Skip to main content
bioRxiv
  • Home
  • About
  • Submit
  • ALERTS / RSS
Advanced Search
New Results

Single cell transcriptome analysis of human pancreas reveals transcriptional signatures of aging and somatic mutation patterns

View ORCID ProfileMartin Enge, H. Efsun Arda, Marco Mignardi, John Beausang, Rita Bottino, Seung K. Kim, Stephen R. Quake
doi: https://doi.org/10.1101/108043
Martin Enge
1Department of Bioengineering, Stanford University, Stanford, California 94305, United States
  • Find this author on Google Scholar
  • Find this author on PubMed
  • Search for this author on this site
  • ORCID record for Martin Enge
H. Efsun Arda
2Department of Developmental Biology, Stanford University School of Medicine, California 94305, United States
  • Find this author on Google Scholar
  • Find this author on PubMed
  • Search for this author on this site
Marco Mignardi
1Department of Bioengineering, Stanford University, Stanford, California 94305, United States
5Division of Visual Information and Interaction, Department of Information Technology, Uppsala University, Uppsala SE-751 05, Sweden.
  • Find this author on Google Scholar
  • Find this author on PubMed
  • Search for this author on this site
John Beausang
1Department of Bioengineering, Stanford University, Stanford, California 94305, United States
  • Find this author on Google Scholar
  • Find this author on PubMed
  • Search for this author on this site
Rita Bottino
4Institute of Cellular Therapeutics, Allegheny Health Network, 320 East North Avenue, Pittsburgh PA 15212, USA
  • Find this author on Google Scholar
  • Find this author on PubMed
  • Search for this author on this site
Seung K. Kim
2Department of Developmental Biology, Stanford University School of Medicine, California 94305, United States
  • Find this author on Google Scholar
  • Find this author on PubMed
  • Search for this author on this site
Stephen R. Quake
1Department of Bioengineering, Stanford University, Stanford, California 94305, United States
3Chan Zuckerberg Biohub, San Francisco, United States
  • Find this author on Google Scholar
  • Find this author on PubMed
  • Search for this author on this site
  • Abstract
  • Full Text
  • Info/History
  • Metrics
  • Preview PDF
Loading

Abstract

As organisms age, cells accumulate genetic and epigenetic changes that eventually lead to impaired organ function or catastrophic transformation such as cancer. Since aging appears to be a stochastic process of increasing disorder1 cells in an organ will be individually affected in different ways - thus rendering bulk analyses of postmitotic adult tissues difficult to characterize. Here we directly measure the effects of aging in primary human tissue by performing single-cell transcriptome analysis of 2544 human pancreas cells from eight donors spanning six decades of life. We find that islet cells from older donors have increased levels of molecular disorder as measured both by noise in the transcriptome and by the number of cells which display inappropriate hormone expression, revealing a transcriptional instability associated with aging. By further analyzing the spectrum of somatic mutations in single cells, we found a specific age-dependent mutational signature characterized by C to A and C to G transversions. These mutations are indicators of oxidative stress and the signature is absent in single cells from human brain tissue or in a tumor cell line. We have used the single cell measurements of transcriptional noise and mutation level to identify molecular pathways correlated with these changes that could influence human disease. Our results demonstrate the feasibility of using single-cell RNA-seq data from primary cells to derive meaningful insights into the genetic processes that operate on aging human tissue and to determine molecular mechanisms coordinated with these processes.

Copyright 
The copyright holder for this preprint is the author/funder, who has granted bioRxiv a license to display the preprint in perpetuity. All rights reserved. No reuse allowed without permission.
Back to top
PreviousNext
Posted February 13, 2017.
Download PDF
Email

Thank you for your interest in spreading the word about bioRxiv.

NOTE: Your email address is requested solely to identify you as the sender of this article.

Enter multiple addresses on separate lines or separate them with commas.
Single cell transcriptome analysis of human pancreas reveals transcriptional signatures of aging and somatic mutation patterns
(Your Name) has forwarded a page to you from bioRxiv
(Your Name) thought you would like to see this page from the bioRxiv website.
CAPTCHA
This question is for testing whether or not you are a human visitor and to prevent automated spam submissions.
Share
Single cell transcriptome analysis of human pancreas reveals transcriptional signatures of aging and somatic mutation patterns
Martin Enge, H. Efsun Arda, Marco Mignardi, John Beausang, Rita Bottino, Seung K. Kim, Stephen R. Quake
bioRxiv 108043; doi: https://doi.org/10.1101/108043
Digg logo Reddit logo Twitter logo Facebook logo Google logo LinkedIn logo Mendeley logo
Citation Tools
Single cell transcriptome analysis of human pancreas reveals transcriptional signatures of aging and somatic mutation patterns
Martin Enge, H. Efsun Arda, Marco Mignardi, John Beausang, Rita Bottino, Seung K. Kim, Stephen R. Quake
bioRxiv 108043; doi: https://doi.org/10.1101/108043

Citation Manager Formats

  • BibTeX
  • Bookends
  • EasyBib
  • EndNote (tagged)
  • EndNote 8 (xml)
  • Medlars
  • Mendeley
  • Papers
  • RefWorks Tagged
  • Ref Manager
  • RIS
  • Zotero
  • Tweet Widget
  • Facebook Like
  • Google Plus One

Subject Area

  • Genomics
Subject Areas
All Articles
  • Animal Behavior and Cognition (3479)
  • Biochemistry (7318)
  • Bioengineering (5296)
  • Bioinformatics (20196)
  • Biophysics (9976)
  • Cancer Biology (7701)
  • Cell Biology (11249)
  • Clinical Trials (138)
  • Developmental Biology (6417)
  • Ecology (9915)
  • Epidemiology (2065)
  • Evolutionary Biology (13276)
  • Genetics (9352)
  • Genomics (12551)
  • Immunology (7673)
  • Microbiology (18937)
  • Molecular Biology (7417)
  • Neuroscience (40887)
  • Paleontology (298)
  • Pathology (1226)
  • Pharmacology and Toxicology (2125)
  • Physiology (3140)
  • Plant Biology (6837)
  • Scientific Communication and Education (1270)
  • Synthetic Biology (1891)
  • Systems Biology (5296)
  • Zoology (1084)