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
Institute of Science and Technology Austria, Klosterneuburg, Austria.
Google Research, Zürich, Switzerland.
Nature. 2025 May 7. doi: 10.1038/s41586-025-08985-1.
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.
大脑细胞网络的信息处理能力取决于神经元之间的物理连接模式及其分子和功能特征。通过具有密集细胞标记的纳米级分辨率的体积成像，可以实现对神经元的映射并解析其单个突触连接。光学显微镜在可视化特定分子方面具有独特的优势，但由于分辨率、对比度和体积成像能力的限制，通过光学显微镜进行密集的、突触水平的电路重建一直无法实现。在这里，我们描述了基于光学显微镜的连接组学（LICONN）。我们将经过特殊工程设计的水凝胶嵌入和扩展与基于深度学习的全面连接性分割和分析相结合，从而将分子信息直接纳入脑组织的突触水平重建中。LICONN将使生物实验中脑组织的突触水平表型分析能够以一种易于采用的方式进行。
