Critical Transition-based Correlation Analysis for Metabolomics

基于关键转变的代谢组学相关分析

• 批准号: 9222459
• 负责人: Peter Nemes
• 金额: $ 8.45万
• 依托单位: GEORGE WASHINGTON UNIVERSITY
• 依托单位国家: 美国
• 财政年份: 2016
• 资助国家: 美国
• 起止时间: 2016-09-14 至 2017-12-31
• 项目状态: 已结题
• 来源: https://reporter.nih.gov/project-details/9222459
• 关键词: Address; Area Under Curve; Biochemical Pathway; Biological; Biopsy; Capillary Electrophoresis; Cell Differentiation process; Cells; Charge; Complex; Computer software; Data; Data Analyses; Data Set; Databases; Dependency; Deposition; Detection; Development; Disease; Electrospray Ionization; Embryo; Emerging Technologies; Ensure; Event; Feedback; Goals; Gold; Health; Heterogeneity; High Pressure Liquid Chromatography; Investigation; Ions; Label; Liquid substance; Manuals; Mass Spectrum Analysis; Measurement; Measures; Metabolic; Metabolism; Metadata; Modeling; Molecular; Monitor; Network-based; Neurons; Neurosciences; Noise; Outcome; Pathway interactions; Physics; Population; Protocols documentation; Research; Research Personnel; Resolution; Resources; Role; Sampling; Signal Transduction; Software Tools; South African; Specimen; Stream; Surveys; System; Technology; Testing; Tissues; Vendor; Work; Xenopus; Xenopus laevis; abstracting; base; biological systems; blastomere structure; commercialization; computerized data processing; data format; design; drug metabolism; improved; instrument; instrumentation; mass spectrometer; metabolome; metabolomics; portability; research study; small molecule; stem

Abstract The metabolome provides a unique window to monitor a system's molecular state as a result of both intrinsic and extrinsic events. Using ultrahigh-performance liquid chromatography (UPLC) high-resolution mass spectrometry (HRMS), the gold standard technology of metabolomics, it is possible to measure hundreds of metabolites in thousands-to-millions of cells to enhance the signal for trace-level compounds. Recent advances in HRMS technology have extended these measurements to also trace-level detection for rare or precious samples, where averaging is not feasible or hinders results interpretation. Despite the availability of high-sensitivity HRMS, a bottleneck in metabolomics is a lack of software tools capable of detecting trace-level signals in the resulting complex metabolomics data. The proposed work fills this technological gap by developing a software suite that surveys HRMS data sets for trace-level signals (Specific Aim 1) and helps find correlations between metabolite variances (Specific Aim 2). The approach stems from manual data processing protocols that have been established and validated for high-sensitivity analyses as well as critical transition models in physics that efficiently indicate transition points in a network. The software is validated using HRMS data sets that have been acquired for differentiating cells in the early developing embryo of the South African clawed frog (Xenopus laevis), a powerful model in cell and developmental studies, and functional experiments that test the developmental significance of select metabolites. The work includes tests designed to ensure the compatibility of the software to HRMS data from broad types of mass spectrometry instrumentation and different types of metabolomics studies, including neuroscience and drug metabolism and data deposited in MetabolomicsWorkbench, a public metabolomics data repository. The final product is metabolomics software that is applicable to broad types of metabolomics investigations. Besides providing new software, the data that are obtained during this work provide new information on metabolomic changes underlying cell differentiation in the developing embryo. The outcomes of the proposed work are matched with the goals of RFA-RM-15-021, “Metabolomics Data Analysis (R03).” The proposed software is scalable to HRMS data from diverse instrument vendors, aids the identification of trace-level metabolite signals, and facilitates the analysis of metabolite- metabolite correlations in the system, which in turn facilitates the design of hypothesis-driven studies to help better understand health.

摘要