Discriminating normal regions within cancerous hen ovarian tissue using multivariate hyperspectral image analysis
Discriminating normal regions within cancerous hen ovarian tissue using multivariate hyperspectral image analysis
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DOI：
10.1002/rcm.8362
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发表时间：
2019-02-28
影响因子：
2
通讯作者：
Abdollahi, Hamid
中科院分区：
文献类型：
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作者：
Lakeh, Mahsa Akbari;Tu, Anqi;Abdollahi, Hamid
在不同病理条件下鉴别癌组织的亚区对了解肿瘤的进展和转移具有重要意义。红外基质辅助激光解吸电喷雾质谱法（IR-MALDESI-MS）可以监测生物组织中代谢物和脂质的空间分布和丰度，因此具有潜在的诊断用途。然而，高光谱数据的大尺寸和高维给分析和解释带来了挑战。为了克服这些障碍，研究人员首次将多变量方法应用于IR-MALDESI数据，旨在有效地解析质谱图像，然后将这些结果用于识别癌组织内的正常区域。方法采用IR-MALDESI-MS建立健康和癌变母鸡卵巢组织的分子图谱。主成分分析（PCA）与颜色编码相结合，构建了一个单一的组织图像，总结了高维数据特征。具有相似颜色的像素表示相似的组成。健康组织的PCA结果进一步用于测试癌组织中的每个像素，以确定它是否健康。采用多变量曲线分辨率-交替最小二乘法（MCR-ALS）获取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。结果主成分分析法是主要的降维方法，前3种主成分分析法捕获了90%以上的光谱方差。PCA图像显示癌组织比健康组织更具有化学异质性，其中至少有四个区域具有不同的m/z剖面可以区分。PCA模型将癌组织的顶部区域指定为健康样。MCR-ALS分别从健康组织和癌变组织中提取三种和四种主要化合物。对分辨光谱的相似性进行评估，揭示了癌变组织中某些区域的化学成分是不同的，作为区分健康和癌变区域的补充方法。结论采用PCA和MCR-ALS两种无监督化学计量方法对母鸡卵巢组织IR-MALDESI-MS数据进行解析和可视化，提高了质谱成像结果的解释。然后从癌组织切片中分化出可能的正常区域。使用这两种化学计量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，这使得人们可以揭示疾病进展过程中发生的分子事件。
Rationale 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. Methods 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. Results 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. Conclusions 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.