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Transcriptomic analysis of severe allergic patch test reactions to different allergens (nickel, 2-hydroxyethylmethacrylate, methylisothiazolinone and formaldehyde)

GSE282562 Homo sapiens (human) RNA-seq (gene expression) 59 samples Submitted 2025/07/16 Platform GPL30173
At a glance

RNA-seq (gene expression) · human · 59 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
59 samples: 59 samples, each with a distinct title — no replicated groupsGroups 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.
Summary (written by the submitters)
Background: Immune mechanisms underlying elicitation in allergic contact dermatitis (ACD) are not disclosed. Previous studies have shown a double-faceted nature of ACD with both common biomarkers between different allergens, as well as allergen-specific imprinting, with discordance in terms of relevant involved immune pathways.
Read the full summary ↓
Objectives: To characterize molecular signatures and their immune mechanisms between different relevant allergens (nickel, 2-hydroxyethylmethacrylate (2-HEMA), methylisothiazolinone (MI), formaldehyde) in strong and extreme patch test reactions of patients without atopic dermatitis using RNA sequencing.
This dataset
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.

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 GSE282562_RAW.tar (it can run to tens of gigabytes).

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.

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 PRJNA1189143 and SRA study SRP546837. Searching any of these in the dataset finder brings you back here.

Study design
59 samples, each with a distinct title — no replicated groups

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

How we read this: Read from 59 sample titles: 59 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.

Samples in this study

+ 19 more — browse all 59 samples with per-sample file links →

Datasets on a similar topic

Search all human RNA-seq datasets in GEO →

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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.