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Multiple Particle Tracking via Velocity Filtering (MPT-vVF): a velocity filtering framework for robust tracking moving organelles in living cells
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Scientists developed a new computer tool that can precisely track tiny cargo-carrying "packages" (vesicles) as they move inside brain cells, even in crowded, busy conditions where older methods struggled. Using this tool, they discovered that exposure to nanoplastics, incredibly tiny plastic particles, slowed down and shortened the transport of a protein essential for brain cell health and communication (BDNF). This matters because it suggests nanoplastics, which are increasingly found in our environment and bodies, could interfere with basic processes cells need to keep neurons healthy and functioning.
Abstract Living cells are highly dynamic and densely crowded environments in which organelles such as vesicles undergo continuous motion that is essential for cellular processes. Therefore, accurate tracking of individual organelles is crucial for understanding intercellular dynamics and functions. However, precise tracking of individual organelles in living cells remains challenging due to high organelle densities, frequent particle overlap, and the coexistence of stationary and motile organelles. In particular, stationary organelles can obscure the trajectories of moving organelles, leading to tracking errors and fragmented tracks. To overcome these challenges, we developed Multiple Particle Tracking via Velocity Filtering (MPT-vVF), an unbiased, semi-automated tracking framework that incorporates a mathematically derived velocity-filtering algorithm to selectively identify and track moving organelles with high accuracy in crowded intracellular environments. MPT-vVF integrates denoising, background subtraction, and a velocity-matching detection step that discriminates true particle motion from noise based on spatiotemporal continuity, followed by robust trajectory linking. We demonstrate that MPT-vVF can accurately resolve nanometer-scale displacements of immobilized beads, highlighting its high tracking precision. We also validate the robustness of MPT-vVF by quantifying the transport of brain-derived neurotrophic factor (BDNF)-mRFP-containing vesicles in living hippocampal neurons. Furthermore, MPT-vVF reveals that exposure to 50-nm nanoplastics impairs vesicular transport, reducing both travel length and speed of BDNF-containing vesicles in living neurons. These findings establish MPT-vVF as a powerful method for quantitative analysis of intracellular organelles in crowded living cells and suggest its broad application to biophysics, cell biology, and soft matter research. Significance Accurate tracking of moving organelles in the crowded living cells remains challenging due to high particle densities, frequent overlap, and the coexistence of stationary and motile organelles. To address these challenges, we developed Multiple Particle Tracking via Velocity Filtering (MPT-vVF), an unbiased semi-automated tracking framework that uses a mathematically derived velocity-filtering algorithm to selectively identify and track motile organelles. By integrating denoising, background subtraction, and spatiotemporal velocity-matching for trajectory linking, MPT-vVF enables precise measurements of nanometer-scale displacements of immobilized beads. Moreover, MPT-vVF accurately quantifies the movement of brain-derived neurotrophic factor (BDNF)-containing vesicles and reveals transport impairment of BDNF-containing vesicles following exposure to nanoplastics. Its versatility makes MPT-vVF broadly applicable to biophysical processes, cellular transport, and particle dynamics in complex systems.
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