Macrophage–Fibroblast Signaling Network Identified by Single-Cell RNA Sequencing in Juvenile Systemic Sclerosis
Single-cell RNA-seq · human · 11 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
- 11 samples: 9 × SCGroups 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.
Read the full summary ↓
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.
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.
Supplementary files
The count tables and other files the authors uploaded. Take only the samples you need, or the whole study as GSE320020_RAW.tar (it can run to tens of gigabytes).
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.
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 PRJNA1426001 and SRA study SRP678273. Searching any of these in the dataset finder brings you back here.
Supports a between-group comparison across 9 samples.
How we read this: 1 replicated groups read from 11 sample titles; they account for 9 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.
- GSM9531900 S-32-C
- GSM9531901 S-27-C
- GSM9533523 SC288 SC
- GSM9533524 SC360 SC
- GSM9533525 SC367 SC
- GSM9533526 SC416 SC
- GSM9533527 SC419 SC
- GSM9533528 SC522 SC
- GSM9533529 SC524 SC
- GSM9533530 SC481 SC
- GSM9533531 SC507 SC
- GSE291890 Single-Cell RNA Sequencing Unveils Macrophage Involvement in Aortic Valve Stenosis Progression 33 samples shares single-cell, macrophage
- GSE312129 Single-nuclei multiome and spatial analyses of early, untreated SSc skin identify a proinflammatory vascular niche of macrophage-fibroblast signaling 38 samples shares macrophage, fibroblast
- GSE253904 High-Dimensional Single-Cell Multimodal Landscape of Human Carotid Atherosclerosis 24 samples shares single-cell, sclerosis
- GSE305971 Integrative single-cell analysis reveals malignant archetypes and proliferative hierarchies in multiple myeloma 2105 samples shares single-cell
- GSE243013 A single-cell atlas of immune heterogeneity in anti-PD1-treated non-small cell lung cancer 243 samples shares single-cell
- GSE263733 Single-cell transcriptome analysis reveals subtype-specific clonal evolution and microenvironmental changes in liver metastasis of pancreatic adenocarcinoma and their clinical implications 218 samples shares single-cell
- GSE282122 A longitudinal single-cell atlas of anti-tumour necrosis factor treatment in inflammatory bowel disease 216 samples shares single-cell
- GSE328048 Single-cell transcriptome reveals keratinocyte subclusters that contribute to altered differentiation and inflammatory responses in atopic dermatitis 116 samples shares single-cell
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.