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. 2016 Jan 26;12(1):e1005793.
doi: 10.1371/journal.pgen.1005793. eCollection 2016 Jan.

Genetic Variation, Not Cell Type of Origin, Underlies the Majority of Identifiable Regulatory Differences in iPSCs

Courtney K Burrows  1 , Nicholas E Banovich  1 , Bryan J Pavlovic  1 , Kristen Patterson  1 , Irene Gallego Romero  1 , Jonathan K Pritchard  2   3 , Yoav Gilad  1
Affiliations

Genetic Variation, Not Cell Type of Origin, Underlies the Majority of Identifiable Regulatory Differences in iPSCs

Courtney K Burrows et al. PLoS Genet. .

Abstract

The advent of induced pluripotent stem cells (iPSCs) revolutionized human genetics by allowing us to generate pluripotent cells from easily accessible somatic tissues. This technology can have immense implications for regenerative medicine, but iPSCs also represent a paradigm shift in the study of complex human phenotypes, including gene regulation and disease. Yet, an unresolved caveat of the iPSC model system is the extent to which reprogrammed iPSCs retain residual phenotypes from their precursor somatic cells. To directly address this issue, we used an effective study design to compare regulatory phenotypes between iPSCs derived from two types of commonly used somatic precursor cells. We find a remarkably small number of differences in DNA methylation and gene expression levels between iPSCs derived from different somatic precursors. Instead, we demonstrate genetic variation is associated with the majority of identifiable variation in DNA methylation and gene expression levels. We show that the cell type of origin only minimally affects gene expression levels and DNA methylation in iPSCs, and that genetic variation is the main driver of regulatory differences between iPSCs of different donors. Our findings suggest that studies using iPSCs should focus on additional individuals rather than clones from the same individual.

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Conflict of interest statement

JKP is on the advisory board of 23andMe with stock options. No other competing interests exist.

Figures

Fig 1
Fig 1. Study design.
A schematic of the study design. Three independent iPSC lines were generated from LCLs and one from fibroblasts.
Fig 2
Fig 2. Hierarchical clustering and principal components analysis.
Hierarchical clustering using the complete linkage method and Euclidean distance from autosomal loci for (a) DNA methylation data (n = 445,277 probes) and (b) gene expression data (n = 10,648 autosomal genes).
Fig 3
Fig 3. Differential methylation and gene expression between the four cell types (L-iPSC, F-iPSC, LCLs and fibroblasts).
(a) A Venn diagram of differentially methylated (DM) loci (FDR of 5%) overlapping between different contrasts. (b) Venn diagram of differentially expressed (DE) genes (FDR of 5%) overlapping between different contrasts. (c) Heatmaps of the DNA methylation and gene expression levels where each row corresponds to a gene (labeled on the right). DNA methylation levels represent the average of all loci DM between L-iPSCs and F-iPSCs nearby the corresponding gene.
Fig 4
Fig 4. Histograms of P-values from DE tests.
Histograms of P-Values from differential expression analysis.
Fig 5
Fig 5. Contribution of individual differences versus cell type of origin to methylation and expression levels.
Estimated contribution of inter-individual differences and cell type of origin effects on variation in (a) methylation and (b) gene expression levels from a linear mixed effect model. There is a significant difference in the mean proportion of variation explained by individual and cell type of origin (P < 10−15).
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