Gene Expression Profiling of Cultured Human Islet Preparations

Abstract

The expression of functional and regulatory genes by islet cells is a key determinant for the success of islet transplantation. The aim of this study is twofold: first, to characterize the cluster of genes expressed in human islet isolations; and second, to validate the capability of gene array technology to assess with accuracy the expression of various transcripts. RNA from isolated islet preparations obtained from three independent donors was converted to cDNA and then transcribed to cRNA. Individual cRNA preparations were then hybridized to U133A microarrays carrying approximately 23,000 genes, and analyzed using GeneSpring (SiliconGenetics, Redwood City, CA) software. Real-time reverse transcription-polymerase chain reaction was performed to validate results obtained by microarray analysis. Microarray analysis identified the expression of about 7,000 genes transcribed in cultured human islet preparations. Enzymes represented the most abundant class of genes identified, followed by nuclear binding proteins, signal transduction molecules, transport proteins, and growth factor receptors and their ligands. Real-time polymerase chain reaction confirmed the identification of various islet-specific genes detected by microarray analysis, but also showed that such genes as pancreatic duodenal homeobox 1 protein and glucagon-like peptide 1 receptor, which were not detected by gene array, can be readily identified and quantified. In addition, gene array produced a suboptimal quantification of genes expressed in large amounts by islet cells. Indeed, the abundance of mRNA for insulin when compared with the level of somatostatin mRNA was not as different as one would have predicated based on the classic knowledge of islet physiology. Gene array analysis appears to be a valuable tool to obtain preliminary information of genes expressed by a given tissue. The expression levels of transcripts expressed in very low or very high quantities need to be confirmed by an independent technique.

Discovery of Novel Glucose-Regulated Proteins in Isolated Human Pancreatic Islets Using LC–MS/MS-Based Proteomics

“…Previous studies have defined comprehensive islet gene [6, 7, 9, 12] and protein [5, 8, 16, 17] expression patterns in both murine and human samples, yet the effect of elevated glucose on islet protein expression has, to date, been thoroughly examined only in mouse models [11, 13]. In the present study, a semi-quantitative proteomics approach was used to determine the human islet proteome under basal (5 mM) or elevated (15 mM) glucose conditions.…”

“…The advent of global “-omics” technologies has enabled researchers to address this issue on a much larger scale than traditional approaches and has resulted in a number of studies examining pancreatic islet mRNA and protein content. Most of these studies have investigated basal -omics expression; − however, the effect of elevated glucose on rodent beta-cells has also been examined. − Two-dimensional (2-D) gel-based proteomics approaches have been used to identify proteins altered in response to high glucose exposure in mouse islets and the rat insulin-producing INS-1E cell line . These efforts identified a number of significant proteins illustrative of known biology and suggestive of new perspectives but were limited by an inherent modest dynamic range.…”

Integrating Global Proteomic and Genomic Expression Profiles Generated from Islet α Cells

“…Other related algorithms are continuously being developed to improve the overall sensitivity and specificity of mRNA detection, especially for lower-abundance transcripts that are often presumed to be of particular biological relevance. 2 For quantitative multisample comparisons, microarray data is usually normalized to minimize technical variability, such as variation in manufacture and processing of the arrays, scanning (and scanner calibration), differences in detection efficiency between the fluorescent dyes, and systematic spatial biases in measured expression levels. Normalization also aims to minimize unwanted variability due to unequal quantities of starting RNA, variable sample preparation, differences in labeling and hybridization efficiencies, the time of day the experiment is performed, and even technician performing the experiment (20,21).…”

“…Although glucagon-producing ␣ cells are the second most abundant islet cell type within the pancreatic islets of Langerhans, a majority of studies aimed at identifying islet gene products have used either a mixture of islet cells, or purified ␤ cells or ␤ cell lines (1)(2)(3)(4)(5). The current paucity of data on gene and protein expression in islet ␣ cells is due in part to the almost insurmountable technical challenges in isolating sufficient numbers of pure primary ␣ cells for exhaustive molecular studies.…”

“…While several laboratories have reported investigations of whole islet or islet ␤ cell gene expression profiles (1,2), only limited information is currently available concerning gene expression and proteomic profiles linked to the specification of the differentiated phenotypes associated with ␣ cells. To this end, we have initiated extensive molecular analyses of gene expression patterns in mature ␣ cells using an islet ␣ cell line as a model system, with the goal of deciphering key biochemical, regulatory, and cell biological properties.…”