Garry Nolan

Research Interests

Control of T cell signaling, machine learning of signaling states by systems biology, and leukemia/cancer autoimmunity are prominent in our studies. We use advanced Flow Cytometric analysis (FACS) of phosphoproteins in single cells to achieve many of our goals. Signaling systems can now be analyzed directly by flow cytometry and Fluorescence Activated Cell Sorting, primarily through technologies developed in our laboratory, focused on following multiple phosphoproteins in complex populations of primary cells such as mouse cells and even clinical samples. Up to 15 simultaneous parameters can be followed in single cells including multiple kinases, phosphoproteins, cell cycle proteins, and other parameters, enabling resolution of cellular activation states.

We are using these techniques to study B and T cell signaling, dendritic cell function, and other immune parameters by analysis of biochemical functions at the single cell level. Recently, we have begun using the approach to distinguish predictive patterns of intracellular signaling to classify patient responses to chemotherapies and to determine how their signaling systems are altered in disease states. We are also using the technique for drug screening in primary cells to truly select for drugs with efficacies in certain cell subsets but not others.

Autoimmune diseases in which we have particular interest include rheumatoid arthritis and systemic lupus erythematosus. In these diseases, we focus on understanding how the immune system becomes dysregulated as disease comes and goes. We can measure and determine the cellular network states in multiple cell subsets. In cancer, we are working in follicular lymphoma as well as acute myelogenous leukemia where we can look at disease progression as a measure of changes in disease states correlated to particular genetic changes in the genome of human cancer cells. Also we have made determined efforts in understanding how the micro-environment of cancers modulates immune signaling. We have made significant strides in understanding this in several solid cancer models and have begun working with human clinical samples.

We put substantial effort into bioinformatics approaches to mine the datasets we collect and to automate the production of network models of the signaling pathways affected. For this, we have collaborations with statisticians, engineering departments, and computer design specialists here at Stanford and UC Berkeley to extend our efforts to make the program in the laboratory extremely cross-disciplinary.