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Lysosomal RNA profiling reveals targeting of specific types of RNAs for degradation

GSE296736 Homo sapiens (human) RNA-seq (gene expression) 99 samples Submitted 2025/12/08 Platform GPL24676
At a glance

RNA-seq (gene expression) · human · 99 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
99 samples: 49 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
Lysosomal RNA profiling reveals targeting of specific types of RNAs for degradationbioRxiv : the preprint server for biology · 2025
Summary (written by the submitters)
Autophagy targets a wide variety of substrates for degradation within lysosomes. Although lysosomes are known to possess RNase activity, the role of lysosomal RNA degradation in post-transcriptional gene regulation is not well understood. We defined RNASET2, PLD3, and both endogenous and exogenous RNase A family members as lysosomal RNases.
Read the full summary ↓
Cells lacking these RNases accumulated large amounts of lysosomal RNA. Quantitative RNA sequencing of lysosomal contents revealed that although all RNA types can enter lysosomes, the SRP RNAs, Y RNAs, 5′ TOP mRNAs, long-lived mRNAs, and mRNAs encoding membrane and secreted proteins were specifically enriched. This specificity was due to autophagy, and lysosomally degraded transcripts had autophagy-dependent changes in stability. To explore the basis of this specificity, we identified sequence motifs of SRP RNAs and 5′ TOP mRNAs that are necessary and sufficient for enhanced lysosomal targeting. Our results establish lysosomes as selective modulators of cellular RNA content.
Published in
Lysosomal RNA profiling reveals targeting of specific types of RNAs for degradation
Ray GJ, Nardini E, Keys HR et al. · bioRxiv : the preprint server for biology 2025 · PMID 40964324 · doi:10.1101/2025.09.09.674968
This dataset
Open GSE296736 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 GSE296736_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 PRJNA1260976 and SRA study SRP584172. Searching any of these in the dataset finder brings you back here.

NCBI GEO page ↗ Paper (PMID 40964324) ↗
Study design
49 conditions, mostly in duplicate
RNASET2 PLD3 RNASE4 tKO no serum whole cell ×2 RNASET2 KO whole cell ×2 WT whole cell ×2 RNASET2 PLD3 RNASE4 tKO no serum Torin1 whole… ×2 RNASET2 KO Torin1 whole cell ×2 WT Torin1 whole cell ×2 RNASET2 PLD3 RNASE4 tKO no serum LysoIP ×2 RNASET2 KO LysoIP ×2 +42 more

Supports a between-group comparison across 98 samples.

How we read this: 49 replicated groups read from 99 sample titles; they account for 98 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

+ 59 more — browse all 99 samples with per-sample file links →

Datasets on a similar topic

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