AM Seminar: Cell-cell communication and the gene regulatory network dynamics of stem cell differentiation

Adam MacLean 
Assistant Professor in the Department of Quantitative and Computational Biology
University of Southern California

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Description: Stem cells differentiate via cell fate decision-making, which thus underlies organ development and disease. Despite great promise, we are yet to harness the high-resolution cell state information that is offered by single-cell genomics data to understand cell fate decision-making as it is controlled by gene regulatory networks. To assist in modeling gene regulatory network dynamics, we developed RVAgene, a recurrent variational autoencoder that learns an accurate and efficient reconstruction of temporal gene profiles from noisy input single-cell data. Its low-dimensional latent space enables feature discovery and by sampling from this space we can generate new data to test models. But no cell is an island: cell-internal gene regulatory dynamics act in concert with external signals to control cell fate. We developed a multiscale model to study the effects of cell-cell communication on gene regulatory network dynamics that control cell fate. We discovered a profound role for cell-cell communication in controlling the fates of single cells, resolving a controversy in the literature regarding hematopoietic stem cell differentiation. Our work highlights the need to consider single-cell resolved dynamic models to understand and predict the fates of cells.

Speaker bio: Adam MacLean is an assistant professor in the Department of Quantitative and Computational Biology at the University of Southern California. Adam studied mathematical physics at the University of Edinburgh, then completed his Ph.D. in systems biology from Imperial College London. He worked as a postdoc at the University of Oxford and the University of California, Irvine, before joining USC in 2019. Adam's lab at USC seeks to understand how cell fate decisions regulate tissue and organ function during development, homeostasis, and cancer. These questions are addressed via the development of new mathematical models and machine learning tools, drawing on recent biological advances in single-cell measurement technologies, and computational advances in Julia for numerical systems biology. His work has appeared in PNAS, Stem Cells, and Cancer Research. He has won multiple fellowships and awards, including an NSF CAREER award and an NIH R35 MIRA award.