Tipping points in epithelial-mesenchymal lineages from single cell transcriptomics data.

Understanding cell fate decision-making during complex biological processes is an open challenge that is now aided by high resolution single cell sequencing technologies. Specifically, it remains challenging to identify and characterize transition states corresponding to "tipping points" whereby cells commit to new cell states. Here, we present a computational method that takes advantage of single cell transcriptomics data to infer the stability and gene regulatory networks (GRN) along cell lineages. Our method uses the unspliced and spliced counts from single cell RNA sequencing (scRNA-seq) data and cell ordering along lineage trajectories to train an RNA splicing multivariate model, from which cell state stability along the lineage is inferred based on spectral analysis of the model's Jacobian matrix. Moreover, the model infers the RNA cross-species interactions resulting in gene regulatory networks (GRN) and their variation along the cell lineage. When applied to epithelial-mesenchymal transition (EMT) in ovarian and lung cancer-derived cell lines, our model predicts a saddle-node transition between the epithelial and mesenchymal states passing through an unstable, intermediate cell state. Furthermore, we show that the underlying GRN controlling EMT rearranges during the transition, resulting in denser and less modular networks in the intermediate state. Overall, our method represents a flexible tool to study cell lineages with a combination of theory-driven modeling and single cell transcriptomics data.