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深度学习,3D超结构分析揭示了COVID-19/SARSCoV2患者衍生的血小板中血小板和器官包装的定量差异

Sagar S Matharu¹, Cassidy S Nordmann¹, Kurtis R Ottman¹

¹Laboratory of Cellular Imaging and Macromolecular Biophysics, National Institute of Biomedical Imaging and Bioengineering, National Institutes of Health, Bethesda, MD, USA.

|November 7, 2023

概括

COVID-19血小板较小,更紧,有更密集的细胞器包装. 这项研究表明外部因素,而不是血小板变化,驱动COVID-19并发症,突出了先进的3D成像方法.

科学领域:

* 血液学血液学
* 超结构性病理学
* 计算生物学计算生物学

背景情况:

* 血小板在COVID-19中发挥作用,特别是在肺微结血中.
* 了解血小板形态和细胞器变化可能会揭示过度激活机制.
*之前的研究缺乏COVID-19血小板的高分辨率3D超结构分析.

研究的目的:

*与健康对照人群相比,研究COVID-19患者血小板的3D超结构差异.
*评估COVID-19中血小板形态,α颗粒和线粒体的诊断潜力.
*评估深度学习在分析血小板超结构中的有用性.

主要方法:

*使用聚焦离子束扫描电子显微镜 (FIB-SEM) 进行高分辨率3D成像.
*利用深度学习计算方法分析大约600个血小板和30,000个细胞器.
*比较了三名健康对照组和三名重病COVID-19患者的血小板.

主要成果:

*COVID-19血小板的α-粒/线粒体/血小板体积比显著更高,表明有机体密度更高.
*COVID-19血小板比对照血小板显著小 (35%的体积减少).
*在COVID-19患者中观察到差异性血小板激活的微小3D超结构证据.

结论:

*COVID-19血小板较小,更紧,有更密集的细胞器包装,但表现出极少的内在过活化的迹象.
*研究结果表明,外部因素,而不是内在的血小板超结构变化,可能是COVID-19并发症的主要原因.
*基于深度学习的3D超结构方法表明,它是未来血小板研究的黄金标准.

关键词:

3D电子显微镜 3D电子显微镜在 COVID-19 疫情中,阿尔法颗粒是什么意思深度学习是一种深度学习.图像分析图像分析血小板血小板是超结构的超结构.

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