Gallivan Cameron P, Ren Honglei, Read Elizabeth L
Department of Chemical & Biomolecular Engineering, University of California, Irvine, CA, United States.
NSF-Simons Center for Multiscale Cell Fate, University of California, Irvine, CA, United States.
Front Genet. 2020 Jan 31;10:1387. doi: 10.3389/fgene.2019.01387. eCollection 2019.
Single-cell transcriptomics is advancing discovery of the molecular determinants of cell identity, while spurring development of novel data analysis methods. Stochastic mathematical models of gene regulatory networks help unravel the dynamic, molecular mechanisms underlying cell-to-cell heterogeneity, and can thus aid interpretation of heterogeneous cell-states revealed by single-cell measurements. However, integrating stochastic gene network models with single cell data is challenging. Here, we present a method for analyzing single-cell gene-pair coexpression patterns, based on biophysical models of stochastic gene expression and interaction dynamics. We first developed a high-computational-throughput approach to stochastic modeling of gene-pair coexpression landscapes, based on numerical solution of gene network Master Equations. We then comprehensively catalogued coexpression patterns arising from tens of thousands of gene-gene interaction models with different biochemical kinetic parameters and regulatory interactions. From the computed landscapes, we obtain a low-dimensional "shape-space" describing distinct types of coexpression patterns. We applied the theoretical results to analysis of published single cell RNA sequencing data and uncovered complex dynamics of coexpression among gene pairs during embryonic development. Our approach provides a generalizable framework for inferring evolution of gene-gene interactions during critical cell-state transitions.
单细胞转录组学正在推动细胞身份分子决定因素的发现，同时也促进了新型数据分析方法的发展。基因调控网络的随机数学模型有助于揭示细胞间异质性背后的动态分子机制，从而有助于解释单细胞测量所揭示的异质细胞状态。然而，将随机基因网络模型与单细胞数据相结合具有挑战性。在此，我们基于随机基因表达和相互作用动力学的生物物理模型，提出了一种分析单细胞基因对共表达模式的方法。我们首先基于基因网络主方程的数值解，开发了一种高通量的基因对共表达景观随机建模方法。然后，我们全面编目了由数万个具有不同生化动力学参数和调控相互作用的基因-基因相互作用模型产生的共表达模式。从计算出的景观中，我们获得了一个低维的“形状空间”，用于描述不同类型的共表达模式。我们将理论结果应用于已发表的单细胞RNA测序数据分析，并揭示了胚胎发育过程中基因对之间共表达的复杂动态。我们的方法为推断关键细胞状态转变过程中基因-基因相互作用的演变提供了一个可推广的框架。
