Modern single cell sequencing technologies enable us to probe cellular processes at unprecedented resolution, while introducing new theoretical challenges. A major difficulty lies in constructing dynamical models of cell behavior, as these technologies capture only a single snapshot of a cell's state, thereby preventing the observation of temporal dynamics. In this talk, I will discuss how we utilized single cell transcriptomics - a technique that measures RNA expression in individual cells - to develop a mathematical framework of gene expression and construct the attractor landscape regulating cell fate decisions. By applying this model under both equilibrium and non-equilibrium conditions, we analyzed the stability, transition paths, and cell type-specific regulatory networks that govern cell fate. Building on this framework, we introduce a single cell transition tensor that harmonizes small stochastic fluctuations in gene expression at a local level (within individual attractors) with global changes in cell fate (transitions between attractors) to dissect cellular dynamics across multiple scales. Finally, we incorporate spatial transcriptomics data into our framework to project cell fate transitions in space.
Key publications:
Bocci, Zhou, Nie, spliceJAC: transition genes and state‐specific gene regulation from single‐cell transcriptome data, Molecular Systems Biology 18(11):e11176 (2022).
Barcenas, Bocci, Nie, Tipping points in epithelial-mesenchymal lineages from single-cell transcriptomics data, Biophysical Journal 123(17):2849-2859 (2024).
Zhou, Bocci, Li, Nie, Spatial transition tensor of single cells, Nature Methods 21, 1053-1062 (2024).