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Single cell resolution analysis of multi-tissue derived human iNKT cells reveals novel transcriptional paradigms

GSE261557 Homo sapiens (human) Single-cell RNA-seq 3 samples Submitted 2025/03/22 Platform GPL24676
At a glance

Single-cell RNA-seq · human · 3 samples

What it is
Single-cell RNA-seqGene activity measured in thousands of individual cells, so cell types can be told apart.
Organism
Homo sapiens (human)
Samples
3 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
Single-cell transcriptomic profiling reveals diversity in human iNKT cells across hematologic tissuesCell reports · 2025
Summary (written by the submitters)
Invariant natural killer T (iNKT) cells are evolutionarily conserved innate lymphocytes important for host defense against pathogens. Further, they are increasingly recognized to play a role in tumor immune surveillance and in protection against graft versus host disease, and they are of particular importance as a universal donor for cellular therapies.
Read the full summary ↓
Therefore, a thorough understanding of the biology of iNKT cells is critical. Murine studies have revealed the existence of transcriptionally and functionally distinct subsets, similar to T helper cell subsets. However, a comprehensive study of human iNKT cell heterogeneity is lacking. Herein, we define the transcriptomic heterogeneity of human iNKT cells derived from multiple immunologically relevant tissues, including peripheral blood, cord blood, bone marrow, and thymus, using single cell RNA-sequencing. We describe human iNKT cells with a naïve/precursor transcriptional pattern, a Th2-like signature, and Th1/17/NK-like gene expression. This combined Th1/17 pattern of gene expression differs from previously described murine iNKT subsets in which Th1- and Th17-like iNKT cells are distinct populations. We also describe transcription factors regulating human iNKT cells with distinct gene expression patterns not previously described in mice. Further, we demonstrate a novel T effector memory RA+ (TEMRA)-like pattern of expression in some human iNKT cells. Additionally, we provide an in-depth transcriptional analysis of human CD8+ iNKT cells, revealing cells with two distinct expression patterns—one consistent with naïve/precursor cells and one consistent with Th1/17/NK-like cells. Collectively, our data provide critical insights into the transcriptional heterogeneity of human iNKT cells, providing a platform to facilitate future functional studies and to inform the development of iNKT-based cellular therapies.
Published in
Single-cell transcriptomic profiling reveals diversity in human iNKT cells across hematologic tissues
Jayasinghe RG, Hollingsworth D, Schedler NC et al. · Cell reports 2025 · PMID 40305288 · doi:10.1016/j.celrep.2025.115587
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 GSE261557_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 PRJNA1087436. Searching any of these in the dataset finder brings you back here.

Samples in this study

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