Deep learning-assisted mapping of dendritic spines using sequential 2D two-photon calcium imaging

Neurons transform complex spatiotemporal synaptic inputs into structured action potential sequences. Excitatory inputs initiate or interact with dendritic spikes or plateau potentials, adding computational layers that diversify input-output transformations. Because synapse location strategically shapes the dendritic events, mapping synaptic organization is critical for understanding neuronal function. Spine calcium imaging offers a direct readout of active contact location but requires access to spines distributed across intricate three-dimensional dendrites. We present a software pipeline for targeted dendritic imaging and analysis using sequential two-dimensional scanning on standard two-photon microscopes. It includes a ScanImage-compatible pre-acquisition tool, ROIpy, which generates dendrite-aligned region-of-interests (ROIs) for depth-specific dendritic imaging, and a post-acquisition suite, Spyne, which includes deep learning modules for spine detection and binary classification of calcium activity (active vs. non-active spines). This method accommodates diverse experimental designs, including two-photon imaging with patch-clamp or all-optical setups, supporting whole-arbor or branch-specific imaging.