Imaging Cells and Tissues with Super-Resolution Structured Illumination Microscopy

使用超分辨率结构化照明显微镜对细胞和组织进行成像

• 批准号: 10515036
• 负责人: Guy Hagen
• 金额: $ 41.3万
• 依托单位: UNIVERSITY OF COLORADO
• 依托单位国家: 美国
• 财政年份: 2018
• 资助国家: 美国
• 起止时间: 2018-07-01 至 2025-08-31
• 项目状态: 未结题
• 来源: https://reporter.nih.gov/project-details/10515036
• 关键词: Adoption; Affect; Algorithmic Analysis; Algorithms; American; Area; Award; B-Lymphocytes; BRAIN initiative; Bayesian Method; Biological; Biological Models; Biology; Biophysics; Cell Surface Receptors; Cell membrane; Cells; Chemistry; Computer Graphics; Computing Methodologies; Data; Data Analyses; Development; Diffusion; Dimensions; Equipment; Failure; Fluorescence; Fluorescence Microscopy; Frequencies; Goals; Image; Image Analysis; Immune; Joints; Lateral; Laws; Lead; Learning; Lighting; Machine Learning; Methods; Microscope; Microscopy; Modeling; Molecular; Morphologic artifacts; Morphology; Nobel Prize; Noise; Optics; Pattern; Proteins; Reaction; Research; Research Personnel; Resolution; Sampling; Signal Transduction; Structure; Supervision; T-Lymphocyte; Techniques; Technology; Testing; Tissue imaging; Tissues; United States National Institutes of Health; Work; algorithmic methodologies; allergic response; base; biological systems; blind; brain pathway; brain tissue; cellular imaging; computerized data processing; denoising; density; design; fluorescence imaging; fluorescence microscope; high resolution imaging; image reconstruction; imaging detector; imaging modality; imaging study; immune activation; improved; innovation; light scattering; live cell imaging; microscopic imaging; multilayer perceptron; negative affect; neural network; optical imaging; prevent; receptor; reconstruction; relating to nervous system; statistics; theories; tool

Imaging Cells and Tissues with Super-Resolution Structured Illumination Microscopy - Project Summary Fluorescence optical microscopy is one of the most important tools available for the study of biological systems at the cellular level. Unfortunately, due to diffraction phenomena the resolution of fluorescence microscopes in the lateral dimension is limited to about 250 nm. As many biological structures within cells are much smaller than this, increasing resolution is of prime importance. Although several methods are now available which are able to extend the resolution of optical microscopes beyond the diffraction limit, imaging cells and tissues with these methods remains a challenge. Super-resolution structured illumination microscopy (SIM), which can achieve a resolution of approximately 100 nm, is a suitable super-resolution method for cells and tissues. However, adoption of this technique by biologists is hindered by the inflexible equipment and artifact-prone image analysis algorithms which are currently available. The solution to this problem demands innovations in both optical design and in data processing methods which are used in SIM. In particular, imaging deeper into tissues with SIM has not been realized so far. The goal of this interdisciplinary project is to develop, improve, and utilize super-resolution microscopy with a focus on imaging both cells and tissues. In Aim 1 we will develop alternative illumination approaches for SIM using economical components, and we will develop and implement improved SIM reconstruction algorithms which produce results with higher resolution, quality, and more reliable results than are available with current methods. These methods will allow imaging into tissues up to 500 micrometers, about 10-fold better than current technology allows. In Aim 2, we will develop new algorithms based on machine learning for optical sectioning microscopy and for denoising of microscopy images. In Aim 3, we will use the newly developed suite of methods for studies of the molecular basis of allergic responses. We will use structured illumination microscopy to study the relationship between cell surface receptors and the morphology of the plasma membrane, and we will develop a reaction-diffusion model to better understand the biophysics of the cell membrane.

具有超分辨率结构化照明显微镜的成像细胞和组织 - 项目摘要 荧光光学显微镜是可用于研究生物系统的最重要工具之一 在细胞水平。不幸的是，由于衍射现象，荧光显微镜在 横向尺寸仅限于约250 nm。由于细胞内的许多生物结构都比 这是提高分辨率的重要性。尽管现在有几种方法可以 将光学显微镜的分辨率扩展到衍射极限之外，成像细胞和组织 方法仍然是一个挑战。 超分辨率结构化照明显微镜（SIM），可以达到约100的分辨率 NM是一种适合细胞和组织的超分辨率方法。但是，生物学家采用这种技术 不灵活的设备和易于人工的图像分析算法阻碍了这些算法 可用的。解决此问题的解决方案需要光学设计和数据处理中的创新 在SIM中使用的方法。尤其是，到目前为止，还没有实现更深入地进入SIM的组织。 这个跨学科项目的目的是开发，改进和利用超分辨率显微镜 专注于成像细胞和组织。在AIM 1中，我们将开发SIM的替代照明方法 使用经济的组件，我们将开发和实施改进的SIM重建算法 与当前相比，它具有更高的分辨率，质量和更可靠的结果的结果 方法。这些方法将允许成像成500微米的组织，比电流好10倍 技术允许。在AIM 2中，我们将基于机器学习开发新算法 显微镜和用于显微镜图像的降解。在AIM 3中，我们将使用新开发的方法套件 为了研究过敏反应的分子基础。我们将使用结构化照明显微镜研究 细胞表面受体与质膜的形态之间的关系，我们将 开发一个反应扩散模型，以更好地了解细胞膜的生物物理学。