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      Machine Learning of Stem Cell Identities From Single-Cell Expression Data via Regulatory Network Archetypes

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          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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          Most cited references 39

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          Isolation of a pluripotent cell line from early mouse embryos cultured in medium conditioned by teratocarcinoma stem cells.

          This report describes the establishment directly from normal preimplantation mouse embryos of a cell line that forms teratocarcinomas when injected into mice. The pluripotency of these embryonic stem cells was demonstrated conclusively by the observation that subclonal cultures, derived from isolated single cells, can differentiate into a wide variety of cell types. Such embryonic stem cells were isolated from inner cell masses of late blastocysts cultured in medium conditioned by an established teratocarcinoma stem cell line. This suggests that such conditioned medium might contain a growth factor that stimulates the proliferation or inhibits the differentiation of normal pluripotent embryonic cells, or both. This method of obtaining embryonic stem cells makes feasible the isolation of pluripotent cells lines from various types of noninbred embryo, including those carrying mutant genes. The availability of such cell lines should made possible new approaches to the study of early mammalian development.
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            From few to many: illumination cone models for face recognition under variable lighting and pose

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              Accounting for technical noise in single-cell RNA-seq experiments.

              Single-cell RNA-seq can yield valuable insights about the variability within a population of seemingly homogeneous cells. We developed a quantitative statistical method to distinguish true biological variability from the high levels of technical noise in single-cell experiments. Our approach quantifies the statistical significance of observed cell-to-cell variability in expression strength on a gene-by-gene basis. We validate our approach using two independent data sets from Arabidopsis thaliana and Mus musculus.
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                Author and article information

                Contributors
                Journal
                Front Genet
                Front Genet
                Front. Genet.
                Frontiers in Genetics
                Frontiers Media S.A.
                1664-8021
                22 January 2019
                2019
                : 10
                Affiliations
                1Centre for Human Development, Stem Cells and Regeneration, Faculty of Medicine, University of Southampton , Southampton, United Kingdom
                2Institute for Life Sciences, University of Southampton , Southampton, United Kingdom
                3Mathematical Sciences, University of Southampton , Southampton, United Kingdom
                Author notes

                Edited by: Sudipto Saha, Bose Institute, India

                Reviewed by: Nathan Weinstein, National Autonomous University of Mexico, Mexico; Carlos Espinosa-Soto, Universidad Autónoma de San Luis Potosí, Mexico

                *Correspondence: Patrick S. Stumpf ps.stumpf@ 123456soton.ac.uk

                This article was submitted to Systems Biology, a section of the journal Frontiers in Genetics

                Article
                10.3389/fgene.2019.00002
                6349820
                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.

                Page count
                Figures: 6, Tables: 0, Equations: 3, References: 68, Pages: 12, Words: 9653
                Funding
                Funded by: Biotechnology and Biological Sciences Research Council 10.13039/501100000268
                Award ID: BB/L000512/1
                Funded by: Medical Research Council 10.13039/501100000265
                Award ID: MC_PC_15078
                Categories
                Genetics
                Original Research

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