GEO series

SLIDE analysis of the scRNA-seq data using an interpretable factor-analysis machine learning framework, that moves beyond predictive biomarkers to try and infer latent factors underlying LS pathophysiology.

GSE288490 Homo sapiens (human) Single-cell RNA-seq 16 samples Submitted 2025/02/28 Platform GPL18573

At a glance

Single-cell RNA-seq · human · 16 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: 16 samples
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: SLIDE: Significant Latent Factor Interaction Discovery and Exploration across biological domainsNature methods · 2024

Summary (written by the submitters)

Using transcriptomic profiling at single-cell resolution, we investigate cell-intrinsic and cell-extrinsic signatures associated with pathogenesis and inflammation-driven fibrosis in both adult and pediatric localized Scleroderma (LS) patients. We perform scRNA-seq on adult and pediatric LS patients and healthy controls. We then analyze the scRNA-seq data using an interpretable factor-analysis machine learning framework, SLIDE, that moves beyond predictive biomarkers to try and infer latent factors underlying LS pathophysiology.

Read the full summary ↓

SLIDE is a novel latent factor regression-based framework that comes with rigorous statistical guarantees regarding identifiability of the latent factors, corresponding inference, and FDR control. We find distinct differences in the characteristics and complexity in the molecular signatures between adult and pediatric LS. SLIDE identified cell type-specific determinants of LS associated with age and severity and revealed insights into signaling mechanisms shared between LS and Systemic Sclerosis (SSc), as well as differences in onset of the disease in the pediatric compared with adult population. Our analyses recapitulate known drivers of LS pathology and find previously unidentified cellular signaling modules that stratify LS subtypes and define a shared signaling axis with SSc.In this study we compared 17 healthy and 27 LS samples evaluated by 10X Genomics single cell sequencing. Samples were processed and clustered for DEG analysis.

Published in

SLIDE: Significant Latent Factor Interaction Discovery and Exploration across biological domains

Rahimikollu J, Xiao H, Rosengart A et al. · Nature methods 2024 · PMID 38374265 · doi:10.1038/s41592-024-02175-z

This dataset

Open GSE288490 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 GSE288490_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 PRJNA1217853 and SRA study SRP560902. Searching any of these in the dataset finder brings you back here.

NCBI GEO page ↗ Paper (PMID 38374265) ↗

Study design

A cohort of 16 individual samples

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

How we read this: Sample titles run SC408 … SC386 and carry no group information.

Samples in this study

GSM8768935 SC408
GSM8768936 SC409
GSM8768937 SC445
GSM8768938 SC446
GSM8768939 SC389
GSM8768940 SC424
GSM8768941 SC442
GSM8768942 SC443
GSM8768943 SC455
GSM8768944 SC457
GSM8768945 SC361
GSM8768946 SC388
GSM8768947 SC391
GSM8768948 SC392
GSM8768949 SC407
GSM8768950 SC386

Datasets on a similar topic

GSE223501 Non-small Cell Lung Cancer Brain Metastasis Atlas [slide-seq] 14 samples shares slide
GSE287118 Multiomics identifies unique modulators of calf muscle pathophysiology in peripheral artery disease and chronic kidney disease 86 samples shares pathophysiology
GSE287174 Patient-Derived Meningioma Organoids: A Reliable Model for Studying Human Tumor Pathophysiology 65 samples shares pathophysiology
GSE302096 Mucus plugs-associated gene expression identifies pathophysiology shared with chronic bronchitis 48 samples shares pathophysiology
GSE265940 Overexpression of extracellular matrix proteins, increased cell death and reduced cell proliferation contribute to the pathophysiology of microphthalmia across in vitro patient-derived models 48 samples shares pathophysiology
GSE247407 Induction ofsteatosis in primary human hepatocytes manifests into hepatic pathophysiology of metabolic dysfunction-associated liver disease 30 samples shares pathophysiology
GSE282371 Combined Th2 and Th17/22 cytokines models atopic dermatitis pathophysiology in vitro 26 samples shares pathophysiology
GSE273766 Pathophysiology of microglial-driven neurodegeneration in Histiocytosis 24 samples shares pathophysiology

Search all human RNA-seq datasets in GEO →

Share this dataset

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.