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Single-cell transcriptome sequencing of 18,787 human induced pluripotent stem cells identifies differentially primed subpopulations

Quan H. Nguyen, Samuel W. Lukowski, Han Sheng Chiu, Anne Senabouth, Timothy J. C. Bruxner, Angelika N. Christ, Nathan J. Palpant, Joseph E. Powell
doi: https://doi.org/10.1101/119255
Quan H. Nguyen
1Institute for Molecular Bioscience, University of Queensland, Brisbane, Queensland, 4072, Australia
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Samuel W. Lukowski
1Institute for Molecular Bioscience, University of Queensland, Brisbane, Queensland, 4072, Australia
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Han Sheng Chiu
1Institute for Molecular Bioscience, University of Queensland, Brisbane, Queensland, 4072, Australia
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Anne Senabouth
1Institute for Molecular Bioscience, University of Queensland, Brisbane, Queensland, 4072, Australia
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Timothy J. C. Bruxner
1Institute for Molecular Bioscience, University of Queensland, Brisbane, Queensland, 4072, Australia
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Angelika N. Christ
1Institute for Molecular Bioscience, University of Queensland, Brisbane, Queensland, 4072, Australia
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Nathan J. Palpant
1Institute for Molecular Bioscience, University of Queensland, Brisbane, Queensland, 4072, Australia
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Joseph E. Powell
1Institute for Molecular Bioscience, University of Queensland, Brisbane, Queensland, 4072, Australia
2Queensland Brain Institute, University of Queensland, Brisbane, Queensland, 4072, Australia
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Summary

For pluripotent stem cells, transcriptional profiling is central to discovering the key genes and gene networks governing the undifferentiated state. However, the heterogeneity of cell states represented in pluripotent cultures have not been described at the transcriptional level. Since gene expression is highly heterogeneous between cells, single-cell RNA sequencing (scRNA-seq) can be used to increase our understanding of how individual pluripotent cells function. Here, we present the scRNA-seq results of 18,787 individual WTC CRISPRi human induced pluripotent stem cells. Four subpopulations were distinguishable on the basis of their pluripotent state including: quiescent (48.3%), proliferative (47.8%), early-primed for differentiation (2.8%) and late-primed for differentiation (1.1%). We identified novel genes and pathways defining each of the subpopulations and developed a multigenic prediction model to accurately classify single cells into subpopulations. This study provides a benchmark single cell dataset that expands our understanding of the cellular complexity of pluripotency.

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The copyright holder for this preprint is the author/funder, who has granted bioRxiv a license to display the preprint in perpetuity. It is made available under a CC-BY 4.0 International license.
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Posted March 22, 2017.
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Single-cell transcriptome sequencing of 18,787 human induced pluripotent stem cells identifies differentially primed subpopulations
Quan H. Nguyen, Samuel W. Lukowski, Han Sheng Chiu, Anne Senabouth, Timothy J. C. Bruxner, Angelika N. Christ, Nathan J. Palpant, Joseph E. Powell
bioRxiv 119255; doi: https://doi.org/10.1101/119255
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Single-cell transcriptome sequencing of 18,787 human induced pluripotent stem cells identifies differentially primed subpopulations
Quan H. Nguyen, Samuel W. Lukowski, Han Sheng Chiu, Anne Senabouth, Timothy J. C. Bruxner, Angelika N. Christ, Nathan J. Palpant, Joseph E. Powell
bioRxiv 119255; doi: https://doi.org/10.1101/119255

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