Inferring the burst dynamics of coupled self-feedback gene expression circuits based on single-cell data.

Abstract

Gene expression involves a series of complex regulatory processes. It is known that self-feedback regulation plays an important role in it. However, self-feedback burst dynamics in single-cell sequencing data has not been extensively studied yet. Here we propose a single-cell stochastic burst model with coupled positive- and negative-feedback gene expression circuits and then analyze its dynamical behavior using mouse fibroblast scRNA-seq data. The burst dynamics parameters are inferred genomewide and the self-feedback regulation patterns are identified. The results show that positive feedback can restore a bimodal distribution of gene expression, while negative feedback and no feedback often lead to a unimodal distribution. Additionally, we analyze the effects of feedback regulation types on burst dynamics, namely, burst size, burst frequency, and noise. It is found that as the gene mean increases, the burst frequency and burst size of the three feedback types show an increasing trend, while noise shows a decreasing trend. On the other hand, the mean burst frequency of negative feedback is greater than that of positive feedback, while the mean burst size and noise of positive feedback are greater than those of negative feedback. This helps us understand the gene expression patterns, cell differentiation, and fate determination.