Analysis of Transcriptional Variability in a Large Human iPSC Library Reveals Genetic and Non-genetic Determinants of Heterogeneity

Ivan Carcamo-Orive; Gabriel E. Hoffman; Paige Cundiff; Noam D. Beckmann; Sunita L. D'Souza; Joshua W. Knowles; Achchhe Patel; Dimitri Papatsenko; Fahim Abbasi; Gerald M. Reaven; Sean Whalen; Philip Lee; Mohammad Shahbazi; Marc Y.R. Henrion; Kuixi Zhu; Sven Wang; Panos Roussos; Eric E. Schadt; Gaurav Pandey; Rui Chang; Thomas Quertermous; Ihor Lemischka

doi:10.1016/j.stem.2016.11.005

Analysis of Transcriptional Variability in a Large Human iPSC Library Reveals Genetic and Non-genetic Determinants of Heterogeneity

Ivan Carcamo-Orive, Gabriel E. Hoffman, Paige Cundiff, Noam D. Beckmann, Sunita L. D'Souza, Joshua W. Knowles, Achchhe Patel, Dimitri Papatsenko, Fahim Abbasi, Gerald M. Reaven, Sean Whalen, Philip Lee, Mohammad Shahbazi, Marc Y.R. Henrion, Kuixi Zhu, Sven Wang, Panos Roussos, Eric E. Schadt, Gaurav Pandey, Rui ChangThomas Quertermous, Ihor Lemischka

Research output: Contribution to journal › Article › peer-review

194 Scopus citations

Abstract

Variability in induced pluripotent stem cell (iPSC) lines remains a concern for disease modeling and regenerative medicine. We have used RNA-sequencing analysis and linear mixed models to examine the sources of gene expression variability in 317 human iPSC lines from 101 individuals. We found that ∼50% of genome-wide expression variability is explained by variation across individuals and identified a set of expression quantitative trait loci that contribute to this variation. These analyses coupled with allele-specific expression show that iPSCs retain a donor-specific gene expression pattern. Network, pathway, and key driver analyses showed that Polycomb targets contribute significantly to the non-genetic variability seen within and across individuals, highlighting this chromatin regulator as a likely source of reprogramming-based variability. Our findings therefore shed light on variation between iPSC lines and illustrate the potential for our dataset and other similar large-scale analyses to identify underlying drivers relevant to iPSC applications.

Original language	English (US)
Pages (from-to)	518-532.e9
Journal	Cell Stem Cell
Volume	20
Issue number	4
DOIs	https://doi.org/10.1016/j.stem.2016.11.005
State	Published - Apr 6 2017
Externally published	Yes

Keywords

Polycomb targets
allelic imbalance
differentiation variability
eQTL
iPSC library
key drivers
network analysis
transcriptional variability
variance partition

ASJC Scopus subject areas

Molecular Medicine
Genetics
Cell Biology

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Carcamo-Orive, I., Hoffman, G. E., Cundiff, P., Beckmann, N. D., D'Souza, S. L., Knowles, J. W., Patel, A., Papatsenko, D., Abbasi, F., Reaven, G. M., Whalen, S., Lee, P., Shahbazi, M., Henrion, M. Y. R., Zhu, K., Wang, S., Roussos, P., Schadt, E. E., Pandey, G., ... Lemischka, I. (2017). Analysis of Transcriptional Variability in a Large Human iPSC Library Reveals Genetic and Non-genetic Determinants of Heterogeneity. Cell Stem Cell, 20(4), 518-532.e9. https://doi.org/10.1016/j.stem.2016.11.005

Carcamo-Orive, I, Hoffman, GE, Cundiff, P, Beckmann, ND, D'Souza, SL, Knowles, JW, Patel, A, Papatsenko, D, Abbasi, F, Reaven, GM, Whalen, S, Lee, P, Shahbazi, M, Henrion, MYR, Zhu, K, Wang, S, Roussos, P, Schadt, EE, Pandey, G, Chang, R, Quertermous, T & Lemischka, I 2017, 'Analysis of Transcriptional Variability in a Large Human iPSC Library Reveals Genetic and Non-genetic Determinants of Heterogeneity', Cell Stem Cell, vol. 20, no. 4, pp. 518-532.e9. https://doi.org/10.1016/j.stem.2016.11.005

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title = "Analysis of Transcriptional Variability in a Large Human iPSC Library Reveals Genetic and Non-genetic Determinants of Heterogeneity",

abstract = "Variability in induced pluripotent stem cell (iPSC) lines remains a concern for disease modeling and regenerative medicine. We have used RNA-sequencing analysis and linear mixed models to examine the sources of gene expression variability in 317 human iPSC lines from 101 individuals. We found that ∼50% of genome-wide expression variability is explained by variation across individuals and identified a set of expression quantitative trait loci that contribute to this variation. These analyses coupled with allele-specific expression show that iPSCs retain a donor-specific gene expression pattern. Network, pathway, and key driver analyses showed that Polycomb targets contribute significantly to the non-genetic variability seen within and across individuals, highlighting this chromatin regulator as a likely source of reprogramming-based variability. Our findings therefore shed light on variation between iPSC lines and illustrate the potential for our dataset and other similar large-scale analyses to identify underlying drivers relevant to iPSC applications.",

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AU - Carcamo-Orive, Ivan

AU - Hoffman, Gabriel E.

AU - Cundiff, Paige

AU - Beckmann, Noam D.

AU - D'Souza, Sunita L.

AU - Knowles, Joshua W.

AU - Patel, Achchhe

AU - Papatsenko, Dimitri

AU - Abbasi, Fahim

AU - Reaven, Gerald M.

AU - Whalen, Sean

AU - Lee, Philip

AU - Shahbazi, Mohammad

AU - Henrion, Marc Y.R.

AU - Zhu, Kuixi

AU - Wang, Sven

AU - Roussos, Panos

AU - Schadt, Eric E.

AU - Pandey, Gaurav

AU - Chang, Rui

AU - Quertermous, Thomas

AU - Lemischka, Ihor

PY - 2017/4/6

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N2 - Variability in induced pluripotent stem cell (iPSC) lines remains a concern for disease modeling and regenerative medicine. We have used RNA-sequencing analysis and linear mixed models to examine the sources of gene expression variability in 317 human iPSC lines from 101 individuals. We found that ∼50% of genome-wide expression variability is explained by variation across individuals and identified a set of expression quantitative trait loci that contribute to this variation. These analyses coupled with allele-specific expression show that iPSCs retain a donor-specific gene expression pattern. Network, pathway, and key driver analyses showed that Polycomb targets contribute significantly to the non-genetic variability seen within and across individuals, highlighting this chromatin regulator as a likely source of reprogramming-based variability. Our findings therefore shed light on variation between iPSC lines and illustrate the potential for our dataset and other similar large-scale analyses to identify underlying drivers relevant to iPSC applications.

AB - Variability in induced pluripotent stem cell (iPSC) lines remains a concern for disease modeling and regenerative medicine. We have used RNA-sequencing analysis and linear mixed models to examine the sources of gene expression variability in 317 human iPSC lines from 101 individuals. We found that ∼50% of genome-wide expression variability is explained by variation across individuals and identified a set of expression quantitative trait loci that contribute to this variation. These analyses coupled with allele-specific expression show that iPSCs retain a donor-specific gene expression pattern. Network, pathway, and key driver analyses showed that Polycomb targets contribute significantly to the non-genetic variability seen within and across individuals, highlighting this chromatin regulator as a likely source of reprogramming-based variability. Our findings therefore shed light on variation between iPSC lines and illustrate the potential for our dataset and other similar large-scale analyses to identify underlying drivers relevant to iPSC applications.

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KW - differentiation variability

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KW - network analysis

KW - transcriptional variability

KW - variance partition

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Analysis of Transcriptional Variability in a Large Human iPSC Library Reveals Genetic and Non-genetic Determinants of Heterogeneity

Abstract

Keywords

ASJC Scopus subject areas

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