Bioarrays (eBook)

Foreword 6
Preface 8
Contents 11
Contributors 13
PART I BIOARRAY TECHNOLOGY PLATFORMS 17
1 Investigation of Tumor Metastasis by Using cDNA Microarrays 19
1. Introduction 19
2. Experimental Outline 22
3. Future of Microarray Analysis and Conclusions 29
References 30
2 From Tissue Samples to Tumor Markers 33
1. Introduction 33
2. Technical Issues: Array Platform, Samples, and Bioinformatics Tools 34
3. Validation of Candidate Genes Through Real-Time Reverse Transcription-PCR and Tissue Microarray 39
4. Perspectives 41
Acknowledgments 41
References 41
3 Experimental Design for Gene Expression Analysis 45
1. cDNA and DNA Microarrays 45
2. Proteomics 53
3. Conclusions 57
Acknowledgments 57
References 57
4 From Microarrays to Gene Networks 61
1. Introduction 61
2. Background 62
3. Biological Network Models 69
4. Analysis of Gene Expression Data in a Networks Setting 70
5. Conclusions 71
References 72
PART II BIOMARKERS AND CLINICAL GENOMICS 75
5 Reduction in Sample Heterogeneity Leads to Increased Microarray Sensitivity 77
1. Introduction 77
2. Materials and Methods 80
3. Results 81
References 94
6 Genomics to Identify Biomarkers of Normal Brain Aging 99
1. Introduction 99
2. Molecular Characterization of Normal Aging 100
3. Aging Is a Continuous and Specific Process Throughout Adult Life 104
4. Summary and Conclusions 105
Acknowledgments 107
References 107
7 Gene Expression Profiling for Biomarker Discovery 110
1. Bioarray and Clinical Applications 110
2. Microarrays for Molecular Biomarker Discovery 113
3. Prospects for Gene Expression Profiling in Clinical Use 120
References 120
8 Array-Based Comparative Genomic Hybridization 122
1. Introduction 122
2. Historical Aspects of CGH 123
3. aCGH and Cancer 124
4. aCGH and Tuberculosis 129
References 133
9 Regional Specialization of Endothelial Cells as Revealed by Genomic Analysis 137
1. Introduction 137
2. Overview of Gene Expression Patterns 138
3. Gene Expression Patterns Between Macrovascular and Microvascular ECs 139
4. Gene Expression Pattern Differences Between Arterial and Venous ECs 142
5. Hey2 Activates Expression of Arterial-Specific Genes 144
6. Genes Differentially Expressed in ECs from Different Tissues 144
7. Conclusions 145
Acknowledgments 146
References 146
PART III BIOMARKER IDENTIFICATION BY USING CLINICAL PROTEOMICS AND GLYCOMICS 149
10 Identification of Target Antigens in CNS Inflammation by Protein Array Technique 151
1. Introduction 151
2. Multiple Sclerosis 152
3. Dissection of Antibody Specificity in MS by Using Human Brain cDNA Protein Macroarrays 155
4. Conclusions 159
Acknowledgments 159
References 159
11 Differential Protein Expression, Protein Profiles of Human Gliomas, and Clinical Implications 163
1. Introduction 163
2. Background, Types, Origin of Gliomas, and Clinical Issues 164
3. Molecular Genetic Changes and Molecular Markers and Importance of Molecular Profiles 168
4. Experimental System: Glioma Cell Lines Vs Primary Tumors 169
5. DNA Microarray Analysis and Transcript Profiles 170
6. Proteomics Approaches and Protein Profiles 171
7. Future Perspective 182
Acknowdgments 183
References 184
12 Antibody-Based Microarrays 188
1. Background 188
2. Antibody Microarrays: A Short Introduction 189
3. Quest for Developing Antibody Microarrys: Our Approach 190
4. Antibody Microarray Applications: Current and Future 197
5. Summary and Conclusions 199
Acknowledgments 199
References 199
13 Glycoprofiling by DNA Sequencer-Aided Fluorophore-Assisted Carbohydrate Electrophoresis 203
1. Introduction 203
2. Glycosylation in Diagnosis: Current Use and Limitations 204
3. Glycosylation Analysis: DNA Sequencer-Aided Fluorophore-Assisted Carbohydrate Electrophoresis (DSA-FACE) 205
4. DSA-FACE in Glycodiagnosis 206
5. Conclusions 210
References 210
14 High-Throughput Carbohydrate Microarray Technology 213
1. Introduction 213
2. Theoretical Considerations in Developing Carbohydrate Microarrays 214
3. Experimental Approach to Establishment of High-Throughput Carbohydrate Microarrays 215
4. Practical Platform of Carbohydrate Microarrays 216
5. Promising Areas for Exploring Carbohydrate Microarray Technology 218
References 222
PART IV EMERGING TECHNOLOGIES IN DIAGNOSTICS 224
16 “Lab-on-a-Chip” Devices for Cellular Arrays Based on Dielectrophoresis 241
1. Introduction 241
2. Theory Supporting DEP-Based Levitation and Movement of Biological and Physical Objects 243
3. Description of Lab-on-a-Chip Platforms for Cell Manipulation 244
4. Biological Applications of Lab-on-a-Chip Platforms 247
5. Conclusions 249
Acknowledgments 250
References 251
17 Genetic Disorders and Approaches to Their Prevention 254
1. Introduction 254
2. Genetic Diseases and Their Patterns of Inheritance 256
3. Approach for Analysing Genetic Disorders 259
4. Illustrative Examples of Individual Diseases 264
5. Future Thoughts 269
References 269
Index 271

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.