Quantifying the individual contributions of comorbid tau neuropathologies using deep learning

项目总结/摘要 随着年龄的增长，不同神经退行性疾病的共同发生越来越常见，并且作为一种疾病， 疾病特异性生物标志物发展中的混杂因素。然而，即使是在黄金标准下， 通过评估尸检大脑中聚集蛋白的免疫染色，病理学的复杂性使得它 不可能通过目视检查可靠地量化疾病的混合物，特别是当共存时 这两种疾病都具有相同的聚集蛋白质，尽管具有不同的疾病特异性模式。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）神经病理学，从而使我们能够去卷积他们的 来自混合病理的磷酸化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， 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 虚拟完整幻灯片图像中的相关“特征”。这些特征将包括不同类型的细胞（例如， 神经元，星形胶质细胞），聚集体（例如富含PSP和AD的簇状星形胶质细胞和老年斑， 分别）和组织区域（灰色与白色物质，其在这些组织中的参与模式不同。 疾病）。第二，基于共病病理表现为纯疾病的混合的假设， 特征，我们将从纯AD和纯PSP病例中构建疾病分类器。鉴于局部有一片tau染色的 组织，这些分类器将返回它们的置信度，即组织表现出这些疾病中的任一种。我们将 评估两种方法，一种是建立在上面确定的“特征”上，另一种是更传统的黑色- 框深度学习方法纯粹依靠图像补丁工作。最后，我们将评估我们的纯病 基于病理学家审查和与抗体一致性的混合病理病例分类器 tau亚型，其个体组织形态学有助于区分AD和PSP。 因为他们将鉴定广泛使用的AT 8磷酸化tau蛋白所证实的已建立的神经病理学特征， 以及3R和4 R tau亚型免疫染色，我们的分类器将是未来数字成像的宝贵资源 基于神经病理学的研究。我们从尸检样本中去卷积合并症的框架可以是 扩展到其他疾病，从而能够更好地整合临床和生物标志物数据，最终， 提高了生前诊断和治疗水平。

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.