Influence of donor age on induced pluripotent stem cells



Induced pluripotent stem cells (iPSCs) are being pursued as a source of cells for autologous therapies, many of which will be aimed at aged patients. To explore the impact of age on iPSC quality, we produced iPSCs from blood cells of 16 donors aged 21–100. We find that iPSCs from older donors retain an epigenetic signature of age, which can be reduced through passaging. Clonal expansion via reprogramming also enables the discovery of somatic mutations present in individual donor cells, which are missed by bulk sequencing methods. We show that exomic mutations in iPSCs increase linearly with age, and all iPSC lines analyzed carry at least one gene-disrupting mutation, several of which have been associated with cancer or dysfunction. Unexpectedly, elderly donors (>90 yrs) harbor fewer mutations than predicted, likely due to a contracted blood progenitor pool. These studies establish that donor age is associated with an increased risk of abnormalities in iPSCs and will inform clinical development of reprogramming technology.

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Figure 1: Study overview and epigenetic analyses.
Figure 2: Somatic mutations in iPSCs.


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This work is supported by a Scripps Translational Science Institute pilot award (5 UL1 TR001114) from Scripps Genomic Medicine, a NIH-NCATS Clinical and Translational Science Award (CTSA; 5 UL1 RR025774) to STSI. Further support is provided by the NextGen Consortium NHLBI 5 U01 HL107436 to STSI and TSRI (E.J.T. and K.K.B.), and U54GM114833 to STSI (A.T.).

Author information




Conceptualization, K.K.B., A.T., E.J.T.; methodology, K.K.B., V.L.S., A.T.; formal analysis, A.T., G.A.E.; investigation, V.L.S., W.F., A.T.; resources, A.T., K.K.B., E.J.T.; writing—original draft, A.T., K.K.B.; writing—review and editing, K.K.B., V.L.S., W.F., A.T., E.J.T.; visualization, V.L.S., K.K.B., A.T.; supervision, A.T., K.K.B., E.J.T.; project administration, A.T., K.K.B., E.J.T.; funding acquisition, A.T., K.K.B., E.J.T.

Corresponding authors

Correspondence to Kristin K Baldwin or Ali Torkamani.

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Competing interests

The authors declare no competing financial interests.

Integrated supplementary information

Supplementary Figure 1 Characterization of PBMC-derived iPSCs

A) Graph shows no correlation (Pearson r -0.22) between the number of chromosomal abnormalities in PBMC-derived iPSCs and the age of the donor. B) Reprogramming efficiency did not correlate with the age of the donor. C) Table showing the characterization of all iPSC lines used in the study. iPSC lines were tested for karyotyping and pluripotency markers, TRA 1-60 and SSEA4, by cytofluorimetric analysis. Table shows the lines used for epigenetic analysis, as well as, the ones used for somatic mutation calls. Passage for each assay is shown. Abbreviations: HTN: Hypertension; CAD: Coronary Artery Disease; MI: Myocardial infarction; CA: Cancer; PAD: Peripheral Artery Disease; GERD: Gastroesophageal reflux disease; SVT: Supraventricular tachycardia. Blood donors were identified either through participation in the Normal Blood Donor service at TSRI (a walk in blood donation program), or through participation in other studies ongoing in the Topol laboratory including the Geneheart (individuals diagnosed with coronary artery disease) and Wellderly (individuals greater than 80 years old with no common chronic conditions) cohorts. While the majority of blood donors appeared to be normal and healthy, some donors had been diagnosed with common aging associated diseases reflective of those seen in the general population. None of these conditions are associated with genome instability in blood. While it is possible that rare donors could have undiagnosed conditions that could impact the number of mutations in blood cells, these are expected to be very rare and due to the large number of donors and cell lines we sampled, such events would not impact overall trends we report. Some of the iPSC lines have been banked and are available at WiCell, in a collection called Topol Lab’s Next Gen Cell Lines. The cell line numbers in Table C correspond to the following accession numbers in WiCell: Young: NBD5332#3 (SCRP4203i), NBD5396#3 (SCRP4403i), NBD5433#5 (SCRP4505i), NBD5532#2 (SCRP4602i), NBD5361#5 (SCRP4305i). Middle: C087#2 (SCRP0202i), C512#17 (SCRP0517i), C151#7 (SCRP0307i), C568#1 (SCRP0601i), C664#9 (SCRP0709i). Elderly: HE463#5 (SCRP2305i), HE463#7 (SCRP2307i), HE463#10 (SCRP2310i), HE787#3 (SCRP2503i), HE787#6 (SCRP2506i), HE787#8 (SCRP2508i), C939#2 (SCRP0802i), HE019#1 (SCRP2101i), HE019#6 (SCRP2106i), HE019#15 (SCRP2115i), HE157#8 (SCRP2208i), HE157#10 (SCRP2210i), HE157#11 (SCRP2211i), HE554#7 (SCRP2407i), HE554#9 (SCRP2409i), HE554#11 (SCRP2411i).

Supplementary Figure 2 Methylation State of Age-related CpGs in iPSCs and PBMCs

This heatmap depicts the methylation state of CpGs that are predictive of biological age and are displayed for iPSCs and PBMCs of donors across all ages. Blue indicates methylated CpGs while red indicates unmethylated CpGs. CpGs are clustered based on their methylation pattern and samples are ordered by sample type (iPSC vs. PBMC) and donor age. Note that many iPSC CpG sites are strongly de-methylated (bright red) whereas CpG sites shift to intermediate methylation (yellow/orange) across older individuals.

Supplementary Figure 3 Methylation State of CpGs that Significantly Demethylate During Reprogramming of PBMCs to iPSCs

A) This boxplot depicts the methylation state of 3,896 CpG sites that significantly decline in methylation (p-value <0.05) and convert from mostly methylated to unmethylated during reprogramming of PBMCs to iPSCs (defined as CpGs with average an M-value > 1 (>67% methylation) in PBMCs and an average M-value < -1 (<33% methylation) in iPSCs). The methylation values of each CpG site are converted to a standardized Z-score. These sites globally display methylation levels that are, on average, ~1/3rd of a standard deviation higher in iPSCs derived from elderly vs. young individuals (center of boxplot - generalized Friedman rank sum test with replicated data, p-value < 2.2·10-16). B) DNA methylation profiles for selected iPSC lines were analyzed at early/intermediate and late passages. The methylation status of 3 CpG sites, whose methylation is not restored with passaging is plotted with black circle representing M-value for iPSCs at early passage and red triangle M-value for late passage in selected iPSC lines. TRAPPC3 (eR2 = 0.38 p-value =0.0002; (lR2)= 0.53 p-value = 0.0002), AHRR (eR2 = 0.24 p-value=0.0035; (lR2)= 0.24 p-value = 0.026), MYADML2 (eR2 = 0.35 p-value=0.0003; (lR2)= 0.20 p-value = 0.04). Pearson correlation with two-tailed p-value. C) Quantitative RT-PCR for TET1, 2A, 2B, 3 and DNMT1, 3A, 3B. Correlation between gene expression and age is plotted. Relative mRNA expression was determined by the ΔΔ-Ct method with the average of young donors used as control. Gene expression was normalized using PPIA (peptidylprolyl isomerase A) as reference gene. TET1 (R2 = 0.21 p-value=0.00015), TET2A (R2 = 0.248 p-value=0.0005), TET2B (R2 = 0.04 p-value=0.15), TET3 (R2 = 0.46 p-value<0.0001). Pearson correlation with two-tailed p-value.

Supplementary Figure 4 Methylation State of CpGs Differentially Methylated During Reprogramming and Associated with Age.

This heatmap depicts the methylation state of all CpGs that show an absolute M-value change of 1.0 (2-fold increase or decrease in the ratio of methylated to unmethylated CpGs in the cellular population) during reprogramming, and whose methylation status in iPSCs is associated with age of the donor. Blue indicates methylated CpGs while red indicates unmethylated CpGs. CpGs are clustered based on their methylation pattern and samples are ordered by sample type (iPSC vs. PBMC) and donor age. Genes are labeled on the right side and the corresponding probe identifier is labeled on the left side of the heatmap.

Supplementary information

Supplementary Text and Figures

Supplementary Figures 1–4 (PDF 6871 kb)

Supplementary Dataset 1

CpG probes predictive of biological age. (XLSX 225 kb)

Supplementary Dataset 2

CpG probes associated with age of the donor. (XLSX 81 kb)

Supplementary Dataset 3

Somatic Mutation Coverage and Molecular Impact. (XLSX 86 kb)

Supplementary Dataset 4

Coverage and Somatic Mutation Counts. (XLSX 11 kb)

Supplementary Dataset 5

Somatic Mosaicism. (XLSX 12 kb)

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Lo Sardo, V., Ferguson, W., Erikson, G. et al. Influence of donor age on induced pluripotent stem cells. Nat Biotechnol 35, 69–74 (2017).

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