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Deciphering the Sperm RNA Code of Aging with PANDORA-seq

GSE256182 Homo sapiens (human) Non-coding RNA profiling 154 samples Submitted 2025/12/04 Platform GPL24676Platform GPL24247Platform GPL34328Platform GPL34284
At a glance

Non-coding RNA profiling · human · 154 samples

What it is
Non-coding RNA profilingLevels of RNAs that do not code for proteins, such as miRNA or lncRNA.
Organism
Homo sapiens (human)
Samples
154 samples: 23 conditions, mostly with about 4 replicates eachGroups 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
Conserved shifts in sperm small non-coding RNA profiles during mouse and human agingThe EMBO journal · 2026
Summary (written by the submitters)
Sperm aging impacts male fertility and offspring health, highlighting the need for reliable aging biomarkers to guide reproductive decisions. Using PANDORA-seq, a novel method to comprehensively profile small non-coding RNAs (sncRNAs), we identified an "aging cliff" in mouse sperm RNA profiles-a sharp age-specific transition marked by significant shifts in genomic and mitochondrial tRNA-derived small RNAs (tsRNAs) and rRNA-derived small RNAs (rsRNAs).
Read the full summary ↓
Notably, rsRNAs embedded in sperm heads exhibited a transformative length shift, with longer rsRNAs increasing and shorter ones decreasing with age, suggesting altered biogenesis with age. Remarkably, this sperm rsRNA length shift was consistently observed in two independent human aging cohorts, highlighting its evolutionary significance. Moreover, a combination of tsRNAs and rsRNAs (tsRNA/rsRNA cocktails) resembling those in aged sperm induced transcriptomic changes in embryonic stem cells, impacting metabolism and neurodegeneration pathways, mirroring the phenotypes observed in offspring of aged sperm. These results reveal new insights of sperm aging, highlighting the conserved rsRNA length shift in mice and humans, with translational potential for fertility and intergenerational health
Published in
Conserved shifts in sperm small non-coding RNA profiles during mouse and human aging
Shi J, Zhang X, Cai C et al. · The EMBO journal 2026 · PMID 41559200 · doi:10.1038/s44318-025-00687-8
This dataset
Open GSE256182 in finder → Find more 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 GSE256182_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 ↗

Also filed as BioProject PRJNA1078386 and SRA study SRP490496. Searching any of these in the dataset finder brings you back here.

NCBI GEO page ↗ Paper (PMID 41559200) ↗
Study design
23 conditions, mostly with about 4 replicates each
mouse sperm, 10-week, PANDORA-seq, ×4 mouse sperm, 30-week, PANDORA-seq, ×4 mouse sperm, 50-week, PANDORA-seq, ×4 mouse sperm, 70-week, PANDORA-seq, ×4 mouse sperm, 90-week, PANDORA-seq, ×4 mouse sperm, 10-week, traditional sncRNA-seq, ×4 mouse sperm, 30-week, traditional sncRNA-seq, ×4 mouse sperm, 50-week, traditional sncRNA-seq, ×4 +78 more

Supports a between-group comparison across 91 samples.

How we read this: 23 replicated groups read from 154 sample titles; they account for 91 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

+ 114 more — browse all 154 samples with per-sample file links →

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