The first community competition designed to objectively compare the performance of particle tracking algorithms provides valuable practical information for both users and developers. Particle tracking is of key importance for quantitative analysis of intracellular dynamic processes from time-lapse microscopy image data. Because manually detecting and following large numbers of individual particles is not feasible, automated computational methods have been developed for these tasks by many groups. Aiming to perform an objective comparison of methods, we gathered the community and organized an open competition in which participating teams applied their own methods independently to a commonly defined data set including diverse scenarios. Performance was assessed using commonly defined measures. Although no single method performed best across all scenarios, the results revealed clear differences between the various approaches, leading to notable practical conclusions for users and developers.

Structured illumination microscopy (SIM) surpasses the optical diffraction limit and offers a two-fold enhancement in resolution over diffraction limited microscopy. However, it requires both intense illumination and multiple acquisitions to produce a single high-resolution image. Using deep learning to augment SIM, we obtain a five-fold reduction in the number of raw images required for super-resolution SIM, and generate images under extreme low light conditions (at least 100× fewer photons). We validate the performance of deep neural networks on different cellular structures and achieve multi-color, live-cell super-resolution imaging with greatly reduced photobleaching.

Insulin stimulates translocation of GLUT4 storage vesicles (GSVs) to the surface of adipocytes, but precisely where insulin acts is controversial. Here we quantify the size, dynamics, and frequency of single vesicle exocytosis in 3T3-L1 adipocytes. We use a new GSV reporter, VAMP2-pHluorin, and bypass insulin signaling by disrupting the GLUT4-retention protein TUG. Remarkably, in unstimulated TUG-depleted cells, the exocytic rate is similar to that in insulin-stimulated control cells. In TUG-depleted cells, insulin triggers a transient, twofold burst of exocytosis. Surprisingly, insulin promotes fusion pore expansion, blocked by acute perturbation of phospholipase D, which reflects both properties intrinsic to the mobilized vesicles and a novel regulatory site at the fusion pore itself. Prolonged stimulation causes cargo to switch from ∼60 nm GSVs to larger exocytic vesicles characteristic of endosomes. Our results support a model whereby insulin promotes exocytic flux primarily by releasing an intracellular brake, but also by accelerating plasma membrane fusion and switching vesicle traffic between two distinct circuits.