The LINCS program seeks to derive molecular signatures resultant from cellular perturbation. Cellular signaling through modulation of protein phosphorylation is an important component of the cellular response to stimuli, and is complementary to transcriptional profiling. This proposal details the development of a high information content multiplex mass spectrometry-based assay to query serine and threonine signaling pathways. A reductionist approach, whereby the natural correlations of multiple phosphorylation sites under disparate cellular conditions are elucidated, will be used to create efficiencies in representing the cellular state. To achieve this, a modest number of quantitative global phosphoproteomic profiles under varying conditions will be obtained using mass spectrometry (MS). From these data, a limited number of representative phosphopeptides will be extracted - the reduced representation set - whose levels and stoichiometries will serve to capture signatures in response to perturbation. Subsequently, the necessary reagents will be procured to allow the design of targeted mass spectrometry assays (multiple reaction monitoring, or MRM-MS) to quantify the peptides in the reduced representation set in a multiplex (~100-plex) fashion. Variability, reproducibility, limits of detection and quantification, and final assay cost will be measured. Multi-laboratory implementation of the assay will also be demonstrated. In parallel to these efforts, the established open access proteomics software Skyline will be extended to provide a standard means of housing important assay parameters, processing data acquired from reduced representation set MRM-MS assays, and integrating QA/QC metrics to determine assay performance. Skyline will also be adapted to allow for inter-laboratory exchange of MRM-MS methods and data, and we will develop a public database that will integrate assay results from multiple LINCS laboratories. The final product will be a high information content multiplex MS assay called the """"""""Accelerated Protein Signaling Signature"""""""" and the informatics infrastructure to support a community resource that contains all of the necessary information and tools for its practical implementation across multiple proteomics laboratories in a collaborative manner.
Development of these experiments will allow us to determine how human cells respond to drug treatment for a fraction of the time and cost of comparable methods. The information provided from these experiments will give us early indicators of potential new therapies and help us differentiate drugs that have desired effects from those that might have undesirable side effects.
