Appasani Bioarrays

From Basics to Diagnostics
1. Auflage 2007
ISBN: 978-1-59745-328-8
Verlag: Humana Press
Format: PDF
Kopierschutz: 1 - PDF Watermark

E-Book, Englisch, 269 Seiten

This book provides an integrated collection of timely articles on the use of bioarray techniques in the fields of biotechnology and molecular medicine. It is the first book to comprehensively integrate molecular diagnostics and molecular pathology. This book serves as an indispensable reference for graduate students, post-docs, and professors as well as an explanatory analysis for executives and scientists in biotechnology and pharmaceutical companies.

Autoren/Hrsg.

Appasani, Krishnarao

Inhaltsverzeichnis

1;Foreword;6
2;Preface;8
3;Contents;11
4;Contributors;13
5;PART I BIOARRAY TECHNOLOGY PLATFORMS;17
5.1;1 Investigation of Tumor Metastasis by Using cDNA Microarrays;19
5.1.1;1. Introduction;19
5.1.2;2. Experimental Outline;22
5.1.3;3. Future of Microarray Analysis and Conclusions;29
5.1.4;References;30
5.2;2 From Tissue Samples to Tumor Markers;33
5.2.1;1. Introduction;33
5.2.2;2. Technical Issues: Array Platform, Samples, and Bioinformatics Tools;34
5.2.3;3. Validation of Candidate Genes Through Real-Time Reverse Transcription-PCR and Tissue Microarray;39
5.2.4;4. Perspectives;41
5.2.5;Acknowledgments;41
5.2.6;References;41
5.3;3 Experimental Design for Gene Expression Analysis;45
5.3.1;1. cDNA and DNA Microarrays;45
5.3.2;2. Proteomics;53
5.3.3;3. Conclusions;57
5.3.4;Acknowledgments;57
5.3.5;References;57
5.4;4 From Microarrays to Gene Networks;61
5.4.1;1. Introduction;61
5.4.2;2. Background;62
5.4.3;3. Biological Network Models;69
5.4.4;4. Analysis of Gene Expression Data in a Networks Setting;70
5.4.5;5. Conclusions;71
5.4.6;References;72
6;PART II BIOMARKERS AND CLINICAL GENOMICS;75
6.1;5 Reduction in Sample Heterogeneity Leads to Increased Microarray Sensitivity;77
6.1.1;1. Introduction;77
6.1.2;2. Materials and Methods;80
6.1.3;3. Results;81
6.1.4;References;94
6.2;6 Genomics to Identify Biomarkers of Normal Brain Aging;99
6.2.1;1. Introduction;99
6.2.2;2. Molecular Characterization of Normal Aging;100
6.2.3;3. Aging Is a Continuous and Specific Process Throughout Adult Life;104
6.2.4;4. Summary and Conclusions;105
6.2.5;Acknowledgments;107
6.2.6;References;107
6.3;7 Gene Expression Profiling for Biomarker Discovery;110
6.3.1;1. Bioarray and Clinical Applications;110
6.3.2;2. Microarrays for Molecular Biomarker Discovery;113
6.3.3;3. Prospects for Gene Expression Profiling in Clinical Use;120
6.3.4;References;120
6.4;8 Array-Based Comparative Genomic Hybridization;122
6.4.1;1. Introduction;122
6.4.2;2. Historical Aspects of CGH;123
6.4.3;3. aCGH and Cancer;124
6.4.4;4. aCGH and Tuberculosis;129
6.4.5;References;133
6.5;9 Regional Specialization of Endothelial Cells as Revealed by Genomic Analysis;137
6.5.1;1. Introduction;137
6.5.2;2. Overview of Gene Expression Patterns;138
6.5.3;3. Gene Expression Patterns Between Macrovascular and Microvascular ECs;139
6.5.4;4. Gene Expression Pattern Differences Between Arterial and Venous ECs;142
6.5.5;5. Hey2 Activates Expression of Arterial-Specific Genes;144
6.5.6;6. Genes Differentially Expressed in ECs from Different Tissues;144
6.5.7;7. Conclusions;145
6.5.8;Acknowledgments;146
6.5.9;References;146
7;PART III BIOMARKER IDENTIFICATION BY USING CLINICAL PROTEOMICS AND GLYCOMICS;149
7.1;10 Identification of Target Antigens in CNS Inflammation by Protein Array Technique;151
7.1.1;1. Introduction;151
7.1.2;2. Multiple Sclerosis;152
7.1.3;3. Dissection of Antibody Specificity in MS by Using Human Brain cDNA Protein Macroarrays;155
7.1.4;4. Conclusions;159
7.1.5;Acknowledgments;159
7.1.6;References;159
7.2;11 Differential Protein Expression, Protein Profiles of Human Gliomas, and Clinical Implications;163
7.2.1;1. Introduction;163
7.2.2;2. Background, Types, Origin of Gliomas, and Clinical Issues;164
7.2.3;3. Molecular Genetic Changes and Molecular Markers and Importance of Molecular Profiles;168
7.2.4;4. Experimental System: Glioma Cell Lines Vs Primary Tumors;169
7.2.5;5. DNA Microarray Analysis and Transcript Profiles;170
7.2.6;6. Proteomics Approaches and Protein Profiles;171
7.2.7;7. Future Perspective;182
7.2.8;Acknowdgments;183
7.2.9;References;184
7.3;12 Antibody-Based Microarrays;188
7.3.1;1. Background;188
7.3.2;2. Antibody Microarrays: A Short Introduction;189
7.3.3;3. Quest for Developing Antibody Microarrys: Our Approach;190
7.3.4;4. Antibody Microarray Applications: Current and Future;197
7.3.5;5. Summary and Conclusions;199
7.3.6;Acknowledgments;199
7.3.7;References;199
7.4;13 Glycoprofiling by DNA Sequencer-Aided Fluorophore-Assisted Carbohydrate Electrophoresis;203
7.4.1;1. Introduction;203
7.4.2;2. Glycosylation in Diagnosis: Current Use and Limitations;204
7.4.3;3. Glycosylation Analysis: DNA Sequencer-Aided Fluorophore-Assisted Carbohydrate Electrophoresis (DSA-FACE);205
7.4.4;4. DSA-FACE in Glycodiagnosis;206
7.4.5;5. Conclusions;210
7.4.6;References;210
7.5;14 High-Throughput Carbohydrate Microarray Technology;213
7.5.1;1. Introduction;213
7.5.2;2. Theoretical Considerations in Developing Carbohydrate Microarrays;214
7.5.3;3. Experimental Approach to Establishment of High-Throughput Carbohydrate Microarrays;215
7.5.4;4. Practical Platform of Carbohydrate Microarrays;216
7.5.5;5. Promising Areas for Exploring Carbohydrate Microarray Technology;218
7.5.6;References;222
8;PART IV EMERGING TECHNOLOGIES IN DIAGNOSTICS;224
8.1;16 “Lab-on-a-Chip” Devices for Cellular Arrays Based on Dielectrophoresis;241
8.1.1;1. Introduction;241
8.1.2;2. Theory Supporting DEP-Based Levitation and Movement of Biological and Physical Objects;243
8.1.3;3. Description of Lab-on-a-Chip Platforms for Cell Manipulation;244
8.1.4;4. Biological Applications of Lab-on-a-Chip Platforms;247
8.1.5;5. Conclusions;249
8.1.6;Acknowledgments;250
8.1.7;References;251
8.2;17 Genetic Disorders and Approaches to Their Prevention;254
8.2.1;1. Introduction;254
8.2.2;2. Genetic Diseases and Their Patterns of Inheritance;256
8.2.3;3. Approach for Analysing Genetic Disorders;259
8.2.4;4. Illustrative Examples of Individual Diseases;264
8.2.5;5. Future Thoughts;269
8.2.6;References;269
9;Index;271

Leseproben

5 Reduction in Sample Heterogeneity Leads to Increased Microarray Sensitivity (S. 61-62)

Amanda J. Williams, Kevin W. Hagan, Steve G. Culp, Amy Medd, Ladislav Mrzljak, Tom R. Defay, and Michael A. Mallamaci

Summary

DNA microarrays are most useful for pharmacogenomic discovery when a clear relationship can be made between gene expression in a targeted tissue and drug affect. Unfortunately, the true target of the drug affect is most often a subpopulation of cells within the tissue. Thus, when heterogeneous tissues containing many diverse cell types are profiled, expression changes, especially in low-abundance genes, are often obscured. In this chapter, two examples are presented where a cellular subpopulation is isolated from its complex background, with minimal cellular activation, resulting in increased microarray detection sensitivity. In the first example, erythrocytes (the most abundant cell population in blood) were removed or whole blood was immediately stabilized before RNA isolation. The removal of erythrocytes resulted in a twofold increase in the detectability of leukocyte-specific genes. During the study, protocols for RNA isolation from rat blood were validated. In addition, a list of 91 genes was generated whose expression correlated with the level of erythrocyte contamination in rat blood. In the second example, laser microbeam microdissection (LMM) was used to isolate a specific neuronal population. Our LMM amplification technique was first validated for reproducibility. After validation, data obtained from pooled neurons, cortical tissue slices, and whole brain were compared. Overall, 20% of the transcripts detected in whole brain and 13% of the transcripts detected in tissue slices were not detected in LMM neurons. Many of these transcripts were specific to neuroglial support cells or noncortical neurons, verifying that our LMM technique captured only the neurons of interest. Conversely, 10% of the transcripts detected in LMM neurons were not detected in cortical tissue slices, and 14% were not detected in whole brain. As expected, these transcripts were neuronal specific and were presumably still present in the broader tissue regions. However, in neurons isolated by LMM, the effective concentration of these previously undetectable transcripts was raised because of the elimination of competing signal noise from extraneous cell types, reinforcing the claim that microdissection can be used to increase microarray sensitivity.

Key Words: Brain, blood, detection sensitivity, DNA microarray, erythrocyte, laser capture microdissection, leukocyte, neuron.

1. Introduction

Gene expression profiling by using DNA microarrays has become an integral part of basic and applied research in both the academic and industrial scientific communities. This technology has been successfully used for many distinct applications ranging from disease classification and functional genomics to pharmacogenomics biomarker identification and single-nucleotide polymorphism analysis (1). Because of the relatively large quantity of starting material necessary for microarray use, initial studies primarily focused on animal disease models or cultured cells. Profiling experiments on human tissue required macrodissected regions to generate sufficient starting material. Unfortunately, owing to the heterogeneous mixture of cells present in complex tissues, it is difficult for such studies to detect genes that are expressed at low levels or within rare subpopulations. When genes are detectable, it is difficult to compare the relative levels of gene expression between two or more samples. Part of the problem is the extraneous signal noise contributed by cell types that do not express the genes of interest.

In addition, variability in the cellular composition of each sample can obscure changes that are occurring within one cell type. Recent technical advances in small-scale RNA isolation and amplification, laser microdissection, and RNA stabilization have now made it possible to stratify, with minimal cellular activation, specific cell populations within complex tissue samples. Thus, the expression profiles of cellular subpopulations previously lost in the transcriptional complexity of heterogeneous tissues can now be uncovered. In this chapter, we provide two examples in which the reduction of biological heterogeneity within a sample is accompanied by an increase in gene expression detectability by using microarrays. In the first example, the advantages and disadvantages of reducing cellular heterogeneity in whole blood are explored.