Akbari Lakeh Mahsa, Tu Anqi, Muddiman David C, Abdollahi Hamid
Department of Chemistry, Institute for Advanced Studies in Basic Sciences (IASBS), Zanjan, 45137-66731, Iran.
Department of Chemistry, FTMS Laboratory for Human Health Research, North Carolina State University, Raleigh, NC, 27695, USA.
Rapid Commun Mass Spectrom. 2019 Feb 28;33(4):381-391. doi: 10.1002/rcm.8362.
Identification of subregions under different pathological conditions on cancerous tissue is of great significance for understanding cancer progression and metastasis. Infrared matrix-assisted laser desorption electrospray ionization mass spectrometry (IR-MALDESI-MS) can be potentially used for diagnostic purposes since it can monitor spatial distribution and abundance of metabolites and lipids in biological tissues. However, the large size and high dimensionality of hyperspectral data make analysis and interpretation challenging. To overcome these barriers, multivariate methods were applied to IR-MALDESI data for the first time, aiming at efficiently resolving mass spectral images, from which these results were then used to identify normal regions within cancerous tissue.
Molecular profiles of healthy and cancerous hen ovary tissues were generated by IR-MALDESI-MS. Principal component analysis (PCA) combined with color-coding built a single tissue image which summarizes the high-dimensional data features. Pixels with similar color indicated similar composition. PCA results from healthy tissue were further used to test each pixel in cancerous tissue to determine if it is healthy. Multivariate curve resolution-alternating least squares (MCR-ALS) was used to obtain major spatial features existing in ovary tissues, and group molecules with the same distribution patterns simultaneously.
PCA as the predominating dimensionality reduction approach captured over 90% spectral variances by the first three PCs. The PCA images show the cancerous tissue is more chemically heterogeneous than healthy tissue, where at least four regions with different m/z profiles can be differentiated. PCA modeling assigns top regions of cancerous tissue as healthy-like. MCR-ALS extracted three and four major compounds from healthy and cancerous tissue, respectively. Evaluating similarities of resolved spectra uncovered the chemical components that were distinct in some regions on cancerous tissue, serving as a supplementary way to differentiate healthy and cancerous regions.
Two unsupervised chemometric methods including PCA and MCR-ALS were applied for resolving and visualizing IR-MALDESI-MS data acquired from hen ovary tissues, improving the interpretation of mass spectrometry imaging results. Then possible normal regions were differentiated from cancerous tissue sections. No prior knowledge is required using either chemometric method, so our approach is readily suitable for unstained tissue samples, which allows one to reveal the molecular events happening during disease progression.
识别癌组织在不同病理条件下的亚区域对于理解癌症进展和转移具有重要意义。红外基质辅助激光解吸电喷雾电离质谱（IR-MALDESI-MS）可潜在地用于诊断目的，因为它可以监测生物组织中代谢物和脂质的空间分布和丰度。然而，高光谱数据的大尺寸和高维度使得分析和解释具有挑战性。为了克服这些障碍，首次将多变量方法应用于IR-MALDESI数据，旨在有效解析质谱图像，然后利用这些结果识别癌组织内的正常区域。
通过IR-MALDESI-MS生成健康和癌性母鸡卵巢组织的分子图谱。主成分分析（PCA）结合颜色编码构建了一个单一的组织图像，该图像总结了高维数据特征。颜色相似的像素表示组成相似。健康组织的PCA结果进一步用于测试癌组织中的每个像素，以确定其是否健康。多变量曲线分辨率交替最小二乘法（MCR-ALS）用于获得卵巢组织中存在的主要空间特征，并同时对具有相同分布模式的分子进行分组。
作为主要降维方法的PCA在前三个主成分中捕获了超过90%的光谱方差。PCA图像显示癌组织比健康组织在化学上更具异质性，其中至少可以区分出四个具有不同m/z谱的区域。PCA建模将癌组织的顶部区域指定为类似健康的区域。MCR-ALS分别从健康组织和癌组织中提取了三种和四种主要化合物。评估解析光谱的相似性揭示了癌组织某些区域中不同的化学成分，作为区分健康和癌区域的补充方法。
应用主成分分析（PCA）和多变量曲线分辨率交替最小二乘法（MCR-ALS）这两种无监督化学计量学方法来解析和可视化从母鸡卵巢组织获得的IR-MALDESI-MS数据，改进了质谱成像结果的解释。然后从癌组织切片中区分出可能的正常区域。使用这两种化学计量学方法都不需要先验知识，因此我们的方法很容易适用于未染色的组织样本，这使得人们能够揭示疾病进展过程中发生的分子事件。
