Munc18 and Munc13 regulate early neurite outgrowth.

BACKGROUND INFORMATION: During development, growth cones of outgrowing neurons express proteins involved in vesicular secretion, such as SNARE (soluble N-ethylmaleimide-sensitive fusion protein-attachment protein receptor) proteins, Munc13 and Munc18. Vesicles are known to fuse in growth cones prior to synapse formation, which may contribute to outgrowth. RESULTS: We tested this possibility in dissociated cell cultures and organotypic slice cultures of two release-deficient mice (Munc18-1 null and Munc13-1/2 double null). Both types of release-deficient neurons have a decreased outgrowth speed and therefore have a smaller total neurite length during early development [DIV1-4 (day in vitro 1-4)]. In addition, more filopodia per growth cone were observed in Munc18-1 null, but not WT (wild-type) or Munc13-1/2 double null neurons. The smaller total neurite length during early development was no longer observed after synaptogenesis (DIV14-23). CONCLUSION: These data suggest that the inability of vesicle fusion in the growth cone affects outgrowth during the initial phases when outgrowth speed is high, but not during/after synaptogenesis. Overall, the outgrowth speed is probably not rate-limiting during neuronal network formation, at least in vitro. In addition, Munc18, but not Munc13, regulates growth cone filopodia, potentially via its previously observed effect on filamentous actin.

Automated quantification of cellular traffic in living cells.

Cellular traffic is a central aspect of cell function in health and disease. It is highly dynamic, and can be investigated at increasingly finer temporal and spatial resolution due to new imaging techniques and probes. Manual tracking of these data is labor-intensive and observer-biased and existing automation is only semi-automatic and requires near-perfect object detection and high-contrast images. Here, we describe a novel automated technique for quantifying cellular traffic. Using local intrinsic information from adjacent images in a sequence and a model for object characteristics, our approach detects and tracks multiple objects in living cells via Multiple Hypothesis Tracking and handles several confounds (merge/split, birth/death, and clutters), as reliable as expert observers. By replacing the related component (e.g. using a different appearance model) the method can be easily adapted for quantitative analysis of other biological samples.