Title:
"Utilizing single-cell RNA sequencing to investigate gene covariances in non-dynamic systems"

Abstract:
Gene covariances have been widely used to infer gene regulatory networks and to link genes to common functions. However their analysis has long been limited to dynamic processes such as development or disease progression. Only recent advances in single-cell sequencing technologies have allowed for the inference of transcriptome-wide covariances within homogeneous cell populations.

Using single-cell sequencing we have traced regulatory networks in a highly homogeneous and non-dynamic population of mouse embryonic stems cells (mESC). We find that gene covariances in a non-dynamic cell population can be partially explained by the actions of transcription factors, miRNA targeting and nuclear architecture. Many of these gene covariances are likely linked to biological functions as they are enriched within gene sets sharing the same gene ontology or pathway annotation. Altogether we present evidence that gene regulatory networks leave a detectable mark on the complex single-cell transcriptome - exceeding technical, and biological noise - and that these covariances are connected to functional biological circuits.