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Chapter 16 Gene Expression Analysis in Diabetes Research Peter White and Klaus H. Kaestner Summary Global gene expression profiling through the use of microarray technology is among the most powerful molecular biological techniques available to diabetes researchers today. In this chapter, we outline how to appropriately perform a microarray experiment using pancreatic islets or total pancreas, based upon over a decade of experience in our laboratory. Through the utilization of careful experimental designs, large numbers of biological replicates, production of high-quality starting material, optimized protocols for hybridization, and sophisticated tools for data processing and statistical analysis, the full potential of high-quality expression profiling can be realized. Key words: Microarray, RNA extraction, Gene expression profiling, Diabetes, Islet, b-Cell
1. Introduction The incidence of both type I and II diabetes continues to increase globally and the effectiveness of pharmaceutical treatments for the disease remains limited. A significant proportion of basic research into the disease has focused on understanding the transcriptional programs that regulate the development and function of the endocrine pancreas. Of the numerous research tools and approaches that have been utilized to address these fundamental questions of development, global gene expression profiling has become one of the most powerful techniques employed to date. The use of microarray technology opened the door for researchers to rapidly assess the transcriptional profile for thousands of genes. Through the application of these techniques, we have gained significant insights into the transcriptional programs and
C. Stocker (ed.), Type 2 Diabetes, Methods in Molecular Biology, vol. 560 DOI 10.1007/978-1-59745-448-3_16, © Humana Press, a part of Springer Science + Business Media, LLC 2009
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signaling mechanisms that control the differentiation of endocrine precursors to mature hormone-expressing endocrine cells of the islet (reviewed in (1–5)). Over the last decade, the methodology involved in expression profiling has become more standardized and the technical variation within the assay has significantly diminished, resulting in highly reproducible gene expression measurements across multiple platforms (6). However, with the power of microarray techniques come significant pitfalls. Initially, the high cost of arrays resulted in inappropriate application of the technology, with studies being conducted and conclusions being drawn from the use of only one or two biological replicates. Even though the price of commercial arrays has now fallen to under $200, this inappropriate practice is still commonplace, resulting in the production of low-quality data sets and consequently wasting time and resources following up on potentially wrong hypotheses. In our hands, the use of four or more true biological replicates per experiment has resulted in the production of numerous highquality data sets (3, 7–11). One of the biggest challenges in conducting genomics research in the diabetes field is the production of high-quality RNA. Microarray analysis is exquisitely sensitive to RNA quality, and requires RNA of very high integrity. The pancreas is the major source of ribonucleases (RNase) in the body and therefore it is very difficult to isolate RNA from this tissue. Furthermore, expression profiling using the whole pancreas may be inappropriate if the aim is to investigate gene expression profiles at the level of the endocrine islet. With islets accoun-ting for less than 5% of total pancreas mass, expression profiling using RNA from the whole pancreas will predominantly provide information on the exocrine pancreas and a small subset of the most highly expressed endocrine genes. To overcome this limitation, islets must first be purified from the pancreas using collagenase digestion in Hanks buffer, followed by separation of islets from exocrine tissue in a Ficoll gradient (12). Finally, once islets have been purified, they must be purity matched before microarray analysis can begin. We have developed a method of purity matching using quantitative real-time reverse transcription polymerase chain reaction (qRT-PCR) which allows the investigator to hybridize islet samples of similar purity by determining the ratio of exocrine and endocrine markers (3). This additional step of purity matching allows the investigator to remove the confounding factors contributed by even slight differences in purity between islet preparations. Having designed the experiment and carefully prepared the samples, the next step is to label and hybridize. Numerous commercial kits are available for this purpose, but in this chapter,
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we focus on an indirect labeling method that we have significantly optimized and in our hands consistently produces better results than the commercial kits we have tested (13). A choice must be made at this point between using a single- or dual-color approach. In the one-color procedure, a single sample is fluorescently labeled and hybridized to each array, whereas in a twocolor approach, samples are labeled in pairs (e.g., test and control) with different fluorophores and hybridized together on a single microarray. There are advantages and disadvantages to both approaches, but we have found that the two-color procedure, which allows for direct comparisons of test and control samples, reduces variability and results in greater sensitivity and accuracy in determining differential gene expression. There are many different array-based platforms for assessing transcript abundance including spotted cDNA arrays (e.g., the PancChips (14)), spotted long oligo arrays, in-situ synthesized short (e.g., Affymetrix GeneChips) or long (e.g., Agilent Technologies Whole Genome Arrays) oligo arrays, and long oligo bead arrays (e.g., Illumina BeadChips). The mouse and human PancChip microarrays developed by the NIDDK-funded Beta Cell Biology Consortium have proved to be invaluable tools for diabetes research and will be the array platform of choice for this chapter. The content of these arrays was chosen based upon transcripts known to play important roles in pathways relating to pancreatic development and glucose homeostasis and for their expression in various stages of pancreatic development. These arrays contain many novel and rare transcripts known to be expressed in the pancreas and yet not available on many commercial platforms. Moreover, these arrays provide a low-cost alternative to commercial platforms and are distributed at cost through the Beta Cell Biology Consortium (http://www.betacell.org/microarrays). The final stumbling blocks for many researchers performing gene expression profiling experiments lie in the data analysis and interpretation steps. Numerous commercial and public domain applications exist for the purpose of data preprocessing and normalization [for detailed reviews see (15, 16)]. Although we provide some recommendations regarding the informatics tools that can be employed, for best results it is our recommendation that you consult a qualified bioinformatics expert for assistance. Once the data has been processed and statistical analyses have been conducted, interpretation of the data remains the final step. There are many resources available for this purpose, and in the following chapter, Dr. Dunbar provides a review of several of these tools. Finally, once a hypothesis has been drawn, it is back to the bench for experimental validation and verification of the results, leading ultimately to discovery and publication of the new findings.
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2. Materials 2.1. Sample Collection
1. Collect samples for expression analysis based upon the optimal experimental design to answer the biological question of interest.
2.2. Preparation of High-Quality RNA
1. Choose the most appripriate kit for your sample. Consult the Qiagen web site for more specifics, or call Qiagen technical support (1 (800) 362-7737): (a) Qiagen RNeasy® Mini Kit (12): Qiagen (Cat. No. 74104). Yields
