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The spatial transcriptomic landscape of fibrosing ILDs [Nanostring]

GSE255174 Homo sapiens (human) RNA-seq (gene expression) 179 samples Submitted 2024/09/30 Platform GPL30173
At a glance

RNA-seq (gene expression) · human · 179 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
179 samples: 179 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.
Paper
Spatially resolved gene expression profiles of fibrosing interstitial lung diseasesScientific reports · 2024
Summary (written by the submitters)
Fibrosing interstitial lung diseases (ILDs) encompass a diverse range of scarring disorders that lead to progressive lung failure. Previous gene expression profiling studies focused on idiopathic pulmonary fibrosis (IPF) and bulk tissue samples. We employed digital spatial profiling to gain new insights into the spatial resolution of gene expression across distinct lung microenvironments (LMEs) in IPF, chronic hypersensitivity pneumonitis (CHP) and non-specific interstitial pneumonia (NSIP).
Read the full summary ↓
We identified differentially expressed genes between LMEs within each condition, and across histologically similar regions between conditions. Uninvolved areas in IPF and CHP were remarkably distinct from normal controls, and displayed potential therapeutic targets. Hallmarks LMEs of each condition retained a distinct gene signature, but these could not be reproduced in matched lung tissue samples. Based on these gene expression signatures and unsupervised clustering, we grouped previously unclassified ILD cases into NSIP or CHP. Lastly, we characterized a gene expression pattern associated with poor outcome . Overall, our work uniquely dissects gene expression profiles between LMEs within and across different types of fibrosing ILDs. This new spatial transcriptomics approach has the potential to reclassify unclassifiable cases, to qualify the transcriptional relevance of smaller biopsies for clinical use, and to predict outcome at the time of diagnosis.
Published in
Spatially resolved gene expression profiles of fibrosing interstitial lung diseases
Kim SJ, Cecchini MJ, Woo E et al. · Scientific reports 2024 · PMID 39488596 · doi:10.1038/s41598-024-77469-5
This dataset
Open GSE255174 in finder → Find more human RNA-seq datasets →
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.

Series matrix ↓
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 GSE255174_RAW.tar (it can run to tens of gigabytes).

All supplementary files ↗ Whole study · RAW.tar ↓
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.

Raw reads on ENA ↗

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 PRJNA1073896 and SRA study SRP488470. Searching any of these in the dataset finder brings you back here.

NCBI GEO page ↗ Paper (PMID 39488596) ↗
Study design
179 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 179 sample titles: 179 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

+ 139 more — browse all 179 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.