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.

中文翻译：

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单细胞转录组学数据中上皮间充质谱系的临界点

了解复杂生物过程中的细胞命运决策是一项公开的挑战，现在高分辨率单细胞测序技术对此有所帮助。具体来说，识别和表征与细胞进入新细胞状态的“临界点”相对应的过渡状态仍然具有挑战性。在这里，我们提出了一种计算方法，利用单细胞转录组学数据来推断沿细胞谱系的稳定性和基因调控网络（GRN）。我们的方法使用单细胞 RNA 测序数据中的未剪接和剪接计数以及沿谱系轨迹的细胞排序来训练 RNA 剪接多元模型，并根据模型雅可比矩阵的光谱分析推断沿谱系的细胞状态稳定性。此外，该模型还推断出 RNA 跨物种相互作用导致 GRN 及其沿细胞谱系的变化。当应用于卵巢癌和肺癌来源的细胞系中的上皮-间质转化时，我们的模型预测上皮和间质状态之间的鞍结转变经过不稳定的中间细胞状态。此外，我们发现控制上皮-间质转化的潜在 GRN 在过渡过程中会重新排列，从而导致中间状态下的网络更密集、模块化程度更低。总的来说，我们的方法代表了一种灵活的工具，可以结合理论驱动的建模和单细胞转录组数据来研究细胞谱系。