Novel image analysis tool for rapid screening of cell morphology in preclinical animal models of disease.
Novel image analysis tool for rapid screening of cell morphology in preclinical animal models of disease.
复制标题
用于快速筛选临床前动物疾病模型中细胞形态的新型图像分析工具。
DOI：
10.1016/j.heliyon.2023.e13449
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发表时间：
2023-02
期刊：
影响因子：
4
通讯作者：
Lein, Pamela J.
中科院分区：
文献类型：
--
作者：
Guignet, Michelle;Schmuck, Martin;Harvey, Danielle J.;Nguyen, Danh;Bruun, Donald;Echeverri, Angela;Gurkoff, Gene;Lein, Pamela J.
关键词：
The field of cell biology has seen major advances in both cellular imaging modalities and the development of automated image analysis platforms that increase rigor, reproducibility, and throughput for large imaging data sets. However, there remains a need for tools that provide accurate morphometric analysis of single cells with complex, dynamic cytoarchitecture in a high-throughput and unbiased manner. We developed a fully automated image-analysis algorithm to rapidly detect and quantify changes in cellular morphology using microglia cells, an innate immune cell within the central nervous system, as representative of cells that exhibit dynamic and complex cytoarchitectural changes. We used two preclinical animal models that exhibit robust changes in microglia morphology: (1) a rat model of acute organophosphate intoxication, which was used to generate fluorescently labeled images for algorithm development; and (2) a rat model of traumatic brain injury, which was used to validate the algorithm using cells labeled using chromogenic detection methods. All ex vivo brain sections were immunolabeled for IBA-1 using fluorescence or diaminobenzidine (DAB) labeling, images were acquired using a high content imaging system and analyzed using a custom-built algorithm. The exploratory data set revealed eight statistically significant and quantitative morphometric parameters that distinguished between phenotypically distinct groups of microglia. Manual validation of single-cell morphology was strongly correlated with the automated analysis and was further supported by a comparison with traditional stereology methods. Existing image analysis pipelines rely on high-resolution images of individual cells, which limits sample size and is subject to selection bias. However, our fully automated method integrates quantification of morphology and fluorescent/chromogenic signals in images from multiple brain regions acquired using high-content imaging. In summary, our free, customizable image analysis tool provides a high-throughput, unbiased method for accurately detecting and quantifying morphological changes in cells with complex morphologies.
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DOI：
10.1523/jneurosci.5399-12.2013
发表时间：
2013-04-03
期刊：
The Journal of neuroscience : the official journal of the Society for Neuroscience
影响因子：
--
作者：
Bachstetter AD;Rowe RK;Kaneko M;Goulding D;Lifshitz J;Van Eldik LJ
通讯作者：
Van Eldik LJ
影响因子：
15.1
作者：
Frank-Cannon TC;Alto LT;McAlpine FE;Tansey MG
通讯作者：
Tansey MG
影响因子：
5.3
作者：
Fernández-Arjona MDM;Grondona JM;Granados-Durán P;Fernández-Llebrez P;López-Ávalos MD
通讯作者：
López-Ávalos MD
影响因子：
15.9
作者：
Kreutzberg, GW
通讯作者：
Kreutzberg, GW
影响因子：
2.9
作者：
Feng, Jun-feng;Gurkoff, Gene G.;Lyeth, Bruce G.
通讯作者：
Lyeth, Bruce G.