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RNA-binding proteins Zfp36l1 and Zfp36l2 protect against premature thymic involution.

GSE315892 Mus musculus (mouse) Single-cell RNA-seq 1 samples Submitted 2026/01/09 Platform GPL34328
At a glance

Single-cell RNA-seq · mouse · 1 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
1 samples
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
RNA-binding proteins Zfp36l1 and Zfp36l2 protect against premature thymic involutionCellular & molecular immunology · 2026
Summary (written by the submitters)
The thymus is a primary lymphoid organ that undergoes progressive age-associated involution, leading to reduced T cell output and impaired adaptive immunity. In this study, we employed scRNA-Seq as well as other approaches to reveal that medullary thymic epithelial cells (mTECs) produce excessive proinflammatory cytokines in the absence of Zfp36l1 and Zfp36l2, two RNA-binding proteins of TTP family.
Read the full summary ↓
Consequently, FOXN1, a master regulator of pro-lymphopoietic genes in thymic epithelial cells, was down-regulated in young mice, which leads to premature thymic involution in knockout mice. These findings reveal a protective role for TTP family proteins in regulating cytokine levels within the thymic microenvironment and preventing premature thymic involution.
Published in
RNA-binding proteins Zfp36l1 and Zfp36l2 protect against premature thymic involution
Han J, Golzari-Sorkheh M, Rajan V et al. · Cellular & molecular immunology 2026 · PMID 41840035 · doi:10.1038/s41423-026-01399-7
This dataset
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.

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 GSE315892_RAW.tar (it can run to tens of gigabytes).

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.

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 PRJNA1399745 and SRA study SRP660604. Searching any of these in the dataset finder brings you back here.

Samples in this study

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