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.

在显微镜下观察细胞揭示了有关细胞过程的大量信息。例如，细胞图像可以揭示细胞类型以及细胞是否健康或患病等。这项研究旨在开发先进的计算模型来测量显微图像中的细胞特征。从图像中获取的细胞测量值可用于进行生物学研究，例如了解疾病的发病机制、诊断患者和寻找有效的治疗方法。所有这些应用对于推进和促进国民健康至关重要。该项目的一个重要方面是，用于测量细胞特征的计算模型将是通用的，并且可以在许多获取显微图像的生物应用中重复使用，而无需手动配置或最少的手动配置。这项研究将设计、开发并向公众提供模型和自动化工具，以促进基础生物研究和其他生物技术系统中基于图像的快速细胞分析。该项目将涉及不同的研究人员，在尖端机器学习技术和细胞生物学图像分析交叉点的包容性环境中工作。多样化的研究生和博士后将在尖端深度学习技术和细胞生物学图像分析相结合的包容性环境中接受培训。从图像中提取细胞形态特征是一个复杂的临时过程，没有完善的标准。通常，鉴于成像技术和实验目标的复杂性和多样性，成像项目从头开始开发定制方法，并且仅测量一些细胞特征。由于缺乏定义和测量单细胞形态状态的通用方法，研究人员无法充分发挥成像技术在推进细胞生物学方面的潜力。该项目旨在创建一个用于收集单细胞形态数据的通用深度学习模型。它可以轻松量化任何显微镜图像中的细胞形态，几乎不需要任何培训。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多维表示的方法，2）制定纠正批次效应和消除技术差异的策略，3）开发分析和解释生物特征的策略。形态特征的意义。对于学习表示，将使用自我监督学习来开发和训练能够自适应处理多通道显微镜图像的神经网络。领域适应技术将被扩展以纠正批次效应。重要的是，学习到的特征将用于绘制细胞群之间的关系，并且将设计可解释的方法来促进它们的解释。这项研究将准备来自各种公共来源的成像数据集用于培训和评估，包括广泛生物图像基准集合（BBBC）、图像数据资源（IDR）和人类蛋白质图谱（HPA）。该项目中创建的模型将适用于大多数显微镜成像协议，将单细胞图像转换为生物研究的定量数据。所有结果、软件工具和模型都将在 http://broad.io/morphem 上公开。该奖项反映了 NSF 的法定使命，并通过使用基金会的智力价值和更广泛的影响审查标准进行评估，被认为值得支持。