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Comparative lipidomic and transcriptomic profiling reveals distinct fatty acid utilization in T and B cells across mouse and human

GSE326689 Homo sapiens (human) RNA-seq (gene expression) 22 samples Submitted 2026/06/30 Platform GPL30173Platform GPL30172
At a glance

RNA-seq (gene expression) · human · 22 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
Homo sapiens (human)
Samples
22 samples: 8 × Tcell_hs vs 8 × Bcell_hs vs 3 × Tcell vs 3 × BcellGroups 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)
Lipid metabolism is increasingly recognized as a critical determinant of immune cell differentiation and function, yet the metabolic programs that distinguish closely related adaptive immune cell subsets remain incompletely defined. Here we demonstrate that T and B cells employ distinct fatty acid utilization strategies across species.
Read the full summary ↓
Integrated lipidomic and transcriptomic analyses of mouse and human lymphocytes revealed enrichment of long-chain phospholipid species in T cells, including prominent increases in arachidonic acid–containing lipids, accompanied by elevated expression of fatty acid elongation–associated genes including Elovl5. In contrast, B cells preferentially accumulated phospholipids with shorter and/or more unsaturated acyl chains, notably with consistent enrichment of eicosapentaenoic acid–containing lipids, together with increased expression of genes involved in fatty acid uptake, desaturation, and phospholipid remodeling, including Cd36, Scd1, and Lpgat1. Acyl-chain pairing analyses further uncovered distinct organizational patterns in lipid assembly, and cross-species comparisons revealed both conserved features and marked species-specific divergence, highlighting shared metabolic principles in mouse and human lymphocytes. Our data indicate that differential fatty acid elongation, uptake, and remodeling programs shape lineage-specific lipid architectures and contribute to metabolic specialization in adaptive immune cells.
This dataset
Open GSE326689 in finder → Find more 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 GSE326689_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 PRJNA1446766 and SRA study SRP688498. Searching any of these in the dataset finder brings you back here.

NCBI GEO page ↗
Study design
8 × Tcell_hs vs 8 × Bcell_hs vs 3 × Tcell vs 3 × Bcell

Supports a between-group comparison across 22 samples.

How we read this: 4 replicated groups read from 22 sample titles; they account for 22 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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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.