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Single-cell analysis of human pancreas reveals transcriptional signatures of aging and somatic mutation patterns
Uppsala University, Disciplinary Domain of Science and Technology, Mathematics and Computer Science, Department of Information Technology, Division of Visual Information and Interaction. Uppsala University, Disciplinary Domain of Science and Technology, Mathematics and Computer Science, Department of Information Technology, Computerized Image Analysis and Human-Computer Interaction. Uppsala University, Science for Life Laboratory, SciLifeLab. Stanford Univ, Dept Bioengn & Appl Phys, Stanford, CA 94305 USA.
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2017 (English)In: Cell, ISSN 0092-8674, E-ISSN 1097-4172, Vol. 171, no 2, p. 321-330.e14Article in journal (Refereed) Published
Abstract [en]

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.

Place, publisher, year, edition, pages
2017. Vol. 171, no 2, p. 321-330.e14
National Category
Medical Biotechnology (with a focus on Cell Biology (including Stem Cell Biology), Molecular Biology, Microbiology, Biochemistry or Biopharmacy)
Research subject
Computerized Image Processing
Identifiers
URN: urn:nbn:se:uu:diva-333672DOI: 10.1016/j.cell.2017.09.004ISI: 000412346100010PubMedID: 28965763OAI: oai:DiVA.org:uu-333672DiVA, id: diva2:1157405
Funder
Knut and Alice Wallenberg Foundation, KAW 2013.0391Swedish Research Council, 2015-00599Available from: 2017-09-28 Created: 2017-11-15 Last updated: 2018-01-10Bibliographically approved

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Mignardi, Marco

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