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Classification and Segmentation of Nanoparticle Diffusion Trajectories in Cellular Micro Environments

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  • Thorsten Wagner
  • Alexandra Kroll
  • Chandrashekara R Haramagatti
  • Hans-Gerd Lipinski
  • Martin Wiemann

Abstract

Darkfield and confocal laser scanning microscopy both allow for a simultaneous observation of live cells and single nanoparticles. Accordingly, a characterization of nanoparticle uptake and intracellular mobility appears possible within living cells. Single particle tracking allows to measure the size of a diffusing particle close to a cell. However, within the more complex system of a cell’s cytoplasm normal, confined or anomalous diffusion together with directed motion may occur. In this work we present a method to automatically classify and segment single trajectories into their respective motion types. Single trajectories were found to contain more than one motion type. We have trained a random forest with 9 different features. The average error over all motion types for synthetic trajectories was 7.2%. The software was successfully applied to trajectories of positive controls for normal- and constrained diffusion. Trajectories captured by nanoparticle tracking analysis served as positive control for normal diffusion. Nanoparticles inserted into a diblock copolymer membrane was used to generate constrained diffusion. Finally we segmented trajectories of diffusing (nano-)particles in V79 cells captured with both darkfield- and confocal laser scanning microscopy. The software called “TraJClassifier” is freely available as ImageJ/Fiji plugin via https://git.io/v6uz2.

Suggested Citation

  • Thorsten Wagner & Alexandra Kroll & Chandrashekara R Haramagatti & Hans-Gerd Lipinski & Martin Wiemann, 2017. "Classification and Segmentation of Nanoparticle Diffusion Trajectories in Cellular Micro Environments," PLOS ONE, Public Library of Science, vol. 12(1), pages 1-20, January.
  • Handle: RePEc:plo:pone00:0170165
    DOI: 10.1371/journal.pone.0170165
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    Cited by:

    1. Wang, Xiaolong & Feng, Jing & Liu, Qi & Li, Yongge & Xu, Yong, 2022. "Neural network-based parameter estimation of stochastic differential equations driven by Lévy noise," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 606(C).
    2. Aleksandra Grzesiek & Radosław Zimroz & Paweł Śliwiński & Norbert Gomolla & Agnieszka Wyłomańska, 2021. "A Method for Structure Breaking Point Detection in Engine Oil Pressure Data," Energies, MDPI, vol. 14(17), pages 1-24, September.
    3. Clement Verkest & Irina Schaefer & Timo A. Nees & Na Wang & Juri M. Jegelka & Francisco J. Taberner & Stefan G. Lechner, 2022. "Intrinsically disordered intracellular domains control key features of the mechanically-gated ion channel PIEZO2," Nature Communications, Nature, vol. 13(1), pages 1-14, December.
    4. Sikora, Grzegorz & Wyłomańska, Agnieszka & Krapf, Diego, 2018. "Recurrence statistics for anomalous diffusion regime change detection," Computational Statistics & Data Analysis, Elsevier, vol. 128(C), pages 380-394.

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