GEO series

DNGR-1 signalling limits dendritic cell activation for optimal antigen cross-presentation

GSE287030 Mus musculus (mouse) RNA-seq (gene expression) 12 samples Submitted 2025/10/28 Platform GPL21103

At a glance

RNA-seq (gene expression) · mouse · 12 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: 12 samples: 12 × BUC5254AGroups 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: Cross-presentation of dead cell-associated antigens shapes the neoantigenic landscape of tumor immunityNature immunology · 2026

Summary (written by the submitters)

Innate immune receptors often induce activation of conventional dendritic cells (cDCs) and enhance antigen (cross-)presentation, favouring immune responses. DNGR-1 (CLEC9A), a receptor expressed by type 1 cDCs (cDC1s) and implicated in immune responses to viruses and cancer, recognises F-actin exposed on dead cell remnants and promotes cross-presentation of associated antigens.

Read the full summary ↓

Here, we show that recruitment of phosphatase SHIP1, a process governed by a single amino acid residue adjacent to the signalling motif of the receptor, partly explains how DNGR-1 fails to trigger cDC1 activation in vitro. Substituting this residue converts DNGR-1 into an activating receptor but decreases induction of cross-presentation of dead cell-associated antigens. Introducing the reverse mutation into the related receptor Dectin-1 impairs its activation capacity while enhancing its ability to promote cross-presentation. These findings reveal a functional trade-off in receptor signalling and suggest that DNGR-1 has evolved to prioritise antigen cross-presentation over cellular activation, possibly to minimise inflammatory responses to dead cells.

Published in

Cross-presentation of dead cell-associated antigens shapes the neoantigenic landscape of tumor immunity

Lim KHJ, Schulz O, Lobon I et al. · Nature immunology 2026 · PMID 41482545 · doi:10.1038/s41590-025-02354-w

This dataset

Open GSE287030 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 GSE287030_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 PRJNA1211014 and SRA study SRP557475. Searching any of these in the dataset finder brings you back here.

NCBI GEO page ↗ Paper (PMID 41482545) ↗

Study design

12 × BUC5254A

Supports a between-group comparison across 12 samples.

How we read this: 1 replicated groups read from 12 sample titles; they account for 12 of them. 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

GSM8736773 BUC5254A1 BUC5254A
GSM8736774 BUC5254A2 BUC5254A
GSM8736775 BUC5254A3 BUC5254A
GSM8736776 BUC5254A4 BUC5254A
GSM8736777 BUC5254A5 BUC5254A
GSM8736778 BUC5254A6 BUC5254A
GSM8736779 BUC5254A7 BUC5254A
GSM8736780 BUC5254A8 BUC5254A
GSM8736781 BUC5254A9 BUC5254A
GSM8736782 BUC5254A10 BUC5254A
GSM8736783 BUC5254A11 BUC5254A
GSM8736784 BUC5254A12 BUC5254A

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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.