Teschendorff, AE;
Enver, T;
(2017)
Single-cell entropy for accurate estimation of differentiation potency from a cell's transcriptome.
Nature Communications
, 8
, Article 15599. 10.1038/ncomms15599.
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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.
Type: | Article |
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Title: | Single-cell entropy for accurate estimation of differentiation potency from a cell's transcriptome |
Location: | England |
Open access status: | An open access version is available from UCL Discovery |
DOI: | 10.1038/ncomms15599 |
Publisher version: | http://doi.org/10.1038/ncomms15599 |
Language: | English |
Additional information: | This work is licensed under a Creative Commons Attribution 4.0 International License. The images or other third party material in this article are included in the article’s Creative Commons license, unless indicated otherwise in the credit line; if the material is not included under the Creative Commons license, users will need to obtain permission from the license holder to reproduce the material. To view a copy of this license, visit http://creativecommons.org/licenses/by/4.0/ |
Keywords: | Cellular signalling networks, Complexity, Network topology, Stochastic networks |
UCL classification: | UCL UCL > Provost and Vice Provost Offices > School of Life and Medical Sciences UCL > Provost and Vice Provost Offices > School of Life and Medical Sciences > Faculty of Medical Sciences UCL > Provost and Vice Provost Offices > School of Life and Medical Sciences > Faculty of Medical Sciences > Cancer Institute UCL > Provost and Vice Provost Offices > School of Life and Medical Sciences > Faculty of Medical Sciences > Cancer Institute > Research Department of Cancer Bio |
URI: | https://discovery.ucl.ac.uk/id/eprint/1558361 |
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