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

自动检测具有独特蛋白质标记且可预测慢性肾脏病的微观结构特征

• 批准号: 10600074
• 负责人: ANDREW David RULE
• 金额: $ 69.3万
• 依托单位: MAYO CLINIC ROCHESTER
• 依托单位国家: 美国
• 财政年份: 2011
• 资助国家: 美国
• 起止时间: 2011-02-10 至 2027-01-31
• 项目状态: 未结题
• 来源: https://reporter.nih.gov/project-details/10600074
• 关键词: Age; Aging; Albuminuria; Anatomy; Area; Arteries; Arteriosclerosis; Artificial Intelligence; Atrophic; Biopsy; Biopsy Specimen; Blood Pressure; Blood Vessels; Blood capillaries; Bowman' s space; Cell Density; Cessation of life; Characteristics; Chronic Kidney Failure; Clinical; Communities; Core Biopsy; Data; Data Set; Detection; Development; Diameter; Discipline; Disease; Disease Outcome; Distal; Epithelial Cells; Excision; Fibrosis; Functional disorder; Glass; Goals; Health; Hypertension; Immunohistochemistry; Intervention; Kidney; Kidney Diseases; Link; Magnetic Resonance Imaging; Manuals; Mass Spectrum Analysis; Measures; Modeling; Nephrectomy; Nephrons; Nephrosclerosis; Obesity; Outcome; Parietal; Pathogenicity; Pathologic Processes; Patients; Persons; Population; Populations at Risk; Proteins; Proteinuria; Proteomics; Renal function; Reproducibility; Resources; Risk Factors; Scanning; Site; Slide; Specimen; Stenosis; Structure; Technology; Testing; Tissues; Tubular formation; Validation; capsule; clinically relevant; comorbidity; deep learning; density; differential expression; follow-up; glomerulosclerosis; graft failure; high risk; image processing; interstitial; kidney biopsy; kidney cortex; kidney imaging; kidney surgery; laser capture microdissection; learning network; living kidney donor; morphometry; novel; patient population; podocyte; prognostic; protein biomarkers; research clinical testing; tool; tumor; whole slide imaging

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.

在“正常”肾脏中看到的主要微观结构属性是肾脏硬化（动脉硬化，全球 肾小球硬化和间质纤维化/管状萎缩），肾单位数和肾小管大小。然而 这些微观结构的手动测量是不切实际的。此外，了解他们的病理生理学可能 导致新的肾脏疾病干预措施。具有深度学习（DL）网络的自动形态计量学可能 能够快速测量肾脏硬化，肾单位数和肾单位大小。新颖的形态法 受肾小球过滤影响的结构（足细胞和顶皮细胞（PEC）密度， 鲍曼（Bowman）的空间以及近端和远端管的直径）和微脉管系统需要改善 了解早期疾病的病理生理。对不同微观结构的蛋白质组学分析 在肾脏与不发展CKD结果的肾脏之间有可能识别预后甚至 早期肾脏疾病的致病蛋白。多学科的多站点衰老肾脏解剖学研究已有 在“正常”肾脏中研究微观结构的独特资源。这包括数据和标本 活着的肾脏供体，包括针线芯活检，捐赠时使用数字化的全幻灯片图像（WSI）和 捐助者和接受者的长期CKD结果。这还包括有关患者的数据和标本 对肿瘤进行了根治性肾切除术，包括肾脏楔形部分的数字化WSI和年度EGFR 在随访期间测试CKD结果。 AIM 1将确定肾脏硬化的自动形态测定法是否 肾单位编号和肾脏大小预测CKD结果，以测试DL工具允许的假设 对这些临床相关的微结构属性的有效定量。此目标以前将使用两者 开发和新的DL网络开发模型来预测自动形态计量学的CKD结果， 并比较自动化和手动形态计量学之间CKD结果的预测。 AIM 2意志 表征与肾功能，CKD风险因素和CKD相关的新型微观结构属性 测试肾脏组织中编码的假设的结果是意想不到的结构属性 反映CKD预后的肾小球过滤和间质微血管状态。这个目标 将自动量化足细胞，PEC，周围毛细血管（PTC），Bowman的空间（音量）和 WSI上使用先前开发的和新开发的DL工具，近端和远端小管（直径） 将这些结构与肾功能，CKD风险因素和CKD结果相关联。 AIM 3会发现 与微结构属性相关的蛋白质标志物，这些属性是CKD结果的预后，以测试 假设肾脏微观结构中包含不同表达的蛋白质预测CKD 结果。该目标将使用激光捕获微分解，基于质谱的蛋白质组学（均 发现和靶向验证方法）和免疫组织化学，以鉴定肾脏活检的蛋白质 预测CKD结果并确定其与微结构属性的关联的部分。