High spatiotemporal optical imaging to study dynamics of 3D cell motion and behavior in living organisms

高时空光学成像用于研究活体生物体中 3D 细胞运动和行为的动力学

• 批准号: 10715637
• 负责人: Juhyun Lee
• 金额: $ 37.54万
• 依托单位: UNIVERSITY OF TEXAS ARLINGTON
• 依托单位国家: 美国
• 财政年份: 2023
• 资助国家: 美国
• 起止时间: 2023-09-15 至 2028-08-31
• 项目状态: 未结题
• 来源: https://reporter.nih.gov/project-details/10715637
• 关键词: 3-Dimensional; Algorithms; Behavior; Behavioral; Biomedical Research; Cell Shape; Cell Size; Cell model; Cell physiology; Cells; Cellular biology; Dependence; Dimensions; Ensure; Evaluation; Event; Fluorescence; Functional disorder; Histopathology; Image; Imaging Device; Imaging Techniques; Individual; Lateral; Light; Maximum Likelihood Estimate; Microscope; Microscopic; Microscopy; Modality; Monitor; Morphogenesis; Morphology; Motion; Noise; Optics; Organism; Organogenesis; Outcome; Phenotype; Research Personnel; Resolution; Signal Transduction; Techniques; Technology; Tissues; Training; attenuation; automated segmentation; cell behavior; cell motility; cellular imaging; deep learning; detector; high resolution imaging; improved; in vivo; in vivo monitoring; learning network; lens; mechanical stimulus; migration; millimeter; nanometer; nanometer resolution; nanoscale; next generation; novel; optical imaging; response; restoration; sensor; spatiotemporal; temporal measurement; ultra high resolution

PROJECT SUMMARY/ABSTRACT Behavioral analysis of individual cells that determines the structural phenotypes in response to the mechanical stimuli or cell migration is important in organogenesis or tissue morphogenesis. Dynamic cell tracking, migration trajectory monitoring, or temporal changes in cell shape or size are essential for modeling cellular interactions. However, optical limits imposed by microscope objective lenses and light attenuation in tissue hinder high- resolution evaluation of cell physiology on the dimension scale of μm to nm. Hence, several super-resolution fluorescence modalities have been developed in recent years to enable nanoscale tissue characterization and less damage to tissue histopathology. On the other hand, isotropic nanometer optical resolution for super- resolution modalities is restricted to micron scale field-of-view (FOV). This physical limitation reflects in vivo characterization up to cellular events only. Therefore, we propose to develop an advanced optical imaging technique to achieve sub-cellular resolution, while providing user capability to modulate millimeter to centimeter FOV for in vivo monitoring. Light-sheet fluorescent microscopy (LSFM) has emerged as a popular optical sectioning modality in biomedical research, owing to rapid camera frame rates in conjunction with long working distance excitation optics. Although LSFM provides intermediate-to-high resolution images, the resolution is highly dependent on the confocal range of the excitation objective. To overcome this challenge, we propose to apply the rolling shutter (RS) based-axially sweeping LSFM technique to de-couple the dependence of light sheet FOV on excitation numerical aperture (NA). RS is a type of image capture in sCMOS camera that record the frame line by line on an image sensor instead of capturing the entire frame all at once to improve signal-to-noise, but it can create some unintended image distortions. Thus, we will incorporate the application of maximum likelihood estimation as a post-acquisition restoration strategy to remove optical distortions introduced by RS image acquisition to ensure isotropic lateral and axial nanometer resolution. After the acquisition, we will segment and extract information on cell motion and morphology using a feature detector framework based on the Hessian difference of Gaussian in combination with the watershed algorithm. The feature detector has been validated to separate adjacent clustered cells in 3D undergoing rapid motion with high sensitivity. In addition, we will further train our segmented cell images for deep-learning network and use for automatic segmentation and tracking. The proposed microscopic technology will enable the quantitative characterization of cellular behavior effectively in arbitrary FOV.

项目总结/摘要 对单个细胞的行为分析，确定响应机械刺激的结构表型。 刺激或细胞迁移在器官发生或组织形态发生中是重要的。动态细胞跟踪、迁移 轨迹监测或细胞形状或大小的时间变化对于模拟细胞相互作用是必不可少的。 然而，显微镜物镜和组织中的光衰减所施加的光学限制阻碍了高分辨率的成像。 细胞生理学在微米到纳米尺度上的分辨率评价。因此，几个超分辨率 近年来已经开发了荧光模态以实现纳米级组织表征 对组织病理学损伤较小。另一方面，各向同性纳米光学分辨率的超， 分辨率形式被限制为微米级视场（FOV）。这种物理限制反映了体内 仅表征细胞事件。因此，我们建议开发一种先进的光学成像技术， 实现亚蜂窝分辨率技术，同时提供用户将毫米调制到厘米的能力 FOV用于体内监测。光片荧光显微镜（LSFM）已成为一种流行的光学 生物医学研究中的切片模式，由于快速的相机帧速率和长时间的工作 远距离激发光学系统虽然LSFM提供了中到高分辨率的图像，但分辨率 高度依赖于激发物镜的共焦范围。为了克服这一挑战，我们建议 采用基于滚动快门的轴向扫描LSFM技术， 激发数值孔径（NA）上的FOV。RS是sCMOS相机中的一种图像捕获类型， 在图像传感器上逐行地帧而不是一次捕获整个帧以改善信噪比， 但它可能会造成一些意外的图像失真。因此，我们将采用最大 似然估计作为一种采集后恢复策略，以消除由RS引入的光学失真 图像采集，以确保各向同性的横向和轴向纳米分辨率。收购后，我们将 并使用基于Hessian的特征检测器框架提取关于细胞运动和形态的信息 高斯差分结合分水岭算法。特征检测器已被验证， 以高灵敏度在3D中分离经历快速运动的相邻群集细胞。此外，我们将进一步 为深度学习网络训练我们分割的细胞图像，并用于自动分割和跟踪。 提出的显微技术将使细胞行为的定量表征有效 在任意FOV中。