Canon Stroke and Vascular Research Center, University at Buffalo, Buffalo, New York, USA
Department of Pathology and Anatomical Sciences, University at Buffalo Jacobs School of Medicine and Biomedical Sciences, Buffalo, New York, USA.
J Neurointerv Surg. 2023 Sep;15(e1):e33-e40. doi: 10.1136/neurintsurg-2022-018898. Epub 2022 Jun 24.
Determining stroke etiology is crucial for secondary prevention, but intensive workups fail to classify ~30% of strokes that are cryptogenic.
To examine the hypothesis that the transcriptomic profiles of clots retrieved during mechanical thrombectomy are unique to strokes of different subtypes.
We isolated RNA from the clots of 73 patients undergoing mechanical thrombectomy. Samples of sufficient quality were subjected to 100-cycle, paired-end RNAseq, and transcriptomes with less than 10 million unique reads were excluded from analysis. Significant differentially expressed genes (DEGs) between subtypes (defined by the Trial of Org 10 172 in Acute Stroke Treatment) were identified by expression analysis in edgeR. Gene ontology enrichment analysis was used to study the biologic differences between stroke etiologies.
In all, 38 clot transcriptomes were analyzed; 6 from large artery atherosclerosis (LAA), 21 from cardioembolism (CE), 5 from strokes of other determined origin, and 6 from cryptogenic strokes. Among all comparisons, there were 816 unique DEGs, 174 of which were shared by at least two comparisons, and 20 of which were shared by all three. Gene ontology analysis showed that CE clots reflected high levels of inflammation, LAA clots had greater oxidoreduction and T-cell processes, and clots of other determined origin were enriched for aberrant platelet and hemoglobin-related processes. Principal component analysis indicated separation between these subtypes and showed cryptogenic samples clustered among several different groups.
Expression profiles of stroke clots were identified between stroke etiologies and reflected different biologic responses. Cryptogenic thrombi may be related to multiple etiologies.
确定中风病因对于二级预防至关重要，但强化检查未能对约 30%的隐源性中风进行分类。
检验血栓切除术取出的血栓转录组谱是否与不同亚型中风具有独特性的假设。
我们从 73 名接受机械血栓切除术的患者的血栓中分离出 RNA。质量足够的样本进行了 100 个循环、配对末端 RNAseq 分析，并且将转录本中少于 1000 万个独特读数的样本排除在分析之外。通过 edgeR 中的表达分析确定了不同亚型（根据急性中风治疗的 Org 10172 试验定义）之间的显著差异表达基因 (DEGs)。基因本体论富集分析用于研究不同中风病因之间的生物学差异。
共分析了 38 个血栓转录组；6 个来自大动脉粥样硬化 (LAA)，21 个来自心源性栓塞 (CE)，5 个来自其他确定来源的中风，6 个来自隐源性中风。在所有比较中，有 816 个独特的 DEGs，其中 174 个至少被两个比较共享，20 个被所有三个比较共享。基因本体论分析表明，CE 血栓反映了高水平的炎症，LAA 血栓具有更高的氧化还原和 T 细胞过程，而其他确定来源的血栓则富含异常血小板和血红蛋白相关过程。主成分分析表明这些亚型之间存在分离，并且显示隐源性样本聚类在几个不同的组中。
中风血栓的表达谱在中风病因之间被确定，并反映了不同的生物学反应。隐源性血栓可能与多种病因有关。
