Intelligent classification of platelet aggregates by agonist type
Intelligent classification of platelet aggregates by agonist type
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DOI：
10.7554/elife.52938
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发表时间：
2020-05
期刊：
影响因子：
7.7
通讯作者：
Yuqi Zhou;A. Yasumoto;Cheng Lei;Chun-Jung Huang;H. Kobayashi;Yunzhao Wu;Sheng Yan;Chia‐Wei Sun;Y. Yatomi;K. Goda
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文献类型：
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作者：
Yuqi Zhou;A. Yasumoto;Cheng Lei;Chun-Jung Huang;H. Kobayashi;Yunzhao Wu;Sheng Yan;Chia‐Wei Sun;Y. Yatomi;K. Goda
血小板是血液中的无核细胞，其主要功能是通过形成聚集体进行止血反应来止血。血小板聚集体除了参与生理性止血外，还参与病理性血栓形成，并在炎症、动脉粥样硬化和癌症转移中发挥重要作用。血小板的聚集是由各种激动剂引起的，但这些血小板聚集长期以来被认为是难以区分和不可能分类的。本文提出了一种基于激动剂类型的智能分类方法。它是基于卷积神经网络训练的高通量成像流式细胞术的血细胞，以识别和区分细微但明显的形态学特征的血小板聚集激活不同类型的激动剂。该方法是研究血小板聚集潜在机制的有力工具，有望为临床诊断、药物计量学和治疗学的全新类别打开一扇窗。
Platelets are anucleate cells in blood whose principal function is to stop bleeding by forming aggregates for hemostatic reactions. In addition to their participation in physiological hemostasis, platelet aggregates are also involved in pathological thrombosis and play an important role in inflammation, atherosclerosis, and cancer metastasis. The aggregation of platelets is elicited by various agonists, but these platelet aggregates have long been considered indistinguishable and impossible to classify. Here we present an intelligent method for classifying them by agonist type. It is based on a convolutional neural network trained by high-throughput imaging flow cytometry of blood cells to identify and differentiate subtle yet appreciable morphological features of platelet aggregates activated by different types of agonists. The method is a powerful tool for studying the underlying mechanism of platelet aggregation and is expected to open a window on an entirely new class of clinical diagnostics, pharmacometrics, and therapeutics.