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: 10444797
• 负责人: ANDREW David RULE
• 金额: $ 69.33万
• 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/10444797
• 关键词: Age; Aging; Albuminuria; Anatomy; Area; Arteries; Arteriosclerosis; Artificial Intelligence; Atrophic; Biopsy; Biopsy Specimen; Blood Pressure; Blood capillaries; Bowman' s space; Caliber; Cell Density; Cessation of life; Characteristics; Chronic Kidney Failure; Clinical; Communities; Core Biopsy; Data; Data Set; Detection; Development; Discipline; Disease; Disease Outcome; Distal; Epithelial Cells; Excision; Fibrosis; Functional disorder; Glass; Goals; Health; Hypertension; Immunohistochemistry; Intervention; Kidney; Kidney Diseases; Lead; Link; Magnetic Resonance Imaging; Manuals; Mass Spectrum Analysis; Measures; Modeling; Nephrectomy; Nephrons; Nephrosclerosis; Obesity; Outcome; Parietal; Pathogenicity; Pathologic Processes; Patients; Persons; Population; Proteins; Proteinuria; Proteomics; Renal function; Reproducibility; Resources; Risk; Risk Factors; Scanning; Site; Slide; Specimen; Stenosis; Structure; Technology; Testing; Tissues; Tubular formation; Validation; base; capsule; clinically relevant; comorbidity; deep learning; density; differential expression; follow-up; glomerulosclerosis; graft failure; high risk; image processing; interstitial; kidney biopsy; 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”肾脏中观察到的主要微观结构属性是肾硬化（动脉硬化，全球性）。 肾小球硬化和间质纤维化/肾小管萎缩）、肾单位数量和肾单位大小。然而 这些微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 导致肾脏疾病的新干预措施。使用深度学习（DL）网络的自动形态测量可以 能够快速测量肾硬化、肾单位数量和肾单位大小。新的形态计量学 受肾小球超滤影响的结构（足细胞和壁上皮细胞（PEC）密度， Bowman间隙、近端小管和远端小管的直径）和微血管的直径需要更好地 了解早期疾病的病理生理学。不同的特定微结构的蛋白质组学分析 肾脏发生与不发生CKD结果之间的差异有可能确定预后， 早期肾脏疾病的致病蛋白。多学科多中心衰老肾脏解剖学研究 研究“正常”肾脏微观结构的独特资源。这包括数据和标本， 活体肾脏捐献者，包括捐献时的针芯活检和数字化全载玻片图像（WSI）， 长期CKD的结果在捐助者和接受者。这还包括患者的数据和标本， 因肿瘤行根治性肾切除术，包括肾楔形切片的数字化WSI和年度eGFR 在随访期间检测CKD结果。目的1将确定是否自动肾硬化形态测量， 肾单位数量和肾单位大小预测CKD结果，以检验DL工具允许 这些临床相关的微观结构属性的有效量化。这一目标将使用两个以前 开发和新的DL网络，开发模型来预测自动形态测量的CKD结果， 并比较自动和手动形态测量对CKD结局的预测。目标2将 表征与肾功能、CKD风险因素和CKD相关的新型显微结构属性 结果，以测试在肾脏组织中编码的假设是未探索的结构属性， 反映肾小球高滤过和间质微血管状态，是CKD的预后指标。这一目标 将自动量化足细胞、佩奇、管周毛细血管（PTC）、Bowman空间（体积）和 使用先前开发和新开发的DL工具，在WSI上测量近端和远端小管（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将发现 与微结构属性相关的蛋白质标记物，这些微结构属性是CKD结果的预后， 肾脏微结构中差异表达蛋白预测CKD的假说 结果。这一目标将使用激光捕获显微切割，质谱为基础的蛋白质组学（两者）， 发现和有针对性的验证方法），以及免疫组织化学来鉴定肾活检中的蛋白质 预测CKD结果并确定其与微结构属性的相关性。