Cytokine stimulated keratinocytes
RNA-seq (gene expression) · human · 800 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
- 800 samples: 200 conditions, mostly in duplicateGroups 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
- GWAS meta-analysis of psoriasis identifies new susceptibility alleles impacting disease mechanisms and therapeutic targetsNature communications · 2025
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 GSE255828_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: 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 PRJNA1076598 and SRA study SRP489802. Searching any of these in the dataset finder brings you back here.
Supports a between-group comparison across 400 samples.
How we read this: 200 replicated groups read from the first 400 of 800 sample titles; they account for 400 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.
- GSM8080554 64588_CGATGT_S1_L001 64588_CGATGT_S1_L
- GSM8080555 64588_CGATGT_S1_L005 64588_CGATGT_S1_L
- GSM8080556 64589_CAGATC_S2_L005 64589_CAGATC_S2_L
- GSM8080557 64589_CAGATC_S2_L001 64589_CAGATC_S2_L
- GSM8080558 64590_GTGAAA_S3_L005 64590_GTGAAA_S3_L
- GSM8080559 64590_GTGAAA_S3_L001 64590_GTGAAA_S3_L
- GSM8080560 64591_TGACCA_S4_L005 64591_TGACCA_S4_L
- GSM8080561 64591_TGACCA_S4_L001 64591_TGACCA_S4_L
- GSM8080562 64592_GAGTGG_S5_L005 64592_GAGTGG_S5_L
- GSM8080563 64592_GAGTGG_S5_L001 64592_GAGTGG_S5_L
- GSM8080564 64593_ATCACG_S6_L005 64593_ATCACG_S6_L
- GSM8080565 64593_ATCACG_S6_L001 64593_ATCACG_S6_L
- GSM8080566 64594_TAGCTT_S7_L005 64594_TAGCTT_S7_L
- GSM8080567 64594_TAGCTT_S7_L001 64594_TAGCTT_S7_L
- GSM8080568 64595_GTGGCC_S8_L005 64595_GTGGCC_S8_L
- GSM8080569 64595_GTGGCC_S8_L001 64595_GTGGCC_S8_L
- GSM8080570 64596_AGTCAA_S9_L005 64596_AGTCAA_S9_L
- GSM8080571 64596_AGTCAA_S9_L001 64596_AGTCAA_S9_L
- GSM8080572 64597_GTCCGC_S10_L005 64597_GTCCGC_S10_L
- GSM8080573 64597_GTCCGC_S10_L001 64597_GTCCGC_S10_L
- GSM8080574 64598_ACAGTG_S11_L005 64598_ACAGTG_S11_L
- GSM8080575 64598_ACAGTG_S11_L001 64598_ACAGTG_S11_L
- GSM8080576 64599_GCCAAT_S12_L005 64599_GCCAAT_S12_L
- GSM8080577 64599_GCCAAT_S12_L001 64599_GCCAAT_S12_L
- GSM8080578 64600_ATGTCA_S13_L005 64600_ATGTCA_S13_L
- GSM8080579 64600_ATGTCA_S13_L001 64600_ATGTCA_S13_L
- GSM8080580 64601_CTTGTA_S14_L001 64601_CTTGTA_S14_L
- GSM8080581 64601_CTTGTA_S14_L005 64601_CTTGTA_S14_L
- GSM8080582 64602_CCGTCC_S15_L005 64602_CCGTCC_S15_L
- GSM8080583 64602_CCGTCC_S15_L001 64602_CCGTCC_S15_L
- GSM8080584 64603_TTAGGC_S16_L005 64603_TTAGGC_S16_L
- GSM8080585 64603_TTAGGC_S16_L001 64603_TTAGGC_S16_L
- GSM8080586 64604_GATCAG_S17_L005 64604_GATCAG_S17_L
- GSM8080587 64604_GATCAG_S17_L001 64604_GATCAG_S17_L
- GSM8080588 64605_ACTGAT_S18_L005 64605_ACTGAT_S18_L
- GSM8080589 64605_ACTGAT_S18_L001 64605_ACTGAT_S18_L
- GSM8080590 64606_ATTCCT_S19_L005 64606_ATTCCT_S19_L
- GSM8080591 64606_ATTCCT_S19_L001 64606_ATTCCT_S19_L
- GSM8080592 64607_GGCTAC_S20_L005 64607_GGCTAC_S20_L
- GSM8080593 64607_GGCTAC_S20_L001 64607_GGCTAC_S20_L
+ 760 more — browse all 800 samples with per-sample file links →
- GSE288617 Paired transcriptomics reveals similarities between cytokine-stimulated organoids and ulcerative colitis epithelial responses 41 samples shares stimulated, cytokine
- GSE315599 Primary human fibroblasts and keratinocytes stimulated with IL-17A and/or TNF 32 samples shares keratinocytes, stimulated
- GSE288517 Paired transcriptomics reveals similarities between cytokine-stimulated organoids and ulcerative colitis epithelial responses 23 samples shares stimulated, cytokine
- GSE237690 Lupus dermal fibroblasts hyper-respond to inflammatory cytokines and exhibit a pro-fibrotic phenotype to TGF-β in patients with scarring skin disease 317 samples shares cytokine
- GSE301357 The airway transcriptome in type2 cytokine biomarker-high and -low severe asthma 129 samples shares cytokine
- GSE298908 Positionally distinct interferon-stimulated dermal immune-acting fibroblasts promote neutrophil recruitment in Sweet's syndrome [RNA-seq] 113 samples shares stimulated
- GSE274606 Differential Induction of Interferon-Stimulated Genes by Cell-Based Versus Egg-Based Quadrivalent Influenza Vaccines in Children During the 2018-2019 Season 103 samples shares stimulated
- GSE314628 Temporal Transcriptomic Profiling Identifies Core Regulators in Cytokine-Induced Gut Barrier Disruption in Differentiated Caco-2 Cells 73 samples shares cytokine
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.