GEO series

STK40 regulates T cell development and effector function through COP1-depenendent degradation of c-Jun transcription factor

GSE193207 Mus musculus (mouse) Single-cell RNA-seq 48 samples Submitted 2025/01/01 Platform GPL24247

At a glance

Single-cell RNA-seq · mouse · 48 samples

What it is: Single-cell RNA-seqGene activity measured in thousands of individual cells, so cell types can be told apart. Read from the summary: GEO's own type field only says 'expression profiling by sequencing', so confirm it in the paper.
Organism: Mus musculus (mouse)
Samples: 48 samples: 12 conditions, mostly with about 4 replicates eachGroups 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)

To examine the effect of Stk40 deletion specifically in T cells during chronic LCMV infection, we have generated a T cell specific knockout mouse line (CD4CreER; Stk40loxp/loxp). We found that mice lacking Stk40 exhibited improved viral clearance and T cell function compared to WT counterparts.

Read the full summary ↓

To gain a broader understanding of how Stk40 regulates T cell response to chronic LCMV infection, we performed single cell transcriptome analysis on magnetically enriched LCMV-specific dextramer+ CD8 T cells isolated from spleens of Stk40 KO or WT mice at days 15 and 22 after infection with LCMV clone 13.

This dataset

Open GSE193207 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 GSE193207_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 PRJNA795431 and SRA study SRP353955. Searching any of these in the dataset finder brings you back here.

NCBI GEO page ↗

Study design

12 conditions, mostly with about 4 replicates each

RNA d15 WT # ×4 RNA d15 KO # ×4 RNA d22 WT # ×4 RNA d22 KO # ×4 HTO d15 WT # ×4 HTO d15 KO # ×4 HTO d22 WT # ×4 HTO d22 KO # ×4 +4 more

Supports a between-group comparison across 48 samples.

How we read this: 12 replicated groups read from 48 sample titles; they account for 48 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

GSM5776494 RNA d15 WT #1 RNA d15 WT #
GSM5776495 RNA d15 WT #2 RNA d15 WT #
GSM5776496 RNA d15 WT #3 RNA d15 WT #
GSM5776497 RNA d15 WT #4 RNA d15 WT #
GSM5776498 RNA d15 KO #1 RNA d15 KO #
GSM5776499 RNA d15 KO #2 RNA d15 KO #
GSM5776500 RNA d15 KO #3 RNA d15 KO #
GSM5776501 RNA d15 KO #4 RNA d15 KO #
GSM5776502 RNA d22 WT #1 RNA d22 WT #
GSM5776503 RNA d22 WT #2 RNA d22 WT #
GSM5776504 RNA d22 WT #3 RNA d22 WT #
GSM5776505 RNA d22 WT #4 RNA d22 WT #
GSM5776506 RNA d22 KO #1 RNA d22 KO #
GSM5776507 RNA d22 KO #2 RNA d22 KO #
GSM5776508 RNA d22 KO #3 RNA d22 KO #
GSM5776509 RNA d22 KO #4 RNA d22 KO #
GSM5776510 HTO d15 WT #1 HTO d15 WT #
GSM5776511 HTO d15 WT #2 HTO d15 WT #
GSM5776512 HTO d15 WT #3 HTO d15 WT #
GSM5776513 HTO d15 WT #4 HTO d15 WT #
GSM5776514 HTO d15 KO #1 HTO d15 KO #
GSM5776515 HTO d15 KO #2 HTO d15 KO #
GSM5776516 HTO d15 KO #3 HTO d15 KO #
GSM5776517 HTO d15 KO #4 HTO d15 KO #
GSM5776518 HTO d22 WT #1 HTO d22 WT #
GSM5776519 HTO d22 WT #2 HTO d22 WT #
GSM5776520 HTO d22 WT #3 HTO d22 WT #
GSM5776521 HTO d22 WT #4 HTO d22 WT #
GSM5776522 HTO d22 KO #1 HTO d22 KO #
GSM5776523 HTO d22 KO #2 HTO d22 KO #
GSM5776524 HTO d22 KO #3 HTO d22 KO #
GSM5776525 HTO d22 KO #4 HTO d22 KO #
GSM5776526 TCR d15 WT #1 TCR d15 WT #
GSM5776527 TCR d15 WT #2 TCR d15 WT #
GSM5776528 TCR d15 WT #3 TCR d15 WT #
GSM5776529 TCR d15 WT #4 TCR d15 WT #
GSM5776530 TCR d15 KO #1 TCR d15 KO #
GSM5776531 TCR d15 KO #2 TCR d15 KO #
GSM5776532 TCR d15 KO #3 TCR d15 KO #
GSM5776533 TCR d15 KO #4 TCR d15 KO #

+ 8 more — browse all 48 samples with per-sample file links →

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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.