Classification and Segmentation of Nanoparticle Diffusion Trajectories in Cellular Micro Environments

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.

OMEGA: a software tool for the management, analysis, and dissemination of intracellular trafficking data that incorporates motion type classification and quality control

“…This manual method can be used in conjunction with an iterative analysis process to obtain homogeneous trajectory segments (see below and Figure 6). In subsequent releases, automated methods for trajectory segmentation will be integrated in OMEGA (Helmuth et al, 2007;Wagner et al, 2017;Huet et al, 2006;Persson et al, 2013;Wang et al, 2017).…”

Detection and quantification of single mRNA dynamics with the Riboglow fluorescent RNA tag

“…1B, Supplementary Table 1). Traces labeled as "subdiffusion" by the TraJClassifier describe restricted movement 53 , resembling a behavior called "corralled" by Fusco et al 3 . Indeed, as Fusco et al 3 ( Supplementary Fig.…”

“…1A, Supplementary Table 1). We further inspected non-directional movement classified as either "normal diffusion" or "subdiffusion" by the TraJClassifier 53 . The diffusion coefficient we observed for normal diffusion was 0.09 ± 0.03 µm 2 /s, comparable with values observed in the literature 3,[7][8][9]11,12,48 ( Supplementary Fig.…”

Multiple particle tracking detects changes in brain extracellular matrix structure and predicts neurodevelopmental age

“…The utility of this approach is already well documented. Wagner et al demonstrated the ability to predict motion type (confined, directed, anomalous, normal) using random forest classifiers trained on trajectory feature datasets (Wagner et al, 2017), and others have employed artificial neural networks to predict agarose gel stiffness and in vitro cell uptake of nanoparticles (Curtis et al, 2019a).…”