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

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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
    University College London
    UCL Cancer Institute, Paul O’Gorman Building, University College London)

  • Tariq Enver

    (UCL Cancer Institute, Paul O’Gorman Building, University College London)

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.

Suggested Citation

  • Andrew E. Teschendorff & Tariq Enver, 2017. "Single-cell entropy for accurate estimation of differentiation potency from a cell’s transcriptome," Nature Communications, Nature, vol. 8(1), pages 1-15, August.
    • Handle: RePEc:nat:natcom:v:8:y:2017:i:1:d:10.1038_ncomms15599
    DOI: 10.1038/ncomms15599
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    Cited by:

    1. Huanhuan Tan & Weixu Wang & Congjin Zhou & Yanfeng Wang & Shu Zhang & Pinglan Yang & Rui Guo & Wei Chen & Jinwen Zhang & Lan Ye & Yiqiang Cui & Ting Ni & Ke Zheng, 2023. "Single-cell RNA-seq uncovers dynamic processes orchestrated by RNA-binding protein DDX43 in chromatin remodeling during spermiogenesis," Nature Communications, Nature, vol. 14(1), pages 1-21, December.
    2. Farshad Farshidfar & Kahn Rhrissorrakrai & Chaya Levovitz & Cong Peng & James Knight & Antonella Bacchiocchi & Juan Su & Mingzhu Yin & Mario Sznol & Stephan Ariyan & James Clune & Kelly Olino & Laxmi , 2022. "Integrative molecular and clinical profiling of acral melanoma links focal amplification of 22q11.21 to metastasis," Nature Communications, Nature, vol. 13(1), pages 1-16, December.
    3. Anthony Baptista & Ben D. MacArthur & Christopher R. S. Banerji, 2024. "Charting cellular differentiation trajectories with Ricci flow," Nature Communications, Nature, vol. 15(1), pages 1-14, December.

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