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

基本信息

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
项目类别：
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

项目摘要

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.

项目概要随着多重 RNA FISH 和多重免疫组织化学现在可以在同时进行“组学”测量这些新技术使我们能够创建新的、对于细胞培养模型，它们提供了对正常和患病组织的描述性理解。有望测量细胞行为的多个方面——从细胞形状到基因表达——全部在同一个细胞中这可以通过配对动态活细胞成像数据来完成。甚至可以进行终点空间基因组学测量。在扰动的情况下，创建了绘制生物网络的强大工具。提案，我试图通过使用使生命科学界可以使用这些方法大规模数据标注、深度学习、云计算解决若干突出问题我还建议开发一个空间基因组学领域面临的细胞图像分析问题。在基于成像的实验中执行扰动的简单、可扩展的方法。这里提出的工作有三个方面：首先，我们将开发深度学习方法。在组织中进行全细胞分割以及分割和谱系构建确保这些模型能够跨组织、细胞系和细胞系进行推广。成像平台，我们将进行大规模的数据注释工作，以创建用单细胞分辨率注释的标准化图像集合。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遗传筛选的新方法。方法中，我们将使用 CRISPR-Display 在细胞核中创建多色空间图案。将使我们能够将图像中的细胞和扰动最小化，同时收集的数量具有数百个或数千个扰动的库只需 1-2 个即可解释。多轮低倍率 4 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，因为它将使研究人员能够以单细胞分辨率成像来研究生命系统一旦完成，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。

项目成果

期刊论文数量（1）

专著数量（0）

科研奖励数量（0）

会议论文数量（0）

专利数量（0）

Accurate single-molecule spot detection for image-based spatial transcriptomics with weakly supervised deep learning.

通过弱监督深度学习，对基于图像的空间转录组学进行准确的单分子点检测。

DOI：
10.1016/j.cels.2024.04.006
发表时间：
2024-05-01
期刊：
Cell systems
影响因子：
9.3
作者：
Emily Laubscher;X. Wang;Nitzan Razin;Tom Dougherty;Rosalind J. Xu;Lincoln Ombelets;Edward Pao;William Graf;Jeffrey R. Moffitt;Yisong Yue;David Van Valen
通讯作者：
David Van Valen