GEO series

A single-cell multiomics approach for simultaneous analysis of replication timing and gene expression

GSE283896 Homo sapiens (human) Single-cell RNA-seq 96 samples Submitted 2025/06/16 Platform GPL30173

At a glance

Single-cell RNA-seq · human · 96 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: 96 samples
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: Development of a Novel Single Cell Multiomics Approach for Simultaneous Analysis of Replication Timing and Gene ExpressionbioRxiv : the preprint server for biology · 2025

Summary (written by the submitters)

Replication timing (RT) allows us to analyze temporal patterns of genome-wide replication, that is, if genes replicate early or late during the S-phase of the cell cycle. RT has been linked to gene expression in normal and diseased states such as cancer. However, studies done to date were on bulk cell populations that required tens of thousands of cells for RT analysis.

Read the full summary ↓

Here, we developed an affordable novel single cell (sc)-multiomics approach to simultaneously analyze RT and gene expression from cells or nuclei. We used this approach to generate sc-RT profiles and sc-gene expression data from the well-established human liver cancer cell line, HepG2. We demonstrated that as few as 17 mid S-phase cells were sufficient to produce cell-type specific pseudo bulk RT profiles, that had a high correlation to previously published HepG2 bulk RT profiles. The sc-RT profiles allowed us to visualize how individual cells progressed through genome replication. We were also able to demonstrate high-resolution correlations between RT and gene expression within each individual cell, which to our knowledge, has not been done before. We observed trends conserved between individual cells, as well as cell-to-cell variations, which has not been possible so far with the bulk RT studies.

Published in

Development of a Novel Single Cell Multiomics Approach for Simultaneous Analysis of Replication Timing and Gene Expression

Shetty AV, Steer CJ, Low WC · bioRxiv : the preprint server for biology 2025 · PMID 39803575 · doi:10.1101/2025.01.02.631160

This dataset

Open GSE283896 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 GSE283896_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 PRJNA1195984 and SRA study SRP550301. Searching any of these in the dataset finder brings you back here.

NCBI GEO page ↗ Paper (PMID 39803575) ↗

Samples in this study

The sample list for this study is not cached yet. Press Load samples and it will be fetched from NCBI.

+ 96 more — browse all 96 samples with per-sample file links →

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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.