Imaging Cells and Tissues with Super-Resolution Structured Illumination Microscopy

用超分辨率结构照明显微镜成像细胞和组织-项目摘要 荧光光学显微镜是研究生物系统的最重要的工具之一 在细胞水平上。不幸的是，由于衍射现象， 横向尺寸被限制为约250纳米。由于细胞内的许多生物结构比 这个，提高分辨率是至关重要的。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衍射极限之外， 方法仍然是一个挑战。 超分辨率结构照明显微镜（SIM），其分辨率可达到约100 nm，是一种适用于细胞和组织的超分辨方法。然而，生物学家采用这种技术， 受到当前不灵活的设备和伪像倾向的图像分析算法的阻碍， available.解决这个问题需要在光学设计和数据处理方面进行创新 在SIM中使用的方法。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， 专注于细胞和组织的成像。在目标1中，我们将为SIM开发替代照明方法 使用经济的组件，我们将开发和实施改进的SIM重建算法 其产生的结果具有更高的分辨率、质量和更可靠的结果， 方法.这些方法将允许成像到高达500微米的组织中，比目前的方法好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 显微镜和显微镜图像的降噪。在目标3中，我们将使用新开发的一套方法 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 开发反应扩散模型，以更好地了解细胞膜的生物物理学。

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.