Spectral imaging toolbox: segmentation, hyperstack reconstruction, and batch processing of spectral images for the determination of cell and model membrane lipid order.
Spectral imaging toolbox: segmentation, hyperstack reconstruction, and batch processing of spectral images for the determination of cell and model membrane lipid order.
复制标题
DOI：
10.1186/s12859-017-1656-2
复制
发表时间：
2017-05-12
影响因子：
3
通讯作者：
Stride E
中科院分区：
文献类型：
--
作者：
Aron M;Browning R;Carugo D;Sezgin E;Bernardino de la Serna J;Eggeling C;Stride E
光谱成像与极性敏感的荧光探针，使量化的细胞和模型膜的物理特性，包括局部水合，流动性，和横向脂质包装，其特征通常是广义偏振（GP）参数。随着配备光谱检测器的商业显微镜的发展，光谱成像已成为测量GP和其他膜性质的方便和强大的技术。然而，用于光谱图像处理的现有工具不足以处理由这种技术进步提供的大数据集，并且不适合处理用快速内化的荧光探针获得的图像。在这里，我们提出了一个MATLAB光谱成像工具箱，旨在克服这些限制。除了常见的操作，如GP值分布的计算，伪彩色GP图的生成和光谱分析，该工具的一个关键亮点是可靠的膜分割的探针，迅速内化。此外，处理超堆栈、3D重建和批处理有助于分析由时间序列、z堆栈和区域扫描显微镜操作生成的数据集。最后，确定物体尺寸分布，这可以提供对膜性质变化的潜在机制的洞察，并且对于例如涉及模型膜和表面活性剂涂覆的颗粒的研究是期望的。使用环境敏感探针Laurdan、羧基修饰的Laurdan（C-Laurdan）、Di-4-ANEPPDHQ和Di-4-AN（F）EPPTEA（FE）对细胞膜、细胞衍生囊泡、模型膜和微泡进行分析，以定量脂质或脂质堆积的局部横向密度。光谱成像分析仪是一个强大的工具，用于分割和处理大型光谱成像数据集，具有可靠的膜分割方法，无需编程能力。光谱成像分析仪可从https://uk.mathworks.com/matlabcentral/fileexchange/62617-spectral-imaging-toolbox下载。
Spectral imaging with polarity-sensitive fluorescent probes enables the quantification of cell and model membrane physical properties, including local hydration, fluidity, and lateral lipid packing, usually characterized by the generalized polarization (GP) parameter. With the development of commercial microscopes equipped with spectral detectors, spectral imaging has become a convenient and powerful technique for measuring GP and other membrane properties. The existing tools for spectral image processing, however, are insufficient for processing the large data sets afforded by this technological advancement, and are unsuitable for processing images acquired with rapidly internalized fluorescent probes. Here we present a MATLAB spectral imaging toolbox with the aim of overcoming these limitations. In addition to common operations, such as the calculation of distributions of GP values, generation of pseudo-colored GP maps, and spectral analysis, a key highlight of this tool is reliable membrane segmentation for probes that are rapidly internalized. Furthermore, handling for hyperstacks, 3D reconstruction and batch processing facilitates analysis of data sets generated by time series, z-stack, and area scan microscope operations. Finally, the object size distribution is determined, which can provide insight into the mechanisms underlying changes in membrane properties and is desirable for e.g. studies involving model membranes and surfactant coated particles. Analysis is demonstrated for cell membranes, cell-derived vesicles, model membranes, and microbubbles with environmentally-sensitive probes Laurdan, carboxyl-modified Laurdan (C-Laurdan), Di-4-ANEPPDHQ, and Di-4-AN(F)EPPTEA (FE), for quantification of the local lateral density of lipids or lipid packing. The Spectral Imaging Toolbox is a powerful tool for the segmentation and processing of large spectral imaging datasets with a reliable method for membrane segmentation and no ability in programming required. The Spectral Imaging Toolbox can be downloaded from https://uk.mathworks.com/matlabcentral/fileexchange/62617-spectral-imaging-toolbox.