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GEN News Highlights : Jul 19, 2012

Absolute Gene Expression Profiling

Scientists at Stanford University School of Medicine are making publicly available an analytical platform for microarray-based gene expression profiling that they claim will allow researchers to determine the absolute expression level of any given gene, rather than just compare its expression level relative to that of other genes evaluated in the same experiment. The web-based Gene Expression Commons (GEC) platform uses a huge set (over 10,000) of publicly available microarray data as a common reference, so that statistical attributes of each probeset, such as the dynamic range and threshold between low and high expression, can be determined through meta-analysis. Essentially, users can now map individual sample data against the common reference to enable empirical estimation of the absolute expression level of the designated gene.

Using current technologies, probesets used for microarray-based profiling exhibit varied efficiencies in terms of how strongly they bind to their target sequences. This, explains Irving L Weissman, M.D., Jun Seita, Ph.D., and colleagues, means that array-based global gene expression analyses are limited to profiling relative differences in expression of the same gene (evaluated using the same probeset) between samples, rather than determining the absolute level of gene expression in a particular sample.

The GEC platform is designed to overcome this drawback, because it is large enough to enable a meta-analysis to be carried out to calculate the distribution of data, dynamic-range, and a threshold to distinguish high expression from low expression for each probeset. Essentially, it takes raw data from individual microarrays and normalizes them against the common reference, giving an instant readout of absolute expression levels.

Reporting in PLoS One, the researchers established a gene expression map of 39 different hematopoietic cell types from mouse bone marrow, spleen, and thymus. They carried out gene expression analysis for each cell type, and incorporated the data into the GEC, to generate a specific mouse hemopoiesis model. Researchers can now, they claim, search and find the absolute gene expression profile of any gene on Affymetrix Mouse Genome 430 2.0 microarray platform simply by searching for the NCBI gene symbol, gene name, keyword, or probeset ID. Gene expression microarray data of publicly available human hematopoietic populations are also available in the GEC.

 “For each gene, we get about 30,000 values,” explains co-author Debashis Sahoo, Ph.D., at Stanford’s Institute for Stem Cell Biology and Regenerative Medicine. "This gives us an idea of the low and high range of expression in many cell types. We can get a sense of the range of the values, and then find where any individual sample fits within this continuum.”

Importantly, GEC has been designed not just as a search engine for existing gene expression data, but as an open platform to profile absolute gene expression of any microarray data. Users can submit gene expression microarray data files of their own, or publish microarray data from public repositories. They can also design their own models by combining ‘populations’ to represent a biological context of interest. Alternatively, its possible to query any cell type within a model to obtain a list of genes expressed exclusively in that cell type, or that are expressed with a defined subset of other cell types.

“It is so simple that a researcher can just type in the name of a gene and, within seconds, see the absolute level of the expression of that gene in ever cell type in a panel,” states professor Weissman, who is director of Stanford’s Institute for Stem Cell Biology and Regenerative Medicine. “We believe that this program will rapidly become the most important tool for discovery in a number of fields, including stem cells, cancer, and regenerative medicine.”