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Modeling of Clinical Phenotypes in Systemic Lupus Erythematosus Based on the Platelet Transcriptome and FCGR2a Genotype

GSE226147 Homo sapiens (human) RNA-seq (gene expression) 69 samples Submitted 2024/03/22 Platform GPL24676
At a glance

RNA-seq (gene expression) · human · 69 samples

What it is
RNA-seq (gene expression)Gene activity measured by sequencing. The summary does not mention single-cell work, so this is most likely bulk: one average per sample, the usual starting point for comparing groups.
Organism
Homo sapiens (human)
Samples
69 samples: 69 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
Modeling of clinical phenotypes in systemic lupus erythematosus based on the platelet transcriptome and FCGR2a genotypeJournal of translational medicine · 2023
Summary (written by the submitters)
Background: The clinical heterogeneity of SLE with its complex pathogenesis remains challenging as we strive to provide optimal management. The contribution of platelets to endovascular homeostasis, inflammation and immune regulation highlights their potential importance in SLE. Prior work from our group showed that the Fcγ receptor type IIa (FcγRIIa)–R/H131 biallelic polymorphism is associated with increased platelet activity and cardiovascular risk in SLE.
Read the full summary ↓
The study was initiated to investigate the platelet transcriptome in patients with SLE and evaluate its association across FcγRIIa genotypes and distinct clinical features. Methods: Fifty-one patients fulfilling established criteria for SLE (mean age = 41.1±12.3, 100% female, 45% Hispanic, 24% black, 22% Asian, 51% white, mean SLEDAI = 4.4±4.2 at baseline) were enrolled and compared with 18 demographically matched control samples. The FCGR2a receptor was genotyped for each sample, and RNA-seq was performed on isolated, leukocyte-depleted platelets. Transcriptomic data were used to create a modular landscape to explore the differences between SLE patients and controls and various clinical parameters in the context of FCGR2a genotypes. Results: There were 2,290 differentially expressed genes enriched for pathways involved in interferon signaling, immune activation, and coagulation when comparing SLE samples vs controls. When analyzing patients with proteinuria, modules associated with oxidative phosphorylation and platelet activity were unexpectedly decreased. Furthermore, genes that were increased in SLE and in patients with proteinuria were enriched for immune effector processes, while genes increased in SLE but decreased in proteinuria were enriched for coagulation and cell adhesion. A low-binding FCG2Ra allele (R131) was associated with decreases in FCR activation, which further correlated with increases in platelet and immune activation pathways. Finally, we were able to create a transcriptomic signature of clinically active disease that performed significantly well in discerning SLE patients with active clinical disease form those with inactive clinical disease. Conclusions: In aggregate, these data demonstrate the platelet transcriptome provides insight into lupus pathogenesis and disease activity, and shows potential use as means of assessing this complex disease using a liquid biopsy.
Published in
Modeling of clinical phenotypes in systemic lupus erythematosus based on the platelet transcriptome and FCGR2a genotype
Cornwell MG, Bannoudi HE, Luttrell-Williams E et al. · Journal of translational medicine 2023 · PMID 37029410 · doi:10.1186/s12967-023-04059-w
This dataset
Open GSE226147 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 GSE226147_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: For RNA-seq the counts are usually a table among the supplementary files (look for 'counts', 'expression', 'FPKM' or 'TPM' in the file name). The series matrix of a sequencing study is sometimes nearly empty.

Also filed as BioProject PRJNA938957 and SRA study SRP424652. Searching any of these in the dataset finder brings you back here.

NCBI GEO page ↗ Paper (PMID 37029410) ↗
Study design
69 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 69 sample titles: 69 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

+ 29 more — browse all 69 samples with per-sample file links →

Datasets on a similar topic

Search all human RNA-seq datasets in GEO →

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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.