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建立高通量运动跟踪方法,用于. 中的多个生物学评估。

Establishing a High-Throughput Locomotion Tracking Method for Multiple Biological Assessments in .

机构信息

Department of Chemistry, Chung Yuan Christian University, Chung-Li 320314, Taiwan.

Department of Bioscience Technology, Chung Yuan Christian University, Chung-Li 320314, Taiwan.

出版信息

Cells. 2022 Jul 28;11(15):2326. doi: 10.3390/cells11152326.

DOI:10.3390/cells11152326
PMID:35954170
原文链接:https://pmc.ncbi.nlm.nih.gov/articles/PMC9367449/
Abstract

Protozoa are eukaryotic, unicellular microorganisms that have an important ecological role, are easy to handle, and grow rapidly, which makes them suitable for ecotoxicity assessment. Previous methods for locomotion tracking in protozoa are largely based on software with the drawback of high cost and/or low operation throughput. This study aimed to develop an automated pipeline to measure the locomotion activity of the ciliated protozoan using a machine learning-based software, TRex, to conduct tracking. Behavioral endpoints, including the total distance, velocity, burst movement, angular velocity, meandering, and rotation movement, were derived from the coordinates of individual cells. To validate the utility, we measured the locomotor activity in either the knockout mutant of the dynein subunit DYH7 or under starvation. Significant reduction of locomotion and alteration of behavior was detected in either the dynein mutant or in the starvation condition. We also analyzed how locomotion was affected by the exposure to copper sulfate and showed that our method indeed can be used to conduct a toxicity assessment in a high-throughput manner. Finally, we performed a principal component analysis and hierarchy clustering to demonstrate that our analysis could potentially differentiate altered behaviors affected by different factors. Taken together, this study offers a robust methodology for locomotion tracking in a high-throughput manner for the first time.

摘要

原生动物是真核的、单细胞微生物,具有重要的生态作用,易于处理且生长迅速,因此适合用于生态毒性评估。先前的原生动物运动追踪方法在很大程度上基于软件,这些软件存在成本高和/或操作通量低的缺点。本研究旨在开发一种自动化管道,使用基于机器学习的软件 TRex 来进行追踪,以测量纤毛原生动物的运动活性。从单个细胞的坐标中得出运动活性的行为终点,包括总距离、速度、爆发运动、角速度、蜿蜒和旋转运动。为了验证其效用,我们测量了动力蛋白亚基 DYH7 缺失突变体或饥饿条件下的运动活性。在动力蛋白突变体或饥饿条件下,均检测到运动活性显著降低和行为改变。我们还分析了铜硫酸盐暴露如何影响运动,并表明我们的方法确实可以用于高通量进行毒性评估。最后,我们进行了主成分分析和层次聚类,表明我们的分析有可能区分不同因素影响的行为改变。总之,本研究首次提供了一种用于原生动物高通量运动追踪的稳健方法。

https://cdn.ncbi.nlm.nih.gov/pmc/blobs/9fff/9367449/a05c465739e7/cells-11-02326-g006.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/9fff/9367449/eea07b7b0466/cells-11-02326-g0A1.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/9fff/9367449/338d2f93931f/cells-11-02326-g0A2.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/9fff/9367449/0366e4191fc5/cells-11-02326-g001.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/9fff/9367449/e55cbdef2ab1/cells-11-02326-g002.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/9fff/9367449/fef5d54192b9/cells-11-02326-g003.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/9fff/9367449/0476578ff8ae/cells-11-02326-g004.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/9fff/9367449/9976ea3cb108/cells-11-02326-g005.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/9fff/9367449/a05c465739e7/cells-11-02326-g006.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/9fff/9367449/eea07b7b0466/cells-11-02326-g0A1.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/9fff/9367449/338d2f93931f/cells-11-02326-g0A2.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/9fff/9367449/0366e4191fc5/cells-11-02326-g001.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/9fff/9367449/e55cbdef2ab1/cells-11-02326-g002.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/9fff/9367449/fef5d54192b9/cells-11-02326-g003.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/9fff/9367449/0476578ff8ae/cells-11-02326-g004.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/9fff/9367449/9976ea3cb108/cells-11-02326-g005.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/9fff/9367449/a05c465739e7/cells-11-02326-g006.jpg

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