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Podosomes are submicron adhesive and mechanosensitive structures formed by macrophages, dendritic cells and osteoclasts that are capable of protruding into the extracellular environment. Built of an F-actin core surrounded by an adhesion ring, podosomes assemble in a network interconnected by acto-myosin cables. They have been shown to display spatiotemporal instability as well as protrusion force oscillations. To analyse the entire population of these unstable structures, we have designed an automated multi-particle tracking adapted to both topographical and fluorescence data. Here we describe in detail this approach and report the measurements of individual and collective characteristics of podosome ensembles, providing an integrated picture of their activity from the complementary angles of organisation, dynamics, mobility and mechanics. We believe that this will lead to a comprehensive view of podosome collective behaviour and deepen our knowledge about the significance of mechanosensing mediated by protrusive structures.
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http://dx.doi.org/10.1016/j.ymeth.2015.09.002 | DOI Listing |
Flagella-driven motility is a conserved feature across eukaryotic lineages, from unicellular plankton to mammals. In marine dinoflagellates, such as , motility underlies diel vertical migration (DVM), a key adaptive strategy that enables access to spatio-temporally segregated resources in the water column. To investigate how pH influences motility, we used and two other dinoflagellates as a model and used a multi-particle tracking algorithm to monitor and quantitatively analyze cellular motility.
View Article and Find Full Text PDFPhys Med Biol
July 2025
Department of Radiation Physics, The University of Texas, MD Anderson Cancer Center, Houston, TX, United States of America.
Hypofractionated radiotherapy requires reliable cell survival models for doses much higher than the standard 2 Gy, for which the linear-quadratic (LQ) model is not applicable. We developed an alternative approach applicable to both low doses and high doses used in hypofractionated treatments and radiobiological experiments.We combined a standard microdosimetric technique with a recently introduced non-LQ cell survival model.
View Article and Find Full Text PDFLab Chip
May 2025
Universidad de Granada, Department of Applied Physics, Nanoparticles Trapping Laboratory, Granada, 18071, Spain.
We demonstrate that a set of microfabricated electrodes can be coupled to a commercial optical tweezers device, implementing a hybrid electro-optical platform with multiple functionalities for the manipulation of micro-/nanoparticles in suspension. We show that the hybrid scheme allows enhanced manipulation capabilities, including hybrid dynamics, controlled accumulation in the dielectrophoretic trap from the optical tweezers, selectivity, and video tracking of the individual trajectories of trapped particles. This creates opportunities for novel studies in statistical physics and stochastic thermodynamics with multi-particle systems, previously limited to investigations with individual particles.
View Article and Find Full Text PDFSensors (Basel)
April 2024
Department of Physics, The Hong Kong University of Science and Technology, Hong Kong 999077, China.
In diverse realms of research, such as holographic optical tweezer mechanical measurements, colloidal particle motion state examinations, cell tracking, and drug delivery, the localization and analysis of particle motion command paramount significance. Algorithms ranging from conventional numerical methods to advanced deep-learning networks mark substantial strides in the sphere of particle orientation analysis. However, the need for datasets has hindered the application of deep learning in particle tracking.
View Article and Find Full Text PDFJ Imaging
January 2024
NanoP, TH Mittelhessen University of Applied Sciences, D 35392 Giessen, Germany.
Detecting micron-sized particles is an essential task for the analysis of complex plasmas because a large part of the analysis is based on the initially detected positions of the particles. Accordingly, high accuracy in particle detection is desirable. Previous studies have shown that machine learning algorithms have made great progress and outperformed classical approaches.
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