Machine Learning Analysis of the Cerebrovascular Thrombi Proteome in Human Ischemic Stroke: An Exploratory Study

• Published in: Frontiers in neurology 2020-11, Vol.11, p.575376-575376
• Main Authors: 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
• Format: Article
• Language: English
• Subjects: Biochemistry, Molecular Biology; Bioengineering; cerebrovascular; Computer Science; Genomics; Life Sciences; Machine Learning; mechanical thrombectomy; Neurology; Neurons and Cognition; proteome; stroke; support vector machine learning; thrombus
• ISSN: 1664-2295; 1664-2295
• DOI: 10.3389/fneur.2020.575376

Objective: 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.Methods: We specifically used n = 32 cardioembolic and n = 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.Results: 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.Conclusion: 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.