Jarmusch Alan K, Alfaro Clint M, Pirro Valentina, Hattab Eyas M, Cohen-Gadol Aaron A, Cooks R Graham
Department of Chemistry and Center for Analytical Instrument Development, Purdue University, West Lafayette, Indiana, United States of America.
Department of Pathology and Laboratory Medicine, University of Louisville School of Medicine, Louisville, Kentucky, United States of America.
PLoS One. 2016 Sep 22;11(9):e0163180. doi: 10.1371/journal.pone.0163180. eCollection 2016.
Desorption electrospray ionization-mass spectrometry (DESI-MS) imaging was used to analyze unmodified human brain tissue sections from 39 subjects sequentially in the positive and negative ionization modes. Acquisition of both MS polarities allowed more complete analysis of the human brain tumor lipidome as some phospholipids ionize preferentially in the positive and others in the negative ion mode. Normal brain parenchyma, comprised of grey matter and white matter, was differentiated from glioma using positive and negative ion mode DESI-MS lipid profiles with the aid of principal component analysis along with linear discriminant analysis. Principal component-linear discriminant analyses of the positive mode lipid profiles was able to distinguish grey matter, white matter, and glioma with an average sensitivity of 93.2% and specificity of 96.6%, while the negative mode lipid profiles had an average sensitivity of 94.1% and specificity of 97.4%. The positive and negative mode lipid profiles provided complementary information. Principal component-linear discriminant analysis of the combined positive and negative mode lipid profiles, via data fusion, resulted in approximately the same average sensitivity (94.7%) and specificity (97.6%) of the positive and negative modes when used individually. However, they complemented each other by improving the sensitivity and specificity of all classes (grey matter, white matter, and glioma) beyond 90% when used in combination. Further principal component analysis using the fused data resulted in the subgrouping of glioma into two groups associated with grey and white matter, respectively, a separation not apparent in the principal component analysis scores plots of the separate positive and negative mode data. The interrelationship of tumor cell percentage and the lipid profiles is discussed, and how such a measure could be used to measure residual tumor at surgical margins.
解吸电喷雾电离质谱成像（DESI-MS）用于以正离子和负离子模式依次分析39名受试者未经修饰的人脑组织切片。两种质谱极性的采集能够更全面地分析人脑肿瘤脂质组，因为一些磷脂在正离子模式下优先电离，而另一些则在负离子模式下优先电离。借助主成分分析和线性判别分析，利用正离子和负离子模式的DESI-MS脂质谱将由灰质和白质组成的正常脑实质与胶质瘤区分开来。正离子模式脂质谱的主成分-线性判别分析能够区分灰质、白质和胶质瘤，平均灵敏度为93.2%，特异性为96.6%，而负离子模式脂质谱的平均灵敏度为94.1%，特异性为97.4%。正离子和负离子模式的脂质谱提供了互补信息。通过数据融合对正离子和负离子模式脂质谱进行主成分-线性判别分析，单独使用时得到的平均灵敏度（94.7%）和特异性（97.6%）与正离子和负离子模式大致相同。然而，当它们结合使用时，通过将所有类别（灰质、白质和胶质瘤）的灵敏度和特异性提高到90%以上，它们相互补充。使用融合数据进行进一步的主成分分析，导致胶质瘤被分为两组，分别与灰质和白质相关，这一分离在单独的正离子和负离子模式数据的主成分分析得分图中并不明显。讨论了肿瘤细胞百分比与脂质谱的相互关系，以及如何利用这种测量方法来测量手术切缘的残留肿瘤。
