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Multichrome encoding-based multiplexed, spatially resolved imaging reveals single-cell RNA epigenetic modifications heterogeneity

GSE280639 Homo sapiens (human) Single-cell RNA-seq 21 samples Submitted 2024/12/23 Platform GPL16791
At a glance

Single-cell RNA-seq · human · 21 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
21 samples: 7 conditions, mostly in triplicateGroups 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
Multichrome encoding-based multiplexed, spatially resolved imaging reveals single-cell RNA epigenetic modifications heterogeneityNature communications · 2025
Summary (written by the submitters)
Understanding the heterogeneity of epigenetic modifications within single cells is pivotal for unraveling the nature of the complexity of gene expression and cellular function. In this study, we have developed a strategy based on multichrome encoding and “AND” Boolean logic recognition for multiplexed, spatially resolved imaging of single-cell RNA epigenetic modifications, termed as PRoximity Exchange-assisted Encoding of Multichrome (PREEM).
Read the full summary ↓
Through the implementation of this strategy, we can now map the expression and nuclear distribution of multiple site-specific RNA epigenetic modifications at the single-molecule resolution level in single-cells, and reveal the previously unknown heterogeneity. Notably, we demonstrate how these patterns change after treatment with various drugs. Moreover, cyclic imaging with tailed DNA self-assembly further suggest the scalability and adaptability of PREEM’s design. As an innovative epigenetic modification imaging tool, PREEM not only broadens the horizons of single-cell epigenetics research, enabling joint analysis of multiple targets beyond the limitations of imaging channels, but also reveals cell-to-cell variability, thereby enhancing our capacity to explore cellular functions.
Published in
Multichrome encoding-based multiplexed, spatially resolved imaging reveals single-cell RNA epigenetic modifications heterogeneity
Mao D, Tang X, Zhang R et al. · Nature communications 2025 · PMID 39843433 · doi:10.1038/s41467-025-56331-w
This dataset
Open GSE280639 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 GSE280639_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 PRJNA1179869 and SRA study SRP541981. Searching any of these in the dataset finder brings you back here.

NCBI GEO page ↗ Paper (PMID 39843433) ↗
Study design
7 conditions, mostly in triplicate
CDDP ×3 DMSO ×3 DOX ×3 LMB ×3 MA ×3 Rhein ×3 Sorafenib ×3

Supports a between-group comparison across 21 samples.

How we read this: 7 replicated groups read from 21 sample titles; they account for 21 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
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