Abstract
Dendritic spines are the main sites for synaptic communication in neurons, and alterations in their density, size, and shapes occur in many brain disorders. Current spine segmentation methods perform poorly in conditions with low signal-to-noise and resolution, particularly in the widefield images of thick (10 μm) brain slices. Here, we combined two open-source machine-learning models to achieve automatic 3D spine segmentation in widefield diffraction-limited fluorescence images of neurons in thick brain slices. We validated the performance by comparison with manually segmented super-resolution images of spines reconstructed from direct stochastic optical reconstruction microscopy (dSTORM). Lastly, we show an application of our approach by combining spine segmentation from diffraction-limited images with dSTORM of synaptic protein PSD-95 in the same field-of-view. This allowed us to automatically analyze and quantify the nanoscale distribution of PSD-95 inside the spine. Importantly, we found the numbers, but not the average sizes, of synaptic nanomodules and nanodomains increase with spine size.