The reconstructed skin micronucleus (RSMN) assay was developed in 2006, as an in vitro alternative for genotoxicity evaluation of dermally applied chemicals or products. In the years since, significant progress has been made in the optimization of the assay, including publication of a standard protocol and extensive validation. However, the diverse morphology of skin cells makes cell preparation and scoring of micronuclei (MN) tedious and subjective, thus requiring a high level of technical expertise for evaluation. This ultimately has a negative impact on throughput and the assay would benefit by the development of an automated method which could reduce scoring subjectivity while also improving the robustness of the assay by increasing the number of cells that can be scored. Imaging flow cytometry (IFC) with the ImageStream^®X Mk II can capture high-resolution transmission and fluorescent imagery of cells in suspension. This proof-of-principle study describes protocol modifications that enable such automated measurement in 3D skin cells following exposure to mitomycin C and colchicine. IFC was then used for automated image capture and the Amnis® Artificial Intelligence (AAI) software permitted identification of binucleated (BN) cells with 91% precision. On average, three times as many BN cells from control samples were evaluated using IFC compared to the standard manual analysis. When IFC MNBN cells were visually scored from within the BN cell images, their frequency compared well with manual slide scoring, showing that IFC technology can be applied to the RSMN assay. This method enables faster time to result than microscope-based scoring and the initial studies presented here demonstrate its capability for the detection of statistically significant increases in MNBN frequencies. This work therefore demonstrates the feasibility of combining IFC and AAI to automate scoring for the RSMN assay and to improve its throughput and statistical robustness.

重建皮肤微核 (RSMN) 检测是在 2006 年开发的，作为体外替代皮肤应用化学品或产品的遗传毒性评估。从那以后的几年里，在优化分析方面取得了重大进展，包括标准协议的发布和广泛的验证。然而，皮肤细胞形态的多样性使得细胞制备和微核 (MN) 评分变得乏味和主观，因此需要高水平的技术专长进行评估。这最终会对吞吐量产生负面影响，并且该检测将受益于自动化方法的开发，该方法可以降低评分主观性，同时还可以通过增加可评分的细胞数量来提高检测的稳健性。使用 ImageStream ^{®X 的}成像流式细胞术 (IFC)Mk II 可以捕获悬浮细胞的高分辨率透射和荧光图像。这项原理验证研究描述了协议修改，可以在暴露于丝裂霉素 C 和秋水仙碱后在 3D 皮肤细胞中进行这种自动测量。然后将 IFC 用于自动图像捕获，Amnis® 人工智能 (AAI) 软件允许以 91% 的精度识别双核 (BN) 细胞。平均而言，与标准手动分析相比，使用 IFC 评估的对照样品的 BN 细胞数量是其三倍。当从 BN 细胞图像中对 IFC MNBN 细胞进行视觉评分时，它们的频率与手动幻灯片评分相比很好，表明 IFC 技术可以应用于 RSMN 检测。与基于显微镜的评分相比，这种方法可以更快地获得结果，这里介绍的初步研究证明了其检测 MNBN 频率在统计上显着增加的能力。因此，这项工作证明了将 IFC 和 AAI 相结合以自动为 RSMN 测定评分并提高其吞吐量和统计稳健性的可行性。