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A specific gene expression program underlies antigen archiving by lymphatic endothelial cells in mammalian lymph nodes [scRNA-seq]

GSE281268 Mus musculus (mouse) Single-cell RNA-seq 28 samples Submitted 2025/07/11 Platform GPL24247
At a glance

Single-cell RNA-seq · mouse · 28 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
28 samples: 24 distinct titles, 4 of them repeatedGroups 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
A specific gene expression program underlies antigen archiving by lymphatic endothelial cells in mammalian lymph nodesNature communications · 2025
Summary (written by the submitters)
Antigens from protein subunit vaccination traffic from the tissue to the draining lymph node, either passively via the lymph or carried by dendritic cells at the local injection site. Lymph node (LN) lymphatic endothelial cells (LEC) actively acquire and archive foreign antigens, and archived antigen can be released during subsequent inflammatory stimulus to improve immune responses.
Read the full summary ↓
Here, we address questions of how LECs achieve durable antigen archiving and whether LECs with high levels of antigen express unique transcriptional programs. We used single cell sequencing in dissociated LN tissue and spatial transcriptomics to quantify antigen levels in LEC and dendritic cell populations at multiple time points after immunization. Our measurement of antigen location and levels over multiple immunizations confirms previous data showing antigen positive LEC-dendritic cell interactions. Increased antigen levels within LECs after a second immunization suggests that LEC antigen acquisition and archiving capacity can be improved over multiple exposures. Using machine learning we defined a unique transcriptional program within archiving LECs that predicted LEC archiving capacity in independent data sets. We validated this modeling, showing we could predict lower levels of LEC antigen archiving in chikungunya virus-infected mice and demonstrated in vivo the accuracy of our prediction. Collectively, our findings establish a unique transcriptional program in LECs that promotes antigen archiving and can be translated to other systems.
Published in
A specific gene expression program underlies antigen archiving by lymphatic endothelial cells in mammalian lymph nodes
Sheridan RM, Doan TA, Lucas CJ et al. · Nature communications 2025 · PMID 40998762 · doi:10.1038/s41467-025-63543-7
This dataset
Open GSE281268 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 GSE281268_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 PRJNA1182855 and SRA study SRP543702. Searching any of these in the dataset finder brings you back here.

NCBI GEO page ↗ Paper (PMID 40998762) ↗
Study design
24 distinct titles, 4 of them repeated
LEC-2 ×2 LEC-1_LEC-2 ×2 LEC-P ×2 DC-P ×2

How we read this: Read from 28 sample titles: 24 distinct titles with little repetition. 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
Datasets on a similar topic

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