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Absence of Clec1a in murine cDC1s decrease IFN responses to necrotic cells

GSE326200 Mus musculus (mouse) RNA-seq (gene expression) 237 samples Submitted 2026/07/31 Platform GPL24247
At a glance

RNA-seq (gene expression) · mouse · 237 samples

What it is
RNA-seq (gene expression)Gene activity measured by sequencing. The summary does not mention single-cell work, so this is most likely bulk: one average per sample, the usual starting point for comparing groups.
Organism
Mus musculus (mouse)
Samples
237 samples: 237 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.
Summary (written by the submitters)
Interferons (IFNs) are proteins that signal and play a critical role in health and disease. IFNs play a protective role gainst pathogens such as during viral, bacteria and fungal infections, but can also mediate many inflammatory and autoimmune diseases. IFNs are characterized by time-dependent activities that can drive opposing outcomes so IFN responses need to be carefully balanced to prevent deleterious effects and for the return to homeostasis.
Read the full summary ↓
We previously characterized the C-type lectin receptor CLEC-1 as a receptor of necrotic cells particularly expressed by cDC1s. Here, we showed that the absence of Clec1a in cDC1s decreases the IFN responses to necrotic cells but not to pathogen such as E. Coli. These results suggest that CLEC-1 in cDC1s potentiates IFN signaling in response to necrotic cells to finely regulate downstream immunity.
This dataset
Open GSE326200 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 GSE326200_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: For RNA-seq the counts are usually a table among the supplementary files (look for 'counts', 'expression', 'FPKM' or 'TPM' in the file name). The series matrix of a sequencing study is sometimes nearly empty.

Also filed as BioProject PRJNA1444120 and SRA study SRP687228. Searching any of these in the dataset finder brings you back here.

NCBI GEO page ↗
Study design
237 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 237 sample titles: 237 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

+ 197 more — browse all 237 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.