← BioTransfer GEO Dataset Finder
GEO series

Genome-wide DNA replication profiling and full-length total RNA sequencing from the same single cell [HAP1 mid-S]

GSE278952 Homo sapiens (human) Single-cell RNA-seq 131 samples Submitted 2025/08/27 Platform GPL20795
At a glance

Single-cell RNA-seq · human · 131 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
131 samples: 63 × RNA_HAP1_scRR1_MidS vs 63 × DNA_HAP1_scRR1_MidS vs 5 × DNA_HAP1_scRR1_G1Groups 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
scRepli-RamDA-seq: a multi-omics technology enabling the analysis of gene expression dynamics during S-phaseNature communications · 2025
Summary (written by the submitters)
Single-cell sequencing has advanced our understanding of cell-type diversity and heterogeneity. However, existing single-cell multi-omics methods lack DNA data resolution and full-length total RNA detection. Here, we introduce single-cell (sc)Repli-RamDA-seq (scRR-seq), a novel multi-omics method that enables high-resolution DNA replication profiling and full-length total RNA sequencing from the same single cell, made possible by the clean separation of DNA and RNA.
Read the full summary ↓
scRR-seq generates DNA replication and RNA sequencing data comparable to individually obtained scRepli-seq and scRamDA-seq data, respectively. scRR-seq also allows one to tell the cell-cycle stage of a given S-phase cell and detects copy-number variation (CNV) in non-S-phase cells. scRR-seq outperforms other scRNA-seq methods in the number of transcripts identified and full-length total RNA detection. Furthermore, scRR-seq allowed haplotype-specific analysis and the identification of novel S-phase markers. Taken together, scRR-seq is a robust single-cell multi-omics method with promising potential for comprehensive genome and transcriptome analysis.
Published in
scRepli-RamDA-seq: a multi-omics technology enabling the analysis of gene expression dynamics during S-phase
Poonperm R, Yoneda T, Imada T et al. · Nature communications 2025 · PMID 41397953 · doi:10.1038/s41467-025-64688-1
This dataset
Open GSE278952 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 GSE278952_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 PRJNA1169834 and SRA study SRP537170. Searching any of these in the dataset finder brings you back here.

NCBI GEO page ↗ Paper (PMID 41397953) ↗
Study design
63 × RNA_HAP1_scRR1_MidS vs 63 × DNA_HAP1_scRR1_MidS vs 5 × DNA_HAP1_scRR1_G1

Supports a between-group comparison across 131 samples.

How we read this: 3 replicated groups read from 131 sample titles; they account for 131 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

+ 91 more — browse all 131 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.