RESUMEN
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.
RESUMEN
BACKGROUND AND PURPOSE: Approximately 30% of strokes are cryptogenic despite an exhaustive in-hospital work-up. Analysis of clot composition following endovascular treatment could provide insight into stroke etiology. T-cells already have been shown to be a major component of vulnerable atherosclerotic carotid lesions. We therefore hypothesize that T-cell content in intracranial thrombi may also be a biomarker of atherothrombotic origin. MATERIALS AND METHODS: We histopathologically investigated 54 consecutive thrombi retrieved after mechanical thrombectomy in acute stroke patients. First, thrombi were classified as fibrin-dominant, erythrocyte-dominant or mixed pattern. We then performed quantitative analysis of CD3+ cells on immunohistochemically-stained thrombi and compared T-cell content between "atherothrombotic", "cardioembolism" and "other causes" stroke subtypes. RESULTS: Fourteen (26%) thrombi were defined as fibrin-dominant, 15 (28%) as erythrocyte-dominant, 25 (46%) as mixed. The stroke cause was defined as "atherothrombotic" in 10 (18.5%), "cardioembolism" in 25 (46.3%), and "other causes" in 19 (35.2%). Number of T-cells was significantly higher in thrombi from the "atherothrombotic" group (53.60 ± 28.78) than in the other causes (21.77 ± 18.31; p<0.0005) or the "cardioembolism" group (20.08 ± 15.66; p<0.0003). CONCLUSIONS: The CD3+ T-cell count in intracranial thrombi was significantly higher in "atherothrombotic" origin strokes compared to all other causes. Thrombi with high content of CD3+ cells are more likely to originate from an atherosclerotic plaque.
