Department of Chemistry , Stanford University , Stanford , California 94305 , United States.
Cardiovascular Research Institute and Department of Medicine , University of California San Francisco , San Francisco , California 94131 , United States.
Anal Chem. 2018 Oct 16;90(20):12198-12206. doi: 10.1021/acs.analchem.8b03410. Epub 2018 Sep 25.
Lipid profile changes in heart muscle have been previously linked to cardiac ischemia and myocardial infarction, but the spatial distribution of lipids and metabolites in ischemic heart remains to be fully investigated. We performed desorption electrospray ionization mass spectrometry imaging of hearts from in vivo myocardial infarction mouse models. In these mice, myocardial ischemia was induced by blood supply restriction via a permanent ligation of left anterior descending coronary artery. We showed that applying the machine learning algorithm of gradient boosting tree ensemble to the ambient mass spectrometry imaging data allows us to distinguish segments of infarcted myocardium from normally perfused hearts on a pixel by pixel basis. The machine learning algorithm selected 62 molecular ion peaks important for classification of each 200 μm-diameter pixel of the cardiac tissue map as normally perfused or ischemic. This approach achieved very high average accuracy (97.4%), recall (95.8%), and precision (96.8%) at a spatial resolution of ∼200 μm. In addition, we determined the chemical identity of 27 species, mostly small metabolites and lipids, selected by the algorithm as the most significant for cardiac pathology classification. This molecular signature of myocardial infarction may provide new mechanistic insights into cardiac ischemia, assist with infarct size assessment, and point toward novel therapeutic interventions.
先前的研究表明，心肌中的脂质谱变化与心肌缺血和心肌梗死有关，但缺血性心脏中的脂质和代谢物的空间分布仍有待充分研究。我们对体内心肌梗死小鼠模型的心脏进行了解吸电喷雾电离质谱成像。在这些小鼠中，通过对左前降支冠状动脉的永久性结扎来限制血液供应，从而诱导心肌缺血。我们表明，应用梯度提升树集成的机器学习算法对环境质谱成像数据进行分析，可实现对像素级的梗死心肌和正常灌注心脏进行区分。机器学习算法选择了 62 个分子离子峰，这些峰对于分类心脏组织图谱中每个 200μm 直径像素的正常灌注或缺血状态非常重要。这种方法在约 200μm 的空间分辨率下实现了非常高的平均准确性（97.4%）、召回率（95.8%）和精度（96.8%）。此外，我们还确定了 27 种物质的化学身份，这些物质是算法选择的对心脏病理学分类最重要的物质，主要是一些小分子代谢物和脂质。这种心肌梗死的分子特征可能为心肌缺血提供新的机制见解，有助于评估梗死面积，并指向新的治疗干预。
