Automated detection of microstructural features that have unique protein markers and are prognostic for chronic kidney disease

正常肾脏的主要显微结构特征是肾硬化症(动脉硬化，全球 肾小球硬化和间质纤维化/肾小管萎缩)、肾单位数和肾单位大小。然而， 手工测量这些微观结构是不切实际的。此外，了解它们的病理生理学可能会 导致了对肾脏疾病的新干预措施。具有深度学习(DL)网络的自动形态测量可以 能够快速测量肾硬化症、肾单位数量和肾单位大小。一种新的形态测量方法 受肾小球高滤过影响的结构(足细胞和壁上皮细胞(PEC)密度， 鲍曼间隙、近端和远端小管的直径)和微血管的直径 了解早期疾病的病理生理学。不同微结构的蛋白质组学分析 在发生和不发生CKD结局的肾脏之间，有可能确定预后或甚至 早期肾脏疾病的致病蛋白。多学科多部位衰老肾脏解剖学研究 研究“正常”肾脏微观结构的独特资源。这包括以下数据和样本： 活体肾脏捐赠者，包括捐献时的针芯活组织检查和数字化全幻灯片图像(WSI)和 供者和受者慢性肾脏病的长期结局。这也包括患者的数据和样本 对肿瘤进行了根治性肾切除术，包括数字化肾脏楔形切片的WSI和每年的EGFR 在随访期间检测CKD的结果。目标1将确定肾硬化症的自动形态测量， 肾单位数和肾单位大小预测CKD结果，以检验DL工具允许的假设 有效量化这些临床相关的微结构属性。这一目标将使用之前的 开发和新的DL网络，开发通过自动形态测量预测CKD结果的模型， 并比较自动形态测量法和手动形态测量法对CKD预后的预测。目标2将 表征与肾功能、慢性肾脏病危险因素和慢性肾脏病相关的新微结构属性 检验在肾脏组织中编码的假设的结果是未被探索的结构属性 反映肾小球高滤过和间质微血管状态可预测CKD的预后。这一目标 将自动量化足细胞、PECs、肾小管周围毛细血管(PTC)、鲍曼间隙(体积)和 使用先前开发和新开发的DL工具和 将这些结构与肾功能、CKD危险因素和CKD结局联系起来。Aim 3将会发现 与CKD预后相关的微结构属性的蛋白质标记物 肾脏微结构中差异表达蛋白预测慢性肾脏病的假说 结果。这个目标将使用激光捕获显微切割，基于质谱学的蛋白质组学(两者 发现和有针对性的验证方法)，以及免疫组织化学在肾脏活检中识别蛋白质 预测CKD结果并确定其与微结构属性的关联的部分。

The primary microstructural attributes seen in “normal” kidneys are nephrosclerosis (arteriosclerosis, global glomerulosclerosis, and interstitial fibrosis/tubular atrophy), nephron number, and nephron size. However manual measures of these microstructures are impractical. Further, understanding their pathophysiology may lead to new interventions for kidney disease. Automated morphometry with deep learning (DL) networks may be able to rapidly measure nephrosclerosis, nephron number, and nephron size. Novel morphometry of structures impacted by glomerular hyperfiltration (podocyte and parietal epithelial cell (PEC) density, Bowman’s space, and the diameter of proximal and distal tubule) and of microvasculature are needed to better understand the pathophysiology of early disease. Proteomic analysis of specific microstructures that differ between kidneys that do versus do not develop CKD outcomes has the potential to identify prognostic or even pathogenic proteins for early kidney disease. The multi-discipline multi-site Aging Kidney Anatomy study has unique resources for the study of microstructure in “normal” kidneys. This includes data and specimens on living kidney donors including needle core biopsies at donation with digitized whole slide images (WSI) and long-term CKD outcomes in the donor and recipient. This also includes data and specimens on patients who had a radical nephrectomy for tumor including digitized WSI of kidney wedge sections and annual eGFR testing for CKD outcomes during follow-up. Aim 1 will determine if automated morphometry of nephrosclerosis, nephron number, and nephron size predicts CKD outcomes to test the hypothesis that DL tools allows for efficient quantification of these clinically relevant microstructural attributes. This aim will use both previously developed and new DL networks, develop models to predict CKD outcomes from automated morphometry, and compare prediction of CKD outcomes between automated and manual morphometry. Aim 2 will characterize novel microstructural attributes that associate with kidney function, CKD risk factors, and CKD outcomes to test the hypothesis that encoded in the kidney tissue are unexplored structural attributes that reflect the glomerular hyperfiltration and interstitial microvascular status that are prognostic for CKD. This aim will automatically quantify podocytes, PECs, peritubular capillaries (PTC), Bowman’s space (volume), and proximal and distal tubules (diameter) on WSI using previously developed and newly developed DL tools and associate these structures with kidney function, CKD risk factors, and CKD outcomes. Aim 3 will discover protein markers linked to the microstructural attributes that are prognostic for CKD outcomes to test the hypothesis that differentially expressed proteins contained within kidney microstructures predict CKD outcomes. This aim will use laser capture microdissection, mass spectroscopy-based proteomics (both discovery and targeted validation approaches), and immunohistochemistry to identify proteins on kidney biopsy sections that predict CKD outcomes and to determine their association with microstructural attributes.