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Single-cell RNA-seq of peripheral blood links cell-type-specific regulation of splicing to autoimmune and inflammatory diseases [scRNAseq_KR_SGI]

GSE281106 Homo sapiens (human) Single-cell RNA-seq 352 samples Submitted 2024/12/05 Platform GPL24676
At a glance

Single-cell RNA-seq · human · 352 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
352 samples: 22 conditions, mostly with about 15 replicates eachGroups 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
Single-cell RNA sequencing of peripheral blood links cell-type-specific regulation of splicing to autoimmune and inflammatory diseasesNature genetics · 2024
Summary (written by the submitters)
Alternative splicing is pivotal in the genetics of complex traits, but a detailed understanding requires relevant cell types from diverse genetic ancestries. Here, we described cell-type-specific, sex-biased, and ancestry-biased alternative splicing in ~1M peripheral blood mononuclear cells from 474 healthy donors from the Asian Immune Diversity Atlas.
Read the full summary ↓
We identified widespread sex- and ancestry-biased differentially splicing, most of which are cell-type-specific. We identified 11,577 independent cis-sQTLs, 607 trans-sQTLs, and 107 dynamic sQTLs. Colocalization between cis-eQTL and trans-sQTL revealed a cell-type-specific regulatory relationship between hnRNPLL and PTPRC. We observed a strong enrichment of cis-sQTL effects in autoimmune and inflammatory disease heritability. Specifically, we functionally validated an Asian-specific sQTL disrupting the 5’ splice site of TCHP exon four to putatively modulate the risk of Graves’ disease in East Asian populations. Our work highlights the critical impact of ancestral diversity and provides a roadmap to dissect splicing mechanisms in complex diseases at single-cell resolution.
Published in
Single-cell RNA sequencing of peripheral blood links cell-type-specific regulation of splicing to autoimmune and inflammatory diseases
Tian C, Zhang Y, Tong Y et al. · Nature genetics 2024 · PMID 39627432 · doi:10.1038/s41588-024-02019-8
This dataset
Open GSE281106 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 GSE281106_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 PRJNA1182290 and SRA study SRP543259. Searching any of these in the dataset finder brings you back here.

NCBI GEO page ↗ Paper (PMID 39627432) ↗
Study design
22 conditions, mostly with about 15 replicates each
PBMCs, Korean cohort, B002_L001, H ×15 PBMCs, Korean cohort, B002_L002, H ×15 PBMCs, Korean cohort, B003_L001, H ×15 PBMCs, Korean cohort, B003_L002, H ×15 PBMCs, Korean cohort, B004_L001, H ×15 PBMCs, Korean cohort, B004_L002, H ×15 PBMCs, Korean cohort, B005_L001, H ×15 PBMCs, Korean cohort, B005_L002, H ×15 +36 more

Supports a between-group comparison across 330 samples.

How we read this: 22 replicated groups read from 352 sample titles; they account for 330 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

+ 312 more — browse all 352 samples with per-sample file links →

Datasets on a similar topic

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