Image-based measurement of cargo traffic flow in complex neurite networks

Neurons depend critically on active transport of cargoes throughout their complex neurite networks for their survival and function. Defects in this process have been strongly associated with many human neurodevelopmental and neurodegenerative diseases. To understand related neuronal physiology and disease mechanisms, it is essential to measure the traffic flow within the neurite networks. Currently, however, image analysis methods required for this measurement are lacking. To address this deficiency, we developed a method that could measure the flow rates of cargo traffic at any specified locations along individual branches of the neurite networks. Our method is based on detecting and counting cargo trajectories passing through the specified locations of measurement in kymographs, which are spatiotemporal maps of cargo movement within one-dimensional neurites. A main focus of our method development is robust performance, which ensures that our method works reliably and accurately under low signal-to-noise ratios. We validated and benchmarked our method using both synthetic and actual image data and found its accuracy to be >85% on average under normal conditions. Our method can be used to measure traffic flow in not just neurite networks but also other intracellular networks such as cytoskeletal filament networks.