Inference engines for phospho-signaling networks, disease mechanism, and clinical outcome at the single cell level

PL07-01

Intracellular assays of signaling systems has been limited by an inability to correlate functional subsets of cells in complex populations based on active kinase states or other nodal signaling junctions. Such correlations could be important to distinguish changes in signaling status that arise in rare cell subsets during functional activation or in disease manifestation. We have demonstrated the ability to simultaneously detect activated kinases and phosphoproteins in simultaneous pathways in subpopulations of complex cell populations by multi-parameter flow cytometric analysis. We have applied this technology to the study of normal human cell populations as well as several disease states including Acute Myelogenous Leukemia, and Follicular Lymphoma, colon cancer and infiltrating immune cells of cancers among others. The tremendous amounts of correlated data generated via phospho-flow allows amalgamation that automates signaling network determination using Bayesian analysis (and a unique computational approach using a new electronic architecture for a "statistics supercomputer"). Bringing these together, we have initiated the generation of a comprehensive network topology map of signaling in all primary immune subsets. Thus, we are bringing single cell analysis of multiple kinase pathways together with novel computational and electronic approaches to increase the depth of drug screening to directly reach screening in primary cells.