To get started, submit a Project Request. For more information on project costs, click here. 

Pre-award support is also available to assist researchers with analytical needs, experimental design, and budgeting for grant proposals.

The Core works with a wide range of sample types, including but not limited to cells, tissues, and biological fluids from human subjects, research animals, and other organisms. Typical assays are listed below but laboratory capabilities are by no means limited to them. Assays can be tailored to investigators’ needs at a small additional cost.

Profile up to 38 steroids by LC/MS/MS (24 Δ4 steroids, 8 Δ5 steroids and 6 sulfate-conjugated steroids) in biological matrices such as plasma, serum, tissue, cells, and cell culture media. Sample preparation and extraction of steroids is by either liquid-liquid extraction (LLE) or supported liquid extraction (SLE) followed by an on-line 2D polishing extraction. Target steroid analytes are chromatographically separated on a reverse-phase (RP) column. All analytes and Internal Standards are measured by ESI ionization–pos and/or neg polarity (analyte dependent) on an Agilent 1290 UHPLC coupled with an Agilent 6490 UHPLC-QQQ mass spectrometer with a “Jet-Spray” source and Ion-funnel using MRM methods. The 6490 QQQ with the Jet-Spray and Ion-funnel is up to 10 times more sensitive than a standard electrospray source and results in the achievement of detection limits in the low pM range. Results are reported as total pG/mL (biofluids) or pM (tissues–normalized to wet tissue weight). CV’s are generally 10%.

To get started, submit a Project Request. For more information on project costs, click here. 

The Core provides untargeted profiles of a significant number of metabolites in a variety of tissues, serum, plasma, and cultured cells using a highly automated workflow.

The Untargeted Metabolomics platform is based on four interwoven and fully integrated aspects:

• Hardware: Because of its high mass resolution and mass accuracy, our platforms are based on HPLC/Q-TOF technology. The current generation of mass specs and software are intensely powerful, but NMR, FTIR, and other systems are available when mass spectrometry is not sufficient.
• Informatics: The science of Metabolomics is driven at least as much by the informatics infrastructure as by the actual hardware platform. This begins with a LIMS system capable of tracking samples, the process through data acquisition and analysis, to the final report. A separate set of tools is available for quality control, initial data analysis, and report generation.
• Robotics: Humans are too good at thinking and being creative to do the repetitive, mundane, error-prone tasks involved in most sample handling. Automating processes wherever possible reduces error and enhances data quality.
• Standards-based QA/QC: The use of standards to monitor all aspects of the sample preparation and analytics assures a continuously improved process in which the level of error is always known. This allows better experiment design, which yields better quality information from fewer samples.

Analyses under targeted lipidomics are performed by extracting major membrane lipids from various tissues, plasma, cells, algae, bacterial extract, determining their fatty acid profile by gas chromatography, separating them into neutral and phospholipids and their corresponding sub fractions via thin-layer chromatography, quantifying the amounts of these lipid classes and subclasses, determining their individual fatty acid compositions, etc. Fatty acids containing ester bond from medium-chain carbon 12 through carbon 26 having fully saturated carbon chain and unsaturated chain with different number and location of double bonds and commonly occurring ether bonds can be conveniently analyzed. Separation of cis/trans fatty acids, conjugated linoleic acid, lactobacilli acid of particular interest, and related items are also performed with developed techniques. Quality control and reproducibility are checked to determine the analytical performances.

Lipidomics approaches can provide valuable new insights into the role of lipid molecular species in human health and disease and may identify potential lipidomic biomarkers that can be developed for diagnostic/prognostic and therapeutic use. Our strategy is to use an ABSCIEX 5600 triple TOF MS that combines high-sensitivity detection, high resolution with fast acquisition speeds, and stable mass accuracy over days of acquisition accompanied by RPUPLC methodology. Identification of lipids is accomplished by data-dependent MS/MS product ion information of human plasma lipid species in both positive and negative ionization modes. During the electrospray ionization, molecular ion adducts such as [M+H]+, [M+Na]+ and [M+NH4]+ or [M−H]−, and [M+CH3COO]− are formed in both positive and negative modes. Data-dependent MS/MS acquisition provides information on the nature of the head group and/or neutral loss of the head group from the molecular ion adducts. The information on fatty acids composition of the lipids is obtained in the negative mode.

More background information about the Shotgun Lipidomics platform. Lipids are extracted from biological samples using a modified Bligh-Dyer method [1] using liquid-liquid extraction at room temperature after spiking with internal standards. Analysis of lipids is performed 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 classes of lipids. Quality Controls (QC) are prepared by pooling equal volumes of each sample, in addition to well-characterized plasma pools, 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 [2]. Lipids are identified using the LipidBlast [3] library (computer-generated tandem mass spectral library of 212,516 spectra covering 119,200 compounds from 26 lipid compound classes), by matching the productions MS/MS data. The method allows us to measure >70% of lipids with an intensity RSD value below 20% belonging to eight different lipid classes which include phospholipids like lysophosphotidylchioline (lysoPC), lysophosphotidylchioline (PC), phosphotidylethanolamine (PE), phoaphotidylserine (PS), phosphotidylglycerol (PG), phosphatidylinositol (PI), glycerolipids like triglyceride (TG), diglyceride (DG) and monoglyceride (MG) and sphingholipods like sphingomyleon (SM) and ceramides (Ce) in a single RP UPLC-QTOF 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. The method is successfully utilized for the comprehensive lipidomic profiling of complex biological samples like tissues, plasma, serum, urine, saliva, cells etc.

REFERENCES 
1. Bligh, E. G.; Dyer, W. J. A Rapid Method of Total Lipid Extraction and Purification. Can. J. Biochem. Psysiol 1959, 37, 911–917.
2. Gika, H. G.; Macpherson, E.; Theodoridis, G. A.; Wilson, I. D. Evaluation of the repeatability of ultra-performance liquid chromatography−TOF-MS for global metabolic profiling of human urine samples. J. Chromatogr., B: Anal. Technol. Biomed. Life Sci. 2008, 871 (2), 299−305.
3. Kind, T.; Liu, K.-H.; Lee, D. Y.; DeFelice, B.; Meissen, J. K.; Fiehn, O. LipidBlast in silico tandem mass spectrometry database for lipid identification, Nat. Meth 2013, 10, 755−758.

Measurement of metabolite concentrations provides only part of the picture of the metabolic activity of a cell, tissue, or organism. An equally important aspect is the rate of turnover of metabolites, termed metabolic flux. Mass spectrometry provides a convenient way of assessing metabolic flux in a large number of pathways simultaneously via the use of stable isotope labeling experiments, termed “fluxomics”. Many of the core lab’s metabolomics services can be adapted to study metabolic flux.

Important note: Once you decide that you want to do a fluxomics experiment, it is necessary to select a targeted assay (or assays) that you want to use to perform the fluxomics study. Fluxomics can be done on many of our targeted assays, depending on the compounds you’re looking for. Appropriate design of the biological experiment is especially important in fluxomics studies.

More background information about fluxomics. Stable isotopes, such as 13C, 15N, 2H, etc., are naturally-occurring, non-radioactive versions of elements that differ in mass from more abundant versions of these atoms (12C, 14N, 1H, etc). Tracer compounds that are enriched in these stable isotopes (such as 13C glucose) are commercially available and can be administered to cultured cells or living organisms. After a determined period of time of exposure to the tracer, the resulting cells, tissues, or bio-fluids can be collected and analyzed by mass-spectrometry based metabolomics. The extent of incorporation of stable isotopes into the metabolome can be used to measure the turnover of a wide range of metabolites simultaneously. (1)

Notes on data analysis in flux studies. The data output which the core lab generates from fluxomics studies is “mass isotopomer distributions.” These data show the extent of incorporation of stable isotope tracers into a variety of metabolites (determined by the assay type selected). These data can be used in several ways. One possibility is to use mass isotopomer data as an input for a comprehensive computational model of flux in major metabolic pathways. While this approach yields the most information, it requires consultation with experts in computational modeling and is often not realistic to implement because of the complexity of mammalian metabolic networks and because of the compartmentalization of metabolic reactions (1). An alternative, a simpler approach is to compare measured mass isotopomer distributions between different experimental groups via flux ratios or statistics; this approach has been termed stable isotope resolved metabolomics (SIRM) or “fluxomics” (2, 3). Please consult with the core lab to discuss options for analysis and interpretation of fluxomics data.

REFERENCES 
1. Sauer U. Metabolic networks in motion: 13C-based flux analysis. Molecular Systems Biology. 2006;2. doi: 10.1038/msb4100109.
2. Fan TWM, Lane AN, Higashi RM, Farag MA, Gao H, Bousamra M, Miller DM. Altered regulation of metabolic pathways in human lung cancer discerned by 13C stable isotope-resolved metabolomics (SIRM). Molecular Cancer. 2009;8(1):41. doi: 10.1186/1476-4598-8-41.
3. Godin J-P, Ross AB, Rezzi S, Poussin C, Martin F-P, Fuerholz A, Cleroux M, Mermoud A-F, Tornier L, Vera FA, Pouteau E, Ramadan Z, Kochhar S, Fay L-B. Isotopomics: A Top-Down Systems Biology Approach for Understanding Dynamic Metabolism in Rats Using [1,2-13C2] Acetate. Analytical Chemistry. 2010;82:646-53.

The Core assists users in the design of metabolomic experiments and provides statistical, informatics, and visualization tools to transform raw data into knowledge to enhance the understanding of biological processes.

Specifically, the Core offers users the following support:

• Assistance in the design of metabolomics studies.
• Data sets from targeted and untargeted metabolomics studies have been evaluated for quality, with estimated errors in the measurements included.
• Development and application of statistical methods in untargeted metabolic analysis to identify plausible predictive metabolites and compare treatment effects.
• Individualized statistical tools for analysis of metabolomics data for Core users.
• Data-driven correlation analysis, using the Metab2Mesh and Metscape 3 for Cytoscape, to explore and visualize metabolites of interest in the context of metabolic pathways for new insights.
• Collaboration on the development of the appropriate procedures for multi-scale integration of phenotypic and metabolomic data.

The Core benefits from close association with the Department of Computational Medicine and Bioinformatics and its National Center for Integrative Biomedical Informatics (NCIBI).

Please contact, Alla Karnovsky, to discuss details.