The (MRC)² provides expertise and method development for both untargeted and untargeted metabolomic analysis. The laboratory is well-equipped with multiple high-resolution, high mass accuracy qTOF and triple-quad LC-MS instrumentation with established assay protocols, authentic standards, and the full range of expertise to successfully complete your sample measurements and analyses.

The directed metabolomics platform has the ability to perform ‘fluxomic’ analysis using stable isotopes to assist investigators in determining the dynamic changes metabolites in vivo and in vitro.

The untargeted metabolomic profiling platform uses LC/MS-TOF and GC/MS to assess the relative levels of nearly >5000 molecular features (metabolites) which are reproducibly found in plasma and other biological samples. Assigning the identity of specific features is aided by a library of nearly 1,000 authentic chemical compounds. Interesting ‘hits’ found in the untargeted platform can be further analyzed in an attempt to confirm chemical identities.

The (MRC)² also provides support for an array of tools for statistical and bioinformatic analysis of both targeted and untargeted metabolomics data. With these tools, Core users have the opportunity to perform multiscalar integration of metabolomic data with clinical, physiological, ‘omics and other data types.

The (MRC)² is well-equipped, with a range of instrumentation for targeted and untargeted metabolomic profiling. Instrumentation includes:

1. Two Agilent 6890 gas chromatograph (GC) equipped with HP5973 mass detector GC/MS system with electron impact ionization (EI) and GC/ECNI/MS and positive/negative ion capabilities. The instruments are equipped with an autosampler and an on-column injector.
2. Two Agilent Triple Quadrupole mass spectrometers (6410 and 6490) coupled with Agilent 1290 UHPLC.
3. An Agilent LC/MSD Time of flight mass spectrometer.
4. Three Quadrupole TOF instrument (Q-TOF) instruments coupled Agilent Rapid Resolution HPLC or UPLC which provide high sensitivity, high mass accuracy, high mass range, and rapid throughput ideal for metabolomic profiling.
5. The Core also has an Agilent 6890 GC and a Jasco HPLC system equipped with variable UV detector, fluorescence detector, electrochemical detector and diode array detector. Both instruments have an autosampler.

The Core personnel have access to other MS facilities in campus which include Biomedical Mass Spectrometry Facility (http://sitemaker.umich.edu/mass-spectrometry/home) which has a Linear Ion Trap mass spectrometer which offers the ability to perform multi-stage MS analyses.

The core also has access to two state-of-the-art NMR spectrometers: Varian 400 with pulsed field gradients, auto tune, auto shim and a 5mm ONE probe and Varian 500 NMR spectrometer has pulsed field gradients, auto tune, auto shim, a 3mm ONE probe and a twelve slot sample changer robot.

Samples submitted to the Core are required to be accompanied by an electronic and hard copy sample submission form (provided by the core) which will list of the investigators sample ID, sample type, sample volume or weight, specific analyses requested for each sample. These data are registered in the Metabolomics Laboratory Information Management System (MetLIMS) system.

We have worked with the University of Michigan Institutional Review Board (IRB) to define rules for core analysis of human samples. For work with human samples, Investigators are required expunge all patient-specific data from their sample submission and written/electronic sample submission forms. It is incumbent upon the individual investigator to provide assurance that their local IRB has approved their work and this is attested to on the sample submission form.

The Lipomics Laboratory

The Lipomics Laboratory provides quantitative and qualitative assays for specific lipids or groups of lipids from an array of biological samples. A number of standard assays are offered by the laboratory, as well as the opportunity to develop assays for specific lipids requested by investigators.

Standard Assays Offered by the Lipomics Laboratory:

• Lipid extraction
• Fatty acid composition of lipid subfractions
• Trans fatty acids from plasma or adipose tissue
• Acyl CoA, ceramides and glucosylceramides
• Acyl carnitine
• Phospholipids
• Steroid profiling

More information on the Lipomics Laboratory, including sample submission form and cost information, is available at http://mmoc.med.umich.edu/CoreMPLipidomics.php.

Directed Metabolomics Laboratory

The Directed Metabolomics Laboratory was created to provide assays for specific metabolites or groups of small organic molecules as requested by individual researchers either as a one-time or routine assay.

Standard Assays Offered by the Directed Metabolomics Laboratory:

• Metabolite extraction
• Glycolytic, tricarboxylic acid (TCA) cycle, pentose phosphate pathway and nucleotide metabolites
• Amino Acid Analysis
• NAD-related metabolome
• Miscellaneous metabolite assays
• ‘Fluxomics’

More information on the Directed Metabolomics Laboratory, including sample submission form and cost information, is available at http://mmoc.med.umich.edu/CoreMPDirected.php.

Untargeted Metabolomics Laboratory

The Untargeted Metabolomics Laboratory is a MS-based Metabolomics facility. Methodology developed here can provide untargeted profiles of a significant number of metabolites in a variety of tissues, serum, plasma and cultured cells using a relatively automated and high throughput manner. The Laboratory has compiled a database of spectral features and retention times of 748 endogenous metabolites and has developed methodology to accurately quantify a large number of metabolites and to assign an identity to more than 400 metabolites in plasma, with similar number from cultured cells and tissue extracts.

Standard Assays Offered by the Untargeted Metabolomics Laboratory:

• HILIC and reversed phase chromatography-MS
• GC-MS

Untargeted Metabolomic Profiling and Molecular Libraries

Data from untargeted metabolomics studies are analyzed using Agilent Mass Hunter software against existing library entries. We have two types of libraries in place: first, the Standard Library, is based on pure authentic standards and allows to identify exact chemical nature of compounds; second type of library consists of “known unknowns” (KU) - mass spectral features consistently detected in a large number of samples but lacking identification. Presently, the KU library is based on the large set (~480) of human plasma samples analyzed as part of an effort to define the human plasma metabolome.

More information on the Untargeted Metabolomics Laboratory, including sample submission information and cost information, is available at http://mmoc.med.umich.edu/CoreMPUndirected.php.

For cost-effective utilization of Core resources, it is imperative that Core users have a full understanding of the benefits and limitations of the technology. The (MRC)² manager, Dr. Steve Brown, or leaders of the individual Cores, will ascertain the scope and feasibility of the proposed project and suggest design and statistical consultation, as appropriate.

This consultation is especially important wishing to perform high-throughput, untargeted metabolomics studies, which could be very sensitive to varying experimental factors. Core personnel will work with the investigators to properly prepare and to randomize sample analysis, which is essential for a successful study.

For ‘fluxomic’ studies, a more extensive consultation is often required, especially for novice users. Users who request steady-state measurements of metabolites in cell cultures and, increasingly, in whole animals, are often better served by performing studies with substrates labeled with stable isotopes, such as ¹³C and ¹⁵N. The choice of specific isotopes to answer specific questions related to intermediary metabolism requires experience, which can be provided the core personnel. In addition, the analysis of the spectral data is more time consuming, and the statistical and mathematical analysis of isotopomer distribution is likewise significantly more involved.

Statistical analysis can be carried out by the investigator on the processed data in their own laboratories, or by using tools available on the MMOC or NCIBI websites, or via consultation with Statistics and Bioinformatics Core personnel.

Directed Metabolomics and Lipomics

In general, the statistical analysis of directed metabolomic data is fairly straightforward as the numbers of metabolites are usually moderate. In most assays, authentic, heavy isotope-labeled standards are utilized to normalize recovery from the biological sample. Alternatively, standard curves for the identified metabolite are constructed and normalized to input. Core personnel will suggest the appropriate statistical tests for assessing differences among experimental groups or to identify classifiers of experimental groups.

Untargeted Metabolomics

• Compound-by-Compound Analysis: At the compound-by-compound level, regular t-test, one-way ANOVA F-test, Wilcoxon signed-rank or rank-sum tests, logistic regression and Fisher exact test will be conducted depending on the objectives of specific study. We will compute the p-value (basic significance), false discovery rate (FDR) and q-value (regular and local false discovery rate control). The results of these analyses will be used as input for various bioinformatics tools including MetScape 2.0 and GeneSpring (Agilent, Santa Clara, CA).
• Dimension-Reduction by Single Value Decomposition (SVD)-based Approaches: The Core will conduct this analysis as a routine practice for the investigators. The standard output will include, among others, outcomes of PCA or PLS components, the prediction for the observed responses based on each approach and K-fold cross-validation estimated error rates as well as classification graphs.
• Further Regularization Utilizing Sparsity Condition: For a very high number of predictors, it is known that regularization methods can produce more reliable outcomes and proper inferences. On this front, we will attempt to improve the regular PCA and PLS predictors by using the regularized sparse versions. The further regularization step provides an efficient way to consider a large number of metabolite variables simultaneously, and to identify the potentially effective predictors. This practice further allows us to group correlated metabolomics variables together when it is appropriate to do so.
• Additional Analysis Tailored for Objectives in Each Study: In addition to the standard data analyses described above the Core will offer services, such as investigation of relationship among phenotypic variables and metabolomics output. Additional services will be done on a case-by-case basis with a standard recharge.

Other specific tools that we expect to be useful for analysis include:

• Metscape 2.0 (http://metscape.ncibi.org/) allows users to upload a list of metabolites with experimentally determined concentrations and significance values, and identify reactions, genes and pathways that are associated, and analyze them in the context of relevant metabolic networks.
• Metab2MeSH (http://metab2mesh.ncibi.org/) uses a statistical approach to reliably and automatically annotate compounds with concepts defined in Medical Subject Headings (MeSH), the National Library of Medicine’s (NLM) controlled vocabulary for biomedical concepts and provide links from compounds to biomedical literature, thus complementing existing resources such as PubChem and the Human Metabolome Database.

All named metabolites will be annotated with relevant pathways information, a set of common identifiers (e.g. PubChem and KEGG IDs), and a set of names / synonyms. Statistics and Bioinformatics Core personnel can provide additional support, depending on the needs of the individual investigator/project.