Light-microscopy-based connectomic reconstruction of mammalian brain tissue

The information-processing capability of the brain’s cellular network depends on the physical wiring pattern between neurons and their molecular and functional characteristics. Mapping neurons and resolving their individual synaptic connections can be achieved by volumetric imaging at nanoscale reso...

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Published in	Nature (London) Vol. 642; no. 8067; pp. 398 - 410
Main Authors	Tavakoli, Mojtaba R., Lyudchik, Julia, Januszewski, Michał, Vistunou, Vitali, Agudelo Dueñas, Nathalie, Vorlaufer, Jakob, Sommer, Christoph, Kreuzinger, Caroline, Oliveira, Bárbara, Cenameri, Alban, Novarino, Gaia, Jain, Viren, Danzl, Johann G.
Format	Journal Article
Language	English
Published	London Nature Publishing Group UK 12.06.2025
Subjects	13/1 13/51 14/19 14/34 14/63 631/1647/245/2221 631/1647/328/2238 631/378/3920 631/378/87 639/624/1107/328/1978 64/110 64/60 Animals Brain - anatomy & histology Brain - cytology Brain - diagnostic imaging Connectome - methods Deep Learning Female Humanities and Social Sciences Hydrogels - chemistry Male Mice Microscopy - methods multidisciplinary Neurons - cytology Science Science (multidisciplinary) Synapses - metabolism
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ISSN	0028-0836 1476-4687 1476-4687
DOI	10.1038/s41586-025-08985-1

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Abstract	The information-processing capability of the brain’s cellular network depends on the physical wiring pattern between neurons and their molecular and functional characteristics. Mapping neurons and resolving their individual synaptic connections can be achieved by volumetric imaging at nanoscale resolution 1 , 2 with dense cellular labelling. Light microscopy is uniquely positioned to visualize specific molecules, but dense, synapse-level circuit reconstruction by light microscopy has been out of reach, owing to limitations in resolution, contrast and volumetric imaging capability. Here we describe light-microscopy-based connectomics (LICONN). We integrated specifically engineered hydrogel embedding and expansion with comprehensive deep-learning-based segmentation and analysis of connectivity, thereby directly incorporating molecular information into synapse-level reconstructions of brain tissue. LICONN will allow synapse-level phenotyping of brain tissue in biological experiments in a readily adoptable manner. A technique called LICONN (light-microscopy-based connectomics) allows mapping of brain tissue at synapse level and simultaneous measurement of molecular information, thus enabling quantification of cellular properties and multimodal analysis of brain tissue.
AbstractList	The information-processing capability of the brain’s cellular network depends on the physical wiring pattern between neurons and their molecular and functional characteristics. Mapping neurons and resolving their individual synaptic connections can be achieved by volumetric imaging at nanoscale resolution 1 , 2 with dense cellular labelling. Light microscopy is uniquely positioned to visualize specific molecules, but dense, synapse-level circuit reconstruction by light microscopy has been out of reach, owing to limitations in resolution, contrast and volumetric imaging capability. Here we describe light-microscopy-based connectomics (LICONN). We integrated specifically engineered hydrogel embedding and expansion with comprehensive deep-learning-based segmentation and analysis of connectivity, thereby directly incorporating molecular information into synapse-level reconstructions of brain tissue. LICONN will allow synapse-level phenotyping of brain tissue in biological experiments in a readily adoptable manner. A technique called LICONN (light-microscopy-based connectomics) allows mapping of brain tissue at synapse level and simultaneous measurement of molecular information, thus enabling quantification of cellular properties and multimodal analysis of brain tissue. The information-processing capability of the brain's cellular network depends on the physical wiring pattern between neurons and their molecular and functional characteristics. Mapping neurons and resolving their individual synaptic connections can be achieved by volumetric imaging at nanoscale resolution with dense cellular labelling. Light microscopy is uniquely positioned to visualize specific molecules, but dense, synapse-level circuit reconstruction by light microscopy has been out of reach, owing to limitations in resolution, contrast and volumetric imaging capability. Here we describe light-microscopy-based connectomics (LICONN). We integrated specifically engineered hydrogel embedding and expansion with comprehensive deep-learning-based segmentation and analysis of connectivity, thereby directly incorporating molecular information into synapse-level reconstructions of brain tissue. LICONN will allow synapse-level phenotyping of brain tissue in biological experiments in a readily adoptable manner. The information-processing capability of the brain's cellular network depends on the physical wiring pattern between neurons and their molecular and functional characteristics. Mapping neurons and resolving their individual synaptic connections can be achieved by volumetric imaging at nanoscale resolution1,2 with dense cellular labelling. Light microscopy is uniquely positioned to visualize specific molecules, but dense, synapse-level circuit reconstruction by light microscopy has been out of reach, owing to limitations in resolution, contrast and volumetric imaging capability. Here we describe light-microscopy-based connectomics (LICONN). We integrated specifically engineered hydrogel embedding and expansion with comprehensive deep-learning-based segmentation and analysis of connectivity, thereby directly incorporating molecular information into synapse-level reconstructions of brain tissue. LICONN will allow synapse-level phenotyping of brain tissue in biological experiments in a readily adoptable manner.The information-processing capability of the brain's cellular network depends on the physical wiring pattern between neurons and their molecular and functional characteristics. Mapping neurons and resolving their individual synaptic connections can be achieved by volumetric imaging at nanoscale resolution1,2 with dense cellular labelling. Light microscopy is uniquely positioned to visualize specific molecules, but dense, synapse-level circuit reconstruction by light microscopy has been out of reach, owing to limitations in resolution, contrast and volumetric imaging capability. Here we describe light-microscopy-based connectomics (LICONN). We integrated specifically engineered hydrogel embedding and expansion with comprehensive deep-learning-based segmentation and analysis of connectivity, thereby directly incorporating molecular information into synapse-level reconstructions of brain tissue. LICONN will allow synapse-level phenotyping of brain tissue in biological experiments in a readily adoptable manner. The information-processing capability of the brain’s cellular network depends on the physical wiring pattern between neurons and their molecular and functional characteristics. Mapping neurons and resolving their individual synaptic connections can be achieved by volumetric imaging at nanoscale resolution 1,2 with dense cellular labelling. Light microscopy is uniquely positioned to visualize specific molecules, but dense, synapse-level circuit reconstruction by light microscopy has been out of reach, owing to limitations in resolution, contrast and volumetric imaging capability. Here we describe light-microscopy-based connectomics (LICONN). We integrated specifically engineered hydrogel embedding and expansion with comprehensive deep-learning-based segmentation and analysis of connectivity, thereby directly incorporating molecular information into synapse-level reconstructions of brain tissue. LICONN will allow synapse-level phenotyping of brain tissue in biological experiments in a readily adoptable manner.
Author	Cenameri, Alban Tavakoli, Mojtaba R. Oliveira, Bárbara Agudelo Dueñas, Nathalie Jain, Viren Novarino, Gaia Lyudchik, Julia Vistunou, Vitali Kreuzinger, Caroline Sommer, Christoph Danzl, Johann G. Januszewski, Michał Vorlaufer, Jakob
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Snippet	The information-processing capability of the brain’s cellular network depends on the physical wiring pattern between neurons and their molecular and functional... The information-processing capability of the brain's cellular network depends on the physical wiring pattern between neurons and their molecular and functional...
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Title	Light-microscopy-based connectomic reconstruction of mammalian brain tissue
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