GEO series

Scalable ultra-high-throughput single-cell chromatin and RNA sequencing reveals gene regulatory dynamics linking macrophage polarization to autoimmune disease [NIH-3T3_K562_ATAC_with_blocking]

GSE253164 Homo sapiens (human) Single-cell RNA-seq 1 samples Submitted 2025/04/10 Platform GPL30202

At a glance

Single-cell RNA-seq · human · 1 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: 1 samples: 1 samples, each with a distinct title — no replicated groupsGroups 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: Single-cell ultra-high-throughput multiplexed chromatin and RNA profiling reveals gene regulatory dynamicsNature methods · 2025

Summary (written by the submitters)

Enhancers and transcription factors (TFs) are crucial in regulating cellular processes, including disease-associated cell states. Current technologies to study these elements in gene regulatory mechanisms lack multiplexing capability and scalability. Here, we present SUM-seq, a cost-effective, scalable Single-cell Ultra-high-throughput Multiomic sequencing method for co-assaying chromatin accessibility and gene expression in single nuclei - enabling experiments at the million cell and hundreds of samples scale.

Read the full summary ↓

SUM-seq outperforms current high-throughput single cell methods in sensitivity. We applied SUM-seq to dissect the gene regulatory mechanisms governing macrophage polarization, and explored their link to traits from genome-wide association studies (GWAS). Our analyses confirmed known TFs orchestrating M1 and M2 macrophage programs, unveiled new regulators, and suggested extensive enhancer rewiring. Integration with GWAS data further pinpointed the impact of specific TFs on a set of immune traits. Notably, enhancers regulated by the STAT1/STAT2/IRF9 (ISGF3) complex were enriched for rheumatoid arthritis (RA)-associated genetic variants, with the strongest link pointing to the CD40 gene, an emerging drug target for RA. This highlights the potential of SUM-seq for dissecting molecular disease mechanisms. In summary, SUM-seq provides a simple workflow for cost-effective, scalable single-cell ultra-high-throughput multiomic sequencing. It is particularly adept at elucidating complex gene regulatory networks in cell differentiation, response to perturbations, and disease contexts.

Published in

Single-cell ultra-high-throughput multiplexed chromatin and RNA profiling reveals gene regulatory dynamics

Lobato-Moreno S, Yildiz U, Claringbould A et al. · Nature methods 2025 · PMID 40419657 · doi:10.1038/s41592-025-02700-8

This dataset

Open GSE253164 in finder → Find more ChIP / ATAC / CUT&Tag 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 GSE253164_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 PRJNA1064140 and SRA study SRP483439. Searching any of these in the dataset finder brings you back here.

NCBI GEO page ↗ Paper (PMID 40419657) ↗

Study design

1 samples, each with a distinct title — no replicated groups

Any grouping that defines a comparison lives in the sample characteristics, which GEO records separately from the titles.

How we read this: Read from 1 sample titles: 1 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

GSM8015427 NIH-3T3 and K562 ATAC (with blocking oligo)

Datasets on a similar topic

GSE253162 Scalable ultra-high-throughput single-cell chromatin and RNA sequencing reveals gene regulatory dynamics linking macrophage polarization to autoimmune disease [NIH-3T3_K562_ATAC_without_blocking] 1 samples shares nih-3t3, k562, atac, ultra-high-throughput, polarization, single-cell, regulatory, macrophage
GSE253163 Scalable ultra-high-throughput single-cell chromatin and RNA sequencing reveals gene regulatory dynamics linking macrophage polarization to autoimmune disease [NIH-3T3_K562_RNA_with_blocking] 1 samples shares nih-3t3, k562, ultra-high-throughput, polarization, single-cell, regulatory, macrophage
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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.