Department of Microbiology, Immunology and Cancer Biology, University of Virginia School of Medicine, Charlottesville, VA, 22908, USA.
UVA Cancer Center, University of Virginia School of Medicine, Charlottesville, VA, 22908, USA.
Sci Rep. 2020 Nov 3;10(1):18941. doi: 10.1038/s41598-020-75899-5.
Mitochondria are highly dynamic organelles that can exhibit a wide range of morphologies. Mitochondrial morphology can differ significantly across cell types, reflecting different physiological needs, but can also change rapidly in response to stress or the activation of signaling pathways. Understanding both the cause and consequences of these morphological changes is critical to fully understanding how mitochondrial function contributes to both normal and pathological physiology. However, while robust and quantitative analysis of mitochondrial morphology has become increasingly accessible, there is a need for new tools to generate and analyze large data sets of mitochondrial images in high throughput. The generation of such datasets is critical to fully benefit from rapidly evolving methods in data science, such as neural networks, that have shown tremendous value in extracting novel biological insights and generating new hypotheses. Here we describe a set of three computational tools, Cell Catcher, Mito Catcher and MiA, that we have developed to extract extensive mitochondrial network data on a single-cell level from multi-cell fluorescence images. Cell Catcher automatically separates and isolates individual cells from multi-cell images; Mito Catcher uses the statistical distribution of pixel intensities across the mitochondrial network to detect and remove background noise from the cell and segment the mitochondrial network; MiA uses the binarized mitochondrial network to perform more than 100 mitochondria-level and cell-level morphometric measurements. To validate the utility of this set of tools, we generated a database of morphological features for 630 individual cells that encode 0, 1 or 2 alleles of the mitochondrial fission GTPase Drp1 and demonstrate that these mitochondrial data could be used to predict Drp1 genotype with 87% accuracy. Together, this suite of tools enables the high-throughput and automated collection of detailed and quantitative mitochondrial structural information at a single-cell level. Furthermore, the data generated with these tools, when combined with advanced data science approaches, can be used to generate novel biological insights.
线粒体是高度动态的细胞器，可以表现出广泛的形态。线粒体的形态在不同的细胞类型中差异很大，反映了不同的生理需求，但也可以快速响应应激或信号通路的激活而发生变化。了解这些形态变化的原因和后果对于充分了解线粒体功能如何为正常和病理生理学做出贡献至关重要。然而，尽管对线粒体形态的稳健和定量分析已经变得越来越容易，但需要新的工具来生成和分析高通量的大量线粒体图像数据集。生成这些数据集对于充分利用数据科学中不断发展的方法（例如神经网络）至关重要，这些方法在提取新的生物学见解和生成新的假设方面显示出了巨大的价值。在这里，我们描述了一组三个计算工具，Cell Catcher、Mito Catcher 和 MiA，我们已经开发了这些工具来从多细胞荧光图像中单细胞水平上提取广泛的线粒体网络数据。Cell Catcher 自动从多细胞图像中分离和隔离单个细胞；Mito Catcher 使用跨线粒体网络的像素强度的统计分布来检测和去除细胞中的背景噪声并分割线粒体网络；MiA 使用二值化的线粒体网络来执行超过 100 个线粒体级和细胞级形态测量。为了验证这套工具的实用性，我们生成了一个包含 630 个个体细胞的形态特征数据库，这些细胞编码线粒体分裂 GTPase Drp1 的 0、1 或 2 个等位基因，并证明这些线粒体数据可以用于以 87%的准确率预测 Drp1 基因型。总之，这套工具集能够以高通量和自动化的方式在单细胞水平上收集详细和定量的线粒体结构信息。此外，这些工具生成的数据与先进的数据科学方法相结合，可用于生成新的生物学见解。
