GEO series

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

GSE81547 Homo sapiens (human) Single-cell RNA-seq 2544 samples Submitted 2017/09/28 Platform GPL18573

At a glance

Single-cell RNA-seq · human · 2544 samples

What it is: Single-cell RNA-seqGene activity measured in thousands of individual cells, so cell types can be told apart.
Organism: Homo sapiens (human)
Samples: 2544 samples
Data you can get: A processed table (series matrix) and supplementary files from NCBI; the raw sequencing reads from ENA. Which of these hold counts depends on the study, so check the file list.

Summary (written by the submitters)

As organisms age, cells accumulate genetic and epigenetic changes that eventually lead to impaired organ function or catastrophic failure such as cancer. Here we describe a single-cell transcriptome analysis of 2544 human pancreas cells from donors, spanning six decades of life. We find that islet cells from older donors have increased levels of 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.

Read the full summary ↓

By analyzing the spectrum of somatic mutations in single cells from previously-healthy donors, we find a specific age-dependent mutational signature characterized by C to A and C to G transversions, indicators of oxidative stress, which is absent in single cells from human brain tissue or in a tumor cell line. Cells carrying a high load of such mutations also express higher levels of stress and senescence markers, including FOS, JUN, and the cytoplasmic superoxide dismutase SOD1, markers previously linked to pancreatic diseases with substantial age-dependent risk, such as type 2 diabetes mellitus and adenocarcinoma. Thus, our single-cell approach unveils gene expression changes and somatic mutations acquired in aging human tissue, and identifies molecular pathways induced by these genetic changes that could influence human disease. Also, 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 which molecular mechanisms are coordinated with these processes.

This dataset

Open GSE81547 in finder → Find more human RNA-seq datasets →

Download · which file do I need?

Direct links to NCBI and ENA, no account and no request form. Pick by what you want to do: take the small processed table to look, the supplementary files to re-analyse, or the original raw data to start over.

1 · Just have a look

Processed table

The series matrix: one table of values per sample as the submitters processed it. The smallest download, and it opens in Excel or R. Start here to see what the data look like.

Series matrix ↓

2 · Re-analyse it

Supplementary files

The count tables and other files the authors uploaded. Take only the samples you need, or the whole study as GSE81547_RAW.tar (it can run to tens of gigabytes).

All supplementary files ↗ Whole study · RAW.tar ↓

3 · Start from the raw reads

Raw sequencing reads

The original FASTQ files, straight from the sequencer, held at ENA. Large, and you align them yourself, so only choose this if you need to re-process the study from scratch.

Raw reads on ENA ↗

What the files usually look like: Single-cell studies usually ship their counts as 10x files (matrix.mtx, barcodes.tsv, features.tsv), an .h5 file, or an .rds / .h5ad object that Seurat or Scanpy can open. Look for those among the supplementary files.

Also filed as BioProject PRJNA322355 and SRA study SRP075496. Searching any of these in the dataset finder brings you back here.

NCBI GEO page ↗ Paper (PMID 28965763) ↗

Samples in this study

The sample list for this study is not cached yet. Press Load samples and it will be fetched from NCBI.

+ 2544 more — browse all 2544 samples with per-sample file links →

Datasets on a similar topic

GSE338521 Somatic mutations reveal the ontogeny of microglia in human aging 36 samples shares mutation, somatic, aging
GSE325674 Leveraging experimental evolution to extract predictive collateral drug response signatures in Ewings Sarcoma 73 samples shares signatures, aging
GSE300879 Identification of replicative aging and inflammatory aging signatures via whole-genome CRISPRi screens 16 samples shares signatures, aging
GSE301458 Single-cell analysis of human fibrous dysplasia bone reveals a fibrotic transcriptome and somatic, mosaic GNAS R201H/C mutations in endothelial, perivascular, and stromal cells 12 samples shares mutation, somatic
GSE298243 Single-Cell Profiling of DNA Methylation in Autism Spectrum Disorder Prefrontal Cortex Reveals Distinct Regulatory and Aging Signatures 49 samples shares signatures, aging
GSE282258 Imaging-based drug screening combined with moleuclar profiling identifies signatures and drivers of therapy resistance in pediatric AML [ATAC-seq] 44 samples shares signatures, aging
GSE282423 Imaging-based drug screening combined with moleuclar profiling identifies signatures and drivers of therapy resistance in pediatric AML [RNA-seq] 28 samples shares signatures, aging
GSE313307 Systemic infections alter cortical transcriptional signatures in Alzheimer′s disease 202 samples shares signatures

Search all human RNA-seq datasets in GEO →

Share this dataset

Metadata from NCBI GEO, cached and refreshed periodically — the NCBI page above is authoritative. Downloads link straight to NCBI/ENA; nothing is proxied through BioTransfer.