Gwatimba Alphons, Rosenow Tim, Stick Stephen M, Kicic Anthony, Iosifidis Thomas, Karpievitch Yuliya V
Wal-Yan Respiratory Research Centre, Telethon Kids Institute, University of Western Australia, Perth, WA 6009, Australia.
School of Computer Science and Software Engineering, University of Western Australia, Nedlands, WA 6009, Australia.
J Pers Med. 2022 May 17;12(5):809. doi: 10.3390/jpm12050809.
The airway epithelium of children with asthma is characterized by aberrant repair that may be therapeutically modifiable. The development of epithelial-targeting therapeutics that enhance airway repair could provide a novel treatment avenue for childhood asthma. Drug discovery efforts utilizing high-throughput live cell imaging of patient-derived airway epithelial culture-based wound repair assays can be used to identify compounds that modulate airway repair in childhood asthma. Manual cell tracking has been used to determine cell trajectories and wound closure rates, but is time consuming, subject to bias, and infeasible for high-throughput experiments. We therefore developed software, EPIC, that automatically tracks low-resolution low-framerate cells using artificial intelligence, analyzes high-throughput drug screening experiments and produces multiple wound repair metrics and publication-ready figures. Additionally, unlike available cell trackers that perform cell segmentation, EPIC tracks cells using bounding boxes and thus has simpler and faster training data generation requirements for researchers working with other cell types. EPIC outperformed publicly available software in our wound repair datasets by achieving human-level cell tracking accuracy in a fraction of the time. We also showed that EPIC is not limited to airway epithelial repair for children with asthma but can be applied in other cellular contexts by outperforming the same software in the Cell Tracking with Mitosis Detection Challenge (CTMC) dataset. The CTMC is the only established cell tracking benchmark dataset that is designed for cell trackers utilizing bounding boxes. We expect our open-source and easy-to-use software to enable high-throughput drug screening targeting airway epithelial repair for children with asthma.
哮喘患儿的气道上皮具有异常修复的特征，这种异常修复可能是可通过治疗手段加以改变的。开发能够促进气道修复的上皮靶向治疗方法可为儿童哮喘提供一种新的治疗途径。利用基于患者来源的气道上皮细胞培养的伤口修复试验进行高通量活细胞成像的药物研发工作，可用于识别调节儿童哮喘气道修复的化合物。人工细胞追踪已被用于确定细胞轨迹和伤口闭合率，但这种方法耗时、易产生偏差，且不适用于高通量实验。因此，我们开发了一款名为EPIC的软件，它利用人工智能自动追踪低分辨率、低帧率的细胞，分析高通量药物筛选实验，并生成多个伤口修复指标以及可供发表的图表。此外，与现有的执行细胞分割的细胞追踪器不同，EPIC使用边界框来追踪细胞，因此对于研究其他细胞类型的研究人员来说，其训练数据生成要求更简单、更快速。在我们的伤口修复数据集中，EPIC在极短的时间内就达到了人类水平的细胞追踪精度，其性能优于公开可用的软件。我们还表明，EPIC不仅限于哮喘患儿的气道上皮修复，在有丝分裂检测挑战细胞追踪（CTMC）数据集中，它通过超越同一软件，还可应用于其他细胞环境。CTMC是唯一专门为使用边界框的细胞追踪器设计的既定细胞追踪基准数据集。我们期望我们这款开源且易于使用的软件能够实现针对哮喘患儿气道上皮修复的高通量药物筛选。
