ABSTRACT
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 (GRNs) along cell lineages. Our method uses the unspliced and spliced counts from single-cell RNA sequencing 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 GRNs and their variation along the cell lineage. When applied to epithelial-mesenchymal transition 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 epithelial-mesenchymal transition 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.
ABSTRACT
Hair follicles (HFs) are stem-cell-rich mammalian mini-organs that can undergo cyclic regenerations over the life span of the organism. The cycle of a HF consists of three consecutive phases: anagen-the active proliferation phase, catagen-the degeneration phase, and telogen-the resting phase. While HFs undergo irreversible degeneration during catagen, recent experimental research on mice shows that when anagen HFs are subject to ionizing radiation (IR), they undergo a transient degeneration, followed by a nearly full regeneration that makes the HFs return to homeostatic state. The mechanisms underlying these IR-induced HF regenerative dynamics and the catagen degenerative dynamics, remain unknown. In this work, we develop an ODE type cell differentiation population model to study the control mechanisms of HF regeneration. The model is built based on current theoretical knowledge in biology and mathematically formulated using feedback mechanisms. Model parameters are calibrated to IR experimental data, and we then provide modeling results with both deterministic ODE simulations and corresponding stochastic simulations. We perform stability and bifurcation analyses on the ODE model, which reveal that for anagen HFs, a low spontaneous apoptosis rate secures the stability of the HF homeostatic steady state, allowing the HF to regenerate even when subject to strong IR. On the other hand, the irreversible degeneration during catagen results from both strong spontaneous apoptosis rate and strong apoptosis feedback. Lastly, we perform sensitivity analysis to identify key parameters in the model to validate these hypotheses.
