Impact Factor 4.151
2017 JCR, Clarivate Analytics 2018

Frontiers journals are at the top of citation and impact metrics

This article is part of the Research Topic

Integrated Omics for Defining Interactomes

Original Research ARTICLE Provisionally accepted The full-text will be published soon. Notify me

Front. Genet. | doi: 10.3389/fgene.2019.00002

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

 Patrick S. Stumpf1, 2, 3* and  Ben MacArthur1, 2, 3, 4
  • 1University of Southampton, United Kingdom
  • 2Institute for Life Sciences, University of Southampton, United Kingdom
  • 3Faculty of Medicine, University of Southampton, United Kingdom
  • 4Department of Mathematical Sciences, Faculty of Social, Human and Mathematical Sciences, University of Southampton, United Kingdom

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.

Keywords: machine learning (artificial intelligence), single-cell data, regulatory network, Eigenface approach, stem cell, Pluripotenct Stem Cells

Received: 12 Oct 2018; Accepted: 07 Jan 2019.

Edited by:

Sudipto Saha, Bose Institute, India

Reviewed by:

Nathan Weinstein, Institute of Ecology, National Autonomous University of Mexico, Mexico
Carlos Espinosa-Soto, Universidad Autónoma de San Luis Potosí, Mexico  

Copyright: © 2019 Stumpf and MacArthur. This is an open-access article distributed under the terms of the Creative Commons Attribution License (CC BY). The use, distribution or reproduction in other forums is permitted, provided the original author(s) and the copyright owner(s) are credited and that the original publication in this journal is cited, in accordance with accepted academic practice. No use, distribution or reproduction is permitted which does not comply with these terms.

* Correspondence: Dr. Patrick S. Stumpf, University of Southampton, Southampton, United Kingdom,