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.

项目概要/摘要 对单个细胞进行行为分析，确定响应机械作用的结构表型 刺激或细胞迁移对于器官发生或组织形态发生很重要。动态细胞追踪、迁移 轨迹监测或细胞形状或大小的时间变化对于建模细胞相互作用至关重要。 然而，显微镜物镜施加的光学限制和组织中的光衰减阻碍了高 在微米到纳米的尺寸尺度上对细胞生理学进行分辨率评估。因此，一些超分辨率 近年来，荧光模式得到了发展，能够实现纳米级组织表征和 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可实现超 分辨率模式仅限于微米级视场（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 技术实现亚细胞分辨率，同时为用户提供从毫米到厘米调制的能力 用于体内监测的 FOV。光片荧光显微镜 (LSFM) 已成为一种流行的光学技术 生物医学研究中的切片模式，由于相机帧速率快且工作时间长 距离激发光学器件。虽然 LSFM 提供中高分辨率图像，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 应用基于卷帘快门（RS）的轴向扫描LSFM技术来解耦光片的依赖性 激发数值孔径 (NA) 上的 FOV。 RS 是 sCMOS 相机中的一种图像捕捉方式，可记录 在图像传感器上逐行扫描，而不是一次捕获整个帧以提高信噪比， 但它可能会造成一些意想不到的图像扭曲。因此，我们将应用最大 似然估计作为采集后恢复策略，以消除 RS 引入的光学畸变 图像采集确保各向同性的横向和轴向纳米分辨率。收购完成后，我们将进行细分 并使用基于 Hessian 的特征检测器框架提取有关细胞运动和形态的信息 高斯差分与分水岭算法的结合。该特征检测器已经过验证 以高灵敏度分离正在进行快速运动的 3D 相邻簇状细胞。此外，我们还将进一步 训练我们的分割细胞图像用于深度学习网络并用于自动分割和跟踪。 所提出的微观技术将能够有效地定量表征细胞行为 在任意视场中。