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Spatially resolved m6A profiling using m6A-ARTR-DBiT

GSE285303 Homo sapiens (human) DNA methylation 40 samples Submitted 2026/01/14 Platform GPL30173Platform GPL24247Platform GPL30172Platform GPL24676
At a glance

DNA methylation · human · 40 samples

What it is
DNA methylationChemical marks on DNA that help switch genes on or off.
Organism
Homo sapiens (human)
Samples
40 samples: 17 conditions, mostly in duplicateGroups 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
Spatially resolved m(6)A profiling using m(6)A-ARTR-DBiTNature methods · 2026
Summary (written by the submitters)
N6-methyladenosine (m6A) on RNA plays diverse regulatory roles, yet its spatial distribution within tissues remains largely unexplored. Here we introduce m6A-ARTR-DBiT, a spatial m6A profiling assay that leverages reverse-transcription-based detection and deterministic barcoding in tissue to map transcriptome-wide m6A distribution while preserving native tissue context.
Read the full summary ↓
Applying m6A-ARTR-DBiT to mouse embryonic tissues and adult brains generates spatially resolved m6A landscapes and reveals region-associated m6A features across different functional domains. Pairwise comparison of spatial m6A profiles with spatial transcriptomes uncovers positive correlations between m6A levels and the expression of its methyltransferases and binding proteins, which also enables systematic identification of tissue-region-specific epitranscriptomic regulation. In the mouse hippocampus, m6A-ARTR-DBiT allows for high-resolution mapping of m6A organization within fine-scale tissue structures. Together, m6A-ARTR-DBiT provides a platform for interrogating RNA modification distribution within intact tissue sections, offering insights into the link between spatially patterned m6A deposition and gene regulation.
Published in
Spatially resolved m(6)A profiling using m(6)A-ARTR-DBiT
Xiao Y, Bai Z, Zou Z et al. · Nature methods 2026 · PMID 42265211 · doi:10.1038/s41592-026-03123-9
This dataset
Open GSE285303 in finder → Find more methylation 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 GSE285303_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: Look for beta-value tables or raw IDAT files among the supplementary files.

Also filed as BioProject PRJNA1202190 and SRA study SRP553453. Searching any of these in the dataset finder brings you back here.

NCBI GEO page ↗ Paper (PMID 42265211) ↗
Study design
17 conditions, mostly in duplicate
HeLa_noAb_deepseq ×2 HeLa_m6A_deepseq ×2 mESC_noAb ×2 mESC_m6A ×2 HeLa_noAb_pRT30 ×2 HeLa_m6A_no2Ab_pRT30 ×2 HeLa_m6A_pRT30 ×2 HeLa_m6A_pRT3 ×2 +15 more

Supports a between-group comparison across 34 samples.

How we read this: 17 replicated groups read from 40 sample titles; they account for 34 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
Datasets on a similar topic

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