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.
Dr. Xiaojie Qiu is an incoming assistant professor at the Department of Genetics, the BASE program, and the Department of Computer Science at Stanford. Xiaojie’s Ph.D. work at University of Washington with Dr. Cole Trapnell made substantial contributions to the field of single-cell genomics, exemplified by the development of Monocle ⅔ (monocle 2 & monocle 3), which can accurately and robustly reconstruct complex developmental trajectories from scRNA-seq data. In his post-doc at Whitehead Institute with Dr. Jonathan Weissman, Xiaojie developed Dynamo (aristoteleo/dynamo-release) to infers absolute RNA velocity with metabolic labeling enabled single-cell RNA-seq, reconstructs continuous vector fields that predict fates of individual cells, employs differential geometry to extract underlying gene regulatory network regulations, and ultimately predicts optimal reprogramming paths and makes nontrivial in silico perturbation predictions. Recently he also developed a powerful toolkit, Spateo (aristoteleo/spateo-release), for advanced multi-dimensional spatiotemporal modeling of single cell resolution spatial transcriptomics. Spateo delivers novel methods for digitizing spatial layers/columns to identify spatially-polar genes, and 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.
The Qiu lab at Stanford will officially start on Dec. 16, 2024. Xiaojie will continue leveraging his unique background in single-cell genomics, mathematical modeling, and machine learning to lead a research team that bridges the gap between the “big data” from single-cell and spatial genomics and quantitative/predictive modeling in order to address fundamental questions in mammalian cell fate transitions, especially that of heart development and disease. There will be mainly four directions in the lab: 1) dissect the mechanisms of mammalian cell differentiation, reprogramming, and maintenance, including that of cardiac cells, through differentiable deep learning frameworks; 2) integrate multi-omics and harmonize short-term RNA velocities with long-term lineage tracing and apply such methods to heart developmental and heart congenital disease; 3) build predictive in silico 3D spatiotemporal models of mammalian organogenesis with a focus on the heart morphogenesis; and 4) establish foundational software ecosystem for predictive and mechanistic modeling of single cell and spatial transcriptomics.