New algorithm tracks protein movement in cells with minimal computing power
Scientists have developed a lightweight method to analyze how proteins move across cell membranes—a process central to drug delivery and disease understanding. Unlike computationally expensive AI approaches, this technique runs faster and cheaper, potentially enabling real-time analysis in research labs and clinical settings.
Originaltitel: A light-weight, data-driven segmentation method for multi-state Brownian trajectories
Abstract Single-particle tracking methods have emerged as a crucial tool for the characterisation of dynamical and diffusive processes in a range of biological and synthetic systems. Here, we propose a simple and light-weight yet accurate method for the segmentation of multi-state Brownian trajectories based on an optimised Gaussian filtering of the displacement time series combined with an automated fitting to a Gaussian mixture model. We verify our method using synthetic, 2-state Brownian trajectories and show that our method provides high levels of accuracy in terms of segmentation and the estimation of self-diffusion coefficients for reasonably well-separated values of the diffusion coefficients. We furthermore demonstrate the feasibility of our method on experimental systems using single-particle tracking data for diffusing membrane proteins bound to a supported lipid bilayer. Compared to methods based on deep learning or hidden Markov models, our method imposes a much lower computational load, making it suitable for fast and accurate online processing of single-particle trajectories from microscopy images.