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

Andrew E. Teschendorff () and Tariq Enver
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Andrew E. Teschendorff: CAS Key Laboratory of Computational Biology, CAS-MPG Partner Institute for Computational Biology, Shanghai Institute for Biological Sciences
Tariq Enver: UCL Cancer Institute, Paul O’Gorman Building, University College London

Nature Communications, 2017, vol. 8, issue 1, 1-15

Abstract: Abstract 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.

Date: 2017
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DOI: 10.1038/ncomms15599

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