Light-microscopy based dense connectomic reconstruction of mammalian brain tissue

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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 labeling. Light microscopy is uniquely positioned to visualize specific molecules but dense, synapse-level circuit reconstruction by light microscopy has been out of reach due to limitations in resolution, contrast, and volumetric imaging capability. Here we developed light-microscopy based connectomics (LICONN). We integrated specifically engineered hydrogel embedding and expansion with comprehensive deep-learning based segmentation and analysis of connectivity, thus directly incorporating molecular information in synapse-level brain tissue reconstructions. LICONN will allow synapse-level brain tissue phenotyping in biological experiments in a readily adoptable manner.

One-Sentence Summary

Hydrogel expansion enables molecularly informed reconstruction of brain tissue at synaptic resolution with light microscopy.

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  1. Excerpt

    LICONN uses molecular labelling and deep learning to reconstruct brain circuitry, bridging the gap between EM and molecular specificity.