BME 280B Seminar: Speaker Dr. Aaron Newman – Molecular and spatial determinants of single-cell developmental states in cancer
Presenter: Dr. Newman, Associate Professor in the Department of Biomedical Data Science, Stanford University
Description: Determining the factors that shape cell potency—the ability of a cell to differentiate into other cell types—is essential for understanding tissue biology in health and disease, including cancer. In previous work, we found that single-cell transcriptional diversity decreases across developmental time, from the fertilized egg to the most mature cells in the body, and in multiple organisms. More recently, we developed CytoTRACE 2, an interpretable AI framework trained on millions of data points from single-cell RNA sequencing data, to determine cell potency on an absolute scale and reveal molecular hallmarks of developmental potential. We are now leveraging this method along with advances in spatial transcriptomics, to identify multicellular ecosystems linked to cancer cell differentiation states and clinical outcomes. I will highlight these tools along with our ongoing work to decode cell plasticity and clinically relevant spatial microenvironments in human malignancies.
Bio: Dr. Newman is an Associate Professor in the Department of Biomedical Data Science at Stanford University and a Chan Zuckerberg Biohub Investigator. He is also a member of the Stanford Cancer Institute and the Stanford Institute for Stem Cell Biology and Regenerative Medicine. Dr. Newman has made significant contributions to computational biology with applications to liquid biopsy, cancer genomics, and tumor immunology. Key contributions include CAPP-Seq for ultrasensitive detection of circulating tumor DNA; CIBERSORT/x for decoding cellular composition from bulk genomic data; CytoTRACE/2 for inferring cellular differentiation states from scRNA-seq data; and EcoTyper for delineating context-dependent cellular ecosystems from bulk, single-cell, and spatial expression data. His research program focuses on developing innovative data science tools to study the phenotypic diversity, differentiation hierarchies, and clinical significance of tumor cells and their surrounding microenvironments. Key results are further explored experimentally, both in the lab and through collaboration, with the goal of translating promising findings into the clinic.
Hosted by: Professor Camilla Forsberg, BME Department