Summary
Differentiation involves bifurcations between discrete cell states, each defined by a distinct gene expression profile. Single-cell RNA profiling allows the detection of bifurcations. However, while current methods capture these events, they do not identify characteristic gene signals. Here we show that BioTIP – a tipping-point theory-based analysis – can accurately, robustly, and reliably identify critical transition signals (CTSs). A CTS is a small group of genes with high covariance in expression that mark the cells approaching a bifurcation. We validated its accuracy in the cardiogenesis with known a tipping point and demonstrated the identified CTSs contain verified differentiation-driving transcription factors. We then demonstrated the application on a published mouse gastrulation dataset, validated the predicted CTSs using independent in-vivo samples, and inferred the key developing mesoderm regulator Etv2. Taken together, BioTIP is broadly applicable for the characterization of the plasticity, heterogeneity, and rapid switches in developmental processes, particularly in single-cell data analysis.
Highlights
Identifying significant critical transition signals (CTSs) from expression noise
A significant CTS contains or is targeted by key transcription factors
BioTIP identifies CTSs accurately and independent of trajectory topologies
Significant CTSs reproducibly indicate bifurcations across datasets
Competing Interest Statement
The authors have declared no competing interest.
Footnotes
1) Focus on the application to single-cell transcriptome. 2) Adding a new analysis on data with a verified tipping point and driving transcription factors.
