Transcriptional heterogeneity is a fundamental biological process impacting health and disease, yet the regulatory mechanisms behind this are poorly understood. Here we identify mechanistic processes of heterogeneity in pluripotent stem cells by manipulating the growth environment. We have developed new computational methods for analysing transcriptional heterogeneity and apply these to inform mechanisms of gene regulation.
First, we show how bulk population transcriptomics can be used to inform mechanisms of single-cell heterogeneity. Second, we have developed a method to infer rates of bursty gene transcription from single-cell RNA-seq transcriptomics. Third, we demonstrate the improved power of single-cell data for inference of biological network structure and have developed a new method to incorporate multiple layers of information, such as ChIP-seq data, into gene regulatory networks.
The complexity of single-cell heterogeneity can be unravelled using concerted computational approaches. Such methods are critical to understand the mechanistic processes regulating transcriptional heterogeneity and how these are disrupted in disease.