Details

Autor(en) / Beteiligte

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

Titel

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

Ist Teil von

• Cell stem cell, 2017-04, Vol.20 (4), p.518-532.e9

Ort / Verlag

United States: Elsevier Inc

Erscheinungsjahr

2017

Link zum Volltext

Quelle

MEDLINE

Beschreibungen/Notizen

• 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. [Display omitted] •Gene expression analysis characterizes 317 human iPSC lines from 101 individuals•eQTLs contribute significantly to a cross individual variation in iPSC lines•Polycomb target genes are a significant source of non-genetic variation•Predictive networks highlight candidate key drivers of differentiation efficiency Using large-scale analyses of over 300 iPSC lines, Chang, Quertermous, Lemischka, and colleagues of the NHLBI NextGen consortium examine sources of gene expression variation between lines and illustrate how this approach can identify genetic and non-genetic drivers relevant to line variation with implications for iPSC characterization and disease modeling.