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Originally published In Press as doi:10.1074/mcp.R500011-MCP200 on March 1, 2005.
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Molecular & Cellular Proteomics 4:458-474, 2005.
© 2005 by The American Society for Biochemistry and Molecular Biology, Inc.

Review

Integrating Global Proteomic and Genomic Expression Profiles Generated from Islet {alpha} Cells

Opportunities and Challenges to Deriving Reliable Biological Inferences*,S

Marlena Maziarz{ddagger},§,¶, Clement Chung||,¶, Daniel J. Drucker{ddagger},§ and Andrew Emili||,**,{ddagger}{ddagger}

From the {ddagger} Banting and Best Diabtetes Centre, University of Toronto, Toronto, Ontario, Canada; § Department of Medicine, Toronto General Hospital, Toronto, Ontario, Canada; and || Program in Proteomics and Bioinformatics, Banting and Best Department of Medical Research, University of Toronto, Toronto, Ontario M5G 1L6, Canada

Systematic profiling of expressed gene products represents a promising research strategy for elucidating the molecular phenotypes of islet cells. To this end, we have combined complementary genomic and proteomic methods to better assess the molecular composition of murine pancreatic islet glucagon-producing {alpha}TC-1 cells as a model system, with the expectation of bypassing limitations inherent to either technology alone. Gene expression was measured with an Affymetrix MG_U74Av2 oligonucleotide array, while protein expression was examined by performing high-resolution gel-free shotgun MS/MS on a nuclear-enriched cell extract. Both analyses were carried out in triplicate to control for experimental variability. Using a stringent detection p value cutoff of 0.04, 48% of all potential mRNA transcripts were predicted to be expressed (probes classified as present in at least two of three replicates), while 1,651 proteins were identified with high-confidence using rigorous database searching. Although 762 of 888 cross-referenced cognate mRNA-protein pairs were jointly detected by both platforms, a sizeable number (126) of gene products was detected exclusively by MS alone. Conversely, marginal protein identifications often had convincing microarray support. Based on these findings, we present an operational framework for both interpreting and integrating dual genomic and proteomic datasets so as to obtain a more reliable perspective into islet {alpha} cell function.

{ddagger}{ddagger} To whom correspondence should be addressed: CH Best Institute, Room 402, 112 College Street, Toronto, Ontario, Canada M5G 1L6. Tel.: 416-946-7281; Fax: 416-978-8528; E-mail: andrew.emili{at}utoronto.ca


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