Inferring gene regulation dynamics from static snapshots of gene expression variability

TL;DR

This paper presents a method to infer gene regulation dynamics from static single-cell data by analyzing co-variability and correlation patterns, enabling detection of feedback loops and cell-cycle effects.

Contribution

It introduces correlation-based criteria to infer regulatory properties and feedback in gene networks from static snapshots, leveraging dual-reporter experiments with different maturation times.

Findings

Correlation conditions detect feedback regulation.

Genes with cell-cycle dependent transcription identified.

Static snapshots can reveal dynamic regulatory interactions.

Abstract

Inferring functional relationships within complex networks from static snapshots of a subset of variables is a ubiquitous problem in science. For example, a key challenge of systems biology is to translate cellular heterogeneity data obtained from single-cell sequencing or flow-cytometry experiments into regulatory dynamics. We show how static population snapshots of co-variability can be exploited to rigorously infer properties of gene expression dynamics when gene expression reporters probe their upstream dynamics on separate time-scales. This can be experimentally exploited in dual-reporter experiments with fluorescent proteins of unequal maturation times, thus turning an experimental bug into an analysis feature. We derive correlation conditions that detect the presence of closed-loop feedback regulation in gene regulatory networks. Furthermore, we show how genes with cell-cycle dependent transcription rates can be identified from the variability of co-regulated fluorescent proteins. Similar correlation constraints might prove useful in other areas of science in which static correlation snapshots are used to infer causal connections between dynamically interacting components.