Del Castillo Esteban, Sementé Lluc, Torres Sònia, Ràfols Pere, Ramírez Noelia, Martins-Green Manuela, Santafe Manel, Correig Xavier
Department of Electronic Engineering, Rovira i Virgili University, IISPV, 43007 Tarragona, Spain.
Spanish Biomedical Research Centre in Diabetes and Associated Metabolic Disorders (CIBERDEM), 28029 Madrid, Spain.
Metabolites. 2019 Aug 2;9(8):162. doi: 10.3390/metabo9080162.
Many MALDI-MS imaging experiments make a case versus control studies of different tissue regions in order to highlight significant compounds affected by the variables of study. This is a challenge because the tissue samples to be compared come from different biological entities, and therefore they exhibit high variability. Moreover, the statistical tests available cannot properly compare ion concentrations in two regions of interest (ROIs) within or between images. The high correlation between the ion concentrations due to the existence of different morphological regions in the tissue means that the common statistical tests used in metabolomics experiments cannot be applied. Another difficulty with the reliability of statistical tests is the elevated number of undetected MS ions in a high percentage of pixels. In this study, we report a procedure for discovering the most important ions in the comparison of a pair of ROIs within or between tissue sections. These ROIs were identified by an unsupervised segmentation process, using the popular k-means algorithm. Our ion filtering algorithm aims to find the up or down-regulated ions between two ROIs by using a combination of three parameters: (a) the percentage of pixels in which a particular ion is not detected, (b) the Mann-Whitney U ion concentration test, and (c) the ion concentration fold-change. The undetected MS signals (null peaks) are discarded from the histogram before the calculation of (b) and (c) parameters. With this methodology, we found the important ions between the different segments of a mouse brain tissue sagittal section and determined some lipid compounds (mainly triacylglycerols and phosphatidylcholines) in the liver of mice exposed to thirdhand smoke.
许多基质辅助激光解吸/电离质谱成像（MALDI-MS）实验对不同组织区域进行病例对照研究，以突出受研究变量影响的重要化合物。这是一项挑战，因为要比较的组织样本来自不同的生物实体，因此它们表现出高度的变异性。此外，现有的统计测试无法适当地比较图像内或图像间两个感兴趣区域（ROI）中的离子浓度。由于组织中存在不同的形态区域，离子浓度之间的高度相关性意味着代谢组学实验中常用的统计测试无法应用。统计测试可靠性的另一个困难是在高比例像素中未检测到的质谱离子数量增加。在本研究中，我们报告了一种在组织切片内或切片间的一对ROI比较中发现最重要离子的程序。这些ROI是通过使用流行的k均值算法的无监督分割过程来识别的。我们的离子过滤算法旨在通过结合三个参数来找到两个ROI之间上调或下调的离子：（a）特定离子未被检测到的像素百分比，（b）曼-惠特尼U离子浓度测试，以及（c）离子浓度变化倍数。在计算（b）和（c）参数之前，将未检测到的质谱信号（空峰）从直方图中剔除。通过这种方法，我们在小鼠脑组织矢状切片的不同段之间找到了重要离子，并确定了暴露于三手烟的小鼠肝脏中的一些脂质化合物（主要是三酰甘油和磷脂酰胆碱）。
