Department of Microbiology and Cell Science, University of Florida.
Department of Microbiology and Cell Science, University of Florida; Department of Computer and Information Science and Engineering, University of Florida.
J Vis Exp. 2021 Oct 14(176). doi: 10.3791/62997.
A rise in the prevalence of neurodegenerative protein conformational diseases (PCDs) has fostered a great interest in this subject over the years. This increased attention has called for the diversification and improvement of animal models capable of reproducing disease phenotypes observed in humans with PCDs. Though murine models have proven invaluable, they are expensive and are associated with laborious, low-throughput methods. Use of the Caenorhabditis elegans nematode model to study PCDs has been justified by its relative ease of maintenance, low cost, and rapid generation time, which allow for high-throughput applications. Additionally, high conservation between the C. elegans and human genomes makes this model an invaluable discovery tool. Nematodes that express fluorescently tagged tissue-specific polyglutamine (polyQ) tracts exhibit age- and polyQ length-dependent aggregation characterized by fluorescent foci. Such reporters are often employed as proxies to monitor changes in proteostasis across tissues. Manual aggregate quantification is time-consuming, limiting experimental throughput. Furthermore, manual foci quantification can introduce bias, as aggregate identification can be highly subjective. Herein, a protocol consisting of worm culturing, image acquisition, and data processing was standardized to support high-throughput aggregate quantification using C. elegans that express intestine-specific polyQ. By implementing a C. elegans-based image processing pipeline using CellProfiler, an image analysis software, this method has been optimized to separate and identify individual worms and enumerate their respective aggregates. Though the concept of automation is not entirely unique, the need to standardize such procedures for reproducibility, elimination of bias from manual counting, and increase throughput is high. It is anticipated that these methods can drastically simplify the screening process of large bacterial, genomic, or drug libraries using the C. elegans model.
神经退行性蛋白构象疾病（PCD）的患病率上升，多年来引起了人们对此课题的极大兴趣。这种关注度的提高要求多样化和改进能够复制 PCD 患者观察到的疾病表型的动物模型。尽管鼠类模型已被证明具有不可估量的价值，但它们昂贵且与费力、低通量的方法相关。利用秀丽隐杆线虫线虫模型研究 PCD 是合理的，因为它易于维持、成本低且世代时间短，允许高通量应用。此外，秀丽隐杆线虫和人类基因组之间的高度保守性使该模型成为一种宝贵的发现工具。表达荧光标记的组织特异性聚谷氨酰胺（polyQ）片段的线虫表现出与年龄和 polyQ 长度相关的聚集，其特征是荧光焦点。这种报告基因通常被用作监测跨组织蛋白稳态变化的替代物。手动聚集体定量既耗时又限制了实验通量。此外，手动焦点定量可能会引入偏差，因为聚集体的识别可能具有高度主观性。在此，我们制定了一个包括线虫培养、图像采集和数据处理的方案，以支持使用表达肠道特异性 polyQ 的秀丽隐杆线虫进行高通量聚集体定量。通过使用图像分析软件 CellProfiler 实施基于秀丽隐杆线虫的图像处理管道，该方法已得到优化，可分离和识别单个线虫并对其各自的聚集体进行计数。虽然自动化的概念并非完全独特，但需要为可重复性、消除手动计数的偏差以及提高通量而标准化这些程序。预计这些方法可以使用秀丽隐杆线虫模型极大地简化大型细菌、基因组或药物文库的筛选过程。
