Doron Betel’s research lies at the intersection of computational biology, genomics, and systems biology, with a focus on understanding how complex cellular systems behave in health and disease. His work centers on developing and applying computational frameworks to analyze high-dimensional data, particularly from single-cell and spatial profiling technologies. By integrating diverse data types, his research aims to uncover the regulatory mechanisms that govern cell state transitions, differentiation, and dysfunction.
A component of Betel’s work examines immune cell plasticity, particularly how T cells transition between functional states in cancer, chronic infection, and autoimmunity. Using single-cell and spatial transcriptomics data, his research has identified transcriptional programs associated with dysfunctional or exhausted immune states and provided insights into variability in responses to immunotherapy. His group contributed to identifying features of T cell dysfunction that emerge early during antigen exposure, refining the understanding of immune regulation in disease.
In parallel, Betel’s research focuses on stem cell–based models of neurodegeneration and aging. He applies computational approaches to characterize how cellular identity and regulatory programs change over time in induced pluripotent stem cell (iPSC)–derived systems. By modeling neuronal and glial differentiation, his work seeks to identify early molecular signatures and trajectories associated with neurodegenerative diseases, as well as broader aging-related processes. These efforts aim to bridge developmental biology with disease modeling, enabling the study of dynamic cellular transitions that are otherwise difficult to observe in human tissues.
More broadly, Betel develops methods to study cellular heterogeneity and lineage relationships, reconstruct developmental trajectories, and identify rare or transient cell populations relevant to disease. His work emphasizes the importance of spatial context, recognizing that cell function is shaped by both intrinsic gene expression and interactions within the microenvironment. Through close collaboration with experimental and clinical researchers, his research advances a more predictive and mechanistic understanding of biological systems, with the goal of informing more precise therapeutic strategies.
Biography
I am a computational biologist specializing in single-cell and spatial genomics, with a focus on immune regulation and disease mechanisms. My work integrates advanced statistical modeling, machine learning, and high-dimensional data analysis to uncover cellular states and gene regulatory networks in cancer and autoimmune conditions. I lead interdisciplinary collaborations with experimental and clinical teams to translate computational insights into biological discovery. I have contributed to developing novel analytical frameworks and led studies published in leading scientific journals. I am committed to mentoring the next generation of computational scientists.
