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Presentation of immunoregulatory sialoglycans on T cells is divergent between mice and humans

GSE276900 Mus musculus (mouse) Single-cell RNA-seq 16 samples Submitted 2025/05/26 Platform GPL16417
At a glance

Single-cell RNA-seq · mouse · 16 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
16 samples: 4 × CD4_SNA_high vs 4 × CD8_SNA_low vs 4 × CD8_SNA_high vs 4 × CD4_SNA_lowGroups 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 Unified Atlas of T cell GlycophysiologybioRxiv : the preprint server for biology · 2024
Summary (written by the submitters)
Glycans are emerging as important regulators of T cell function but remain poorly characterized across the functionally distinct populations that exist in vivo. Here, we couple single-cell analysis technologies with soluble lectins and chemical probes to interrogate glycosylation patterns on major T cell populations across multiple mouse and human tissues.
Read the full summary ↓
Our analysis focused on terminal glycan epitopes with immunomodulatory functions, including sialoglycan ligands for Siglecs. We demonstrate that glycosylation patterns are diverse across the resting murine T cell repertoire and dynamically remodelled in response to antigen-specific stimulation. Surprisingly, we find that human T cell populations do not share the same glycoprofiles or glycan remodelling dynamics as their murine counterparts. We show that these differences can be explained by divergent regulation of glycan biosynthesis pathways between the species. These results highlight fundamental glycophysiological differences between mouse and human T cells and reveal features that are critical to consider for glycan-targeted therapies.
Published in
A Unified Atlas of T cell Glycophysiology
Izzati FN, Choksi H, Giuliana P et al. · bioRxiv : the preprint server for biology 2024 · PMID 39229125 · doi:10.1101/2024.08.24.609521
This dataset
Open GSE276900 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 GSE276900_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 PRJNA1159371 and SRA study SRP531926. Searching any of these in the dataset finder brings you back here.

NCBI GEO page ↗ Paper (PMID 39229125) ↗
Study design
4 × CD4_SNA_high vs 4 × CD8_SNA_low vs 4 × CD8_SNA_high vs 4 × CD4_SNA_low

Supports a between-group comparison across 16 samples.

How we read this: 4 replicated groups read from 16 sample titles; they account for 16 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
Datasets on a similar topic

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