Comparison of Spatial Transcriptomics (ST) Technologies and ST Gradient Analysis
To Know
About this Class
Abstract: Spatial transcriptomics (ST) is evolving rapidly as a pivotal technology in studying the biology of tumors and their associated tumor microenvironments (TME). However, where/how it can impact research remain unclear. In this talk, Dr. Chen will describe their recent study that performs rigorous benchmarking amongst the single cell ST platforms CosMx, MERFISH, and Xenium (uni/multi-modal) platforms (PMID: 41006245) and several computational approaches that performs multimodal ST data fusion, identifies locations and directions of spatial transcriptomic gradients (STG) (PMID: 38562886) and infers metabolic flux (PMID: 37573313) from ST data. Finally, he will describe their experience in studying factors deriving immune checkpoint therapy resistance in HPV+ H&N cancer patients.
Bio: Dr. Chen obtained B. Eng. from Tsinghua University (Beijing) and Ph.D. in Electrical and Computer Engineering from University of Illinois at Urbana-Champaign. He is currently a full professor in the department of Bioinformatics and Computational Biology at the University of Texas MD Anderson cancer center. His primary interest is to develop computational methods to analyze and interpret high-throughput human genetic, functional and clinical data towards understanding the evolution of cancer as a consequence of genetics and environment and identifying molecular targets useful for cancer diagnosis and therapeutics. Among the computational tools he developed, BreakDancer, VarScan and Monovar have been widely used for characterizing genomes and transcriptomes of tumor tissues and single cells.