Single-cell entropy for accurate estimation of differentiation potency from a cell's transcriptome

By Andrew E. Teschendorff, Tariq Enver. Nature Communications, 2017

304 citations recorded by the source corpus.

The ability to quantify differentiation potential of single cells is a task of critical importance. Here we demonstrate, using over 7,000 single-cell RNA-Seq profiles, that differentiation potency of a single cell can be approximated by computing the signalling promiscuity, or entropy, of a cell’s transcriptome in the context of an interaction network, without the need for feature selection. We show that signalling entropy provides a more accurate and robust potency estimate than other entropy-based measures, driven in part by a subtle positive correlation between the transcriptome and connectome. Signalling entropy identifies known cell subpopulations of varying potency and drug resistant cancer stem-cell phenotypes, including those derived from circulating tumour cells. It further reveals that expression heterogeneity within single-cell populations is regulated. In summary, signalling entropy allows in silico estimation of the differentiation potency and plasticity of single cells and bulk samples, providing a means to identify normal and cancer stem-cell phenotypes. Robust quantification of the differentiation potential of single cells is a task of great importance. Here the authors integrate single-cell RNA-Seq profiles with a cellular interaction network to compute the signaling entropy, and show that it can identify normal and cancer stem-cell phenotypes.

Subfields: Biophysics, Molecular Biology

View Paper: https://www.semanticscholar.org/paper/3bf08b7c4de42d15544d4f82212e040e65d416b4