Imaging Cells and Tissues with Super-Resolution Structured Illumination Microscopy

超分辨率结构照明显微镜成像细胞和组织-项目总结 荧光光学显微镜是研究生物系统最重要的工具之一。 在细胞水平上。不幸的是，由于衍射现象，荧光显微镜的分辨率在 横向尺寸被限制在约250 nm。因为细胞内的许多生物结构比 这一点，提高分辨率是最重要的。尽管现在有几种方法可以用来 将光学显微镜的分辨率扩展到超过衍射极限，用这些技术为细胞和组织成像 方法仍然是一个挑战。 超分辨率结构照明显微镜(SIM)，可达到约100的分辨率 NM，是一种适用于细胞和组织的超分辨方法。然而，生物学家采用这项技术 由于设备不灵活，图像分析算法容易出现伪影，目前 可用。这个问题的解决需要在光学设计和数据处理方面进行创新 SIM中使用的方法。特别是，使用SIM对组织进行更深层次的成像到目前为止还没有实现。 这个跨学科项目的目标是开发、改进和利用具有 专注于细胞和组织的成像。在目标1中，我们将为SIM卡开发替代照明方法 使用经济的组件，我们将开发和实现改进的SIM重建算法 其产生的结果具有比当前可用的更高的分辨率、质量和更可靠的结果 方法：研究方法。这些方法将允许对长达500微米的组织进行成像，大约是目前的10倍 技术允许。在目标2中，我们将开发基于机器学习的光学切片新算法 用于显微图像的去噪处理。在目标3中，我们将使用新开发的方法套件 用于研究过敏反应的分子基础。我们将使用结构照明显微镜来研究 细胞表面受体和质膜形态之间的关系，我们将 建立反应-扩散模型以更好地理解细胞膜的生物物理。

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.