Chemical Isotope Labeling LC-MS for Tissue, Serum and Urine Metabolomics

  • Author / Creator
  • Metabolomics is the ultimate reflection of organisms influenced by both genetic and environmental factors. Compared with other omics areas, it is the most appropriate and closest description of phenotype, which is the comprehensive characteristics of an organism. It is a powerful tool for global study of composition, dynamics and responses of metabolites in cells, biofluids, tissues and organs. Metabolomics has been widely used for studying the effects of system perturbations on organisms, such as environmental factors or diseases.
    Metabolomics can be divided into two categories, targeted or untargeted. Untargeted metabolomics is global in scope and it simultaneously detects the entire set of metabolites, which is more significant and more promising. However, due to the complexity of metabolites, it is impossible to profile the metabolome by using one single platform. Since the whole metabolome can be divided into different submetabolomes based on different chemical functional groups, it is better to analyze each submetabolome separately to improve metabolite coverage. Of those methods, chemical isotope labeling (CIL) method has been developed due to its various advantages. CIL can add one isotope tag to target different submetabolomes to improve separation, sensitivity and capability of relative quantification. This “divide and conquer” technology enables the study of the whole metabolome using one platform, such as reverse phase liquid chromatography mass spectrometry (RPLC-MS) in positive mode only.
    In this work, I applied CIL LC-MS technique to profile the amine/phenol submetabolome of tissue samples. Tissue metabolomics can reveal organ-specific metabolic fingerprints and play a crucial role in investigating specific diseases and sites of toxicity. I first developed a workflow for carrying out tissue sample processing with a solvent system of methanol/dichloromethane/water. Then, dansylation labeling was applied to profile the amine/phenol submetabolome of mouse brain tissues with Alzheimer’s disease (AD). The differences in submetabolome between AD transgenic and wild-type mice were investigated. Several metabolite biomarker candidates have been found with good discriminating power. Then, my developed workflow was also used for amine/phenol submetabolome profiling of rat brain, heart, liver, kidney and muscle tissues with Dexamethasone (Dex) treatment. The side effects of Dex treatment on metabolome were studied with these five kinds of tissues, and some common changes were observed using pathway analysis.
    Biofluid samples, such as serum, plasma, urine, saliva, are considered as a pool of metabolites of the body that can reflect some systemic metabolic changes. These samples are relatively easy to obtain and widely used for metabolomics studies. In this work, I applied CIL LC-MS to analyze amine/phenol and carboxyl submetabolomes of serum samples from two cohorts of rheumatoid arthritis (RA) patients. We characterized the submetabolome changes between the early RA and healthy control groups and several potential biomarker candidates were discovered. Besides, 13C-/12C-dansylation labeling LC-MS method were also applied for parallel profiling urine metabolome changes of a mouse model with AD. The metabolic differences between AD and control were observed for both male and female mouse urine samples. The findings of these works indicate that, CIL LC-MS is a comprehensive technique and it is applicable and promising for untargeted metabolomics.

  • Subjects / Keywords
  • Graduation date
    Fall 2018
  • Type of Item
  • Degree
    Master of Science
  • DOI
  • License
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