Mohammad Haj Dezfulian, PhD
Assistant Professor,
University of Pennsylvania
Featured in the Session: Harnessing Big Data to Decode Immune Mechanisms in Type 1 Diabetes
When
Saturday, June 6
at 8:00 a.m. CT
Where
343 (Level 3)
Ernest N. Morial Convention Center
What is your presentation about?
My presentation will focus on the development of genome-scale, high-throughput platforms to systematically discover the antigens recognized by T cells. These technologies are designed to enable de novo epitope discovery at scale and to help define antigen specificity across complex settings including autoimmunity, infection, and cancer. I will discuss how platform engineering can open new ways to map T cell recognition and uncover disease-relevant immune targets that have remained difficult to identify with conventional approaches.
How do you hope your presentation will impact diabetes research or care?
A major barrier in type 1 diabetes is the limited ability to define, in an unbiased way, the antigens recognized by disease-relevant T cells. Scalable epitope discovery platforms may help address this by enabling more direct characterization of antigen specificity, with implications for mechanistic studies, immune monitoring, and target prioritization. More broadly, these approaches may provide a framework for studying pathogenic T cell responses across immune-mediated disease.
How did you become involved with this area of diabetes research or care?
I became interested in this area through a broader effort to build technologies for high-dimensional analysis of T cell antigen recognition. Type 1 diabetes presents a clear translational and biological need for such approaches, given the central role of T cells in disease and the persistent gaps in our understanding of their antigenic targets. That intersection of technology development and fundamental immunology has shaped this line of work.