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Spatial transcriptomic profiling identifies lacrimal gland epithelial cell-driven mechanisms underlying autoimmunity in Sjögren’s disease

GSE312237 Mus musculus (mouse) RNA-seq (gene expression) 90 samples Submitted 2026/03/25 Platform GPL24247
At a glance

RNA-seq (gene expression) · mouse · 90 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
90 samples: 8 conditions, mostly with about 12 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.
Paper
Spatial transcriptomic profiling identifies lacrimal-gland-epithelial cell-driven mechanisms underlying autoimmunity in Sjögren's diseaseFrontiers in immunology · 2026
Summary (written by the submitters)
Sjögren’s disease (SjD) is a second most prevalent rheumatic disease involving autoimmune pathology of tear-producing lacrimal glands that leads to a common clinical manifestation of chronic ocular surface disease. Despite advances in understanding of SjD, lacrimal gland pathology remains incompletely understood limiting diagnosis and treatment.
Read the full summary ↓
Here we analyze spatial transcriptomic profile of lacrimal glands from wild-type (C57Bl/6) mice and Thrombospondin (TSP)-1-/- mice, a spontaneous mouse model of SjD. We uncover molecular mechanisms underlying functional loss of major epithelial cell subtypes – acinar, duct and myoepithelial cells. We identify potential early mechanisms and markers of glandular damage. By integrating spatial and cellular profiles, we uncover the presence of antigen presenting cells in the proximity of duct epithelial cells that were not described previously in lacrimal glands. We further identify role of epithelial cells as active participants in promoting or sustaining inflammation. Our findings help reveal potential molecular and cellular cues that drive periductal infiltrates containing B cells and Tfh cells that form germinal centers to facilitate local autoantibody production. Overall, our study can provide a framework for therapeutic targeting of epithelial cell types and multicellular interactions underlying autoimmune pathology.
Published in
Spatial transcriptomic profiling identifies lacrimal-gland-epithelial cell-driven mechanisms underlying autoimmunity in Sjögren's disease
Gupta S, Ploumakis A, Kalavros N et al. · Frontiers in immunology 2026 · PMID 41869322 · doi:10.3389/fimmu.2026.1759347
This dataset
Open GSE312237 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 GSE312237_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 PRJNA1372709 and SRA study SRP650784. Searching any of these in the dataset finder brings you back here.

NCBI GEO page ↗ Paper (PMID 41869322) ↗
Study design
8 conditions, mostly with about 12 replicates each
DSP-1001660035646-A-B ×12 DSP-1001660035646-A-C ×12 DSP-1001660035646-A-D ×12 DSP-1001660035646-A-E ×12 DSP-1001660035646-A-F ×12 DSP-1001660035646-A-G ×12 DSP-1001660035646-A-A ×11 DSP-1001660035646-A-H ×7

Supports a between-group comparison across 90 samples.

How we read this: 8 replicated groups read from 90 sample titles; they account for 90 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

+ 50 more — browse all 90 samples with per-sample file links →

Datasets on a similar topic

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