Back

Somatic mutation landscape revealed by non-invasive iPSC derivation from urine cells

Bae, T.; Tomasini, L.; Klimczak, L. J.; Kayastha, M.; Suvakov, M.; Jang, Y.; Jourdon, A.; Gordenin, D. A.; Vaccarino, F. M.; Abyzov, A.

2026-04-14 genomics
10.64898/2026.04.11.717904 bioRxiv
Show abstract

Somatic mutations that arise post-zygotically create genetic diversity among normal human cells and provide key insights into human development and aging. Fibroblast-derived induced pluripotent stem cells (iPSCs) have proved to be a useful system for disease modelling; however, due to their clonal nature, iPSC lines carry somatic mutations inherited from the founder cells, raising concerns about their genomic integrity. At the same time, this clonality enables single-cell-level discovery of somatic mutations and the reconstruction of developmental lineages. In living individuals, though, this approach requires invasive biopsies and is limited to skin-derived lineages. Here, we generated 33 urine-derived iPSC lines from four males representing two father- son relationships, performed shallow whole-genome sequencing of the lines and analyzed somatic mutations. Derived iPSCs representing single cells from urine carried a few hundred of somatic single-nucleotide variants per genome, dominated by endogenous, clock-like mutational signatures and lacking environmental imprints such as UV-associated mutations. Copy-number analysis identified somatic CNVs in most of the lines and revealed higher CNV burdens in fathers than in sons, consistent with age-related structural mosaicism. Shared mutations across lines enabled reconstruction of cell lineage phylogenetic trees. In summary, urine-derived iPSCs showed genomic alterations comparable to those in fibroblast-derived iPSC lines and represent a valuable non-invasive alternative for disease modeling. Overall, this study provides the first genome-wide characterization of somatic mutations in urine-derived iPSCs and establishes them as a practical and non-invasive platform for charting somatic mutation landscapes and tracing developmental lineages in living humans.

Matching journals

The top 8 journals account for 50% of the predicted probability mass.

1
Nature Communications
4913 papers in training set
Top 11%
14.4%
2
Genome Medicine
154 papers in training set
Top 0.3%
12.4%
3
Cell Genomics
162 papers in training set
Top 0.4%
6.8%
4
Nucleic Acids Research
1128 papers in training set
Top 4%
4.9%
5
Scientific Reports
3102 papers in training set
Top 24%
4.9%
6
Cell Reports
1338 papers in training set
Top 18%
2.9%
7
Science Translational Medicine
111 papers in training set
Top 2%
2.1%
8
Cell Reports Methods
141 papers in training set
Top 2%
1.9%
50% of probability mass above
9
Frontiers in Genetics
197 papers in training set
Top 4%
1.9%
10
Nature Medicine
117 papers in training set
Top 2%
1.8%
11
Nature Aging
51 papers in training set
Top 0.9%
1.8%
12
Genome Biology
555 papers in training set
Top 4%
1.7%
13
Communications Biology
886 papers in training set
Top 8%
1.7%
14
EMBO Molecular Medicine
85 papers in training set
Top 2%
1.7%
15
eLife
5422 papers in training set
Top 43%
1.7%
16
Genomics, Proteomics & Bioinformatics
171 papers in training set
Top 4%
1.5%
17
Stem Cell Reports
118 papers in training set
Top 0.5%
1.5%
18
Computational and Structural Biotechnology Journal
216 papers in training set
Top 6%
1.3%
19
Aging Cell
144 papers in training set
Top 2%
1.3%
20
Nature Genetics
240 papers in training set
Top 6%
1.2%
21
PLOS Genetics
756 papers in training set
Top 11%
1.2%
22
Human Genomics
21 papers in training set
Top 0.2%
1.2%
23
The American Journal of Human Genetics
206 papers in training set
Top 3%
1.1%
24
Human Molecular Genetics
130 papers in training set
Top 3%
0.9%
25
iScience
1063 papers in training set
Top 27%
0.9%
26
Disease Models & Mechanisms
119 papers in training set
Top 2%
0.9%
27
Advanced Science
249 papers in training set
Top 16%
0.9%
28
BMC Biology
248 papers in training set
Top 4%
0.8%
29
Genome Research
409 papers in training set
Top 4%
0.7%
30
PLOS ONE
4510 papers in training set
Top 69%
0.7%