Cutaneous lupus features specialized stromal niches and altered retroelement expression
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. 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
- 16 samples: 14 distinct titles, 1 of them repeatedGroups 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
- RNA in situ hybridization for ISG15 and IFI6 highlights IFN-I activity in discoid lupus erythematosusJID innovations : skin science from molecules to population health · 2026
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 GSE294296_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 PRJNA1248962 and SRA study SRP578158. Searching any of these in the dataset finder brings you back here.
How we read this: Read from 16 sample titles: 14 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.
- GSM8901866 CLE_Patient1_lesional
- GSM8901878 CLE_Patient1_non-lesional
- GSM8901884 CLE_Patient2_lesional
- GSM8901885 CLE_Patient2_non-lesional
- GSM8901886 CLE_Patient3_lesional
- GSM8901887 CLE_Patient3_non-lesional
- GSM8901888 CLE_Patient4_non-lesional
- GSM8901889 CLE_Patient5_lesional
- GSM8901890 CLE_Patient5_non-lesional
- GSM8901891 HC_Subject1
- GSM8901892 HC_Subject2
- GSM8901893 HC_Subject3
- GSM8901894 SCLE_pretreatment_sp
- GSM8901895 SCLE_posttreatment_sp
- GSM8901896 DLE_Patient _sp
- GSM8901897 HC_Subject_sp
- GSE334588 Single-cell transcriptomic atlas of adult dermatomyositis, cutaneous lupus erythematosus and healthy skin 48 samples shares cutaneous, lupus
- GSE224198 Single-cell multi-omics analysis revealing immune features of inactivated SARS-CoV-2 vaccination in systemic lupus erythematosus patients 26 samples shares features, lupus
- GSE315668 Robust effector memory features of human T-bet-hi B cells induced by repeated mRNA vaccination 42 samples shares features
- GSE248132 LARP6 promotes carcinogenic features of triple negative breast cancer cell line by directly modulating alternative splicing of associated genes 10 samples shares features
- GSE239775 Quantification of Systemic Inflammation by a Gene Score Uncovers Relevant Features on Acutely Decompensated Cirrhosis 1177 samples shares features
- GSE308462 ALS molecular subtypes are a combination of cellular, genetic, and pathological features learned by deep multiomics classifiers (post-mortem cortex) 260 samples shares features
- GSE287022 Cutaneous adipose tissue has a strong inflammatory signature in psoriasis patients, and it is partly IL-17 dependent. 241 samples shares cutaneous
- GSE312983 A single-nucleus RNA-seq atlas of colorectal cancer across the age continuum reveals distinct features of early-onset colorectal cancer 189 samples shares features
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.