AutoMitoNetwork:用于分析自发荧光图像中线粒体网络的软件,可实现无标记细胞分类。

IF 2.5 4区 生物学 Q3 BIOCHEMICAL RESEARCH METHODS
Shannon Handley, Ayad G. Anwer, Aline Knab, Akanksha Bhargava, Ewa M. Goldys
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引用次数: 0

摘要

高分辨率线粒体成像与图像分析工具相结合,极大地促进了我们对健康和疾病中细胞功能的了解。然而,大多数用于线粒体研究的图像分析工具都只适用于荧光标记图像。此外,将描述线粒体网络的特征与用于区分细胞类型的机器学习技术相结合的努力也很有限。在本文中,我们介绍了 AutoMitoNetwork 软件,该软件利用一系列可解释的形态、强度和纹理特征,对无标记自发荧光图像中的线粒体网络进行基于图像的评估。为了证明该软件的实用性,我们对健康视网膜细胞和暴露于两种处理方式的视网膜细胞中未染色的线粒体网络进行了特征描述:鱼藤酮直接抑制线粒体呼吸和ATP的产生,碘乙酸则通过抑制无氧糖酵解对线粒体网络产生较温和的影响。对于这两种情况,我们的多维特征分析结合支持向量机分类器可区分健康细胞和使用鱼藤酮或碘乙酸处理的细胞。我们测量了形态特征的微妙变化,包括经处理的视网膜细胞碎片增多,这表明与新陈代谢机制有关。AutoMitoNetwork 为基于图像的机器学习在无标记成像、诊断和线粒体疾病药物开发方面提供了新的选择。
本文章由计算机程序翻译,如有差异,请以英文原文为准。

AutoMitoNetwork: Software for analyzing mitochondrial networks in autofluorescence images to enable label-free cell classification

AutoMitoNetwork: Software for analyzing mitochondrial networks in autofluorescence images to enable label-free cell classification

High-resolution mitochondria imaging in combination with image analysis tools have significantly advanced our understanding of cellular function in health and disease. However, most image analysis tools for mitochondrial studies have been designed to work with fluorescently labeled images only. Additionally, efforts to integrate features describing mitochondrial networks with machine learning techniques for the differentiation of cell types have been limited. Herein, we present AutoMitoNetwork software for image-based assessment of mitochondrial networks in label-free autofluorescence images using a range of interpretable morphological, intensity, and textural features. To demonstrate its utility, we characterized unstained mitochondrial networks in healthy retinal cells and in retinal cells exposed to two types of treatments: rotenone, which directly inhibited mitochondrial respiration and ATP production, and iodoacetic acid, which had a milder impact on mitochondrial networks via the inhibition of anaerobic glycolysis. For both cases, our multi-dimensional feature analysis combined with a support vector machine classifier distinguished between healthy cells and those treated with rotenone or iodoacetic acid. Subtle changes in morphological features were measured including increased fragmentation in the treated retinal cells, pointing to an association with metabolic mechanisms. AutoMitoNetwork opens new options for image-based machine learning in label-free imaging, diagnostics, and mitochondrial disease drug development.

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来源期刊
Cytometry Part A
Cytometry Part A 生物-生化研究方法
CiteScore
8.10
自引率
13.50%
发文量
183
审稿时长
4-8 weeks
期刊介绍: Cytometry Part A, the journal of quantitative single-cell analysis, features original research reports and reviews of innovative scientific studies employing quantitative single-cell measurement, separation, manipulation, and modeling techniques, as well as original articles on mechanisms of molecular and cellular functions obtained by cytometry techniques. The journal welcomes submissions from multiple research fields that fully embrace the study of the cytome: Biomedical Instrumentation Engineering Biophotonics Bioinformatics Cell Biology Computational Biology Data Science Immunology Parasitology Microbiology Neuroscience Cancer Stem Cells Tissue Regeneration.
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