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Connectome-seq: High-throughput Mapping of Neuronal Connectivity at Single-Synapse Resolution via Barcode Sequencing [snRNA-seq, ssRNA-seq]

GSE312903 Mus musculus (mouse) Single-nucleus RNA-seq 47 samples Submitted 2025/12/16 Platform GPL34328Platform GPL34475
At a glance

Single-nucleus RNA-seq · mouse · 47 samples

What it is
Single-nucleus RNA-seqGene activity measured in individual nuclei (used when whole cells are hard to isolate, for example from frozen tissue).
Organism
Mus musculus (mouse)
Samples
47 samples: 47 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
Connectome-seq: high-throughput mapping of neuronal connectivity at single-synapse resolution via barcode sequencingNature methods · 2026
Summary (written by the submitters)
Understanding neuronal connectivity at single-cell resolution remains a fundamental challenge in neuroscience, with current methods constrained by an inherent trade-off between throughput and synaptic precision. Here we present Connectome-seq, a high-throughput method that combines engineered synaptic proteins, RNA barcoding, and single-compartment sequencing to map neuronal connectivity at single-synapse resolution.
Read the full summary ↓
We validate this approach in the mouse pontocerebellar circuit, successfully identifying thousands of connections including the predominant excitatory projections from pontine neurons to cerebellar granule cells, while also revealing potentially novel synaptic partnerships. By enabling systematic mapping of neuronal connectivity across brain regions with single-cell precision, Connectome-seq provides a scalable platform for comprehensive circuit analysis across different experimental conditions and biological states. This advance in connectivity mapping methodology opens new possibilities for understanding circuit organization in complex mammalian brains.
Published in
Connectome-seq: high-throughput mapping of neuronal connectivity at single-synapse resolution via barcode sequencing
Chen D, Isakova A, Wan Z et al. · Nature methods 2026 · PMID 41820665 · doi:10.1038/s41592-026-03026-9
This dataset
Open GSE312903 in finder → Find more mouse 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 GSE312903_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 PRJNA1377577 and SRA study SRP653433. Searching any of these in the dataset finder brings you back here.

NCBI GEO page ↗ Paper (PMID 41820665) ↗
Study design
47 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 47 sample titles: 47 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

+ 7 more — browse all 47 samples with per-sample file links →

Datasets on a similar topic

Search all mouse RNA-seq datasets in GEO →

Share this dataset

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.