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Selective CAR-T cell mediated B cell depletion suppresses interferon signature in SLE [PBMC]

GSE263931 Homo sapiens (human) Single-cell RNA-seq 28 samples Submitted 2024/05/10 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
Selective CAR T cell-mediated B cell depletion suppresses IFN signature in SLEJCI insight · 2024
Summary (written by the submitters)
Applying advanced molecular profiling together with highly specific targeted therapies offers the possibility to better dissect the mechanisms underlying immune mediated inflammatory diseases such as systemic lupus erythematosus (SLE) in humans. Here we apply a combination of single cell RNA sequencing and T/B cell repertoire analysis to perform an in-depth characterization of molecular changes in the immune-signature upon CD19 CAR T cell-mediated depletion of B cells in SLE patients.
Read the full summary ↓
The resulting datasets do not only confirm a selective CAR T cell-mediated reset of the B cell response, but simultaneously reveal consequent changes in the transcriptional signature of monocyte and T cell subsets that respond with a profound reduction in type 1 interferon signaling. Our current data thus provide evidence for a causal relationship between the B cell response and the increased interferon signature observed in SLE and additionally demonstrate the usefulness of combining targeted therapies and novel analytic approaches to decipher molecular mechanisms of immune-mediated inflammatory diseases in humans.
Published in
Selective CAR T cell-mediated B cell depletion suppresses IFN signature in SLE
Wilhelm A, Chambers D, Müller F et al. · JCI insight 2024 · PMID 38722688 · doi:10.1172/jci.insight.179433
This dataset
Open GSE263931 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 GSE263931_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 PRJNA1100404 and SRA study SRP501670. Searching any of these in the dataset finder brings you back here.

NCBI GEO page ↗ Paper (PMID 38722688) ↗
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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