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使用自动跟踪和进化算法对单层培养中的贴壁细胞进行表征和分类。

Characterization and classification of adherent cells in monolayer culture using automated tracking and evolutionary algorithms.

作者信息

Zhang Zhen, Bedder Matthew, Smith Stephen L, Walker Dawn, Shabir Saqib, Southgate Jennifer

机构信息

Department of Electronics, University of York, Heslington, York YO10 5DD, UK.

Department of Computer Science, University of York, Heslington, York YO10 5GW, UK.

出版信息

Biosystems. 2016 Aug;146:110-21. doi: 10.1016/j.biosystems.2016.05.009. Epub 2016 Jun 3.

DOI:10.1016/j.biosystems.2016.05.009
PMID:27267455
原文链接:https://pmc.ncbi.nlm.nih.gov/articles/PMC5028014/
Abstract

This paper presents a novel method for tracking and characterizing adherent cells in monolayer culture. A system of cell tracking employing computer vision techniques was applied to time-lapse videos of replicate normal human uro-epithelial cell cultures exposed to different concentrations of adenosine triphosphate (ATP) and a selective purinergic P2X antagonist (PPADS), acquired over a 24h period. Subsequent analysis following feature extraction demonstrated the ability of the technique to successfully separate the modulated classes of cell using evolutionary algorithms. Specifically, a Cartesian Genetic Program (CGP) network was evolved that identified average migration speed, in-contact angular velocity, cohesivity and average cell clump size as the principal features contributing to the separation. Our approach not only provides non-biased and parsimonious insight into modulated class behaviours, but can be extracted as mathematical formulae for the parameterization of computational models.

摘要

本文提出了一种用于追踪和表征单层培养中贴壁细胞的新方法。采用计算机视觉技术的细胞追踪系统应用于在24小时内获取的、暴露于不同浓度三磷酸腺苷（ATP）和选择性嘌呤能P2X拮抗剂（PPADS）的正常人尿道上皮细胞重复培养物的延时视频。特征提取后的后续分析表明，该技术能够使用进化算法成功分离受调制的细胞类别。具体而言，进化出了一个笛卡尔遗传程序（CGP）网络，该网络将平均迁移速度、接触角速度、内聚性和平均细胞团大小确定为有助于分离的主要特征。我们的方法不仅为受调制的类别行为提供了无偏差且简洁的见解，还可以提取为计算模型参数化的数学公式。

https://cdn.ncbi.nlm.nih.gov/pmc/blobs/5e0e/5028014/7aa676fc1bcf/gr21.jpg
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https://cdn.ncbi.nlm.nih.gov/pmc/blobs/5e0e/5028014/28cd466d5124/gr10.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/5e0e/5028014/c05c0d9d39aa/gr11.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/5e0e/5028014/b3e8406d4d7b/gr12.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/5e0e/5028014/b636bd101f96/gr13.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/5e0e/5028014/0cc6c917d952/gr14.jpg
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https://cdn.ncbi.nlm.nih.gov/pmc/blobs/5e0e/5028014/8e404232f6ac/gr17.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/5e0e/5028014/5fd889f984b7/gr18.jpg
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https://cdn.ncbi.nlm.nih.gov/pmc/blobs/5e0e/5028014/7aa676fc1bcf/gr21.jpg

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