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I am thrilled to join Keck Medicine of University of Southern California as Associate Professor in the Departments of Biochemistry & Molecular Medicine, Translational Genomics, and Norris Comprehensive Cancer Center!

We are hiring, see details in Join Us

 

About the Principal Investigator

                                     Dr. Sheng Li is an Associate Professor in the Department of Biochemistry and                                                 Molecular Medicine, with a secondary appointment in the Department of Translational                                         Genomics, at the Keck School of Medicine, University of Southern California (USC).                                             She is the Program Co-Leader of Epigenetic Regulation in Cancer (ERC) at the USC                                             NCI-designated Norris Comprehensive Cancer Center. Dr. Li received her PhD in                                                 Computational Biology from Cornell University in 2014, where she focused on the                                                 epigenome dynamics of leukemia relapse. Following her PhD, she served as an                                                   Instructor of Bioinformatics at Weill Cornell Medicine. In 2016, Dr. Li joined the Jackson                                   Laboratory for Genomic Medicine and was promoted to Associate Professor in 2022.

In 2024, her lab transitioned to the USC Keck School of Medicine. Dr. Li leads a research program centered on understanding the impact of somatic mutations and aging on blood cancer initiation by identifying critical epigenetic aberrations that disrupt gene expression regulating hematopoiesis. Her work leverages multi-omics and integrative data mining to study how age-related inflammation shapes the evolutionary trajectories of mutant hematopoietic stem cells in leukemogenesis. The long-term goal of her research is to identify novel therapeutics to mitigate leukemogenesis and extend human health span and life span. Dr. Li recevied NextGen Star Award from American Association for Cancer Research and Maximizing Investigators' Research Award from NIH National Institute of General Medical Sciences.

Research interests

Our ongoing research lays a conceptual and technological foundation for transformative advances in blood cancer prevention and therapy, by revealing the inner workings of cancer cells – especially the epigenetic plasticity and heterogeneity that drive cancer initiation and progression – in the context of aging.

        We are leveraging: i) unique insights into the epigenetic mechanisms controlling hematopoietic stem cells aging, clonal hematopoiesis, and hematopoietic malignancies, ii) powerful and cutting-edge genome technologies and computational algorithms that I developed for studying epigenetic plasticity and heterogeneity, iii) aged genetically engineered mouse models. Building on the important prior research discoveries and on new interdisciplinary collaborations, we have set three main research areas: 

  1. Single-cell Spatial Multi-omics: We will elucidate how aging and clonal hematopoiesis influence the propensity of blood stem cells to adapt to altered environments (“evolvability”) via single-cell multi-omics, which could not only forge a new paradigm for our understanding of leukemia onset but also provide candidate pathways for the development of preventative interventions. We leverage spatial transcriptomic to map the aged tissue microenvironment ("ecosystem") and cell-to-cell communications  including immune cell

  2. 3D epigenomics: We will systematically define the key epigenomics and 3D genomics features of mutated blood stem cells that change with age. This study will support the development of new therapies to prevent blood stem cell over-proliferation and thereby improve the healthspan of aging individuals

  3. Long-read sequencing: We are developing a computational framework for integrative analysis of epigenomics data including nanopore sequencing data that will decipher the cell fate decisions during stem cell differentiation and cancer via advanced deep learning algorithms and statistical methods


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