Multi-modal insights of spatially distributed cells with associations of diseases and drug response

空间分布细胞与疾病和药物反应关联的多模式见解

• 批准号: 10714602
• 负责人: Qianqian Song
• 金额: $ 37.94万
• 依托单位: WAKE FOREST UNIVERSITY HEALTH SCIENCES
• 依托单位国家: 美国
• 财政年份: 2023
• 资助国家: 美国
• 起止时间: 2023-08-10 至 2028-06-30
• 项目状态: 未结题
• 来源: https://reporter.nih.gov/project-details/10714602
• 关键词: Address; Alzheimer' s Disease; Biological; Biological Markers; Biological Process; Biomedical Research; Cells; Clinical Data; Collaborations; Communities; Complex; Comprehensive Cancer Center; Computing Methodologies; Data; Diagnosis; Disease; Epigenetic Process; Etiology; Genetic; Genomics; Goals; Heterogeneity; Joints; Location; Machine Learning; Metastatic malignant neoplasm to brain; Methodology; Methods; Molecular; Multiomic Data; Pharmaceutical Preparations; Pharmacogenomics; Phenotype; Research; Series; Slice; Software Tools; Source; Statistical Methods; Technology; Therapeutic; Tissues; Training; Work; computer framework; deep learning; disorder prevention; drug response prediction; empowerment; epigenomics; forest; genome wide association study; insight; knowledge base; laboratory experiment; multidisciplinary; multimodality; multiple data sources; novel; open source; precision medicine; programs; response; statistical learning; transcriptomics; transfer learning; Address; Alzheimer' s Disease; Biological; Biological Markers; Biological Process; Biomedical Research; Cells; Clinical Data; Collaborations; Communities; Complex; Comprehensive Cancer Center; Computing Methodologies; Data; Diagnosis; Disease; Epigenetic Process; Etiology; Genetic; Genomics; Goals; Heterogeneity; Joints; Location; Machine Learning; Metastatic malignant neoplasm to brain; Methodology; Methods; Molecular; Multiomic Data; Pharmaceutical Preparations; Pharmacogenomics; Phenotype; Research; Series; Slice; Software Tools; Source; Statistical Methods; Technology; Therapeutic; Tissues; Training; Work; computer framework; deep learning; disorder prevention; drug response prediction; empowerment; epigenomics; forest; genome wide association study; insight; knowledge base; laboratory experiment; multidisciplinary; multimodality; multiple data sources; novel; open source; precision medicine; programs; response; statistical learning; transcriptomics; transfer learning

Project Summary Spatial cellular heterogeneity contributes to the complexity of diseases, therapeutic treatment, and drug response, which commonly involve the interplay between different molecular levels including genetic, epigenetic, and cellular levels. Recent technological advances of spatial technologies have enabled the elucidation of single cell heterogeneity with rich information and spatial locations that offer remarkable opportunities to understand biological processes and molecular interplays involved in disease and therapeutics. Moreover, traditional approaches mostly focus on a single type of data that cannot fully address this complexity and heterogeneity. Therefore, there is a lack of integrative approaches that leverage the strengths of data from multiple sources (e.g., genomics, epigenomics, clinical data) to achieve full insights into the pathobiology of complex disease and drug response. Given these challenges and my unique multi-disciplinary training, the overall goals of my research program are to develop a novel class of machine learning, statistical and deep learning approaches for the enhancement, prioritization and interpretation of spatially organized cells in complex tissue, to better understand the molecular mechanisms underpinning diseases and drug response, which will empower precision medicine by identifying individualized biomarkers for disease prevention, diagnosis and treatment. Specifically, in the next five years, my team will (i) develop a novel transfer learning approach to impute the transcriptomics and epigenomics profiles in spatial slices; (ii) develop a computational framework to reveal disease-associated phenotypes in spatially distributed cells, through leveraging Genome-Wide Association Studies (GWAS) studies; (iii) develop a novel domain adaptation method to predict drug responses of spatial cells, using pharmacogenomics knowledge base; (iv) develop a novel class of statistical methods for the joint analysis of spatial transcriptomics and single-cell multi-omics data, thus unveil the underlying regulatory mechanisms in diseases and drug response. In the meantime, supported by Wake Forest Comprehensive Cancer Center, we will apply the methodologies to different studies such as Brain Metastasis and Alzheimer’s Disease for novel scientific findings. We will work closely with collaborating biostatisticians and biologists to interpret the biological discoveries. Importantly, we will work with experimental labs to validate the findings. In line with our previous work, we will continue to make all developed methods into open-source software tools that are accessible and useful to the biomedical research community.

项目摘要 空间细胞异质性有助于疾病，治疗和药物的复杂性 反应通常涉及不同分子水平之间的相互作用，包括遗传，表观遗传学， 和细胞水平。空间技术的最新技术进步使得阐明了单一的技术 细胞异质性具有丰富的信息和空间位置，提供了极大的理解机会 疾病和治疗涉及的生物过程和分子相互作用。而且，传统 方法主要集中在无法完全解决这种复杂性和异质性的单一类型数据上。 因此，缺乏综合方法来利用来自多个来源的数据优势 （例如，基因组学，表观基因组学，临床数据），以充分了解复杂疾病的病理学和 药物反应。考虑到这些挑战和我独特的多学科培训，我的总体目标 研究计划是为了开发一种新颖的机器学习，统计和深度学习方法 复杂组织中空间组织细胞的增强，优先级和解释，以更好地 了解支撑疾病和药物反应的分子机制，这将赋予精度 通过鉴定用于预防疾病，诊断和治疗的个性化生物标志物，医学。具体来说， 在接下来的五年中，我的团队将（i）开发一种新颖的转移学习方法来估算转录组学 和空间切片中的表观基因组学谱； （ii）开发一个计算框架以揭示与疾病相关的 通过利用全基因组关联研究（GWAS）研究，空间分布的细胞中的表型； （iii）开发一种新型域的适应方法，以预测空间细胞的药物反应，并使用 药物基因组学知识库； （iv）开发了一种新型的统计方法，用于联合分析 空间转录组学和单细胞多态数据，因此揭示了基本的调节机制 疾病和药物反应。同时，在Wake Forest综合癌症中心的支持下，我们 将把这些方法应用于不同研究的方法，例如脑转移和阿尔茨海默氏病 科学发现。我们将与合作的生物统治者和生物学家紧密合作，以解释生物学 发现。重要的是，我们将与实验实验室合作以验证发现。与我们以前的 工作，我们将继续将所有开发的方法用于可访问的开源软件工具，并且 对生物医学研究界有用。