Abstract:

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 (https://github.com/aristoteleo/dynamo-release) 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 Spateo, a general framework for quantitative spatiotemporal modeling of single-cell resolution spatial transcriptomics. Spateo develops a comprehensive framework of cell-cell interaction to reveal spatial effects of niche factors and cell type-specific ligand-receptor interactions. Furthermore, Spateo reconstructs 3D models of whole embryos, and performs 3D morphometric analyses. Lastly, Spateo introduces the concept of "morphometric vector field" of cell migrations and integrates spatial differential geometry to unveil regulatory programs underlying various organogenesis patterns of Drosophila and mouse. Thus, Spateo enables the study of the ecology of organs at a molecular level in 3D space, beyond isolated single cells. Moving forward, my lab will try to integrate advances in machine learning and advances in genomics to learn spatially and temporally resolved models of cell fate transition at whole mouse embryo level in 3D space.

https://umich-health.zoom.us/j/93929606089?pwd=SHh6R1FOQm8xMThRemdxTEFMWWpVdz09