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细胞微环境中纳米颗粒扩散轨迹的分类与分割

Classification and Segmentation of Nanoparticle Diffusion Trajectories in Cellular Micro Environments.

作者信息

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

机构信息

Biomedical Imaging Group, Department of Informatics, University of Applied Sciences and Arts Dortmund, Dortmund, Germany.

EAWAG, Swiss Federal Institute of Aquatic Science and Technology, Dübendorf, Switzerland.

出版信息

PLoS One. 2017 Jan 20;12(1):e0170165. doi: 10.1371/journal.pone.0170165. eCollection 2017.

DOI:10.1371/journal.pone.0170165
PMID:28107406
原文链接:https://pmc.ncbi.nlm.nih.gov/articles/PMC5249096/
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.

摘要

暗场显微镜和共聚焦激光扫描显微镜都能够同时观察活细胞和单个纳米颗粒。因此,在活细胞内对纳米颗粒摄取和细胞内移动性进行表征似乎是可行的。单粒子追踪能够测量靠近细胞的扩散粒子的大小。然而,在细胞细胞质这个更为复杂的系统中,可能会出现正常扩散、受限扩散或反常扩散以及定向运动。在这项工作中,我们提出了一种方法,能够自动将单个轨迹分类并分割为各自的运动类型。研究发现单个轨迹包含不止一种运动类型。我们用9种不同特征训练了一个随机森林。合成轨迹在所有运动类型上的平均误差为7.2%。该软件已成功应用于正常扩散和受限扩散阳性对照的轨迹。通过纳米颗粒追踪分析捕获的轨迹用作正常扩散的阳性对照。插入二嵌段共聚物膜中的纳米颗粒用于产生受限扩散。最后,我们对通过暗场显微镜和共聚焦激光扫描显微镜捕获的V79细胞中扩散(纳米)粒子的轨迹进行了分割。名为“TraJClassifier”的软件可通过https://git.io/v6uz2作为ImageJ/Fiji插件免费获取。

https://cdn.ncbi.nlm.nih.gov/pmc/blobs/80d9/5249096/bf5b3636ee1f/pone.0170165.g007.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/80d9/5249096/b20ff8a92c76/pone.0170165.g001.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/80d9/5249096/1738f1d4e2cf/pone.0170165.g002.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/80d9/5249096/f94eb8d5c607/pone.0170165.g003.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/80d9/5249096/c6ea91a3d55c/pone.0170165.g004.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/80d9/5249096/2f8b03a9f5e9/pone.0170165.g005.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/80d9/5249096/874f8e88cd46/pone.0170165.g006.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/80d9/5249096/bf5b3636ee1f/pone.0170165.g007.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/80d9/5249096/b20ff8a92c76/pone.0170165.g001.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/80d9/5249096/1738f1d4e2cf/pone.0170165.g002.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/80d9/5249096/f94eb8d5c607/pone.0170165.g003.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/80d9/5249096/c6ea91a3d55c/pone.0170165.g004.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/80d9/5249096/2f8b03a9f5e9/pone.0170165.g005.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/80d9/5249096/874f8e88cd46/pone.0170165.g006.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/80d9/5249096/bf5b3636ee1f/pone.0170165.g007.jpg

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