Emergence of embryonic pattern through contact inhibition of locomotion.

The pioneering cell biologist Michael Abercrombie first described the process of contact inhibition of locomotion more than 50 years ago when migrating fibroblasts were observed to rapidly change direction and migrate away upon collision. Since then, we have gleaned little understanding of how contact inhibition is regulated and only lately observed its occurrence in vivo. We recently revealed that Drosophila macrophages (haemocytes) require contact inhibition for their uniform embryonic dispersal. Here, to investigate the role that contact inhibition plays in the patterning of haemocyte movements, we have mathematically analysed and simulated their contact repulsion dynamics. Our data reveal that the final pattern of haemocyte distribution, and the details and timing of its formation, can be explained by contact inhibition dynamics within the geometry of the Drosophila embryo. This has implications for morphogenesis in general as it suggests that patterns can emerge, irrespective of external cues, when cells interact through simple rules of contact repulsion.

Bayesian localization microscopy reveals nanoscale podosome dynamics.

We describe a localization microscopy analysis method that is able to extract results in live cells using standard fluorescent proteins and xenon arc lamp illumination. Our Bayesian analysis of the blinking and bleaching (3B analysis) method models the entire dataset simultaneously as being generated by a number of fluorophores that may or may not be emitting light at any given time. The resulting technique allows many overlapping fluorophores in each frame and unifies the analysis of the localization from blinking and bleaching events. By modeling the entire dataset, we were able to use each reappearance of a fluorophore to improve the localization accuracy. The high performance of this technique allowed us to reveal the nanoscale dynamics of podosome formation and dissociation throughout an entire cell with a resolution of 50 nm on a 4-s timescale.

Spatial segregation of polarity factors into distinct cortical clusters is required for cell polarity control.

Cell polarity is regulated by evolutionarily conserved polarity factors whose precise higher-order organization at the cell cortex is largely unknown. Here we image frontally the cortex of live fission yeast cells using time-lapse and super-resolution microscopy. Interestingly, we find that polarity factors are organized in discrete cortical clusters resolvable to ~50-100 nm in size, which can form and become cortically enriched by oligomerization. We show that forced co-localization of the polarity factors Tea1 and Tea3 results in polarity defects, suggesting that the maintenance of both factors in distinct clusters is required for polarity. However, during mitosis, their co-localization increases, and Tea3 helps to retain the cortical localization of the Tea1 growth landmark in preparation for growth reactivation following mitosis. Thus, regulated spatial segregation of polarity factor clusters provides a means to spatio-temporally control cell polarity at the cell cortex. We observe similar clusters in Saccharomyces cerevisiae and Caenorhabditis elegans cells, indicating this could be a universal regulatory feature.

Faster and better: a machine learning approach to corner detection.

The repeatability and efficiency of a corner detector determines how likely it is to be useful in a real-world application. The repeatability is important because the same scene viewed from different positions should yield features which correspond to the same real-world 3D locations. The efficiency is important because this determines whether the detector combined with further processing can operate at frame rate. Three advances are described in this paper. First, we present a new heuristic for feature detection and, using machine learning, we derive a feature detector from this which can fully process live PAL video using less than 5 percent of the available processing time. By comparison, most other detectors cannot even operate at frame rate (Harris detector 115 percent, SIFT 195 percent). Second, we generalize the detector, allowing it to be optimized for repeatability, with little loss of efficiency. Third, we carry out a rigorous comparison of corner detectors based on the above repeatability criterion applied to 3D scenes. We show that, despite being principally constructed for speed, on these stringent tests, our heuristic detector significantly outperforms existing feature detectors. Finally, the comparison demonstrates that using machine learning produces significant improvements in repeatability, yielding a detector that is both very fast and of very high quality.