Levels of Service

The Center has several levels of interaction with Clients as follows:

  1. Basic Analytical Services
    • The Client will supply raw tissue/cells/media with a request for sample processing with NMR and/or MS analyses
    • OR the Clients supply metabolite extracts with a request for standard NMR and/or MS analyses.
    • Then the Center prepares the extracts accordingly, records the appropriate spectra under standard operating conditions, and returns the raw data to the Client
    • OR the Center prepares the extracts accordingly, records the appropriate spectra under standard operating conditions, and reduces the data to a list of quantified identified metabolites with respect to the current databases, with isotopomer and isotopologue distributions when appropriate.
  2. Experimental Design
    • The Client consults with the Center leadership on the overall goals to develop a detailed plan for the experimental design, numbers of samples, sample handling and integrity, numbers of analytes and analytical platforms, number and types of tracers to be used.
  3. Guidance on Mechanism-Based Analysis and ‘Omics Integration
    • The Center will assist in mechanism-based biochemical interpretation, which includes relevant metabolic pathway analysis and integration of metabolic data with other streams of information such as transcriptomics, proteomics, and functional annotations.
    • Beyond rudimentary guidance, this will require collaboration.
  4. Custom Services
    • The Center will work with Clients to develop new methods and standard operating procedures that address their specific needs.
    • In vivo NMR experiments (e.g. preclinical mouse model studies) will always be under this category.
    • Custom Services will usually incur the highest rates, and will require collaboration where new methods or assays are developed
    • Data acquired elsewhere is considered Custom Services, because data analysis and interpretation are integral with experimental design and sample analysis execution.  In most cases, data required elsewhere will require very considerable data analysis labor charges due to creation of new workflow, and likely to be turned down due to shortage of resources.  In ALL cases, raw instrument data must be provided, and CESB may not be able to process such instrument-specific data files.


Richard M. Higashi, Director, mass spectrometry

Teresa W-M. Fan, Director, sample preparation

Andrew N. Lane, Director NMR

Rate Schedule

All rates stated are based on estimates from recharge rate calculation spreadsheets (RRC) that annually balance Center expenditures with projected income for each chargeable operation (e.g. each instrument, sample preparation technique, informatics computer usage).

See cost estimator here.


Common Isotopically Enriched Precursors

CompoundCIL #Isotec #
[U-13C]-glucoseCLM-1396-PK *389374-1G *
[U13C, 15N]-GlnCNLM-1275-H-PK607983-500MG
13C1,2 GlucoseCLM-504-1 *453188-1G
[U-13C, 15N] L SerineCNLM-474-H-0.5608130-250MG
99.9% D2ODLM-11-100151882-100G
70% D2ODLM-2259-70-1L613428-1KG

  • * means not microbiology nor S*&P tested. OK for cell culture.
  • Conversions:
    • 1 g glucose = 5.37 mmol
    • 0.5 g Gln = 3.27 mmol
    • 0.5 g serine = 4.13 mmol
    • 0.5 g palmitate = 1.85 mmol

NMR Tubes

  • 3 mm Shigemi: $95 each
  • Wilmad Tubes
    • Regular 3 mm (Wilmad): $17.18 each - 335-PP-7
    • Regular 5 mm (Wilmad): $19.19 each - 535-PP-7

Data Reduction

All non-automated and semi-automated data analyses will cost $50/h for academic Clients and $150/h for industrial Clients.

  • All automated analyses, requiring less than 1 CPU-core h of execution time, will cost $5 per sample dataset.
  • Automated analyses requiring more than 1 CPU-core h will be charged $5 for the first CPU-core h and $1 for each additional CPU-core h.

Service Examples

13C Dispersal from Glucose into Krebs Cycle, Glycolysis

Cost RateRate/Sample
Sample Processing$30
Tier M GCMS$55
14.1 T NMR$20a

aNMR and its data analysis charges are hourly.
Thus per sample charges are estimated.


13C and 15N Dispersal from Glutamine into Mononucleotides

Cost RateRate/Sample
Sample Processing-
Tier E FTMS$75
14.1 T NMR$25a

aNMR and its data analysis charges are hourly.
Thus per sample charges are estimated.
bFurther detailed isotope labeling is data processing costs only.


"Global" 13C Lipidomics from labeled Glucose or Glutamine

Cost RateRate/Sample
Sample Processing$30
Tier B FTMS$75
Additional Data Processing$40a

aData processing costs can be a black hole,
depending on level of detail required.


Key Questions

Before contacting the Center for services, researchers should be prepared to discuss the following:

  • What is the scientific question or problem?
    • Examples:
      • I need to determine the change in molecule X when I do Y.
      • I want to know whether the oxidative branch of the pentose phosphate pathway is contributing to the production of NADPH.
  • What is the experimental system?
    • Cell culture, spheroids, animal models, human subject, PDX, etc.
  • Targeted or global metabolomics? Pathway analysis or flux analysis?
    • For these questions, often the researcher will not have an answer, but should expect that these aspects will be discussed.
    • It is highly recommended that the client be sure of the term flux, and refresh themselves on network dynamics and reaction diffusion systems under inhomogeneous conditions.
  • What information is already available?The consultation will then focus on the experimental design, which will also need to include biostatisticians for estimating sample sizes.
    • Transcriptomics data may imply metabolic consequences and may be partially validated by protein analysis (cf. Westerns, MS, enzyme activity).
    • The Seahorse XRF provides information about the rate of proton extrusion (ECAR) and the rate of oxygen consumption (OCR) by adherent cells under normoxic conditions.
      • By varying the extracellular nutrient source (e.g. glucose, glutamine, fatty acids) and with the use of specific inhibitors, it is possible to estimate net lactic fermentative flux, glycolytic reserve, respiration potential and the degree of coupling to oxidative phosphorylation. Such information is extremely valuable in designing metabolomics analyses or whether metabolism is a significant correlate of the biological function of interest.
  • It is also important to establish whether the user will do their own sample work up, what analytical techniques are needed, and what level of data reduction is required.
  • The Center has SOPs. Questions that cannot be answered using these SOPs will be considered in terms of development time to establish specific metabolic assays.
    • This is relevant mainly in the context of a small number of metabolites (targeted metabolomics).
  • Costs.
    • What are the costs associated with the samples being analyzed?
    • What are the costs of the metabolomics experiments, including tracers?
  • It is VERY common for data analysis to take 90% of the metabolomics labor, so the researcher should be prepared to discuss the time frame and cost structure with this consideration, versus obtaining data analysis training for their own laboratory.
    • We have found, however, that the latter approach still incurs considerable labor for the CESB/RCSIRM staff, as data analysis is highly nuanced and extensive consulting is still typically needed.
    • Once an experimental approach has been worked out, the user will be introduced to laboratory personnel who will carry out the analyses and oversee the project progress- this person will then be the primary contact.Data collection is not the rate limiting step. Global analyses often produce hundreds to thousands of identifiable and quantifiable compounds or “features”, but this means the reduction of raw data is extremely time consuming.

Useful Publications

Examples of the use of stable isotope resolved metabolomics can be found in the publications listed below:

    • Fan, T.WM., Lane, A.N., Higashi, R.M., Farag, M.A., Gao, H., Bousamra, M. & Miller, D.M. (2009) Altered Regulation of Metabolic Pathways in Human Lung Cancer Discerned by 13C Stable Isotope-Resolved Metabolomics (SIRM). Molecular Cancer. 8:41
    • Fan, T. W-M., Lane, A.N., Higashi, R.M., Yan, J. (2011) Stable Isotope Resolved Metabolomics of Lung Cancer in a SCID Mouse Model. Metabolomics 7, 257-269
    • Le, A., Lane, A.N., Hamaker, M., Bose, S., Barbi, J., Tsukamoto, T., Rojas, C.J., Slusher, B.S., Zhang, H., Zimmerman. L.J., Liebler, D.C., Slebos, R.J.C., Lorkiewicz, P.K., Higashi, R.M., Fan, T.W-M., and Dang, C.V. (2012) Myc induction of hypoxic glutamine metabolism and a glucose-independent TCA cycle in human B lymphocytes. Cell Metabolism. 15, 110-121
    • Yang, Y., Lane, A.N., Fan, T.W-M., Rickets, C., Wu, M., Boros, L., Linehan, W.M. (2013) Understanding How Fumarate Hydratase (FH) Null Cells Use its Central Carbon for Energy and Malignant Development PlosOne 8, e72179
    • Sellers, K., Fox, M.P., Bousamra, M., Slone, S., Higashi, R.M.,Miller, D.M., Wang, Y., Yan, J., Yuneva, M., Deshpande, R., Lane, A.N., Fan, T. W-M. (2015) Pyruvate carboxylase is upregulated in NSCLC. J Clin Invest. 125(2): 687-698
    • The review by Fan, T.W-M., Lorkiewicz, P., Sellers, K., Moseley, H.N.B., Higashi, R.M., Lane, A.N. (2012). Stable isotope-resolved metabolomics and applications to drug development. Pharmacology & Therapeutics. 133:366-391 is also a good background source of examples.
    • Our Handbook of Metabolomics (“The Handbook of Metabolomics Methods in Pharmacology and Toxicology, vol. 17” Humana Press 2012. DOI 10.1007/978-1-61779-618-0_4)

An example study and pipeline

What is the role of tumorin in tumor development and survival in TNBC?
The goal is to understand how central metabolism is impacted by the knockdown, including sources of NADPH needed for proliferation. The user may already have information from Seahorse analysis, and phenotypic effects of the knockdown. Cell cycle distribution analysis is also important for the overall interpretation.
(i) 3 cell lines ±shRNA against tumorin- triplicate experiments, 3 tracers ([U-13C]-Glc, [13C1,2]-Glc, [U-13C,15N]-Gln = 54 experiments.
Polar + non polar metabolites = 108 analytical samples.
Protein may be used for additional experiments (e.g. expression), and/or normalization.
Polar metabolites analyzed by NMR and GC-MS or IC/FT-MS= 216 experiments,
+ 54 FT-MS of nonpolar fraction
total = 270 analyses.
Media samples at 5 time points for each dish =270 media samples, analyzed by NMR and MS
total= 540 analyses.
Total analyses = 810
(ii) Same cell lines as orthotopic xenograft in NSG mice (5 mice/group), two tracers = 60 mice.
Tumor tissue + nontumor tissue + 2 blood samples per mouse = 240 samples.
120 blood analyses by MS and NMR = 240 analytical samples.
Tissue polar and non polar = 480 analyses.
Total mouse sample analyses = 720
Grand total = 1530 analyses.
The number of analytes including isotopomers and isotopologues is > 200,000 quantified analytes for this study.
At this point, the data are reduced to lists of compounds, their isotopomers and their amounts for biological interpretation.
Outline Procedure
Design experiment->execute biological experiment with tracers-> harvest sample->prepare sample for analysis->analytical data acquisition->data reduction->information retrieval and interpretation.
Detailed SOPs are available
Cell culture
Grow cells in culture in triplicate with each tracer ± shRNA. 18 experiments (x3 for each cell line).
Sample media at defined time points and store (e.g. 0, 3,6,9,24 h-the zero time point is critical) (90 samples)
Harvest and extract cells
Store metabolite fractions (polar, non-polar, protein)
Dried samples must be reconstituted in appropriate volume of buffers for different analytical platforms, and loaded into the correct labeled sample tubes.
Prepare for NMR – run first for quality control on sample and extraction integrity
Prepare for GC or IC MS
Prepare for FT-MS
Record spectra on the different platforms
Reduce data to raw isotopologues distributions for each tracer
Repeat any bad experiments
Repeat for next cell line
Tumor bearing mice:
treat with tracer, sample blood. Harvest tissue
Extract tissues and blood
Prepare for analytical spectroscopy
Record spectra on the different platforms
Reduce data to raw isotopologues distributions for each tracer
Repeat any bad experiments
Data acquisition and reduction.
GC-MS adn IC-MS take 1 h per sample to run. QC/standard samples must be run in interleaved mode.
NMR spectra take 1.2 h per cell or tissue sample to run, 0.5 h for plasma extracts
FT-MS for lipids takes 10-15 min per sample.
Data reduction for this density of data is 1-1.5 h/spectrum.
The results can be interpreted in terms of specific networks related to cell growth or survival, with limited flux information (exclusively in this design for inputs and outputs).
Data Processing and Analysis
The “data” comprise several components, as follows.
  1. Metadata that describe in exact detail the entire workflow from sample receipt to final products. No useful results can be obtained without these data. An Excel spreadsheet is required for these data, and a template is provided.
  2. Raw analytical data, i.e. the streams of bits coming from the instruments. For FT-MS and NMR these represent digitized electrical signals in the form of free induction decays comprising both real and imaginary parts. For other MS data, these are digital representations of ion counts.
  3. Raw analytical data have to be transformed into a usable form, which for FT-MS and NMR is the discrete fourier transform and associated digital processing to suppress truncation artefacts, optimize signal-to-noise ratios etc. The resulting output is a spectrum of intensity versus frequency. For NMR, the frequency is usually transformed to chemical shift, in ppm, that is independent of magnetic field strength. For FT-MS, the frequencies are mapped onto an m/z range.
  4. Intensities (ordinate values) must be internally normalized to obtain amounts of materials (i.e. numbers of moles of substances or of ions), and back calculated to the values associated with the original spectrum, on an agreed upon measure of the amount of that species (such as biomass weight, protein mass etc.). This absolutely requires accurate metadata. The amounts are proportional to peak areas (or volumes) NOT peak heights; appropriate numerical or analytical integration procedures must be correctly applied, taking due account of baseline drift, phasing errors and peak overlap.
  5. For isotopomer and isotopologue analyses, the intensities are usually expressed as mole fractions (“enrichments”). As these are ratios, normalization to cell amount is not needed. For MS, the natural abundance needs to be corrected. Software is available for this [cf. Moseley (2010) Correcting for the effects of natural abundance in stable isotope resolved metabolomics experiments involving ultra-high resolution mass spectrometry. BMC Bioinformatics 11,139]
  6. Spectral features need to be mapped onto identifiable molecules (“assignment”), using the available spectral information, and by reference to our own and other public databases.
  7. For “profiling” typically one is concerned with case-control comparisons, which require large numbers of specimens (each unique). Multivariate statistics are generally appropriate for initial analyses- are the groups different? What is different about these groups? PCA and OPLSDA (SimcaP) may be used.
  8. Normalization. To compare case and control, the quantity of each metabolite must be normalized to the appropriate amount of specimen. Cell number is generally not appropriate as cell volumes vary widely among types, and also in response to treatment. Total biomass or a surrogate is appropriate (e.g. dry weight, total protein total DNA).
Total DNA may not be appropriate in a case-control study because the amount of DNA per cell varies twofold during the cell cycle, and the control and treated samples do not necessary have the same cell cycle distribution. Comparison of different cell types is then further compromised where there are different numbers of chromosomes present (diploid G1 normal cell, triploid cancer cell, tetraploid cells arrested at G2/M).
  1. With SIRM studies, a question is often what pathways were impacted, which requires pathway tracing (SIRM) and biochemical expertise.
Quantitative analyses may also be carried out (e.g. what is the rate of nutrient utilization and waste product excretion). Kinetic models (flux analysis) based on enzymology can also be applied where needed. These studies need careful consideration of the time dependence of the biomass as a function of time for accurate normalization of rates. Flux: the number of moles of nutrients (e.g. glucose, glutamine) consumed and the number of moles of product excreted (e.g. lactate, alanine, glutamate) is measured as a function of time, producing a time course of consumption and excretion. To determine rates, it is essential to normalize to the functional unit of metabolism which is the amount of enzymes present in the system. This is proportional to the concentration of the enzymes times the relevant intracellular volume (unknown).
  1. With tracers, the time course of the isotopomer distributions can be determined, as can the fraction of glucose (glutamine) consumed that is converted to excreted product (e.g. lactate, alanine, glutamate).
  2. Further statistical analyses as needed should be carried out by statisticians versed in multivariate analyses.