Despite significant advancements in high-throughput data acquisition in genomics and cell biology, our understanding of the diverse cell types within the human body remains limited. Particularly, the principles governing intracellular molecular spatial organization and cellular spatial organization within complex tissues are still largely unclear. A major challenge lies in developing computational methods capable of integrating heterogeneous and multiscale molecular, cellular, and tissue information. In this talk, I will discuss our recent work on creating integrative approaches for single-cell spatial epigenomics and transcriptomics. These methods hold the potential to reveal new insights into the fundamental genome structure and cellular function, as well as the spatial organization of cells within complex tissues, across a wide range of biological contexts in health and disease.
Jian Ma is the Ray and Stephanie Lane Professor of Computational Biology at the School of Computer Science at Carnegie Mellon University. His lab focuses on developing computational methods to study the structure and function of the human genome and cellular organization and their implications for evolution, health and disease. His group has recently pioneered a series of new machine learning methods for 3D epigenomics, comparative genomics, spatial genomics, and single-cell analysis. He currently leads a multi-disciplinary NIH Center, as part of the NIH 4D Nucleome Program. His recent work has been supported by NIH, NSF, CZI, Google, and the Mark Foundation. He has received several awards, including an NSF CAREER Award and a Guggenheim Fellowship (in Computer Science), and is an elected Fellow of AAAS.