The Deeper Correlations:  Single Cell Measures of Kinase Signaling for

Mechanistic and Clinical Analyses

Garry P. Nolan, Ph.D.

Baxter Laboratory for Genetic Pharmacology

Dept. of Microbiology and Immunology

Stanford School of Medicine

Stanford, CA 94117

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. Simultaneous detection of activated kinases and phosphoproteins in simultaneous pathways in subpopulations of complex cell populations by multi-parameter flow cytometric analysis allows identification of signaling cascades for disease states by ordering of kinase activation and phosphoprotein status in signaling hierarchies.  Importantly, we demonstrate that ordering of these activations requires multiple interrogations of cells, and that the networks discovered are reflective of deeper correlations. Using Bayesian Network analysis (a form of machine learning) one can infer pathway connectivity in an automated fashion, allowing for high throughput derivations of signaling system networks graphs in PRIMARY CELLS.  The approach has powerful applications in mechanistic understanding, drug screening, and patient stratification for prediction of disease outcome in cancer, autoimmunity, infection, based on signaling network status.

(1) Irish J.M., Hovland R., Krutzik P.O., Perez O.D., Bruserud O., Gjertsen B.T., Nolan G.P. (2004) Single Cell Profiling of Potentiated Phospho-Protein Networks in Cancer Cells. Cell. 118:217-228.

(2) Sachs K., Perez O., Pe'er D., Lauffenburger D.A and Nolan G.P. 2005. Causal protein-signaling networks derived from multiparameter single-cell data. Science. 308:523-9.