Learning dynamics from single cell genomics and lineage tracing

About this Class

Abstract: Single-cell genome-wide profiling offers an approach to map transitional cell states during cell differentiation, disease onset, and drug response. Lineage-tracing, in which cells are labeled with hereditary markers, offers an approach to establishing dynamic relationships between cell states. By combining these two approaches, we can overlay cell dynamics and fate decision boundaries onto classically-defined differentiation hierarchies. I will survey progress in this area, and present a computational method to learn stochastic dynamics from lineage tracing genomic assays. We extend the statistical problem of compressed sensing to enforce coherent, sparse clonal relationships in time series data. In datasets representing hematopoiesis, reprogramming, and in vitro differentiation, the resulting approach identifies fate biases not previously detected, consistent with heterogeneity in the expression of transcription factors. Short bio: Dr. Klein is an Associate Professor of Systems Biology at Harvard Medical School. He obtained his PhD in physics from Cambridge University, and a postdoc in experimental systems biology from Harvard Medical School. Dr. Klein studies how cells make fate choices in developing and adult tissues. He pioneered droplet microfluidics for single-cell RNA-Seq, computational methods for analyzing single-cell genomics data, and methods for quantitative clonal analysis. His work includes the discovery of novel cell types, discovering regulators of tissue regeneration, mapping immune cells in cancer, and establishing maps of how cells develop from stem cells to mature cell types. In 2018, Dr. Klein’s work was recognized as part of the the AAAS “Breakthrough of the Year”. In 2020 he received the Dr. Susan Lim Award for Outstanding Young Investigator from the International Society for Stem Cell Research (ISSCR). In 2021, he was awarded the inaugural James Prize for Science and Technology Integration by the USA National Academy of Sciences. ZoomGov link for all the individual meetings and the seminar: https://nih.zoomgov.com/j/1619988709?pwd=dEIwM0ExOXFtSjNqd0xmcGJCVEk5QT09 Meeting ID: 161 998 8709 Passcode: 20892