Reconstruction of Axonal Projections of Single Neurons Using PointTree

The morphology of single-neuron axonal projections is critical for deciphering neural circuitry and information flow in the brain. Yet, manually reconstructing these complex, long-range projections from high-throughput whole-brain imaging data remains an exceptionally labor-intensive and time-consuming task. Here, we developed a points assignment-based method for axonal reconstruction, named PointTree. PointTree enables the precise identification of the individual axons from densely packed axonal population using a minimal information flow tree model to suppress the snowball effect of reconstruction errors. In this protocol, we have elaborated on how to configure the required environment for PointTree software, prepare suitable data for it, and run the software. This protocol can assist neuroscience researchers in more easily and rapidly obtaining the reconstruction results of neuronal axons. Key features • Optimized for mapping long-range axons that connect distant brain regions in dense or crossover scenarios. • Enables high-fidelity (F1-score > 80%) reconstruction of hundreds of GB of large-volume imaging data. • Compatible with LSM, fMOST, and HD-fMOST systems for diverse neuroimaging datasets.