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Single-cell transcriptomes and T cell receptor sequences of CD4 or CD8 single positive T lymphocytes from peripheral blood or urine of lupus nephritis patients at flare.

GSE322527 Homo sapiens (human) Single-cell RNA-seq 26 samples Submitted 2026/07/01 Platform GPL30173Platform GPL18573
At a glance

Single-cell RNA-seq · human · 26 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
26 samples: 26 samples, each with a distinct title — no replicated groupsGroups 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)
Proliferative lupus nephritis (LN) is triggered by deposition of autoantibodies in glomeruli and paralleled by a T cell-rich kidney infiltrate. Although these T cells have been attributed with propagation of tissue injury, it is unclear how they are activated and whether T cell autoreactivity drives the local inflammation. Kidney-infiltrating T cells are also observed in urine, where they have high resemblance with interstitial T cells.
Read the full summary ↓
Therefore, urinary T cells are a proxy to investigate tissue pathogenesis. Here, we analyzed urinary T cells to elucidate if a kidney-specific T cell autoimmune reaction contributes to tubulointerstitial inflammation in LN. Using single cell RNA sequencing, we compared transcriptomes and clonotypes of T cells from blood and urine of patients with active LN and show that urinary T cells were mostly activated CD8 effector memory cells recruited from a circulating CX3CR1+ subset. Several urinary CD8 T cell clones were expanded. However, upon in vitro testing of their T cell receptors, we did not observe autoreactivity against autologous tubular epithelial cells. Instead, approximately 20% of expanded clonotypes were Epstein-Barr virus- or cytomegalovirus specific, but respective viral antigens were undetectable in kidney biopsies or urine. Conversely, kidney-infiltrating T cells had access to interleukin-15 and interferon (IFN) β and stimulation with these cytokines was sufficient to trigger degranulation and production of tumor necrosis factor, IFN γ and granzyme K. Together, these results show that CD8+CX3CR1+ T cells are recruited into the kidney in LN, where they are activated by cytokines enabling them to contribute to local inflammation.
This dataset
Open GSE322527 in finder → Find more human 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 GSE322527_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 PRJNA1430361 and SRA study SRP680189. Searching any of these in the dataset finder brings you back here.

NCBI GEO page ↗
Study design
26 samples, each with a distinct title — no replicated groups

Any grouping that defines a comparison lives in the sample characteristics, which GEO records separately from the titles.

How we read this: Read from 26 sample titles: 26 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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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.