;118
9.6;References;120
10;Chapter 7: Mass Spectrometry Based Proteomics in Cancer Research;124
10.1;7.1 Introduction;125
10.2;7.2 Sample Preparation;126
10.2.1;7.2.1 Proteome Analysis Challenged by the Large Concentration Range;126
10.2.2;7.2.2 Fractionation Using Chromatographic Techniques;128
10.2.2.1;7.2.2.1 Gel Filtration;129
10.2.2.2;7.2.2.2 Ion Exchange Chromatography;129
10.2.2.3;7.2.2.3 Chromatofocusing;129
10.2.2.4;7.2.2.4 Reversed Phase Chromatography;130
10.2.2.5;7.2.2.5 Metal Chelate Chromatography;130
10.2.2.6;7.2.2.6 Affinity Chromatography;130
10.2.3;7.2.3 Fractionation by Gel Electrophoresis;131
10.2.4;7.2.4 Quantitative Proteomics;132
10.2.4.1;7.2.4.1 Pre-biosynthetic Labeling;133
10.2.4.2;7.2.4.2 Post-biosynthetic Labeling;135
10.2.4.3;7.2.4.3 The Absolute Quantification Strategy;136
10.2.4.4;7.2.4.4 Label-Free Quantification;137
10.3;7.3 Mass Spectrometric Analysis;138
10.3.1;7.3.1 Ionization;139
10.3.1.1;7.3.1.1 MALDI;139
10.3.1.2;7.3.1.2 ESI;140
10.3.2;7.3.2 Mass Analyzers;141
10.3.2.1;7.3.2.1 Quadrupole Mass Analyzer;141
10.3.2.2;7.3.2.2 Time of Flight;142
10.3.2.3;7.3.2.3 Ion Trap;142
10.3.2.4;7.3.2.4 Ion Cyclotron Resonance;143
10.3.2.5;7.3.2.5 Orbitrap;143
10.3.3;7.3.3 Using a Mass Spectrometer to Identify Species in a Mixture;144
10.3.3.1;7.3.3.1 Collision-Induced Dissociation;144
10.3.3.2;7.3.3.2 Electron Capture Dissociation;145
10.3.3.3;7.3.3.3 Electron Transfer Dissociation;146
10.3.3.4;7.3.3.4 Infrared Multiphoton Dissociation;146
10.4;7.4 Data Analysis;146
10.4.1;7.4.1 Identification;146
10.4.2;7.4.2 Quantification;148
10.5;7.5 Applications;149
10.5.1;7.5.1 Post-translational Modifications;149
10.5.1.1;7.5.1.1 Isolation of Modified Proteins;151
10.5.1.2;7.5.1.2 PTM Mapping of a Purified Protein;151
10.5.1.3;7.5.1.3 PTM Mapping of Protein Populations;153
10.5.2;7.5.2 Identification of Protein Complexes;154
10.5.2.1;7.5.2.1 Affinity Purification;154
10.5.2.2;7.5.2.2 Biochemical Fractionation in Protein Complex Analysis;155
10.5.2.3;7.5.2.3 Crosslinking of Protein Complexes;155
10.5.2.4;7.5.2.4 Complex Stoichiometry;156
10.6;7.6 Summary;156
10.7;References;157
11;Chapter 8: Tissue Microarrays in Cancer Research;164
11.1;8.1 Background;164
11.2;8.2 Collection, Fixation, and Processing of Tissues;165
11.3;8.3 TMA Design;167
11.4;8.4 TMA Construction;169
11.5;8.5 Microtomy and Slide Storage;171
11.6;8.6 Commercial Slides;172
11.7;8.7 Immunodetection;173
11.7.1;8.7.1 Immunodetection: Signal Amplification;173
11.7.2;8.7.2 Immunodetection: Antigen Retrieval;175
11.7.3;8.7.3 Immunodetection: Validation and Controls;176
11.8;8.8 TMA Imaging Systems;177
11.9;8.9 Image Analysis;178
11.9.1;8.9.1 Manual Scoring;178
11.9.2;8.9.2 Image Analysis: Segmentation of Images;180
11.9.3;8.9.3 Measurement of Staining;182
11.10;8.10 TMA Data Management;183
11.11;8.11 DNA In Situ Hybridization;184
11.12;8.12 Summary;185
11.13;Box 8.1 Steps in Constructing a TMA Using a Manual Arrayer;171
11.14;References;186
12;Index;192