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Anchored screening identifies transcription factor blueprints underlying dendritic cell diversity and subset-specific anti-tumor immunity [mouse scRNA-seq]

GSE245838 Mus musculus (mouse) Single-cell RNA-seq 21 samples Submitted 2025/08/29 Platform GPL24247
At a glance

Single-cell RNA-seq · mouse · 21 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
21 samples: 10 conditions, mostly in duplicateGroups 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
Anchored screening identifies transcription factor blueprints underlying dendritic cell diversity and subset-specific anti-tumor immunityImmunity · 2025
Summary (written by the submitters)
Transcription factor cooperation is essential for specifying the heterogeneous dendritic cell (DC) lineages that orchestrate adaptive immunity, yet how it drives subset diversification remains poorly understood. Here, we employed a sequential anchored screen of 70 transcription factors using direct cellular reprogramming to identify regulators that specify type 2 conventional DCs (cDC2s) and plasmacytoid DCs (pDCs).
Read the full summary ↓
We identified PU.1, IRF4, and PRDM1 as inducers of a pro-inflammatory cDC2B-like fate and SPIB, IRF8, and IKZF2 as mediators of an immature lymphoid DC program. Transcriptomic profiling linked these triads to lineage-specific signatures and demonstrated their requirement for subset identity. Mechanistically, lineage divergence was driven by chromatin co-engagement at subset-specific sites early in reprogramming. Functionally, reprogrammed DCs employed distinct immune mechanisms to elicit orthogonal anti-tumor responses in different tumor models. Collectively, our findings uncover transcriptional circuits that control DC diversification and pave the way to generate patient-tailored DC subsets for cancer immunotherapy.
Published in
Anchored screening identifies transcription factor blueprints underlying dendritic cell diversity and subset-specific anti-tumor immunity
Henriques-Oliveira L, Altman AR, Kurochkin I et al. · Immunity 2025 · PMID 40885192 · doi:10.1016/j.immuni.2025.08.001
This dataset
Open GSE245838 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 GSE245838_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 PRJNA1030047 and SRA study SRP467443. Searching any of these in the dataset finder brings you back here.

NCBI GEO page ↗ Paper (PMID 40885192) ↗
Study design
10 conditions, mostly in duplicate
MEF ×2 PI8 day ×2 PIB day ×2 cDC ×2 PI4 day ×2 PIP day ×2 SI8 Clec9a day ×2 SI8 CD2 day ×2 +3 more

Supports a between-group comparison across 20 samples.

How we read this: 10 replicated groups read from 21 sample titles; they account for 20 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
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