■血小板功能由专门的表面标志物的表达驱动。近年来出现了不同循环血小板亚群的概念,但它们的确切性质仍有争议。
■为了设计基于光谱流式细胞术的表型工作流程,以提供更全面的表征,在全球和个人层面,静息和激活的健康血小板中的表面标志物,并应用此工作流程来调查反应如何根据血小板年龄而有所不同。
■开发了14标记流式细胞术小组,并将其应用于从健康志愿者获得的赋形剂或激动剂刺激的富含血小板的血浆和全血样品,或根据SYTO-13(ThermoFisherScientific)染色强度分选的血小板作为血小板年龄的指标。使用用户主导和独立的方法分析数据,并结合基于机器学习的新算法。
■该测定检测到健康血小板中标志物表达的差异,在休息和激动剂激活时,在富含血小板的血浆和全血样本中,这与文献是一致的。机器学习以高准确度(>80%)识别刺激的血小板群体。同样,年轻和老年血小板群体之间的机器学习差异达到76%的准确率,主要由前向散射加权,分化簇(CD)41,侧向散射,糖蛋白VI,CD61和CD42b表达模式。
■我们的方法提供了强大的表型分析,以及强大的生物信息学和机器学习工作流程,可深入分析血小板亚群。可裂解的受体,糖蛋白VI和CD42b,有助于定义共享和独特的亚群。这个可收养的,低容量方法在疾病中血小板的深度表征中将是有价值的。
UNASSIGNED: Platelet function is driven by the expression of specialized surface markers. The concept of distinct circulating subpopulations of platelets has emerged in recent years, but their exact nature remains debatable.
UNASSIGNED: To design a spectral flow cytometry-based phenotyping workflow to provide a more comprehensive characterization, at a global and individual level, of surface markers in resting and activated healthy platelets, and to apply this workflow to investigate how responses differ according to platelet age.
UNASSIGNED: A 14-marker flow cytometry panel was developed and applied to vehicle- or agonist-stimulated platelet-rich plasma and whole blood samples obtained from healthy volunteers, or to platelets sorted according to SYTO-13 (Thermo Fisher Scientific) staining intensity as an indicator of platelet age. Data were analyzed using both user-led and independent approaches incorporating novel machine learning-based algorithms.
UNASSIGNED: The assay detected differences in marker expression in healthy platelets, at rest and on agonist activation, in both platelet-rich plasma and whole blood samples, that are consistent with the literature. Machine learning identified stimulated populations of platelets with high accuracy (>80%). Similarly, machine learning differentiation between young and old platelet populations achieved 76% accuracy, primarily weighted by forward scatter, cluster of differentiation (CD) 41, side scatter, glycoprotein VI, CD61, and CD42b expression patterns.
UNASSIGNED: Our approach provides a powerful phenotypic assay coupled with robust bioinformatic and machine learning workflows for deep analysis of platelet subpopulations. Cleavable receptors, glycoprotein VI and CD42b, contribute to defining shared and unique subpopulations. This adoptable, low-volume approach will be valuable in deep characterization of platelets in disease.