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Machine learning of stem cell identities from single-cell expression data via regulatory network archetypes

Machine learning of stem cell identities from single-cell expression data via regulatory network archetypes
Machine learning of stem cell identities from single-cell expression data via regulatory network archetypes
The molecular regulatory network underlying stem cell pluripotency has been intensively studied, and we now have a reliable ensemble model for the “average” pluripotent cell. However, evidence of significant cell-to-cell variability suggests that the activity of this network varies within individual stem cells, leading to differential processing of environmental signals and variability in cell fates. Here, we adapt a method originally designed for face recognition to infer regulatory network patterns within individual cells from single-cell expression data. Using this method we identify three distinct network configurations in cultured mouse embryonic stem cells—corresponding to naïve and formative pluripotent states and an early primitive endoderm state—and associate these configurations with particular combinations of regulatory network activity archetypes that govern different aspects of the cell's response to environmental stimuli, cell cycle status and core information processing circuitry. These results show how variability in cell identities arise naturally from alterations in underlying regulatory network dynamics and demonstrate how methods from machine learning may be used to better understand single cell biology, and the collective dynamics of cell communities.
machine learning (artificial intelligence), single-cell data, regulatory network, eigenface approach, stem cell, pluripotent stem cells
1664-8021
Stumpf, Patrick
dfdb286c-b321-46d3-8ba2-85b3b4a7f092
Macarthur, Benjamin
2c0476e7-5d3e-4064-81bb-104e8e88bb6b
Stumpf, Patrick
dfdb286c-b321-46d3-8ba2-85b3b4a7f092
Macarthur, Benjamin
2c0476e7-5d3e-4064-81bb-104e8e88bb6b

Stumpf, Patrick and Macarthur, Benjamin (2019) Machine learning of stem cell identities from single-cell expression data via regulatory network archetypes. Frontiers in Genetics, 10, [2]. (doi:10.3389/fgene.2019.00002).

Record type: Article

Abstract

The molecular regulatory network underlying stem cell pluripotency has been intensively studied, and we now have a reliable ensemble model for the “average” pluripotent cell. However, evidence of significant cell-to-cell variability suggests that the activity of this network varies within individual stem cells, leading to differential processing of environmental signals and variability in cell fates. Here, we adapt a method originally designed for face recognition to infer regulatory network patterns within individual cells from single-cell expression data. Using this method we identify three distinct network configurations in cultured mouse embryonic stem cells—corresponding to naïve and formative pluripotent states and an early primitive endoderm state—and associate these configurations with particular combinations of regulatory network activity archetypes that govern different aspects of the cell's response to environmental stimuli, cell cycle status and core information processing circuitry. These results show how variability in cell identities arise naturally from alterations in underlying regulatory network dynamics and demonstrate how methods from machine learning may be used to better understand single cell biology, and the collective dynamics of cell communities.

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fgene-10-00002 - Version of Record
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Accepted/In Press date: 7 January 2019
e-pub ahead of print date: 22 January 2019
Keywords: machine learning (artificial intelligence), single-cell data, regulatory network, eigenface approach, stem cell, pluripotent stem cells

Identifiers

Local EPrints ID: 427807
URI: http://eprints.soton.ac.uk/id/eprint/427807
ISSN: 1664-8021
PURE UUID: ca865a03-065d-4412-918b-4c0bd6d861ab
ORCID for Patrick Stumpf: ORCID iD orcid.org/0000-0003-0862-0290

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Date deposited: 29 Jan 2019 17:30
Last modified: 09 Jan 2022 05:09

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Contributors

Author: Patrick Stumpf ORCID iD

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