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

项目摘要 空间细胞异质性导致疾病、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， 和细胞水平。空间技术的最新技术进步使得能够阐明单个 具有丰富信息和空间位置的细胞异质性提供了极佳的理解机会 疾病和治疗中涉及的生物过程和分子相互作用。此外，传统的 方法主要集中在不能完全解决这种复杂性和异构性的单一类型的数据。 因此，缺乏综合的方法来利用来自多个来源的数据的优势 (例如，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和 药物反应。鉴于这些挑战和我独特的多学科培训，我的总体目标是 研究计划是开发一类新的机器学习、统计和深度学习方法 对复杂组织中空间组织细胞的增强、优先排序和解释，以更好地 了解支持疾病和药物反应的分子机制，这将增强精确度 通过识别用于疾病预防、诊断和治疗的个性化生物标记物来进行医学研究。具体来说， 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来归因于转录本 和表观基因组学图谱；(Ii)开发一个计算框架，以揭示与疾病相关的 空间分布细胞的表型，通过利用全基因组关联研究(GWAS)研究； (Iii)开发了一种新的区域适应方法来预测空间细胞的药物响应，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 疾病和药物反应。同时，在维克森林综合癌症中心的支持下，我们 将把这些方法应用于不同的研究，如脑转移和阿尔茨海默病 科学发现。我们将与合作的生物统计学家和生物学家密切合作，解释生物 发现。重要的是，我们将与实验实验室合作来验证这些发现。与我们之前的 工作中，我们将继续将所有开发的方法转化为可访问的开源软件工具 对生物医学研究界很有用。

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.