SINGLE CELL ASSAYS TO UNDERSTAND SIGNALING NETWORKS

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DESCRIPTION: (Applicant's Abstract) This proposal has two overall objectives: The first objective is to develop and apply a new research strategy in functional genomics by combining a genome wide view of all signaling proteins with single cell signaling assays in order to break down the fundamental mechanisms of cellular signal transduction networks. The second objective is to give the candidate first-rate training in the fields of Genomics, Bioinformatics, and Signal Transduction under the guidance of Drs. David Botstein and Tobias Meyer. The candidate currently has significant experience in the development of biological instrumentation and would like to use the K25 Career Award to become a tenure track faculty investigating signal transduction networks from a functional genomics perspective. There is more and more evidence for the existence of cross-talk and feedback in signaling pathways, particularly in those involved in growth and differentiation where several thousand gene products may be involved. Signaling pathways can no longer be thought of as independent, linear sets of events, but rather must be understood as a dynamic network. While there are presently excellent assays to establish the identity of different players in the network - for example, by yeast two-hybrid screens - or to obtain final readouts by using microarrays, there is a lack of tools with which to study networks dynamically and to understand how the players interact in the context of different receptor stimuli. The candidate has recently co-developed an Evanescent-wave Single Cell Array Macroscope (E-SCAM) and has used it to show that timecourses of protein translocation and activation can be measured in thousands of single cells simultaneously. By continuing to develop this E-SCAM system for monitoring multiple signaling events over time, along with methodology to quantitatively perturb such a network, the proposed work will be able to establish quantitative kinetic relationships between signaling network parameters and begin to generate models of how cellular signal transduction networks function. PDGF-stimulation of NIH-3T3 fibroblasts was chosen as a model system since the complexity of the resulting signaling responses is well established and since many complementary experimental approaches have provided data that will be useful for the proposed study.