Collaborative Research: Image-based Readouts of Cellular State using Universal Morphology Embeddings

协作研究：使用通用形态学嵌入基于图像的细胞状态读出

• 批准号: 2348683
• 负责人: Juan Caicedo
• 金额: $ 51.27万
• 依托单位: Morgridge Institute for Research, Inc.
• 依托单位国家: 美国
• 项目类别: Standard Grant
• 财政年份: 2023
• 资助国家: 美国
• 起止时间: 2023-10-01 至 2025-04-30
• 项目状态: 未结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=2348683&HistoricalAwards=false
• 关键词: Collaborative; Research; Image; based; Readouts

Observing cells under the microscope reveals an incredible amount of information about cellular processes. For example, images of cells can reveal cell types, and whether the cells are healthy or sick, among others. This research aims to develop advanced computational models to measure cellular traits in microscopy images. Cellular measurements taken from images are useful to conduct biological research such as understanding how diseases work, diagnosing patients, and searching for effective cures. All of these applications are fundamental to advance and promote national health. An important aspect of this project is that the computational models for measuring cellular traits will be of general purpose and reusable across many biological applications where microscopy images are acquired, with minimal or no manual configuration. This research will design, develop and make publicly available the models and automated tools to facilitate rapid image-based cellular analysis in basic biological research and other biotechnology systems. This project will involve diverse researchers working in an inclusive environment at the intersection of cutting-edge machine learning technologies and image analysis for cell biology. Diverse graduate students and postdocs will be trained in an inclusive environment at the intersection of cutting-edge deep learning technologies andimage analysis for cell biology.Extracting cell morphological features from images is a complex, ad-hoc process without well established standards. Typically, imaging projects develop custom approaches from scratch and measure only a few cellular features given the complexity and diversity of imaging techniques and experimental goals. This lack of a common methodology to define and measure the morphological state of single cells prevents researchers from realizing the full potential of imaging for advancing cell biology. This project aims to create a universal deep-learning model for collecting single-cell morphological data. It will readily quantify cell morphology in any microscopy image, requiring little to no training. The specific goals of this research are: 1) develop methods for learning and extracting multidimensional representations of cell morphology from diverse imaging experiments, 2) formulate strategies for correcting batch effects and removing technical variation, and 3) develop strategies for analyzing and interpreting the biological significance of morphological features. For learning representations, neural networks that can adaptively process multi-channel microscopy images will be developed and trained using self-supervised learning. Domain adaptation techniques will be extended for correcting batch effects. Importantly, learned features will be used to map relations between populations of cells and explainable methods will be designed to facilitate their interpretation. This research will prepare imaging datasets from various public sources for training and evaluation, including the Broad Bioimage Benchmark Collections (BBBC), the Image Data Resource (IDR), and the Human Protein Atlas (HPA). The models created in this project will be applicable to most microscopy imaging protocols to transform images of single cells into quantitative data for biological research. All the results, software tools and models will be publicly available at http://broad.io/morphemThis 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.

观察显微镜下的细胞揭示了有关细胞过程的大量信息。例如，细胞的图像可以揭示细胞类型，以及细胞是否健康还是病人等。该研究旨在开发先进的计算模型，以测量显微镜图像中的细胞性状。从图像中获得的细胞测量对于进行生物学研究很有用，例如了解疾病的工作方式，诊断患者以及寻找有效治疗方法。所有这些应用都是进步和促进国家健康的基础。该项目的一个重要方面是，用于测量细胞性状的计算模型将具有通用性，并且可以在获取显微镜图像的许多生物学应用中重复使用，并具有最小或无手动配置。这项研究将设计，开发和公开可用的模型和自动化工具，以促进基本生物学研究和其他生物技术系统中基于图像的快速细胞分析。该项目将涉及在尖端机器学习技术与细胞生物学图像分析的交集中在包容性环境中工作的多元化研究人员。多样化的研究生和博士后将在尖端深度学习技术和图像分析的相交中对细胞生物学的交集进行培训。从图像中提取细胞形态特征是一个复杂的，临时的过程，没有确定的标准。通常，成像项目从头开始开发自定义方法，并且只要鉴于成像技术和实验目标的复杂性和多样性，仅测量少数细胞特征。缺乏定义和测量单细胞的形态状态的常见方法使研究人员无法意识到成像的全部潜力，以推进细胞生物学。该项目旨在创建一个通用的深度学习模型，用于收集单细胞形态数据。它将很容易在任何显微镜图像中量化细胞形态，几乎不需要训练。这项研究的具体目标是：1）开发从不同成像实验中学习和提取细胞形态的多维表示的方法，2）制定策略以纠正批处理效应和消除技术变化，3）制定策略，以分析和解释形态特征的生物学意义。对于学习表征，将使用自我监督的学习来开发和训练可以自适应处理多通道显微镜图像的神经网络。域适应技术将扩展以纠正批处理效果。重要的是，学习的特征将用于绘制细胞种群之间的关系，可解释的方法将被设计以促进其解释。这项研究将准备来自各种公共资源的成像数据集进行培训和评估，包括广泛的生物图像基准收集（BBBC），图像数据资源（IDR）和人类蛋白质图集（HPA）。该项目中创建的模型将适用于大多数显微镜成像协议，以将单个细胞的图像转换为定量数据进行生物学研究。所有结果，软件工具和模型都将在http://broad.io/morphemthis Award上公开可用，反映了NSF的法定任务，并且使用基金会的知识分子优点和更广泛的影响审查标准，认为值得通过评估来获得支持。