Amnis Flow Cytometry, Luminex Corporation;
Amnis Flow Cytometry, Luminex Corporation.
J Vis Exp. 2023 Jan 27(191). doi: 10.3791/64549.
The micronucleus (MN) assay is used worldwide by regulatory bodies to evaluate chemicals for genetic toxicity. The assay can be performed in two ways: by scoring MN in once-divided, cytokinesis-blocked binucleated cells or fully divided mononucleated cells. Historically, light microscopy has been the gold standard method to score the assay, but it is laborious and subjective. Flow cytometry has been used in recent years to score the assay, but is limited by the inability to visually confirm key aspects of cellular imagery. Imaging flow cytometry (IFC) combines high-throughput image capture and automated image analysis, and has been successfully applied to rapidly acquire imagery of and score all key events in the MN assay. Recently, it has been demonstrated that artificial intelligence (AI) methods based on convolutional neural networks can be used to score MN assay data acquired by IFC. This paper describes all steps to use AI software to create a deep learning model to score all key events and to apply this model to automatically score additional data. Results from the AI deep learning model compare well to manual microscopy, therefore enabling fully automated scoring of the MN assay by combining IFC and AI.
微核（MN）试验被世界各地的监管机构用于评估化学物质的遗传毒性。该试验可以通过两种方式进行：在一次分裂、细胞分裂阻断的双核细胞中或完全分裂的单核细胞中计数 MN；或者通过对细胞分裂阻断后的双核细胞进行镜检来计数 MN。历史上，光学显微镜一直是评分该试验的金标准方法，但这种方法既繁琐又主观。近年来，流式细胞术已被用于评分该试验，但由于无法直观确认细胞图像的关键方面而受到限制。成像流式细胞术（IFC）结合了高通量图像捕获和自动化图像分析，已成功用于快速获取 MN 试验的所有关键事件的图像并对其进行评分。最近，已经证明基于卷积神经网络的人工智能（AI）方法可用于对 IFC 采集的 MN 试验数据进行评分。本文描述了使用 AI 软件创建深度学习模型以对所有关键事件进行评分，并将该模型应用于自动评分额外数据的所有步骤。AI 深度学习模型的结果与手动显微镜检查结果非常吻合，因此通过将 IFC 和 AI 相结合，可以实现 MN 试验的全自动评分。
