Dargazanli Cyril, Zub Emma, Deverdun Jeremy, Decourcelle Mathilde, de Bock Frédéric, Labreuche Julien, Lefèvre Pierre-Henri, Gascou Grégory, Derraz Imad, Riquelme Bareiro Carlos, Cagnazzo Federico, Bonafé Alain, Marin Philippe, Costalat Vincent, Marchi Nicola
Institut de Génomique Fonctionnelle, Univ. Montpellier, UMR 5203 CNRS - U 1191 INSERM, Montpellier, France.
Diagnostic and Interventional Neuroradiology Department, Gui de Chauliac Hospital, Montpellier, France.
Front Neurol. 2020 Nov 5;11:575376. doi: 10.3389/fneur.2020.575376. eCollection 2020.
Mechanical retrieval of thrombotic material from acute ischemic stroke patients provides a unique entry point for translational research investigations. Here, we resolved the proteomes of cardioembolic and atherothrombotic cerebrovascular human thrombi and applied an artificial intelligence routine to examine protein signatures between the two selected groups. We specifically used = 32 cardioembolic and = 28 atherothrombotic diagnosed thrombi from patients suffering from acute stroke and treated by mechanical thrombectomy. Thrombi proteins were successfully separated by gel-electrophoresis. For each thrombi, peptide samples were analyzed by nano-flow liquid chromatography coupled to tandem mass spectrometry (nano-LC-MS/MS) to obtain specific proteomes. Relative protein quantification was performed using a label-free LFQ algorithm and all dataset were analyzed using a support-vector-machine (SVM) learning method. Data are available via ProteomeXchange with identifier PXD020398. Clinical data were also analyzed using SVM, alone or in combination with the proteomes. A total of 2,455 proteins were identified by nano-LC-MS/MS in the samples analyzed, with 438 proteins constantly detected in all samples. SVM analysis of LFQ proteomic data delivered combinations of three proteins achieving a maximum of 88.3% for correct classification of the cardioembolic and atherothrombotic samples in our cohort. The coagulation factor XIII appeared in all of the SVM protein trios, associating with cardioembolic thrombi. A combined SVM analysis of the LFQ proteome and clinical data did not deliver a better discriminatory score as compared to the proteome only. Our results advance the portrayal of the human cerebrovascular thrombi proteome. The exploratory SVM analysis outlined sets of proteins for a proof-of-principle characterization of our cohort cardioembolic and atherothrombotic samples. The integrated analysis proposed herein could be further developed and retested on a larger patients population to better understand stroke origin and the associated cerebrovascular pathophysiology.
从急性缺血性中风患者体内机械性取出血栓物质为转化研究调查提供了一个独特的切入点。在此，我们解析了心源性栓塞性和动脉粥样硬化血栓性脑血管人类血栓的蛋白质组，并应用人工智能程序来检查所选两组之间的蛋白质特征。我们特别使用了32例心源性栓塞性和28例动脉粥样硬化血栓性确诊血栓，这些血栓来自急性中风患者且接受了机械取栓治疗。血栓蛋白质通过凝胶电泳成功分离。对于每个血栓，肽样本通过纳流液相色谱-串联质谱（nano-LC-MS/MS）进行分析以获得特定的蛋白质组。使用无标记LFQ算法进行相对蛋白质定量，所有数据集使用支持向量机（SVM）学习方法进行分析。数据可通过ProteomeXchange获得，标识符为PXD020398。临床数据也使用SVM单独或与蛋白质组联合进行分析。通过nano-LC-MS/MS在分析的样本中总共鉴定出2455种蛋白质，在所有样本中持续检测到438种蛋白质。对LFQ蛋白质组学数据的SVM分析得出三种蛋白质的组合，在我们的队列中对心源性栓塞性和动脉粥样硬化血栓性样本进行正确分类时，其准确率最高达到88.3%。凝血因子XIII出现在所有SVM蛋白质三联体中，与心源性栓塞性血栓相关。与仅蛋白质组相比，LFQ蛋白质组和临床数据的联合SVM分析并未给出更好的判别分数。我们的结果推进了对人类脑血管血栓蛋白质组的描绘。探索性SVM分析勾勒出了一组蛋白质，用于对我们队列中心源性栓塞性和动脉粥样硬化血栓性样本进行原理验证表征。本文提出的综合分析可以在更大的患者群体上进一步开发和重新测试，以更好地理解中风起源和相关的脑血管病理生理学。
