Connectomics by Sequencing Reveals Self-Organizing Principles of Neuronal Networks in Cerebral Organoids
- 1Institute of Molecular Biotechnology of the Austrian Academy of Science (IMBA), Vienna BioCenter (VBC), Vienna, Austria.
Abstract
Functional neural networks emerge as developing neurons form synaptic connections. Revealing how these connections are organized and perturbed in disease requires linking single-neuron connectivity to molecular state across thousands of networks, which is challenging with current methods. We developed COSCO (Connectomics by Sequencing in Cerebral Organoids), combining barcoded monosynaptic rabies tracing with single-cell RNA sequencing to reconstruct over 10,000 networks across organoids. We demonstrate that organoid circuitry self-organizes into stereotyped network motifs governed by cell-intrinsic wiring rules aligning with features of mouse cortical connectivity. Projecting network topology onto transcriptomic space reveals that wiring correlates with cell-adhesion and synaptic gene expression programs. In a Tuberous Sclerosis Complex model, pathological hyperexcitability coincides with topological rewiring: mutant networks become more heterogeneous, driven by the transcriptional distribution of upper layer excitatory cell states. This work establishes cerebral organoids as a model to study self-organization of human connectivity, and its disruption in neurodevelopmental disease.
O_FIG O_LINKSMALLFIG WIDTH=199 HEIGHT=200 SRC="FIGDIR/small/755792v1_ufig1.gif" ALT="Figure 1"> View larger version (66K): [email protected]@c97320org.highwire.dtl.DTLVardef@1a53a47org.highwire.dtl.DTLVardef@9520ad_HPS_FORMAT_FIGEXP M_FIG C_FIG
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BibTeX
@misc{vertesy2026connectomics,
title = {{Connectomics by Sequencing Reveals Self-Organizing Principles of Neuronal Networks in Cerebral Organoids}},
author = {A. Vertesy and R. Najm and B. Doleschall and S. Isaev and M. Zabolocki and M. Novatchkova and T. R. Burkard and C. Ziegler and S. Krautberger and C. V. Li and I. Adameyko and J. A. Knoblich},
howpublished = {bioRxiv},
year = {2026},
month = oct,
doi = {10.64898/2026.10.01.755792},
url = {https://www.biorxiv.org/content/10.64898/2026.10.01.755792v1},
note = {CC BY-NC-ND},
}
RIS
TY - UNPB
TI - Connectomics by Sequencing Reveals Self-Organizing Principles of Neuronal Networks in Cerebral Organoids
AU - A. Vertesy
AU - R. Najm
AU - B. Doleschall
AU - S. Isaev
AU - M. Zabolocki
AU - M. Novatchkova
AU - T. R. Burkard
AU - C. Ziegler
AU - S. Krautberger
AU - C. V. Li
AU - I. Adameyko
AU - J. A. Knoblich
PY - 2026
DA - 2026/10/01
PB - bioRxiv
DO - 10.64898/2026.10.01.755792
UR - https://www.biorxiv.org/content/10.64898/2026.10.01.755792v1
AB - Functional neural networks emerge as developing neurons form synaptic connections. Revealing how these connections are organized and perturbed in disease requires linking single-neuron connectivity to molecular state across thousands of networks, which is challenging with current methods. We developed COSCO (Connectomics by Sequencing in Cerebral Organoids), combining barcoded monosynaptic rabies tracing with single-cell RNA sequencing to reconstruct over 10,000 networks across organoids. We demonstrate that organoid circuitry self-organizes into stereotyped network motifs governed by cell-intrinsic wiring rules aligning with features of mouse cortical connectivity. Projecting network topology onto transcriptomic space reveals that wiring correlates with cell-adhesion and synaptic gene expression programs. In a Tuberous Sclerosis Complex model, pathological hyperexcitability coincides with topological rewiring: mutant networks become more heterogeneous, driven by the transcriptional distribution of upper layer excitatory cell states. This work establishes cerebral organoids as a model to study self-organization of human connectivity, and its disruption in neurodevelopmental disease. O_FIG O_LINKSMALLFIG WIDTH=199 HEIGHT=200 SRC="FIGDIR/small/755792v1_ufig1.gif" ALT="Figure 1"> View larger version (66K): [email protected]@c97320org.highwire.dtl.DTLVardef@1a53a47org.highwire.dtl.DTLVardef@9520ad_HPS_FORMAT_FIGEXP M_FIG C_FIG
ER -