Evidence map›Paper›PMID 41030982›Full record

ArticlebioRxiv : the preprint server for biology2025

Connectome-seq: High-throughput Mapping of Neuronal Connectivity at Single-Synapse Resolution via Barcode Sequencing.

Danping Chen, Alina Isakova, Zhou Joe Wan, Mark J Wagner, Yunming Wu, Boxuan Simen Zhao

Abstract readPreprint
In one paragraph

Article in bioRxiv : the preprint server for biology, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.

0numbers the graph read from it
0cells of the map it votes in
0citing papers in PubMed
–field-weighted citation impact
1 · What the graph read from it

What it found

Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.

The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.

2 · The registry

The trial behind it

Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.

Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.

3 · Its place in the literature

Who cites it

0 citing papers in PubMed.

No citing paper in PubMed yet.

4 · The record

Corrections and comments

5 · Who and what money

Authors and funding

6 authors.

Danping ChenDepartment of Cell and Developmental Biology, University of Illinois Urbana-Champaign, Urbana, IL, USA.
Alina IsakovaDepartment of Bioengineering, Stanford University, Stanford, CA, USA.ORCID 0000-0003-1113-6889
Zhou Joe WanDepartment of Cell and Developmental Biology, University of Illinois Urbana-Champaign, Urbana, IL, USA.
Mark J WagnerNational Institute of Neurological Disorders & Stroke, National Institutes of Health, Bethesda, MD, USA.ORCID 0000-0003-1816-6407
Yunming WuDepartment of Biology and Howard Hughes Medical Institute, Stanford University, Stanford, CA, USA.
Boxuan Simen ZhaoDepartment of Cell and Developmental Biology, University of Illinois Urbana-Champaign, Urbana, IL, USA.ORCID 0000-0002-3729-4511

Funding

Four Laser, 18 Color Cell Sorter in the SSFFS10RR025518 · NCRR · STANFORD UNIVERSITY · PI HERZENBERG, LEONARD A · 2009 to 2009
$500k
NCRR NIH HHS S10 RR025518
6 · The paper itself

Abstract

Understanding neuronal connectivity at single-cell resolution remains a fundamental challenge in neuroscience, with current methods particularly limited in mapping long-distance circuits and preserving cell type information. Here we present Connectome-seq, a high-throughput method that combines engineered synaptic proteins, RNA barcoding, and parallel single-nucleus and single-synaptosome sequencing to map neuronal connectivity at single-synapse resolution. This AAV-based approach enables simultaneous capture of both synaptic connections and molecular identities of connected neurons. We validated this approach in the mouse pontocerebellar circuit, identifying both established projections and potentially novel synaptic partnerships. Through integrated analysis of connectivity and gene expression, we identified molecular markers enriched in connected neurons, suggesting potential determinants of circuit organization. By enabling systematic mapping of neuronal connectivity across brain regions with single-cell precision and gene expression information, 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.

Identifiers

PMID41030982
PMCPMC12478398

What OpenQuestion holds

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Registered trials

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Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.