Cited by 581Single-cell and spatial transcriptomicsPancreatic function and diabetesGenetics, Aging, and Longevity in Model Organisms

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Single-Cell Analysis of Human Pancreas Reveals Transcriptional Signatures of Aging and Somatic Mutation Patterns

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Enge, Martin, Arda, H. Efsun, Mignardi, Marco, Beausang, John, Bottino, Rita, Kim, Seung K., Quake, Stephen R.·2017·Cell

DOI 10.1016/j.cell.2017.09.004PMC PMC6047899

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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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STAR: ultrafast universal RNA-seq aligner
Dobin, A., Davis, C. A., Schlesinger, F., Drenkow, J., Zaleski, C., Jha, S., Batut, P., Chaisson, M., & Gingeras, T. R. (2013) · Bioinformatics · doi:10.1093/bioinformatics/bts635 · Cited by 51,709
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Martin, M. (2011) · EMBnet.journal · doi:10.14806/ej.17.1.200 · Cited by 33,889
Untitled work
(2008) · Journal of Machine Learning Research · Cited by 33,051
The Genome Analysis Toolkit: A MapReduce framework for analyzing next-generation DNA sequencing data
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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.
ORCID: Enge, Martin
Arda, H. Efsun
Mignardi, Marco
Bottino, Rita
Kim, Seung K.
Quake, Stephen R.
Venue: Cell
Publisher: Elsevier BV
Published: 2017-10
Volume: 171
Issue: 2
Pages: 321-330.e14
ISSN: 0092-8674 (ISSN-L)
DOI: 10.1016/j.cell.2017.09.004
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: www.elsevier.com/tdm/userlicense/1.0/ (tdm)
www.elsevier.com/open-access/userlicense/1.0/ (vor, 369 d)
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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Single-Cell Analysis of Human Pancreas Reveals Transcriptional Signatures of Aging and Somatic Mutation Patterns

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