Background: Axon growth and regeneration depend on kinesin-1-dependent transport. Results: JIP3 binding to KHC promotes kinesin-1 motility along microtubules and is essential for axon elongation and regeneration. Conclusion: JIP3 regulation of kinesin-1 motility is critical for axon elongation and regeneration. Significance: Regulation of intracellular transport is important for proper neuronal development.

Background: Axon regeneration following nerve injury depends on retrograde injury signals. Results: An increased tyrosinated ␣-tubulin level at the injury site is required for the retrograde transport of injury signals and timely activation of a pro-regenerative program. Conclusion: An injury-induced increase in tyrosinated ␣-tubulin is important for axon regeneration. Significance: Deciphering the mechanisms regulating the retrograde transport of injury signals is crucial for our understanding of regenerative mechanisms in peripheral neurons.

High-resolution live-cell imaging studies of neuronal structure and function are characterized by large variability in image acquisition conditions due to background and sample variations as well as low signal-to-noise ratio. The lack of automated image analysis tools that can be generalized for varying image acquisition conditions represents one of the main challenges in the field of biomedical image analysis. Specifically, segmentation of the axonal/dendritic arborizations in brightfield or fluorescence imaging studies is extremely labor-intensive and still performed mostly manually. Here we describe a fully automated machine-learning approach based on textural analysis algorithms for segmenting neuronal arborizations in high-resolution brightfield images of live cultured neurons. We compare performance of our algorithm to manual segmentation and show that it combines 90% accuracy, with similarly high levels of specificity and sensitivity. Moreover, the algorithm maintains high performance levels under a wide range of image acquisition conditions indicating that it is largely condition-invariable. We further describe an application of this algorithm to fully automated synapse localization and classification in fluorescence imaging studies based on synaptic activity. Textural analysis-based machine-learning approach thus offers a high performance condition-invariable tool for automated neurite segmentation.

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