The Underlying Dermal Scaffold Affects Molecular Differentiation Markers in Three-Dimensional Human Epidermal Equivalents
RNA-seq (gene expression) · human · 12 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
- 12 samples: 5 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
- The underlying dermal scaffold affects molecular differentiation markers in 3-dimensional human epidermal equivalentsThe Journal of investigative dermatology · 2026
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 GSE310114_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 PRJNA1365149 and SRA study SRP645378. Searching any of these in the dataset finder brings you back here.
Supports a between-group comparison across 12 samples.
How we read this: 5 replicated groups read from 12 sample titles; they account for 12 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.
- GSM9288611 ACD, day3, graft1 ACD, day3, graft
- GSM9288612 ACD, day3, graft2 ACD, day3, graft
- GSM9288613 ACD, day3, graft3 ACD, day3, graft
- GSM9288614 ACD, day7, graft4 ACD, day7, graft
- GSM9288615 ACD, day7, graft5 ACD, day7, graft
- GSM9288616 ACD, day7, graft6 ACD, day7, graft
- GSM9288617 CC, day3, graft2 CC, day3, graft
- GSM9288618 CC, day3, graft3 CC, day3, graft
- GSM9288619 CC, day7, graft4 CC, day7, graft
- GSM9288620 CC, day7, graft5 CC, day7, graft
- GSM9288621 CC, day14, graft7 CC, day14, graft
- GSM9288622 CC, day14, graft9 CC, day14, graft
- GSE305032 Tape strips capture immune and epidermal hyperplasia markers in the major orphan ichthyoses 45 samples shares epidermal, markers, dermal
- GSE245608 Transcriptomic evaluation of skin tape-strips in children with allergic asthma uncovers epidermal barrier dysfunction and asthma-associated biomarkers abnormalities 32 samples shares epidermal, markers, dermal
- GSE292207 Functional interaction of Epidermal growth factor and Angiotensin II in female human primary vascular smooth muscle cells 44 samples shares epidermal, dermal
- GSE283265 Filaggrin status modulates gene transcription, DNA damage and epidermal barrier repair during clinical response to methotrexate in atopic eczema 44 samples shares epidermal, dermal
- GSE243188 Epithelial barrier function and its underlying mechanical regulation affects the biology of Macro-Metastasis Parenchyma Interface (MMPI). 28 samples shares underlying, affects
- GSE338364 Lentiviral hTERT overexpression extends the lifespan of primary human epidermal melanocytes and reshapes transcriptional programs 24 samples shares epidermal, dermal
- GSE270765 Disseminated tumor cell of colorectal cancer adopt the wound healing program of epidermal keratinocyte [RNA-seq] 17 samples shares epidermal, dermal
- GSE288490 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. 16 samples shares underlying, markers
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.