Unraveling the genetic basis of cellular behaviors with deep learning and imaging-based reverse genetics

通过深度学习和基于成像的反向遗传学揭示细胞行为的遗传基础

• 批准号: 10472362
• 负责人: David A VAN VALEN
• 金额: $ 117.36万
• 依托单位: CALIFORNIA INSTITUTE OF TECHNOLOGY
• 依托单位国家: 美国
• 财政年份: 2022
• 资助国家: 美国
• 起止时间: 2022-09-08 至 2025-08-31
• 项目状态: 未结题
• 来源: https://reporter.nih.gov/project-details/10472362
• 关键词: Back; Behavior; Biological; Biological Sciences; Biology; Cell Culture Techniques; Cell Line; Cell Nucleus; Cell Shape; Cells; Cloud Computing; Clustered Regularly Interspaced Short Palindromic Repeats; Collection; Color; Communities; Computing Methodologies; Data; Disease; Ensure; Gene Expression; Generations; Genetic; Genetic Screening; Genomics; Goals; Image; Image Analysis; Imaging technology; Immunohistochemistry; Libraries; Link; Measurement; Measures; Methods; Microscope; Modeling; Modernization; Organism; Pattern; RNA; Research Personnel; Resolution; Standardization; Tissues; Variant; Vision; Work; base; cell behavior; cellular imaging; deep learning; experimental study; high reward; high risk; imaging platform; large scale data; learning strategy; live cell imaging; movie; multimodality; new technology; novel strategies; preservation; reverse genetics; skills; tool; unsupervised learning

Project Summary Imaging and genomics are becoming increasingly intertwined, as multiplexed RNA FISH and multiplexed immunohistochemistry now make it possible to perform “omic” measurements while preserving spatial information. These new technologies are allowing us to create a new, descriptive understanding of normal and diseased tissues. For cell culture models, they offer the promise of measuring multiple facets of cellular behavior – ranging from cell shape to gene expression – all in the same cell. This can be done by pairing dynamic live-cell imaging data with end-point spatial genomics measurements. Such measurements could even be performed in the setting of perturbations, creating a powerful tool for mapping biological networks. In this proposal, I seek to make these methods accessible to the life science community by using large-scale data annotation, deep learning, and cloud computing to solve several outstanding cellular image analysis problems facing the spatial genomics field. I also propose to develop a simple, scalable approach to performing perturbations in imaging-based experiments. The work proposed here is three-fold. First, we will develop deep learning methods for performing whole cell segmentation in tissues as well as segmentation and lineage construction in live-cell imaging movies. To ensure these models generalize across tissues, cell lines, and imaging platforms we will undertake a large-scale data annotation effort to create a standardized collection of images that have been annotated with single cell resolution. Second, we will also develop new deep learning methods for unsupervised learning of cellular behaviors. Third, we will create a new approach to imaging-based reverse genetic screens. In this approach, we will use CRISPR-Display to create multi-color spatial patterns in cell nuclei. This will allow us to link cells and perturbations in images while minimizing the number of collected images. Libraries with 100’s of thousands of perturbations would be interpretable with only 1-2 rounds of low-magnification 4 color imaging. Achieving these high-risk, high-reward goals will constitute a transformative advance as it will empower researchers studying living systems with imaging at the resolution of a single cell with both ease and scale. Once finished, this work will place the microscope back at the center of the biologist’s toolkit and enable images to become a universal datatype for biology.

项目摘要 成像和基因组学越来越相互交织，如多重的RNA鱼和 多路复用的免疫组织化学使得执行“ OMIC”测量是可能的 保留空间信息。这些新技术使我们能够创建一个新的 对正常组织和解剖组织的描述性理解。对于细胞培养模型，它们提供了 测量细胞行为多个方面的承诺 - 从细胞形状到基因 表达 - 全部在同一细胞中。这可以通过配对动态活细胞成像数据来完成 与终点的空间基因组学测量。甚至可以执行此类测量 在扰动的环境中，创建了用于映射生物网络的强大工具。在这个 提案，我试图通过使用来使生活科学界可以访问这些方法 大规模的数据注释，深度学习和云计算，以解决几个出色的 细胞图像分析问题面临空间基因组学场。我还建议开发 在基于成像的实验中执行扰动的简单，可扩展的方法。 这里提出的工作是三倍。首先，我们将为 在组织中进行全细胞分割以及分割和谱系结构 在现场电池成像电影中。确保这些模型跨组织，细胞系和 成像平台我们将进行大规模的数据注释努力，以创建一个 已通过单细胞分辨率注释的标准化图像集合。第二， 我们还将开发新的深度学习方法，用于无监督的细胞行为学习。 第三，我们将创建一种基于成像的反向遗传筛选的新方法。在这个 方法，我们将使用CRISPR-Display在细胞核中创建多色空间模式。这 将允许我们在最小化收集的数量的同时将图像中的单元格和扰动连接起来 图像。具有100千的扰动的图书馆只能以1-2的速度解释 低磁化4彩色成像。 实现这些高风险，高回报的目标将构成变革性的进步 授权研究人员通过成像研究生命系统，并以单个单元的分辨率与 轻松和比例。完成后，这项工作将将显微镜放回 生物学家的工具包并使图像成为生物学的通用数据类型。