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 成像和基因组学正变得越来越相互交织，因为多重RNA鱼类和 现在，多路免疫组织化学技术使人们能够同时进行“基因组”测量 保存空间信息。这些新技术使我们能够创造一种新的、 对正常组织和病变组织的描述性理解。对于细胞培养模型，他们提供了 承诺测量细胞行为的多个方面-从细胞形状到基因 表达式-所有内容都在同一个单元格中。这可以通过配对动态活细胞成像数据来实现 用终点空间基因组学测量。这样的测量甚至可以进行 在扰动的背景下，创造了绘制生物网络图的强大工具。在这 提议，我试图通过使用以下方法使生命科学界能够使用这些方法 大规模数据标注、深度学习和云计算解决几个突出问题 空间基因组学领域面临的细胞图像分析问题。我还建议开发一种 在基于成像的实验中执行微扰的简单、可扩展的方法。 这里提出的工作有三个方面。首先，我们将开发深度学习方法，用于 在组织中执行全细胞分割以及分割和谱系构建 在活细胞成像电影中。为了确保这些模型在组织、细胞系和 成像平台我们将进行大规模的数据注释工作，以创建 已使用单个单元格分辨率进行注释的图像的标准化集合。第二, 我们还将开发新的深度学习方法，用于细胞行为的无监督学习。 第三，我们将创造一种基于成像的反向基因筛查的新方法。在这 我们将使用CRISPR-Display在细胞核中创建多色空间图案。这 将允许我们链接图像中的细胞和扰动，同时将收集的数量降至最低 图像。S百千次扰动的文库只需1-2次即可解释 低倍4色成像轮次。 实现这些高风险、高回报的目标将是一种变革性的进步 为研究生命系统的研究人员提供单细胞分辨率的成像 既要宽松，又要有规模。一旦完成，这项工作将把显微镜放回中心 这是生物学家的工具包，并使图像成为生物学的通用数据类型。