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Series GSE4175

Query DataSets for GSE4175

Status

Public on Feb 25, 2006

Title

Genome-Scale Identification of Membrane-Associated Human mRNAs

Organism

Homo sapiens

Experiment type

Expression profiling by array

Summary

The subcellular localization of proteins is critical to their biological roles. Moreover, whether a protein is membrane-bound, secreted, or intracellular affects the usefulness of, and the strategies for, using a protein as a diagnostic marker or a target for therapy. We employed a rapid and efficient experimental approach to classify thousands of human gene products as either membrane-associated/secreted (MS) or cytosolic/nuclear (CN). Using subcellular fractionation methods, we separated mRNAs associated with membranes from those associated with the soluble cytosolic fraction and analyzed these two pools by comparative hybridization to DNA microarrays. Analysis of 11 different human cell lines, representing lymphoid, myeloid, breast, ovarian, hepatic, colon, and prostate tissues, identified more than 5,000 previously uncharacterized MS and more than 6,400 putative CN genes at high confidence levels. The experimentally determined localizations correlated well with in silico predictions of signal peptides and transmembrane domains, but also significantly increased the number of human genes that could be cataloged as encoding either MS or CN proteins. Using gene expression data from a variety of primary human malignancies and normal tissues, we rationally identified hundreds of MS gene products that are significantly overexpressed in tumors compared to normal tissues and thus represent candidates for serum diagnostic tests or monoclonal antibody-based therapies. Finally, we used the catalog of CN gene products to generate sets of candidate markers of organ-specific tissue injury. The large-scale annotation of subcellular localization reported here will serve as a reference database and will aid in the rational design of diagnostic tests and molecular therapies for diverse diseases.
Set of arrays organized by shared biological context, such as organism, tumors types, processes, etc.
Keywords: Logical Set

Overall design

Using regression correlation

Contributor(s)

Diehn M

Citation(s)

16415983

Submission date

Feb 03, 2006

Last update date

Jul 29, 2013

Organization

Stanford Microarray Database (SMD)