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Metabolomics core offers an unbiased metabolomics analysis of any biological specimen for a wide array of polar and non-polar metabolites. This approach with a workflow that includes the use of High Resolution Thermo Orbitrap IQX carried out on extracted metabolites run in both positive and negative modes using newly lanced Thermo Orbitrap IQX. The data is then searched using databases of known metabolites with known masses, retention times, and fragmentation patterns. Once metabolites are matched with high confidence, peak intensities are then measured and normalized using the appropriate statistical analysis.

The Unbiased metabolomics is able to provide a wide range of metabolites using the mzCloud-Advanced Mass Spectral Database (Thermo), NIST library, Human Metabolome Database (HMDB), and an in-house retention-based library. Metabolomics Core has also built an extensive in-house retention-based library in collaboration with IROA Technologies, which will provide an extra layer of confidence in metabolite identification.

Expert technical assistance tailored to meet the needs of investigators in all aspects of the studies can be provided for investigators who are either novices or knowledgeable in mass spectrometry applications. This includes advice on methods of appropriate sample storage and preparation, and assistance in uploading the data for publication.

Lipids and metabolites play essential roles in energy metabolism, membrane structure, and signaling. Thus, alterations in lipid metabolism have been linked to the development of many diseases, including cancer and non-cancer. By profiling the overall lipids, unbiased lipidomic approach provides a snapshot of the physiological and pathological status of a biological system. Such a bird’s-eye view of the metabolism might help to determine the biochemical and lipid-related biochemical pathways that are altered in diseased compared to healthy and generate novel hypotheses on molecular targets for diagnostics and therapeutics.

Liquid chromatography (Shimadzu Nexera UPLC) combined with mass spectrometry (AB Sciex 5600 Triple-TOF) is the method of choice for unbiased lipidomics due to the sensitivity and selectivity of analysis. Advancement in analytical technology and data processing allows the profiling of hundreds of lipid species in biological samples. 

Lipids are extracted from biological samples using a modified Bligh-Dyer method using liquid-liquid extraction at room temperature after spiking with internal standards. Lipids are analyzed on reversed-phase HPLC, followed by MS analysis that alternates between MS and data-dependent MS2 scans using dynamic exclusion in both positive and negative polarity and yields excellent separation of all lipids. Quality Controls (QC) are prepared by pooling equal volumes of each sample and are injected at the beginning and end of each analysis and after every 10 sample injections to provide a measurement of the system's stability and performance as well as reproducibility of the sample preparation method. Lipids are identified using the LipidBlast library by matching the product ions MS/MS data. The technique allows us to measure >70 percent of lipids with an intensity RSD value below 20 percent belonging to eight different lipid classes which includes phospholipids like lysophosphatidylcholine (lysoPC), phosphatidylcholine (PC), phosphatidylethanolamine (PE), phosphatidylserine (PS), phosphatidylglycerol (PG), phosphatidylinositol (PI), glycerolipids like triglyceride (TG), diglyceride (DG) and monoglyceride (MG) and sphingolipids like sphingomyelin (SM) and ceramides (Ce) in Triple-TOF MS/MS acquisition. The lipids are quantified using MultiQuant and normalized by internal standards. This method displays excellent reproducibility, mass accuracy and no significant carryover.

Metabolomics is an emerging and potentially potent tool in healthcare and biomarker discovery to understand the disease states and mechanistic oversight of the disease. Though biological processes operate through complex interactions between genes, RNA, proteins, and metabolites, the composite of this complex interaction network is described as the interactome. Metabolomics is a complementary tool to understand the interactome in disease progression including cancers, which studies endogenous and exogenous metabolites in biological systems.

Metabolomics core has the capability to quantify relative abundances of more than 800 metabolites of different pathway-specific metabolites such as amino sugars, amino acids, prostaglandins, carnitines, polyamines, glycolysis intermediates, TCA cycle, nucleotides, vitamins, steroids, bile acids, short-chain fatty acids, methylated metabolites, fatty acids, Neurotransmitters and related metabolites using mass spectrometry.

All analysis steps, including sample extraction, running the LC-MS, data analysis, and data interpretation, are conducted by well-trained mass spectrometry scientists. The core directors and staff ensure quality control during metabolites extraction using highly curated spiked internal and matrix pools. Afterward, the core director monitors and supervises the entire procedure, from sample extraction to data analysis. Core Standard operation Procedure (SOP), we have been using quality control (QC) along with biological experimental samples to check the extraction efficiency /instrument variations.  

Liquid chromatography (Agilent 1290 Infinity II) separates the compounds from biological matrixes and is then identified by mass spectrometry (Agilent QQQ 6495 and 6495B). In addition, the metabolite compounds are often analyzed in Single Reaction Monitoring (SRM) mode. All assays performed in the metabolomics core are standardized with proper validation in different matrices. 

If the investigator is interested in performing other assays not listed on the webpage, either relative or absolute quantification, please get in touch with Core Directors.

Cellular metabolism consists of hundreds to thousands of genes, enzymes, and metabolites that convert nutrients into biosynthetic building blocks and energy. Metabolic flux analysis is one of the keys to metabolic regulation and engineering. Metabolic flux is crucial when learning about homeostasis and regulating these reaction pathways. Assessing the flux of metabolites through pathways is a leading area of research in cancer metabolism. Metabolic flux analysis is based on mass spectrometry (MS)-tracing patterns of stable isotopes (13C-labeled Glucose and 13C-labeled Glutamine) substrates. The metabolic flux analysis takes advantage of the fact the incorporation of carbon pool into various pathways. When a partly labeled 13C-substrate is fed to live cells, alternative pathways produce a characteristic 13C pattern in the common products of a pathway, e.g., phosphoenolpyruvate molecules derived through glycolysis exhibit a distinct pattern from those derived through the pentose phosphate (PP) pathway.

Metabolomics core has a refined metabolic flux assay to understand the complex regulation of alerted and targeted pathways using isotope-labeled metabolic precursors such as glucose, and glutamine in live cells. Briefly, nutrients or isotopic labeled metabolic precursors feed the live cells that perform as in vitro models. Relative uptake and absorption of isotopes labeled precursors are utilized to form labeled products. Liquid chromatography (Agilent 1290 Infinity II) separates the compounds from biological matrixes and is then identified by mass spectrometry (Agilent QQQ 6495, and 6495B). Mass spectrometry (MS) has measured the labeled product using Multiple Reaction Monitoring (MRM) mode. Metabolomics core strictly maintained quality control (QC) in every study with QC samples.  

Metabolomics Core currently offers Glucose, Glutamine, and Fatty acids Flux in cell lines. If the investigator is interested in performing other fluxes, please get in touch with Core Directors.

The initial tier-1 analysis of metabolomic data has been using a series of steps including a preliminary review of data, normalization, and visualization for experimental results pattern. Data cleaning and basic statistical analyses will include identifying potential outliers, checking for normality, and examining the proportion and variance for each variable. These types of analysis involve the identification of differentially expressed metabolites. Specifically, differentially expressed compounds across two classes will be identified using parametric (t-tests) and non-parametric (rank-sum) tests, while multiple classes of data will employ analysis of variance models (ANOVA). The bioinformatics core, BCM has been supported the data analysis of metabolomics and lipidomics.

The tier-2 analysis of metabolomic data is already in place utilizing the state-of-the-art tools like Oncomine Concept Map (OCM), Ingenuity Pathway Analysis (IPA), Gene Set Enrichment Analysis (GSEA), and Network-based Gene Set Enrichment Analysis (NETGSEA) to do the advanced integrative analysis. Metabolomics core closely work with OMICS core for data integration and pathway analysis.

Project requests are submitted via the on the advanced technology cores website. Upon receipt of the request, the core director sets up an initial meeting with the principal investigators and the bioinformatics core director to discuss project design and define different methods required for the study. Upon submission of samples to the core, the core staff processes samples using standard operating protocols (SOPs) and measures the metabolites and lipids. 
Once the data is ascertained for the quality, then analyzed and visualized as heat maps or box plots by Omics core, BCM. The core Director conducts a meeting with the principal investigator to discuss the results. After a successful meeting with the principal investigator, all the normalized data and results will be sent to the investigator.