Dr. Adam MacLean
University of California, Irvine
Profile
Multiscale modeling and inference of stem cell lineage dynamics with single-cell data
Thursday, February 8
2:00 PM
RRI 101
Abstract: The rapid expansion of single-cell data (e.g. scRNA-seq) offers great potential to characterize cell types and their interactions in new depth. Significant challenges for the analysis of such data include clustering cells, predicting lineage relationships, and building cell-cell communication networks. However analysis alone is not enough: models are essential to explain complex phenomena and identify underlying mechanisms. Here we present models of multiscale stem cell dynamics that describe phenomena from fast - phosphorylation/diffusion - to slow scales - cell division/differentiation, and the resultant tissue-level effects. We have developed methods for parameter-free inference to characterize these models when time course data are lacking, and demonstrate their effectiveness on models of the Wnt pathway. We go on to develop an optimization-based framework for single-cell data analysis that outperforms current clustering methods and infers cell-cell signaling networks. Using hematopoiesis as a model system, we demonstrate its ability to reconstruct a multi-branched lineage and predict the timing of key differentiation events. By combining single-cell analysis tools with models of transcriptional, pathway, and lineage dynamics, we are able to explore cell state stability landscapes. Our analysis of hematopoietic landscapes has led to the prediction of new cell states and transition paths. As ongoing and future work, we discuss how to extend models to be able to determine the stability properties of single cells.
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