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Immune profiling-based targeting of pathogenic T cells with ustekinumab in ANCA-associated glomerulonephritis

GSE253633 Homo sapiens (human) Single-cell RNA-seq 28 samples Submitted 2024/07/01 Platform GPL24676
At a glance

Single-cell RNA-seq · human · 28 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
Homo sapiens (human)
Samples
28 samples: 28 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.
Paper
Immune profiling-based targeting of pathogenic T cells with ustekinumab in ANCA-associated glomerulonephritisNature communications · 2024
Summary (written by the submitters)
Antineutrophil cytoplasmic antibody (ANCA)–associated vasculitis is a life-threatening autoimmune disease that often results in kidney failure caused by crescentic glomerulonephritis (ANCA-GN). To date, treatment of most patients with ANCA-GN relies on unspecific immunosuppressive agents that harbor serious adverse effects and limited efficiency.
Read the full summary ↓
By performing spatial and single-cell transcriptome analysis, we characterized inflammatory niches in the kidneys of 34 patients with ANCA-GN and identified pro-inflammatory, cytokine producing CD4+ (TH1 and TH17 subsets) and CD8+ T cells (TC1 and TC17-like subsets) as a key pathogenic signature. Digital pharmacology identified ustekinumab, a monoclonal antibody targeting IL-12 and IL-23 in these T cells, as the most promising therapeutic drug to target. Based on these findings, four patients with relapsing ANCA-GN were treated with ustekinumab in combination with low-dose cyclophosphamide. Ustekinumab was given subcutaneously (90 mg) at weeks 0, 4, 12, and 24 and clinical and renal responses were evaluated at week 26. This treatment was well-tolerated and induced clinical response in all ANCA-GN patients, including an improved kidney function and Birmingham Vasculitis Activity Score. Our findings suggest that the pathogenesis-based treatment of ANCA-GN patients with ustekinumab is efficacious and warrants further investigation in clinical trials.
Published in
Immune profiling-based targeting of pathogenic T cells with ustekinumab in ANCA-associated glomerulonephritis
Engesser J, Khatri R, Schaub DP et al. · Nature communications 2024 · PMID 39300109 · doi:10.1038/s41467-024-52525-w
This dataset
Open GSE253633 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 GSE253633_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 PRJNA1066376 and SRA study SRP484629. Searching any of these in the dataset finder brings you back here.

NCBI GEO page ↗ Paper (PMID 39300109) ↗
Study design
28 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 28 sample titles: 28 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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