Single-cell Labeling in the Whole Brain - From Neuronal Tracing to Systems-Level Neuroanatomy

Understanding the brain’s functional architecture requires a comprehensive map of its cellular components and their long-range projections. While traditional neuroanatomical techniques have provided deep insights into local circuitry, the ability to visualize individual neurons within the context of the entire brain remains a significant challenge. Bridging the gap between micro-scale resolution and macro-scale volume is essential for deciphering the logic of neural circuits and their role in complex behaviors and neurological disorders.This Research Topic focuses on the latest advancements in single-cell labeling and whole-brain imaging technologies. Furthermore, we seek to explore the integration of these labeling techniques with modern imaging modalities, including light-sheet microscopy and micro-optical sectioning tomography (MOST/fMOST), as well as chemical approaches like tissue clearing that facilitate deep-tissue visualization.Beyond image acquisition, this collection addresses the computational bottlenecks inherent in whole-brain mapping. We encourage submissions detailing new algorithms for automated neuron reconstruction, image registration to standard atlases, and the management of petabyte-scale datasets. By consolidating these cross-disciplinary efforts, we hope to establish a robust framework for high-throughput, high-resolution neuroanatomy.We invite researchers to contribute original research, methods, reviews, and perspectives that push the boundaries of whole-brain cellular mapping. Relevant themes include, but are not limited to, the development of multi-modal tracers, improvements in high-speed optical sectioning, and the application of these tools to study neurodevelopmental or neurodegenerative models in both rodents and larger mammals. Topic editors: Qingming Luo, Xiangning Li Source: https://www.frontiersin.org/research-topics/84745/single-cell-labeling-in-the-whole-brain---from-neuronal-tracing-to-systems-level-neuroanatomy