An Automated High-Content Imaging Platform for Caenorhabditis elegans

亚利桑那大学被授予开发和传播先进的高内容成像平台，用于对秀丽线虫的全面、长期研究。线虫是一种广泛应用于生物学研究的实验系统，因其寿命短、实验室培养简单、成本低以及可获得强大的分子工具而受到青睐。即使有了这些优势，评估生理和分子特征的标准方法往往是劳动密集型的，通常仅限于观察一个或几个特征。该项目旨在改变这一过程，整合自动图像采集、机器学习和专业数据分析方面的最新进展，以同时捕获和解释众多生理和分子特征，显著提高数据收集效率。该平台将进一步允许对相同动物的整个生命周期进行持续监测，提供一个独特的机会来观察分子过程随时间的动态变化，以及种群内个体之间的随机变化。该平台将与数百种现有的转基因荧光生物标记物菌株兼容，并向更广泛的科学界开放，促进跨不同生物领域的合作和创新研究，包括衰老、发育、新陈代谢、应激反应、毒理学、炎症和免疫。该项目还通过为学生提供机器人、成像技术、机器学习、数据库系统和基因工程方面的真实世界培训来促进体验式教育。该研究项目的核心是开发和验证用于初级数据收集的机器人成像系统，并由一个补充数据库和分析套件支持，以实现高效的数据处理、存储和分析。该项目将进一步产生一组经过验证的转基因线虫菌株，每个菌株都表达多个荧光生物标记物，旨在报告优化用于成像平台的不同关键分子过程，并支持不同学科的研究人员。这一平台将使研究人员能够通过提高实验效率、范围和吞吐量，观察生理结果(例如，生存、身体大小、活动)和潜在分子系统(例如，核心分子信号或应激反应通路的激活)之间的动态相互作用，同时通过限制人类在测量和分析中的偏见来提高重复性。通过收集同一个体动物随着时间的推移的数据，该项目将允许研究人员深入研究种群内分子和生理特征之间的动态相互作用。总而言之，这个项目将产生一个创新的成像平台，它将增强我们研究基本生物过程的能力，并加速生物科学的不同学科的发现。该奖项反映了NSF的法定使命，并通过使用基金会的智力优势和更广泛的影响审查标准进行评估，被认为值得支持。

An award is made to the University of Arizona to develop and disseminate an advanced high-content imaging platform for comprehensive, long-term study of the roundworm Caenorhabditis elegans. C. elegans, a widely used experimental system in biological research, is favored for its short lifespan, easy and cost-effective lab cultivation, and the availability of powerful molecular tools. Even with these advantages, standard methods to assess physiological and molecular characteristics are often labor intensive and are typically limited to observing only one or a few traits. This project aims to transform this process, integrating recent advancements in automated image acquisition, machine learning, and specialized data analysis to concurrently capture and interpret numerous physiological and molecular traits, significantly enhancing data collection efficiency. The platform will further allow for continuous monitoring of the same animals throughout their lifespan, offering a unique opportunity to observe dynamic changes in molecular processes over time, as well as stochastic variation among individuals within a population. The platform will be compatible with hundreds of existing transgenic fluorescent biomarker strains and made accessible to the wider scientific community, fostering collaboration and promoting innovative research across diverse biological fields, including aging, development, metabolism, stress response, toxicology, inflammation, and immunity. This project also promotes experiential education by providing students with real-world training in robotics, imaging technology, machine learning, database systems, and genetic engineering.The core of this research project is the development and validation of a robotic imaging system for primary data collection, supported by a complementary database and analysis suite for efficient data processing, storage, and analysis. The project will further generate a panel of validated transgenic C. elegans strains, each expressing multiple fluorescent biomarkers designed to report on different key molecular processes optimized for use with the imaging platform and supporting researchers in various subdisciplines. This platform will enable researchers to observe dynamic interactions between physiological outcomes (e.g., survival, body size, activity) and underlying molecular systems (e.g., activation of core molecular signaling or stress response pathways) by enhancing experimental efficiency, scope, and throughput while improving reproducibility by limiting human bias in measurement and analysis. By collecting data on the same individual animals over time, the project will allow researchers to delve into dynamic interactions between molecular and physiological signatures within a population. In summary, this project will produce an innovative imaging platform that will enhance our ability to study fundamental biological processes and accelerate discovery across diverse disciplines of biological sciences.This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.