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 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 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

204 Citations (Scopus)

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
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
Publication status	Published - 6 Apr 2017
Externally published	Yes

Keywords

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

UN SDGs

This output contributes to the following UN Sustainable Development Goals (SDGs)

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10.1016/j.stem.2016.11.005

204 Citations
1 Other contribution

Erratum: Analysis of Transcriptional Variability in a Large Human iPSC Library Reveals Genetic and Non-genetic Determinants of Heterogeneity
Carcamo-Orive, I., Hoffman, G. E., Cundiff, P., Beckmann, N. D., D'Souza, S. L., Knowles, J. W., Patel, A., Hendry, C., Papatsenko, D., Abbasi, F., Reaven, G. M., Whalen, S., Lee, P., Shahbazi, M., Henrion, M., Zhu, K., Wang, S., Roussos, P., Schadt, E. E. & Pandey, G. & 3 others, Chang, R., Quertermous, T. & Lemischka, I., 6 Oct 2022, 1 p.
Research output: Other contribution

Open Access
2 Citations (Scopus)

Cite this

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., 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

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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.",

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

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

year = "2017",

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doi = "10.1016/j.stem.2016.11.005",

language = "English",

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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, M, 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

TY - JOUR

T1 - Analysis of Transcriptional Variability in a Large Human iPSC Library Reveals Genetic and Non-genetic Determinants of Heterogeneity

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

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

Y1 - 2017/4/6

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.

KW - allelic imbalance

KW - differentiation variability

KW - eQTL

KW - iPSC library

KW - key drivers

KW - network analysis

KW - Polycomb targets

KW - transcriptional variability

KW - variance partition

U2 - 10.1016/j.stem.2016.11.005

DO - 10.1016/j.stem.2016.11.005

M3 - Article

SN - 1934-5909

VL - 20

SP - 518-532.e9

JO - Cell Stem Cell

JF - Cell Stem Cell

IS - 4

ER -

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

Abstract

Keywords

UN SDGs

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Research output

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

Cite this