Quantifying the individual contributions of comorbid tau neuropathologies using deep learning

项目摘要/摘要 不同的神经退行性疾病的共同发生随着年龄的增长越来越常见，并作为一种 疾病特异性生物标志物发展中的混杂因素。然而，即使按照黄金标准 评估尸检脑中聚集蛋白的免疫染色，病理复杂性使其 不可能通过肉眼检查可靠地量化疾病的混合物，尤其是当共存时 这两种疾病的特征都是相同的聚集蛋白，尽管具有不同的疾病特异性模式。在这里，我们 假设深度学习的最新进展可以识别阿尔茨海默病的独特模式 (AD)和进行性核上性麻痹(PSP)的神经病理学，从而使我们能够去卷积他们的 混合病理的磷酸化tau免疫染色的个体贡献。 我们将分三步解决这个问题。首先，为了融入生物学知识并使 为了提高疾病预测的可解释性，我们将开发一套深度学习分类器来识别疾病 虚拟整个幻灯片图像中的相关“特征”。这些功能将包括不同类型的单元(例如 神经元、星形胶质细胞)、聚集体(例如聚集的星形胶质细胞和富含PSP和AD的衰老斑块， 和组织区域(灰质和白质，它们在这些区域中的受累模式不同 疾病)。第二，基于一种假设，即共病病理表现为纯疾病的混合体 特征，我们将从纯AD和纯PSP病例中构建疾病分类器。给了一块局部的tau染色的 组织，这些分类器将恢复他们对组织表现出这两种疾病之一的信心。我们会 评估两种方法，一种建立在上面确定的“特征”上，另一种建立在更传统的黑色- 方框深度学习的方法，工作纯粹的图像补丁。最后，我们将评估我们纯正的疾病 基于病理学家回顾和与抗体的一致性对混合病理病例的分类 Tau亚型的个体组织形态有助于区分AD和PSP。 因为他们将确定广泛使用的AT8磷酸化tau所显示的已建立的神经病理特征 以及3R和4R tau亚型免疫组织化学染色，我们的分类器将是未来数字成像的宝贵资源 神经病理学的基础研究。我们从尸检样本中分离合并症的框架可以是 扩展到其他疾病，从而能够更好地与临床和生物标记物数据相结合，最终， 改进了生前诊断和治疗。

PROJECT SUMMARY/ABSTRACT Co-occurrence of different neurodegenerative diseases is increasingly common with age and acts as a confounding factor in the development of disease-specific biomarkers. Yet, even by the gold standard of evaluating immunostaining for aggregated proteins in autopsy brains, pathologic complexity makes it impossible to reliably quantify the mixture of diseases by visual inspection, especially when coexistent disorders both feature the same aggregated protein, albeit in different disease-specific patterns. Here, we hypothesize that recent advances in deep learning can identify the distinctive patterns of Alzheimer disease (AD) and progressive supranuclear palsy (PSP) neuropathology, thereby allowing us to de-convolve their individual contributions from phospho-tau immunostaining of mixed pathologies. We will tackle this problem in three steps. First, in order to incorporate biological knowledge and enable interpretability of our disease predictions, we will develop a set of deep learning classifiers to identify disease relevant “features” in virtual whole slide images. These features will include different types of cells (e.g. neurons, astrocytes), aggregates (e.g. tufted astrocytes and senile plaques that are enriched in PSP and AD, respectively) and tissue regions (gray vs. white matter, which differ in pattern of involvement in these diseases). Second, based on the assumption that comorbid pathologies exhibit a mixture of pure disease features, we will build disease classifiers from pure AD and pure PSP cases. Given a local patch of tau-stained tissue, these classifiers will return their confidence that tissue exhibited either of these diseases. We will evaluate two approaches, one building on the “features” identified above and the other a more traditional black- box deep learning approach working purely off of image patches. Finally, we will evaluate our pure disease classifiers on cases with mixed pathologies based on pathologist review and concordance with antibodies to tau isoforms whose individual histomorphologies help to distinguish between AD and PSP. As they will identify established neuropathology features demonstrated by the widely-used AT8 phospho-tau and 3R and 4R tau isoform immunostaining, our classifiers will be a valuable resource for future digital imaging based studies in neuropathology. Our framework for de-convolving comorbidities from autopsy samples can be extended to other diseases, thus enabling better integration with clinical and biomarker data, and ultimately, improved antemortem diagnosis and therapy.