Analysis of transient behavior in complex trajectories: application to secretory vesicle dynamics

A novel supervised trajectory segmentation algorithm identifies distinct types of human adenovirus motion in host cells

Biological trajectories can be characterized by transient patterns that may provide insight into the interactions of the moving object with its immediate environment. The accurate and automated identification of trajectory motifs is important for the understanding of the underlying mechanisms. In this work, we develop a novel trajectory segmentation algorithm based on supervised support vector classification. The algorithm is validated on synthetic data and applied to the identification of trajectory fingerprints of fluorescently tagged human adenovirus particles in live cells. In virus trajectories on the cell surface, periods of confined motion, slow drift, and fast drift are efficiently detected. Additionally, directed motion is found for viruses in the cytoplasm. The algorithm enables the linking of microscopic observations to molecular phenomena that are critical in many biological processes, including infectious pathogen entry and signal transduction.

Primed vesicles can be distinguished from docked vesicles by analyzing their mobility

Neurotransmitters are released from nerve terminals and neuroendocrine cells by calcium-dependent exocytosis of vesicles. Before fusion, vesicles are docked to the plasma membrane and rendered release competent through a process called priming. Electrophysiological methods such as membrane capacitance measurements and carbon fiber amperometry accurately measure the fusion step of exocytosis with high time resolution but provide only indirect information about priming and docking. Total internal reflection fluorescence microscopy (TIRFM) enables the real-time visualization of vesicles, near the plasma membrane, as they undergo changes from one molecular state to the other. We devised a new method to analyze the mobility of vesicles, which not only allowed us to classify the movement of vesicles in three different categories but also to monitor dynamic changes in the mobility of vesicles over time. We selectively enhanced priming by treating bovine chromaffin cells with phorbol myristate acetate (PMA) or by overexpressing Munc13-1 (mammalian Unc) and analyzed the mobility of large dense-core vesicles. We demonstrate that nearly immobile vesicles represent primed vesicles because the pool of vesicles displaying this type of mobility was significantly increased after PMA treatment and Munc13-1 overexpression and decreased during tetanus toxin expression. Moreover, we showed that the movement of docked but unprimed vesicles is restricted to a confined region of approximately 220 nm diameter. Finally, a small third population of undocked vesicles showed a directed and probably active type of mobility. For the first time, we can thus distinguish the molecular state of vesicles in TIRFM by their mobility.

Studying molecular motor-based cargo transport: what is real and what is noise?

Noise is a major problem in analyzing tracking data of cargos moved by molecular motors. We use Bayesian statistics to incorporate what is known about the noise in parsing the trajectory of a cargo into a series of constant velocity segments. Tracks with just noise and no underlying motion are fit with constant velocity segments to produce a calibration curve of fit quality versus average segment duration. Fits to tracks of moving cargos are compared to the calibration curves with similar noise. The fit with the optimum number of constant velocity states has the least number of segments needed to match the fit quality of the calibration curve. We have tested this approach using tracks with known underlying motion generated by computer simulations and with a specially designed in vitro experiment. We present the results of using this parsing approach to analyze transport of lipid droplets in Drosophila embryos.