Description

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

Martin Enge^1,6, H. Efsun Arda², Marco Mignardi^1,5, John Beausang¹, Rita Bottino ⁴, Seung K. Kim² & Stephen R. Quake^1,3

¹Department of Bioengineering, Stanford University, Stanford, California 94305, United States; ²Department of Developmental Biology, Stanford University School of Medicine, California 94305, United States; ³Chan Zuckerberg Biohub, San Francisco, United States; ⁴Institute of Cellular Therapeutics, Allegheny Health Network, 320 East North Avenue, Pittsburgh PA 15212, USA; ⁵Division of Visual Information and Interaction, Department of Information Technology, Uppsala University, Uppsala SE-751 05, Sweden; ⁶Current address: Department of Oncology-Pathology Karolinska Institutet, Stockholm, Sweden

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 disorder cells in an organ will be individually affected in different ways - thus rendering bulk analyses of postmitotic adult tissues difficult to characterize. We directly measured 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.