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.

项目总结

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