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Genetically targeted mTORC1 inhibitor reveals transcriptional control by nuclear mTORC1

GSE320090 Mus musculus (mouse) RNA-seq (gene expression) 15 samples Submitted 2026/02/24 Platform GPL24247
At a glance

RNA-seq (gene expression) · mouse · 15 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
15 samples: 5 conditions, mostly in triplicateGroups 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
Genetically targeted mTORC1 inhibitor reveals transcriptional control by nuclear mTORC1Nature chemical biology · 2026
Summary (written by the submitters)
mTORC1 is a nutrient sensor which integrates diverse inputs to regulate protein translation and cell growth. While mTORC1 is activated on the lysosome in the classical model, it has become increasingly clear that this multifaceted signaling complex is active at various subcellular locations, such as the nucleus. However, what specific functions mTORC1 serves at these locations and how its signaling is compartmentalized are not unclear.
Read the full summary ↓
To interrogate subcellular pools of mTORC1, we developed TerminaTOR, a genetically encodable inhibitor of mTORC1 that can be targeted to specific subcellular locations. When TerminaTOR is directed to the lysosome, it inhibits canonical lysosomal mTORC1 and induces autophagy. Furthermore, TerminaTOR targeted to the nucleus specifically inhibits nuclear mTORC1, uncovering non-canonical roles of nuclear mTORC1 in regulating the transcription of CCAAT motif-containing genes. Thus, mTORC1 exhibits functional spatial compartmentalization, and TerminaTOR serves as a powerful tool for unraveling spatially regulated functions of mTORC1 across different scales
Published in
Genetically targeted mTORC1 inhibitor reveals transcriptional control by nuclear mTORC1
Zhong Y, Sahan AZ, Shao Z et al. · Nature chemical biology 2026 · PMID 42135521 · doi:10.1038/s41589-026-02188-z
This dataset
Open GSE320090 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 GSE320090_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 PRJNA1426370 and SRA study SRP678407. Searching any of these in the dataset finder brings you back here.

NCBI GEO page ↗ Paper (PMID 42135521) ↗
Study design
5 conditions, mostly in triplicate
H2A-mCherry cells, no PDGF treatment, ×3 H2A-mCherry cells, PDGF treatment, ×3 H2A-mCherry cells, Rapamycin and PDGF treatme… ×3 H2A-mCherry cells, cycloheximide and PDGF tre… ×3 H2A-TerminaTOR cells, cycloheximide and PDGF … ×3

Supports a between-group comparison across 15 samples.

How we read this: 5 replicated groups read from 15 sample titles; they account for 15 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.

Samples in this study
Datasets on a similar topic

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