Investigating systems physiology with multi-omics data

利用多组学数据研究系统生理学

基本信息

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
项目类别：
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

项目摘要

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.

项目概要 NIH 共同基金项目产生了许多有价值的数据集，包括大型 RNA- 来自多个组织和物种的测序数据以及日益可用的高质量质谱基于测量法的蛋白质组学数据。尽管蛋白质组学为各种病理生理学提供了独特的见解目前，许多蛋白质组学数据集的规模仍小于相应的 RNA-seq 数据集，并且如何最好地整合蛋白质组学和 RNA-seq 数据以最大限度地发挥其效用仍有待研究。该试点项目的目标是评估整合多组学数据以生成新的可行性关于跨组织生理学的假设。我们将在资助期间执行三项综合任务：首先，我们将在两个 NIH 共同基金中整合跨组织、物种和组学数据类型的数据项目，即 GTEx v8 和 MoTrPAC Release v1.0，借助迁移学习方法。我们的目标是学习人体组织基因表达结构的低维表示，可以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开发的 RNA 测序引导的蛋白质组学流程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用于检查基因表达的多组织相关性。最后，我们将利用这个数据分析管道来预测跨组织通信模式来自转录组学和蛋白质组学数据的燕鸥，然后对这些数据进行有限的实验验证使用人类诱导多能干细胞 (iPSC) 衍生细胞的计算结果。如果成功的话，我们预计结果将为数据集成策略提供信息，从而有助于进一步提高数据的效用公共领域的大规模蛋白质组学和 RNA-seq 数据，并生成可检验的假设跨多个组织的基因共表达可以为未来的工作提供信息。