Electrochemical Monitoring of Real-Time Vesicle Dynamics Induced by Tau in a Confined Nanopipette.

The microtubule-associated protein tau participates in neurotransmission regulation via its interaction with synaptic vesicles (SVs). The precise nature and mechanics of tau's engagement with SVs, especially regarding alterations in vesicle dynamics, remain a matter of discussion. We report an electrochemical method using a synapse-mimicking nanopipette to monitor vesicle dynamics induced by tau. A model vesicle of ~30 nm is confined within a lipid-modified nanopipette orifice with a comparable diameter to mimic the synaptic lipid environment. Both tau and phosphorylated tau (p-tau) present two-state dynamic behavior in this biomimetic system, showing typical ionic current oscillation, induced by lipid-tau interaction. The results indicate that p-tau has a stronger affinity to the lipid vesicles in the confined environment, blocking the vesicle movement to a higher degree. Taken together, this method bridges a gap for sensing synaptic vesicle dynamics in a confined lipid environment, mimicking vesicle movement near the synaptic membrane. These findings contribute to understanding how different types of tau protein regulate synaptic vesicle motility and to underlying its functional and pathological behaviours in disease.

HFM-Tracker: a cell tracking algorithm based on hybrid feature matching.

Cell migration is known to be a fundamental biological process, playing an essential role in development, homeostasis, and diseases. This paper introduces a cell tracking algorithm named HFM-Tracker (Hybrid Feature Matching Tracker) that automatically identifies cell migration behaviours in consecutive images. It combines Contour Attention (CA) and Adaptive Confusion Matrix (ACM) modules to accurately capture cell contours in each image and track the dynamic behaviors of migrating cells in the field of view. Cells are firstly located and identified via the CA module-based cell detection network, and then associated and tracked via a cell tracking algorithm employing a hybrid feature-matching strategy. This proposed HFM-Tracker exhibits superiorities in cell detection and tracking, achieving 75% in MOTA (Multiple Object Tracking Accuracy) and 65% in IDF1 (ID F1 score). It provides quantitative analysis of the cell morphology and migration features, which could further help in understanding the complicated and diverse cell migration processes.