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: 10796461
• 负责人: Guy Hagen
• 金额: $ 9.99万
• 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/10796461
• 关键词: Adoption; Affect; Algorithmic Analysis; Algorithms; American; Biological; Biophysics; Cell Surface Receptors; Cell membrane; Cells; Diffusion; Dimensions; Equipment; Fluorescence; Fluorescence Microscopy; Goals; Image; Image Analysis; Lateral; Lighting; Machine Learning; Methods; Microscope; Microscopy; Modeling; Molecular; Morphologic artifacts; Morphology; Optics; Reaction; Reliability of Results; Resolution; Structure; Techniques; Technology; Tissue imaging; Tissues; United States National Institutes of Health; allergic response; biological systems; cellular imaging; computerized data processing; denoising; design; fluorescence microscope; improved; innovation; meter; microscopic imaging; reconstruction; superresolution microscopy; tool; ultra high resolution

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.

用超分辨率结构照明显微镜成像细胞和组织-项目 总结 荧光光学显微镜是用于研究的最重要的工具之一， 细胞水平的生物系统。不幸的是，由于衍射现象， 荧光显微镜在横向尺寸上的分辨率被限制在约250 nm。作为 细胞内的许多生物结构比这小得多，增加分辨率是必要的。 头等重要尽管现在有几种方法可以扩展 光学显微镜的分辨率超过衍射极限， 这些方法仍然是一个挑战。 超分辨率结构照明显微镜（SIM），它可以实现分辨率 大约100 nm的波长是用于细胞和组织的合适的超分辨率方法。然而，在这方面， 生物学家采用这种技术受到不灵活的设备和人工制品的阻碍， 倾向于图像分析算法，这是目前可用的。这个问题的解决方案 需要在光学设计和数据处理方法方面进行创新 SIM.特别地，到目前为止还没有实现利用SIM更深地成像到组织中。 这个跨学科项目的目标是开发、改进和利用超分辨率 显微镜的重点是成像细胞和组织。在目标1中，我们将开发 照明方法SIM使用经济的组件，我们将开发和 实施改进的SIM重建算法，其产生具有更高分辨率的结果， 质量和更可靠的结果比现有的方法。这些方法将 允许在高达500微米的组织中成像，比现有技术好10倍 允许.在目标2中，我们将开发基于机器学习的新算法， 切片显微镜和显微镜图像的降噪。在目标3中，我们将使用新的 开发了一套研究过敏反应分子基础的方法。我们将使用 结构照明显微镜研究细胞表面受体和 质膜的形态，我们将开发一个反应扩散模型， 更好地理解细胞膜的生物物理学。

项目成果

Optimization of laser deposited silver nanoparticle substrates for surface-enhanced raman spectroscopy.

• DOI: 10.1088/1361-6528/ac622e
• 发表时间: 2022-05-13
• 期刊: Nanotechnology
• 影响因子: 3.5

Super-Resolution Imaging of Neuronal Structures with Structured Illumination Microscopy.

• DOI: 10.3390/bioengineering10091081
• 发表时间: 2023-09-13
• 期刊: BIOENGINEERING-BASEL
• 影响因子: 4.6
• 作者: Paul, Tristan C.;Johnson, Karl A.;Hagen, Guy M.
• 通讯作者: Hagen, Guy M.

Evaluation of MRI Denoising Methods Using Unsupervised Learning.

• DOI: 10.3389/frai.2021.642731
• 发表时间: 2021
• 期刊: Frontiers in artificial intelligence
• 影响因子: 4
• 作者: Moreno López M;Frederick JM;Ventura J
• 通讯作者: Ventura J

Fast Switching Dual-Frequency Nematic Liquid Crystal Tunable Filters.

• DOI: 10.1021/acsphotonics.1c00151
• 发表时间: 2021-04-21
• 期刊: ACS photonics
• 影响因子: 7
• 作者: Melnyk O;Jones R;Macêdo R;Garbovskiy Y;Hagen G;Glushchenko AV;Spendier K;Camley RE
• 通讯作者: Camley RE

Raman Spectroscopy in Open-World Learning Settings Using the Objectosphere Approach.

• DOI: 10.1021/acs.analchem.2c02666
• 发表时间: 2022-11-08
• 期刊: ANALYTICAL CHEMISTRY
• 影响因子: 7.4
• 作者: Balytskyi, Yaroslav;Bendesky, Justin;Paul, Tristan;Hagen, Guy M.;McNear, Kelly
• 通讯作者: McNear, Kelly