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Co-profiling of in situ RNA-protein interactions and transcriptome in single cells and tissues

GSE278418 Mus musculus (mouse) Single-cell RNA-seq 278 samples Submitted 2025/06/02 Platform GPL29480Platform GPL34284Platform GPL28330Platform GPL34382Platform GPL24676
At a glance

Single-cell RNA-seq · mouse · 278 samples

What it is
Single-cell RNA-seqGene activity measured in thousands of individual cells, so cell types can be told apart.
Organism
Mus musculus (mouse)
Samples
278 samples: 95 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
Co-profiling of in situ RNA-protein interactions and transcriptome in single cells and tissuesNature methods · 2025
Summary (written by the submitters)
RNA-binding proteins (RBPs) are essential regulators of RNA fate and function. A long-standing challenge in studying RBP regulation has been mapping RNA interactomes within the dynamic transcriptomic landscape, especially in single-cell contexts and primary tissues. Here we introduce MAPIT-seq (modification added to RBP interacting transcript-sequencing), which uses an antibody-directed editing strategy to map genome-wide in situ RBP–RNA interactions and gene expression concurrently.
Read the full summary ↓
We demonstrate MAPIT-seq’s robustness across multiple RBPs and systematically analyze RNA substrates associated with core polycomb repressive complex 2 (PRC2) components. MAPIT-seq is also applicable to frozen tissue sections, enabling the mapping of RBP roles during brain development. Importantly, we develop high-throughput single-cell MAPIT-seq (scMAPIT-seq) to reveal cell stage-specific RBP regulation. In summary, MAPIT-seq expands multi-omics profiling, providing an effective framework to study post-transcriptional regulation in dynamic biological processes and clinically relevant scenarios. This SuperSeries is composed of the SubSeries listed below.
Published in
Co-profiling of in situ RNA-protein interactions and transcriptome in single cells and tissues
Cheng QX, Xie G, Zhang X et al. · Nature methods 2025 · PMID 40784921 · doi:10.1038/s41592-025-02774-4
This dataset
Open GSE278418 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 GSE278418_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 PRJNA1167221 and SRA study SRP535305. Searching any of these in the dataset finder brings you back here.

NCBI GEO page ↗ Paper (PMID 40784921) ↗
Study design
95 conditions, mostly in duplicate
HeLa_FA_FRISCR_Anti-G3BP1_cell ×16 HeLa_DSP_FRISCR_Anti-G3BP1_cell ×16 HeLa_DSP_FRISCR_IgG_cell ×16 HeLa_FA_FRISCR_IgG_cell ×15 HeLa_untreated_FRISCR_cell ×15 OE-FLAG_Anti-FLAG ×2 OE-DF2-FLAG_IgG ×2 OE-DF2-FLAG_NC ×2 +107 more

Supports a between-group comparison across 258 samples.

How we read this: 95 replicated groups read from 278 sample titles; they account for 258 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

+ 238 more — browse all 278 samples with per-sample file links →

Datasets on a similar topic

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