Purchase of a light microscopy system for high-throughput and high-resolution live cell imaging

购买用于高通量和高分辨率活细胞成像的光学显微镜系统

• 批准号: 10582350
• 负责人: Kwonmoo Lee
• 金额: $ 17.55万
• 依托单位: BOSTON CHILDREN'S HOSPITAL
• 依托单位国家: 美国
• 财政年份: 2019
• 资助国家: 美国
• 起止时间: 2019-09-15 至 2024-08-31
• 项目状态: 已结题
• 来源: https://reporter.nih.gov/project-details/10582350
• 关键词: Address; Area; Award; Behavioral; Big Data; Biological Process; Cell division; Cells; Cellular Structures; Cellular biology; Computer Analysis; Cytoskeleton; Data; Data Set; Detection; Equipment; Event; Fluorescence; Frequencies; Goals; Heterogeneity; Human; Image; Image Analysis; Image Enhancement; Individual; Label; Laboratories; Learning; Light Microscope; Machine Learning; Manuals; Maps; Mechanics; Membrane; Microfluidics; Microscope; Microscopy; Monitor; Noise; Optics; Organelles; Parents; Pharmaceutical Preparations; Pharmacotherapy; Phenotype; Physiological; Play; Research; Resolution; Role; Scanning; Structure; Supervision; System; Technology; Testing; Training; Transcend; analytical tool; base; cell motility; cellular imaging; cellular targeting; cost; deep learning; deep neural network; denoising; design; digital imaging; high dimensionality; high resolution imaging; imaging system; innovation; interest; learning strategy; lens; light microscopy; live cell imaging; live cell microscopy; microscopic imaging; movie; multidimensional data; novel; operation; response; spatiotemporal; tool

PROJECT SUMMARY/ABSTRACT High-throughput microscopy (HTM) can generate vast spatiotemporal information on cellular structures and dynamics under various conditions. Computational analyses of the whole aspect of such data could produce unbiased, systematic representations of cellular heterogeneity across multiple scales. Current HTM, however, is constructed by combining high-magnification microscopes with scanning stages; this configuration would entail high complexity in the system design and operation, high cost, and slow image acquisition rates. We reason that we can transcend the current HTM by integrating light microscopy and machine learning (ML). ML is potent in discovering intricate, hidden structures in high-dimensional datasets with limited human supervision. We will leverage ML’s power to learn important cellular features to create a high-throughput high- resolution live cell imaging system. Our goal is to develop a new HTM platform, termed Machine Learning Microscopy (MLM), that autonomously acquires high-resolution live cell movies in a high-throughput manner. The proposed MLM platform will leverage deep neural networks to allow for i) high-resolution, continuous imaging on live cells and ii) automated acquisition of single-cell and subcellular movies specific to target phenotypes. In the parent award, we will apply MLM to construct a detailed phenotypic map of cell migration and subcellular morphodynamics. The MLM will bring unprecedented analytical power to HTM by imaging large numbers of single cells at high spatial resolution and facilitating extracting many cellular and subcellular phenotypes. MLM can be applied to various areas of cell biology, such as cell division, cytoskeleton, membrane remodeling, and membrane-bound organelles.

项目摘要/摘要 高通量显微镜(HTM)可以产生关于细胞结构和 不同条件下的动态变化。对这些数据的所有方面进行计算分析可以产生 对多个尺度上的细胞异质性的公正、系统的表示。然而，目前的HTM， 是通过将高倍率显微镜与扫描平台相结合而构建的；这种配置将 系统设计和操作复杂，成本高，图像采集速度慢。我们 原因是我们可以通过集成光学显微镜和机器学习(ML)来超越当前的HTM。毫升 在用有限的人力在高维数据集中发现错综复杂的隐藏结构方面是有效的 监督。我们将利用ML的力量来学习重要的蜂窝功能，以创建高通量的高 分辨率活细胞成像系统。我们的目标是开发一个新的HTM平台，称为机器学习 显微镜(MLM)，它以高通量的方式自主获取高分辨率的活细胞电影。 拟议的传销平台将利用深度神经网络来实现i)高分辨率、连续 在活细胞上成像以及ii)自动获取特定于目标的单细胞和亚细胞电影 表型。在家长奖中，我们将应用MLM来构建详细的细胞迁移表型图 和亚细胞形态动力学。传销将通过成像为HTM带来前所未有的分析能力 高空间分辨率的大量单个细胞，便于提取许多细胞和亚细胞 表型。传销可以应用于细胞生物学的各个领域，如细胞分裂、细胞骨架、 膜重塑和膜结合细胞器。