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（阿基）是两种常见的交叉性肾脏疾病。CKD已被公认为全球主要的公共卫生问题，影响全球约15%的人口。阿基可导致CKD，每年影响美国超过20万人，并在大脑，心脏和肺等远端器官中留下后遗症。为了更好地了解肾脏疾病的发病机制，并有可能防止阿基转变为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中的复杂相互作用。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，从而深入了解肾脏健康和疾病中的异质细胞类型。然而，与具有更大结构特征的器官（如大脑）相比，肾脏被组织成超过一百万个肾单位，代表着100多种细胞类型，排列得很近。在确定共定位细胞类型和阐明纤维化、免疫相互作用和上皮修复的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AI的计算方法，用于基于空间转录组数据研究肾脏疾病。首先，我们将建立一个深度学习框架，以解决肾损伤中的异质、稀疏和马赛克样细胞类型分布问题，该框架由图神经网络以自监督学习训练方式提供支持。其次，我们将比较健康和受损的细胞状态，以说明CKD和阿基损伤的内在机制。第三，我们将用一个可解释的生成过程来预测肾损伤的影响。我们将使用来自人类细胞图谱（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多组学数据来评估方法的性能。我们的长期目标是创建一个生态社区，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。