SCH: Graph-based Spatial Transcriptomics Computational Methods in Kidney Diseases

SCH：肾脏疾病中基于图的空间转录组学计算方法

• 批准号: 10816929
• 负责人: Michael Thomas Eadon
• 金额: $ 29.98万
• 依托单位: INDIANA UNIV-PURDUE UNIV AT INDIANAPOLIS
• 依托单位国家: 美国
• 财政年份: 2023
• 资助国家: 美国
• 起止时间: 2023-07-15 至 2027-05-31
• 项目状态: 未结题
• 来源: https://reporter.nih.gov/project-details/10816929
• 关键词: Acute Renal Failure with Renal Papillary Necrosis; Address; Affect; Atlases; Brain; Cells; Chronic Kidney Failure; Communities; Complex; Computing Methodologies; Data; Disease; Distant; Fibrosis; Genomics; Goals; Graph; Health; Heart; Heterogeneity; Human; Human BioMolecular Atlas Program; Immune; Individual; Injury; Injury to Kidney; Kidney; Kidney Diseases; Lung; Methods; Molecular Profiling; Multiomic Data; Nephrons; Organ; Pathogenesis; Performance; Physicians; Population; Process; Public Health; Research; Technology; Training; cell injury; cell type; deep learning; empowerment; epithelial repair; graph neural network; insight; mosaic; multiple omics; precision medicine; prevent; supervised learning; transcriptome; transcriptomics

Chronic Kidney Disease (CKD) and Acute Kidney Injury (AKI) are two common intersecting kidney diseases. CKD has been recognized as a leading public health problem worldwide, affecting about 15% of the global population. AKI can lead to CKD and affects more than 200,000 individuals across the US annually, with sequelae in distant organs such as the brain, heart, and lungs. To better understand the pathogenesis of kidney disease and potentially prevent the transition of AKI into CKD, it is necessary to define the heterogeneity of cell types and states, their associated molecular signatures, and complex interactions within the microenvironment. Emerging spatial transcriptomic technologies (e.g., 10X Genomics Visium) generate high-throughput spatial transcriptome data, which provides insights into the heterogeneous cell types within kidney health and disease. However, in contrast to organs with larger structural features like the brain, the kidney is organized into over a million nephrons with representation from more than 100 cell types arranged in close proximity. There are still tremendous computational challenges in identifying the colocalizing cell types and elucidating mechanisms in fibrosis, immune interactions, and epithelial repair. To fill such gaps, we propose to develop AI-based computational methods for studying kidney diseases based on spatial transcriptome data. First, we will build a deep learning framework to address heterogeneous, sparse, and mosaic-like cell type distribution in kidney injury, empowered by graph neural networks in a self-supervised learning training style. Second, we will compare the healthy and injured cell states to illustrate the inherent mechanism beneath the injury of CKD and AKI. Third, we will predict the effects of kidney injury with an interpretable generative process. We will evaluate the methods’ performances using the multi-omics data from the cell atlas of the healthy and injured kidneys in the Human Cell Atlas (HCA), Human Biomolecular Atlas Program (HuBMAP), and Kidney Precision Medicine project (KPMP). Our long-term goal is to create an eco-community for analyzing, sharing, and disseminating spatial transcriptomics data for physicians and bioinformaticians in kidney research.

慢性肾脏病 (CKD) 和急性肾损伤 (AKI) 是两种常见的交叉肾脏疾病，CKD 已被认为是全球主要的公共卫生问题，影响全球约 15% 的人口，AKI 可导致 CKD，并影响超过 10% 的人口。美国每年有 200,000 人在大脑、心脏和肺等远处器官中出现后遗症，以更好地了解肾脏疾病的发病机制，并有可能预防 AKI 转变为肾病。 CKD，有必要定义细胞类型和状态的异质性、其相关的分子特征以及微环境内的复杂相互作用新兴的空间转录组技术（例如 10X Genomics Visium）生成高通量空间转录组数据，从而提供深入了解。然而，与大脑等具有较大结构特征的器官相比，肾脏被组织成超过一百万个肾单位，其代表来自超过一百万个肾单位。 100 种紧密排列的细胞类型在识别共定位细胞类型和阐明纤维化、免疫相互作用和上皮修复机制方面仍然存在巨大的计算挑战，为了填补这些空白，我们建议开发基于人工智能的计算方法来研究肾脏。首先，我们将建立一个深度学习框架，以解决肾损伤中异质、稀疏和马赛克状的细胞类型分布问题，并通过自监督学习训练方式的图神经网络进行支持。其次，我们将比较健康和受损的细胞状态，以说明 CKD 和 AKI 损伤的内在机制。第三，我们将通过可解释的生成过程来预测肾损伤的影响。 -来自人类细胞图谱 (HCA)、人类生物分子图谱计划 (HuBMAP) 和肾脏精准医学项目 (KPMP) 中健康和受损肾脏细胞图谱的组学数据 我们的长期目标是创建一个为肾脏研究中的医生和生物信息学家分析、共享和传播空间转录组学数据的生态社区。