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MIA Talks

Towards predictive spatiotemporal modeling of single cells

September 28, 2022
Postdoctoral Fellow in the Weissman Lab of MIT and Whitehead Institute

Single-cell RNA-seq, together with RNA velocity and metabolic labeling, reveals cellular states and transitions at unprecedented resolution. Fully exploiting these data, however, requires kinetic models capable of unveiling governing regulatory functions. 

In the first part of my talk, I will introduce an analytical framework dynamo (, and highlight dynamo’s power to overcome fundamental limitations of conventional splicing-based RNA velocity analyses to enable accurate velocity estimations on a metabolically labeled human hematopoiesis scRNA-seq dataset. Furthermore, differential geometry analyses reveal mechanisms driving early megakaryocyte appearance and elucidate asymmetrical regulation within the PU.1-GATA1 circuit. Leveraging the least-action-path method, dynamo accurately predicts drivers of numerous hematopoietic transitions. Finally, in silico perturbations predict cell-fate diversions induced by gene perturbations. Dynamo, thus, represents an important step in advancing quantitative and predictive theories of cell-state transitions. 

Cells do not live in a vacuum, but in a milieu defined by cell–cell communication that can be quantified via recent advances in spatial transcriptomics. In my second section of my talk, I will talk about Stereo-seq, arguably the best sequencing-based spatial transcriptomic technology, as it has subcellular resolution, the highest sensitivity, and the largest field of view, providing an important complement to Slide-seq, STARmap and other spatial transcriptomic technologies. We used Stereo-seq to define tissue domains via novel spatially constrained clustering, build a panoramic atlas of mouse organogenesis from E9.5 to E16.5 and investigated the spatiotemporal dynamics of tangential and radial migration of telencephalon neurons.