GEO series

Functional phenotyping of genomic variants using joint multiomic single-cell DNA-RNA sequencing

GSE268646 Homo sapiens (human) Single-cell RNA-seq 17 samples Submitted 2025/06/17 Platform GPL30173Platform GPL30202Platform GPL18573Platform GPL24676

At a glance

Single-cell RNA-seq · human · 17 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: 17 samples: 13 distinct titles, 4 of them repeatedGroups 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: Functional phenotyping of genomic variants using joint multiomic single-cell DNA-RNA sequencingNature methods · 2025

Summary (written by the submitters)

Genetic variants (both coding and noncoding) can impact gene function and expression, driving disease mechanisms such as cancer progression. The systematic study of endogenous genetic variants is hindered by inefficient precision editing tools, combined with technical limitations in confidently linking genotypes to gene expression at single-cell resolution.

Read the full summary ↓

We developed single-cell DNA-RNA sequencing (SDR-seq) to simultaneously profile up to 480 genomic DNA loci and genes in thousands of single cells, enabling accurate determination of coding and noncoding variant zygosity alongside associated gene expression changes. Using SDR-seq, we associate coding and noncoding variants with distinct gene expression in human induced pluripotent stem cells. Furthermore, we demonstrate that in primary B cell lymphoma samples, cells with a higher mutational burden exhibit elevated B cell receptor signaling and tumorigenic gene expression. SDR-seq provides a powerful platform to dissect regulatory mechanisms encoded by genetic variants, advancing our understanding of gene expression regulation and its implications for disease.

Published in

Functional phenotyping of genomic variants using joint multiomic single-cell DNA-RNA sequencing

Lindenhofer D, Bauman JR, Hawkins JA et al. · Nature methods 2025 · PMID 40890552 · doi:10.1038/s41592-025-02805-0

This dataset

Open GSE268646 in finder → Find more 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 GSE268646_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 PRJNA1118023 and SRA study SRP510726. Searching any of these in the dataset finder brings you back here.

NCBI GEO page ↗ Paper (PMID 40890552) ↗

Study design

13 distinct titles, 4 of them repeated

02_Panel_size_gDNA_120 ×2 02_Panel_size_gDNA ×2 02_Panel_size_RNA_120 ×2 02_Panel_size_RNA ×2

How we read this: Read from 17 sample titles: 13 distinct titles with little repetition. 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

GSM8295887 01_POP
GSM8295888 02_Panel_size_gDNA_120-1
GSM8295889 02_Panel_size_gDNA_120-2
GSM8295890 02_Panel_size_gDNA_240
GSM8295891 02_Panel_size_gDNA_480
GSM8295892 02_Panel_size_RNA_120-1
GSM8295893 02_Panel_size_RNA_120-2
GSM8295894 02_Panel_size_RNA_240
GSM8295895 02_Panel_size_RNA_480
GSM8295896 03_CRISPRi_gDNA
GSM8295897 03_CRISPRi_RNA
GSM8295898 04_PE_gDNA
GSM8295899 04_PE_RNA
GSM8295900 05_BE_gDNA
GSM8295901 05_BE_RNA
GSM8819331 07_Species_Mix_gDNA
GSM8819332 07_Species_Mix_RNA

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