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Stem cells tightly regulate dead cell clearance to maintain tissue fitness [scRNA-seq]

GSE271100 Mus musculus (mouse) Single-cell RNA-seq 1037 samples Submitted 2024/06/29 Platform GPL19057
At a glance

Single-cell RNA-seq · mouse · 1037 samples

What it is
Single-cell RNA-seqGene activity measured in thousands of individual cells, so cell types can be told apart.
Organism
Mus musculus (mouse)
Samples
1037 samples: 1037 samples, each with a distinct title — no replicated groupsGroups are read from the sample names, not from the paper. Check the list below.
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.
Paper
Stem cells tightly regulate dead cell clearance to maintain tissue fitnessNature · 2024
Summary (written by the submitters)
Macrophages and dendritic cells have long been appreciated for their ability to migrate to and engulf dying cells and debris, including some of the billions of cells that are naturally eliminated from our body daily. However, a substantial amount of these dying cells are cleared by local tissue cells, so-called ‘non-professional phagocytes’, critical to preserve organismal fitness.
Read the full summary ↓
How non-professional phagocytes are able to sense and digest nearby apoptotic corpses while still performing their normal tissue functions is unclear. Here, we explore the molecular mechanisms underlying this balancing act. Exploiting the cyclical bouts of tissue regeneration and degeneration during the hair cycle, we show that stem cells can transiently become non-professional phagocytes when confronted with dying cells. Adoption of this phagocytic state requires not only local lipids produced by apoptotic corpses, which are necessary for RXRα activation, but also tissue-specific retinoids able to activate RARg. The dual factor dependency enables tight regulation of the genes requisite to activate phagocytic apoptotic clearance. The tunable phagocytic program we describe here offers an attractive mechanism to offset phagocytic duties against the primary stem cell function of replenishing differentiated cells to preserve tissue integrity during homeostasis. Our findings have broad implications for other non-motile stem or progenitor cells which experience cell death in an immune-privileged niche.
Published in
Stem cells tightly regulate dead cell clearance to maintain tissue fitness
Stewart KS, Abdusselamoglu MD, Tierney MT et al. · Nature 2024 · PMID 39169186 · doi:10.1038/s41586-024-07855-6
This dataset
Open GSE271100 in finder → Find more mouse 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 GSE271100_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 PRJNA1129850 and SRA study SRP517007. Searching any of these in the dataset finder brings you back here.

NCBI GEO page ↗ Paper (PMID 39169186) ↗
Study design
1037 samples, each with a distinct title — no replicated groups

Any grouping that defines a comparison lives in the sample characteristics, which GEO records separately from the titles.

How we read this: Read from the first 400 of 1037 sample titles: 400 distinct titles with little repetition. These groups are guessed from the sample names, not taken from the paper, so check them against the sample list below before relying on them.

Samples in this study

+ 997 more — browse all 1037 samples with per-sample file links →

Datasets on a similar topic

Search all mouse RNA-seq datasets in GEO →

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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.