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

项目摘要 成像和基因组学正变得越来越交织在一起，因为多重RNA FISH和 多重免疫组织化学现在使得进行“组学”测量成为可能， 保存空间信息。这些新技术使我们能够创造一个新的， 对正常和病变组织的描述性理解。对于细胞培养模型，他们提供 测量细胞行为的多个方面的承诺-从细胞形状到基因 表达式-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完成 与终点空间基因组学测量。这样的测量甚至可以 在扰动的情况下，为绘制生物网络图创造了一个强大的工具。在这 我的建议，我试图使这些方法获得生命科学界使用 大规模数据标注、深度学习和云计算解决了几个突出问题， 空间基因组学领域面临的细胞图像分析问题。我还建议制定一项 在基于成像的实验中进行扰动的简单、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， 进行组织中的全细胞分割以及分割和谱系构建， 活细胞成像电影。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， 已用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，用于细胞行为的无监督学习。 第三，我们将创建一种基于成像的反向基因筛选的新方法。在这 我们将使用CRISPR-Display在细胞核中创建多色空间模式。这 将使我们能够将图像中的细胞和扰动联系起来，同时最大限度地减少收集的细胞数量。 图像.具有成百上千个扰动的库将仅用1-2个就可解释 几轮低倍四色成像 实现这些高风险，高回报的目标将构成一个变革性的进步，因为它将 使研究人员能够以单细胞的分辨率研究生命系统， 既方便又有规模。一旦完成，这项工作将把显微镜放回中心， 生物学家的工具包，使图像成为生物学的通用数据类型。

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.