Patient-Derived Xenografts (PDXs) are the preclinical models which best recapitulate inter- and intra-patient complexity of human breast malignancies, and are also emerging as useful tools to study the normal breast epithelium. However, data analysis generated with such models is often confounded by the presence of host cells and can give rise to data misinterpretation. For instance, it is important to discriminate between xenografted and host cells in histological sections prior to performing immunostainings. We developed Single Cell Classifier (SCC), a data-driven deep learning-based computational tool that provides an innovative approach for automated cell species discrimination based on a multi-step process entailing nuclei segmentation and single cell classification. We show that human and murine cells contextual features, more than cell-intrinsic ones, can be exploited to discriminate between cell species in both normal and malignant tissues, yielding up to 96% classification accuracy. SCC will facilitate the interpretation of H&E stained histological sections of xenografted human-in-mouse tissues and it is open to new in-house built models for further applications. SCC is released as an open-source plugin in ImageJ/Fiji available at the following link: https://github.com/Biomedical-Imaging-Group/SingleCellClassifier.

中文翻译：

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深度学习通过分析语境特征实现组织学图像中的异种移植细胞分类

患者衍生异种移植物（PDXs）是临床前模型，可以最好地概括人类乳腺恶性肿瘤的患者间和患者内部复杂性，并且也正在成为研究正常乳腺上皮的有用工具。但是，使用此类模型生成的数据分析通常会因宿主细胞的存在而混淆，并可能引起数据误解。例如，在进行免疫染色之前区分组织学切片中的异种移植细胞和宿主细胞很重要。我们开发了单细胞分类器（SCC），这是一种基于数据驱动的基于深度学习的计算工具，它基于核分割和单细胞分类的多步过程提供了一种自动化的细胞物种识别的创新方法。我们证明了人类和鼠类细胞的背景特征，除细胞内源性细胞外，还可以利用它来区分正常组织和恶性组织中的细胞种类，从而产生高达96％的分类精度。SCC将有助于解释异种移植的人体内小鼠组织的H＆E染色组织学切片，它对新的内部构建模型开放供进一步应用。SCC作为ImageJ / Fiji中的开源插件发布，可通过以下链接获取：https：//github.com/Biomedical-Imaging-Group/SingleCellClassifier。