Department of Quantitative and Computational Biology, University of Southern California, 1050 Childs Way, Los Angeles, CA 90089, USA.
Department of Integrative Biology and Physiology, University of California Los Angeles, Los Angeles, CA, USA.
J R Soc Interface. 2023 Jun;20(203):20230172. doi: 10.1098/rsif.2023.0172. Epub 2023 Jun 7.
Single-cell genomic technologies offer vast new resources with which to study cells, but their potential to inform parameter inference of cell dynamics has yet to be fully realized. Here we develop methods for Bayesian parameter inference with data that jointly measure gene expression and Ca dynamics in single cells. We propose to share information between cells via transfer learning: for a sequence of cells, the posterior distribution of one cell is used to inform the prior distribution of the next. In application to intracellular Ca signalling dynamics, we fit the parameters of a dynamical model for thousands of cells with variable single-cell responses. We show that transfer learning accelerates inference with sequences of cells regardless of how the cells are ordered. However, only by ordering cells based on their transcriptional similarity can we distinguish Ca dynamic profiles and associated marker genes from the posterior distributions. Inference results reveal complex and competing sources of cell heterogeneity: parameter covariation can diverge between the intracellular and intercellular contexts. Overall, we discuss the extent to which single-cell parameter inference informed by transcriptional similarity can quantify relationships between gene expression states and signalling dynamics in single cells.
单细胞基因组学技术为研究细胞提供了大量新的资源，但它们在为细胞动力学的参数推断提供信息方面的潜力尚未得到充分实现。在这里，我们开发了一种联合测量单细胞基因表达和 Ca 动力学数据的贝叶斯参数推断方法。我们建议通过迁移学习在细胞之间共享信息：对于一系列细胞，一个细胞的后验分布用于为下一个细胞的先验分布提供信息。在应用于细胞内 Ca 信号动力学时，我们对具有不同单细胞反应的数千个细胞进行了动态模型参数拟合。我们表明，无论细胞如何排序，迁移学习都可以加速细胞序列的推断。然而，只有根据转录相似性对细胞进行排序，我们才能从后验分布中区分 Ca 动态谱和相关标记基因。推断结果揭示了细胞异质性的复杂和竞争来源：细胞内和细胞间环境之间的参数协变可能会出现分歧。总体而言，我们讨论了在多大程度上，受转录相似性启发的单细胞参数推断可以量化单细胞中基因表达状态和信号动力学之间的关系。
