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

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，治疗治疗和药物的复杂性 反应，这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， 和细胞水平。空间技术的最新技术进步使得能够阐明单一的 细胞异质性，具有丰富的信息和空间位置， 疾病和治疗中涉及的生物过程和分子相互作用。而且，传统的 这些方法大多集中于单一类型的数据，无法完全解决这种复杂性和异质性。 因此，缺乏利用多种来源数据优势的综合方法 (e.g.,基因组学、表观基因组学、临床数据），以全面了解复杂疾病的病理生物学， 药物反应考虑到这些挑战和我独特的多学科培训，我的总体目标是 研究计划旨在开发一类新型的机器学习、统计和深度学习方法， 对复杂组织中空间组织细胞的增强、优先化和解释， 了解疾病和药物反应的分子机制，这将使精确 通过识别用于疾病预防、诊断和治疗的个体化生物标志物来治疗疾病。具体地说， 在接下来的五年里，我的团队将（i）开发一种新的迁移学习方法， 空间切片中的表观基因组学特征;（ii）开发一个计算框架，以揭示疾病相关的 通过利用全基因组关联研究（GWAS）研究，在空间分布的细胞中进行表型分析; (iii)开发一种新的域适应方法来预测空间细胞的药物反应，使用 药物基因组学知识库;（iv）开发一类新的统计方法，用于联合分析 空间转录组学和单细胞多组学数据，从而揭示了潜在的调节机制 疾病和药物反应。同时，在维克森林综合癌症中心的支持下， 将把这些方法应用到不同的研究中，如脑转移和阿尔茨海默病， 科学发现。我们将与生物统计学家和生物学家密切合作， 发现。重要的是，我们将与实验室合作，以验证这些发现。符合我们之前 工作，我们将继续使所有开发的方法成为开源软件工具， 对生物医学研究界有用。

项目成果

Detection of lineage-reprogramming efficiency of tumor cells in a 3D-printed liver-on-a-chip model.

• DOI: 10.7150/thno.86921
• 发表时间: 2023
• 期刊: Theranostics
• 影响因子: 12.4
• 作者: Lu Z;Miao X;Song Q;Ding H;Rajan SAP;Skardal A;Votanopoulos KI;Dai K;Zhao W;Lu B;Atala A
• 通讯作者: Atala A