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 a-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 tandem mass spectrometry on a nuclear-enriched cell extract. Both analyses were carried out in triplicate to control for experimental variability. Using a stringent detection P-value cut-off of 0.04, 48% of all potential mRNA transcripts were predicted to be expressed (probes classified as present in at least 2 of 3 replicates), while 1651 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 mass spectrometry 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.