GEO series

Systematic discovery of subcellular RNA patterns in the gut epithelium

GSE267163 Mus musculus (mouse) RNA-seq (gene expression) 14 samples Submitted 2025/09/05 Platform GPL24247

At a glance

RNA-seq (gene expression) · mouse · 14 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: Mus musculus (mouse)
Samples: 14 samples: 14 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: Systematic discovery of subcellular RNA patterns in the gut epitheliumGenome biology · 2025

Summary (written by the submitters)

Subcellular RNA localization is crucial for the spatio-temporal control of protein synthesis and underlies key processes during development, homeostasis and disease. In epithelial cells, RNA can localize asymmetrically along the apico-basal axis. Yet, we ignore the localization of most transcripts as well as the diversity of patterns that they adopt.

Read the full summary ↓

Here, we utilized proximity labeling (APEX-seq) and spatial transcriptomics (MERFISH) to map subcellular transcript localization in intestinal organoids and adult mice tissue. Many transcripts presented localization bias, often localizing in granular structures. We described the intrinsic and environmental factors that influence the formation of these patterns. We identified translation-dependent and -independent localization patterns and pintointed the role of 3’ UTRs and RNA-binding proteins. This subcellular RNA atlas can be referenced to extract new biological insights and provide hypothesis-free observation on spatial transcriptomics.

Published in

Systematic discovery of subcellular RNA patterns in the gut epithelium

Lee M, Acar IE, Eletto D et al. · Genome biology 2025 · PMID 41163196 · doi:10.1186/s13059-025-03786-1

This dataset

Open GSE267163 in finder → Find more mouse 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 GSE267163_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 PRJNA1109980 and SRA study SRP506900. Searching any of these in the dataset finder brings you back here.

NCBI GEO page ↗ Paper (PMID 41163196) ↗

Study design

14 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 14 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.

Samples in this study

GSM8261280 DPP4-1-sIOs
GSM8261281 DPP4-2-sIOs
GSM8261282 DPP4-3-sIOs
GSM8261283 DPP4-4-sIOs
GSM8261284 GFP-1-sIOs
GSM8261285 GFP-2-sIOs
GSM8261286 GFP-3-sIOs
GSM8261287 GFP-4-sIOs
GSM8261288 ACTB-1-sIOs
GSM8261289 ACTB-2-sIOs
GSM8261290 ACTB-3-sIOs
GSM8261291 ACTB-4-sIOs
GSM8261292 MITO-1-sIOs
GSM8261293 MITO-1c-sIOs

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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.