Investigating systems physiology with multi-omics data

利用多组学数据研究系统生理学

• 批准号: 10356548
• 负责人: Edward Lau
• 金额: $ 31.1万
• 依托单位: UNIVERSITY OF COLORADO DENVER
• 依托单位国家: 美国
• 财政年份: 2021
• 资助国家: 美国
• 起止时间: 2021-09-22 至 2023-09-21
• 项目状态: 已结题
• 来源: https://reporter.nih.gov/project-details/10356548
• 关键词: Adipose tissue; Biological; Biological Assay; Cardiac Myocytes; Cells; Collection; Communication; Complex; Computer software; Data; Data Analyses; Data Set; Dimensions; Disease; Endocrine; Funding; Future; Gene Expression; Gene Proteins; Generations; Genes; Genotype-Tissue Expression Project; Goals; Heart; Hepatocyte; Human; In Vitro; Individual; Investments; Learning; Liver; Mass Spectrum Analysis; Methods; Modality; Modeling; Monitor; Multiomic Data; Muscle; Organ; Pathology; Pattern; Peptides; Physiological; Physiology; Pilot Projects; Post-Translational Protein Processing; Process; Protein Isoforms; Proteins; Proteomics; Psychological Transfer; Public Domains; RNA; Reaction; Regulation; Research; Sampling; Signal Transduction; Structure; System; Technology; Testing; Tissues; Training; United States National Institutes of Health; Validation; Work; base; big biomedical data; cell type; computerized data processing; data analysis pipeline; data integration; experience; human tissue; induced pluripotent stem cell; insight; large datasets; learning strategy; multiple omics; novel; programs; stem cell model; transcriptome; transcriptome sequencing; transcriptomics

PROJECT SUMMARY Many valuable datasets have been generated from the NIH Common Fund programs, including large RNA- sequencing data from multiple tissues and species as well as increasingly available high-quality mass spec- trometry-based proteomics data. Although proteomics offers unique insight into various pathophysiological processes, currently many proteomics datasets remain smaller in scale than their RNA-seq counterparts and it remains to be investigated how best to integrate proteomics and RNA-seq data to maximize their utility. The goal of this pilot project is to assess the feasibility of integrating multi-omics data to generate new hypotheses about cross-tissue physiology. We will perform three integrated tasks during the funding period: First, we will integrate data across tissues, species, and omics data type in two NIH Common Fund projects, namely GTEx v8 and MoTrPAC Release v1.0, with the aid of transfer learning methods. Our goal is to learn a low-dimension representation of gene expression structure across human tissues that can be applied to other datasets to facilitate integrative analysis. Second, we will apply an RNA-sequencing guided proteomics pipeline and software that we recently developed in order to extract hidden peptide information from Common Fund proteomics data, including isoform and post-translational modifications. We will then evaluate the feasibility and utility of integrating transcriptomics and proteomics data to examine multi-tissue correlations in gene expression. Lastly, we will utilize this data analysis pipeline in order to predict cross-tissue communication pat- terns from transcriptomics and proteomics data, then perform limited experimental validation of the computational findings using human induced pluripotent stem cell (iPSC)-derived cells. If successful, we envision that the results will inform on data integration strategies that can help further increase the utility of large-scale proteomics and RNA-seq data in the public domain, as well as generate testable hypotheses on gene co-expression across multiple tissues that can inform future work.

项目摘要 NIH共同基金计划已经产生了许多有价值的数据集，包括大型RNA- 测序来自多个组织和物种的数据，以及越来越多的高质量质量表格 基于Trigetry的蛋白质组学数据。尽管蛋白质组学为各种病理生理提供了独特的见解 流程，目前许多蛋白质组学数据集的规模仍然小于其RNA-seq对应物和 尚待研究如何最好地整合蛋白质组学和RNA-seq数据以最大化其效用。 该试点项目的目的是评估整合多摩管数据以生成新的可行性 关于跨组织生理学的假设。我们将在资金期间执行三个集成任务： 首先，我们将以两种NIH共同基金的组织，物种和OMIC数据类型整合数据 项目，即GTEX V8和MOTRPAC版本v1.0，借助转移学习方法。我们的目标是 学习跨人类组织的基因表达结构的低维表示 应用于其他数据集，以促进综合分析。 其次，我们将应用我们最近的RNA测序引导的蛋白质组学管道和软件 开发的目的是从共同基金蛋白质组学数据中提取隐藏的肽信息，包括 同工型和翻译后修饰。然后，我们将评估整合的可行性和效用 转录组学和蛋白质组学数据检查基因表达中的多组织相关性。 最后，我们将利用此数据分析管道，以预测跨组织通信 转录组学和蛋白质组学数据的Terns，然后对实验验证有限 使用人类诱导多能干细胞（IPSC）衍生细胞的计算结果。如果成功，我们 设想结果将告知数据集成策略，以帮助进一步提高实用性 公共领域中的大规模蛋白质组学和RNA-seq数据，并在上产生可检验的假设 基因跨多个组织共表达，这些组织可以为未来的工作提供信息。