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Single-Cell Analysis of Human Pancreas Reveals Transcriptional Signatures of Aging and Somatic Mutation Patterns
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Abstract
As organisms age, cells accumulate genetic and epigenetic errors that eventually lead to impaired organ function or catastrophic transformation such as cancer. Because aging reflects a stochastic process of increasing disorder, cells in an organ will be individually affected in different ways, thus rendering bulk analyses of postmitotic adult cells difficult to interpret. Here, we directly measure the effects of aging in human tissue by performing single-cell transcriptome analysis of 2,544 human pancreas cells from eight donors spanning six decades of life. We find that islet endocrine cells from older donors display increased levels of transcriptional noise and potential fate drift. By determining the mutational history of individual cells, we uncover a novel mutational signature in healthy aging endocrine cells. Our results demonstrate the feasibility of using single-cell RNA sequencing (RNA-seq) data from primary cells to derive insights into genetic and transcriptional processes that operate on aging human tissue.
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“We also considered alternative measures of cell–cell heterogeneity using heuristically selected subsets of genes as in previous reports (Enge et al. 2017; Angelidis et al. 2019) and found that results were highly correlated with our primary method (Supplemental Fig. S12; Supplemental Methods).”
From Murine single-cell RNA-seq reveals cell-identity- and tissue-specific trajectories of aging (2019) · cites this paper as Enge et al. 2017
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“Two data sets of the human cell atlas were used for that, a Drop-seq data set measured in lung tissue52 and a Smart-seq2 data set measured in pancreas tissue51.”
From scPower accelerates and optimizes the design of multi-sample single cell transcriptomic studies (2021) · cites this paper as [51]
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“These observations are supported by a rapidly growing number of single cell RNA sequencing studies showing greater cell-to-cell variability in gene expression in human T cells and pancreatic cells, as well as differences in cellular mutation rates with age.47,48”
From Asynchronous, contagious and digital aging (2021) · cites this paper as [48]
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Metadata
- Authors
- Enge, M., Arda, H. E., Mignardi, M., Beausang, J., Bottino, R., Kim, S. K., & Quake, S. R.
- Affiliations
- Stanford University (US)
- Stanford Medicine (US)
- Uppsala University (SE)
- Allegheny Health Network (US)
- Corresponding author
- Quake, Stephen R.
- Venue
- Cell
- Publisher
- Elsevier BV
- Published
- 2017-10
- Volume
- 171
- Issue
- 2
- Pages
- 321-330.e14
- ISSN
- 0092-8674 (ISSN-L)
- PMC
- PMC6047899
- OpenAlex
- W2758815122
- Cited by
- 568
- References
- 56
- Citations by year
- 2026: 40 · 2025: 51 · 2024: 69 · 2023: 78 · 2022: 77 · 2021: 82 · 2020: 79 · 2019: 46 · 2018: 42 · 2017: 3
- FWCI
- 18.98
- Type
- journal article
- Language
- en
- Open access
- bronze
- doi.org
- Licence
- Locations
- Cell (publishedVersion)
- bioRxiv (Cold Spring Harbor Laboratory) (acceptedVersion)
- PubMed (publishedVersion)
- Europe PMC (PubMed Central) (acceptedVersion)
- PubMed Central (acceptedVersion)
- Funders
- California Institute for Regenerative Medicine (GC1R-06673)
- Center of Excellence for Stem Cell Genomics and NIH (U01-HL099999, U01-HL099995)
- NIH (UC4DK104211, DK10261201, P30DK116074-01)
- Helmsley Charitable Trust
- H.L. Snyder Foundation
- Elser Foundation
- JDRF (3-APF-2016-172-A-N)
- Wallenberg Research link at Stanford University (2013.0391)
- NIDDK (5T32DK007217-39)
- Swedish Research Council (2015-00599)
- Topics
- Life Sciences › Biochemistry, Genetics and Molecular Biology › Molecular Biology › Single-cell and spatial transcriptomics
- Health Sciences › Medicine › Endocrinology, Diabetes and Metabolism › Pancreatic function and diabetes
- Life Sciences › Biochemistry, Genetics and Molecular Biology › Aging › Genetics, Aging, and Longevity in Model Organisms
- Keywords
- human aging, pancreatic islets, endocrine cells, somatic mutations, mutational signatures, transcriptional noise, single-cell transcriptomics, tissue aging, cellular heterogeneity
- MeSH terms
- Adult, Aging, Child, Child, Preschool, Humans, Infant, Middle Aged, Mutation, Pancreas, Transcription, Genetic, Cellular Senescence, Sequence Analysis, RNA, Polymorphism, Single Nucleotide, Single-Cell Analysis
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