Visualizing ToF-SIMS Hyperspectral Imaging Data Using Color-Tagged Toroidal Self-Organizing Maps
Visualizing ToF-SIMS Hyperspectral Imaging Data Using Color-Tagged Toroidal Self-Organizing Maps
复制标题
DOI：
10.1021/acs.analchem.9b03322
复制
发表时间：
2019-11-05
影响因子：
7.4
通讯作者：
Pigram, Paul J.
中科院分区：
文献类型：
--
作者：
Gardner, Wil;Cutts, Suzanne M.;Pigram, Paul J.
飞行时间二次离子质谱（ToF-SIMS）是一种强大的表面表征技术，能够产生高空间分辨率的高光谱图像，其中每个像素包括整个质谱。这样的图像可以提供对整个表面的化学成分的洞察。然而，由于所产生的数据的大小和复杂性，出现了问题。生物样品的数据特别复杂，主要是由于相似组分产生的重叠光谱。选择单个离子峰作为特定组分的代表的传统方法对于这样复杂的数据集是不够的。多变量分析（MVA）可以帮助克服这一重大障碍。我们证明了Kohonen自组织映射（SOM）与环形拓扑结构可以用来分析ToF-SIMS高光谱成像数据集，并确定像素之间的光谱相似性。我们提出了一种方法，用于颜色标记的环形SOM输出，它减少了整个数据集到一个单一的RGB图像中，类似的像素-基于其相关的质谱-被分配一个类似的颜色。该方法使用装载抗生素头孢托仑酯（CP）的干燥大多层囊泡（LMV）的ToF-SIMS图像进行了示例。我们成功地确定了CP加载和空LMV，而不需要任何先验知识的样品，尽管他们高度相似的光谱。我们还确定了哪些特定的离子峰是最重要的区分两个LMV人口。这种方法是完全无监督的，需要最少的实验者输入。它的开发旨在提供一个用户友好但复杂的工作流程，用于使用ToF-SIMS图像理解复杂的生物样品。
Time-of-flight secondary ion mass spectrometry (ToF-SIMS) is a powerful surface characterization technique capable of producing high spatial resolution hyperspectral images, in which each pixel comprises an entire mass spectrum. Such images can provide insight into the chemical composition across a surface. However, issues arise due to the size and complexity of the data produced. Data are particularly complicated for biological samples, primarily due to overlapping spectra produced by similar components. The traditional approach of selecting individual ion peaks as representative of particular components is insufficient for such complex data sets. Multivariate analysis (MVA) can help to overcome this significant hurdle. We demonstrate that Kohonen self-organizing maps (SOMs) with a toroidal topology can be used to analyze a ToF-SIMS hyperspectral imaging data set and identify spectral similarities between pixels. We present a method for color-tagging the toroidal SOM output, which reduces the entire data set to a single RGB image in which similar pixels-based on their associated mass spectra-are assigned a similar color. This method was exemplified using a ToF-SIMS image of dried large multilamellar vesicles (LMVs), loaded with the antibiotic cefditoren pivoxil (CP). We successfully identified CP-loaded and empty LMVs without the need for any prior knowledge of the sample, despite their highly similar spectra. We also identified which specific ion peaks were most important in differentiating the two LMV populations. This approach is entirely unsupervised and requires minimal experimenter input. It was developed with the aim of providing a user-friendly yet sophisticated workflow for understanding complex biological samples using ToF-SIMS images.