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Single-Cell Multi-Omics Sequencing Reveals the Dynamic Epigenome Landscape of Human Adult Spermatogenesis

GSE149407 Homo sapiens (human) Single-cell RNA-seq 1435 samples Submitted 2025/10/01 Platform GPL20301
At a glance

Single-cell RNA-seq · human · 1435 samples

What it is
Single-cell RNA-seqGene activity measured in thousands of individual cells, so cell types can be told apart.
Organism
Homo sapiens (human)
Samples
1435 samples: 70 conditions, mostly with about 6 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.
Summary (written by the submitters)
Stepwise and sequential cell fate transition accomplished by global transcriptional changes occurs across human spermatogenesis, but the epigenome roadmap and regulatory networks underlying this process have not been established. Here, we explore a single-cell multi-omics landscape of human adult testicular cells using the single-cell chromatin overall omic-scale landscape sequencing (scCOOL-seq) technique, deriving an unbiased multi-omics reference map of human adult spermatogenesis.
Read the full summary ↓
The initial commitment to meiosis coincides with a novel dramatic DNA demethylation process in non-coding regions that highly overlapped with crossover and double strand break (DSB) hotspots. Chromatin accessibility mainly accounts for the global expression dynamics in human spermatogenesis. Further studies identify the cis- regulatory elements of several key regulators such as STRA8, ID3, HMGA1 and TEX29 in human spermatogenesis. Thus, our data characterize the regulatory networks orchestrating gene expression and chromatin configuration under the state of normal human spermatogenesis and its perturbation imposed by disease lesion, providing novel insights into the association of epigenome with germ cell development and male infertility.
This dataset
Open GSE149407 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 GSE149407_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: Single-cell studies usually ship their counts as 10x files (matrix.mtx, barcodes.tsv, features.tsv), an .h5 file, or an .rds / .h5ad object that Seurat or Scanpy can open. Look for those among the supplementary files.

Also filed as BioProject PRJNA628638. Searching any of these in the dataset finder brings you back here.

NCBI GEO page ↗
Study design
70 conditions, mostly with about 6 replicates each
RNA_Normal_3 ×8 NOA_1_12 ×8 NOA_2_9 ×8 NOA_3_3 ×8 NOA_3_5 ×8 Normal_1_1_9 ×8 Normal_1_2_11 ×8 Normal_1_2_6 ×8 +65 more

Supports a between-group comparison across 397 samples.

How we read this: 70 replicated groups read from the first 400 of 1435 sample titles; they account for 397 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

+ 1395 more — browse all 1435 samples with per-sample file links →

Datasets on a similar topic

Search all human RNA-seq datasets in GEO →

Share this dataset

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.