Genomic-Scale Gene Expression Analysis: Advancing from DNA to Disease

 

1. INTRODUCTION: The Need for Genomic-Scale Gene Expression Analysis

Given the explosion in genomic information, the “one-gene-at-a-time” approach to gene expression analysis is wholly inadequate. Rather, large-scale methods for analyzing gene expression patterns are needed.

 

2. GENE EXPRESSION ANALYSIS: An Overview

Proteomics is one of the best ways to analyze gene expression, but this approach is currently cumbersome and expensive. However, another approach—examining mRNA levels—offers high-throughput potential and can be performed through technology that is accessible to nearly every researcher. Even so, this method has its drawbacks.

 

2.1 What Gene Expression Analysis Can Tell Us

 

2.2 Key Means of Gene Expression Analysis

 

3. SCIENTIFIC OVERVIEW: Technologies for Gene Expression Analysis

In addition to hybridization arrays, several other alternatives for gene expression analysis are available (e.g., differential display, Serial Analysis of Gene Expression [SAGE] and quantitative reverse transcription-polymerase chain reaction [QRT-PCR]). Although proteomics is the ideal approach for analyzing gene function, this technology is currently inaccessible to most institutions, and for the next five to ten years, most researchers will prefer to use DNA hybridization arrays. 

 

3.1 Non-Hybridization-Array-Based Technologies for Assessing Gene Expression

 

            Serial Analysis of Gene Expression

            Differential Display

            Subtraction Cloning

            Kinetic Quantitative Reverse Transcription-Polymerase Chain Reaction

            Proteomics

 

3.2 DNA Hybridization-Array Technologies

 

4. HYBRIDIZATION ARRAYS: Current and Emerging Technologies

In their initial efforts to use DNA hybridization arrays to study gene function, most researchers have opted to employ premanufactured commercial arrays. The key types of premanufactured arrays are macroarrays, microarrays, oligonucleotide arrays, and microelectronic arrays. Although most organizations that use arrays are considering the possibility of designing custom arrays, the expense and long lead times required for in-house production of arrays make it much easier, in the short term, to use ready-made arrays.

 

            4.1 Premanufactured Arrays: Hybridization-Array Types

 

                        Macroarrays

                        Microarrays

                        High-Density Oligonucleotide Arrays/GeneChips

                        Microelectronic Arrays

           

            4.2 Custom Arrays

                       

                        Robotic Workstations for Array Construction

                        Clone Libraries

 

5. DETECTION: Imaging Instruments

Phosphorimagers are used to quantify the radioactive signal (an indication of the level of hybridization) produced by macroarrays. Fluorescence detection is used with microarrays, high-density oligonucleotide arrays, and microelectronic chips. The main advantage of fluorescence-based approaches over radioactivity is that they allow both experimental and control samples to be hybridized to the same array.

 

            5.1 Phosphorimagers

 

            5.2 Fluorescence Scanners

 

            5.3 Dye/Probe Advances

 

6. USE OF HYBRIDIZATION ARRAYS IN LEADING/POTENTIAL APPLICATIONS: Examples and Limitations

Arrays will be a critical technology for basic research as the emphasis in genomics moves increasingly from sequencing to analysis of genes’ functions. In particular, they are providing important insights into mechanisms involved in cell-cycle control, cancer, and neurobiology. Arrays are also becoming crucial tools for clinical profiling, drug discovery, and toxicology studies.

 

            6.1 Basic Science

 

                        Cell-Cycle Control/Yeast Experiments

                        Cancer

                        Neurobiology

 

            6.2 Clinical Profiling

 

            6.3 Drug Discovery

 

            6.4 Toxicology

 

            6.5 Other

 

7. BIOINFORMATICS: Mining Information from Gene Expression Studies

Bioinformatic tools are the key to mining data from gene-expression-analysis experiments. The various software programs available for bioinformatic analysis can be categorized according to their abilities: image analysis, data analysis, and data integration (relational databases).

 

            7.1 Image Analysis

 

            7.2 Data Analysis

 

            7.3 Data Integration/Relational Databases

 

8. BUSINESS AND STRATEGIC OUTLOOK: Great Promise Amid Challenges

The gene-expression-analysis field is going through a period of dramatic innovation, and tremendous opportunities lie ahead for companies in this field.  However, technical hurdles must be overcome before arrays achieve routine, widespread use in research and clinical settings. For example, reliability and ease of use will have to improve, and costs will have to decrease.

 

            8.1 Overview

 

8.2 Dealmaking, and Key Dealmakers, in Gene Expression Monitoring

 

            8.3 Expert Commentaries

 

APPENDIX A. Profiles of Selected Gene-Expression-Analysis Companies

APPENDIX B. List of Gene-Expression-Analysis Companies

 

GLOSSARY

 

COMPANY INDEX