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.

​​​​​​​Example: Cell culture, spheroids, animal models, human subject, PDX, etc.

For these questions, often the researcher will not have an answer, but should expect that these aspects will be discussed as it impacts experimental design and statistical considerations.

It is highly recommended that the client be sure of the meaning of the term flux, and refresh themselves on network dynamics and reaction diffusion systems under inhomogeneous conditions.

The consultation will then focus on the experimental design, which should also include biostatisticians for estimating sample sizes and considering what approaches are most appropriate.

Transcriptomics data may imply metabolic consequences and may be partially validated by protein analysis (cf. Westerns, RPPA, 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 fermentation 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.

• What are the costs associated with the samples being analyzed?
• What are the costs of the metabolomics experiments, including tracers?
• A list of approved service costs is provided as a spreadsheet, here. 

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.
• As global analyses produce very large quantities of data, it can be tempting to make large numbers of comparisons, with disastrous effects on power.  For hypothesis generation, which should always be followed up with more targeted experiments, consultations= with the biostatisticians is strongly recommended. 
• If global analysis is chosen, it is useful to have a specific hypothesis in mind, that can be explicitly tested using a pre-chosen set of metabolites, and then the remaining collected data used for cross-validation and/or hypothesis generation.
• For targeted analyses a small number of prechosen analytes are determined to answer a specific question. A recent example of a targeted approach is analysis of SAM and SAH (Yang et al. (2021))

It is also important to establish whether the user will do their own sample work up (recommended), 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).

Examples of the use of stable isotope resolved metabolomics in a variety of systems 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)
• Wang, T., Gnanaprakasam, J., Hong, B., Sun, H., Liu, L., Miller, E., Song, X., Cassel, T.A., Sun, Q., Vicente-Munoz, S., Warmoes, M.O., Lane, A.N., Fan, T W-M., & Wang, R. (2020) Inosine as an alternative carbon supply supports effector T cell proliferation and anti-tumor function under glucose-restriction.   Nat. Metab. 2, 635-647
• Vicente-Muñoz, S., Lin, P., Fan, T. W-M. & Lane, A.N.(2021) Analysis of ¹³C carboxylate isotopomers of metabolites using ¹⁵N cholamine chemoselection. Anal Chem 93, 6639-6637
• K.L. Fulghum, T.N. Audam, P. K Lorkiewicz, Y. Zheng, M. Merchant, T. D. Cummins, W. L Dean, T. A Cassel, T. W-M Fan, B.G. Hill. (2022) In vivo deep network tracing reveals phosphofructokinase-mediated coordination of biosynthetic pathway activity in the myocardium. J. Molecular and Cellular Cardiology  162, 32-42
• Sun, Q., Fan, T. W-M., Lane, A.N. & Higashi, R.M. (2021) Ion Chromatography-Ultra High-resolution MS¹/MS² Method for Stable Isotope-Resolved Metabolomics (SIRM) Reconstruction of Metabolic Networks. Anal. Chem. 93:2749-2757  
• Yang. J.S., Fan, T. W-M., Brandon, J.A., Lane, A.N. & Higashi, R.M. (2021) Rapid analysis of S-Adenosylmethionine (SAM) and S-Adenosylhomocysteine (SAH) isotopologues in stable isotope-resolved metabolomics (SIRM) using direct infusion nanoelectrospray ultra-high-resolution Fourier transform mass spectrometry (DI-nESI-UHR-FTMS). Anal Chim Acta 118, 338873
• T. W.-M. Fan, R. M. Higashi, M. Purdom, T. J. Bocklage, T. A. Pittman, D. He, C. Wang, A. N. Lane. (2021) Variable metabolic and immune action of checkpoint inhibition in ex vivo patient-derived lung cancer tissue cultures. eLife 10:e69578
• X. Chen, B. Sunkel, M. Wang, S. Kang, T. Wang, JN Rashida Gnanaprakasam, L. Liu, T. A. Cassel^, D. A. Scott, A. M. Muñoz-Cabello, J. Lopez-Barneo, J. Yang, A. N. Lane, B. Stanton, T. W.-M. Fan, and R. Wang (2022) Succinate dehydrogenase/complex II is critical for metabolic and epigenetic regulation of T cell proliferation and inflammation. Science Immunol. 7: eabm8161
• S. Kang, L. Liu, T Wang, M. Cannon, T. A. Cassel, T. W.-M. Fan, D. A. Scott, H-S. J. Wu, A. N. Lane, and R. Wang (2022) GAB functions as a bioenergetic and signaling gatekeeper to control T cell inflammation. Nat. Metab. 4:1322-1335
• Y. Tong, Y. Q. Xiong, T. L Scott , J.Chen, L. Li, D. He, C. Wang, A. N. Lane, and R. Xu (2023) PLOD2-induced succinate elevation enhances cancer cell stemness and 5hmC accumulation. PNAS 120: e2214942120

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) Cell culture

3 cell lines ±shRNA against tumorin- triplicate experiments, 3 tracers ([U-¹³C]-Glc, [¹³C_1,2]-Glc, [U-¹³C,¹⁵N]-Gln = 54 experiments.
Polar + non polar metabolites = 108 analytical samples.
Protein may be used for additional experiments (e.g. expression using RPPA), and/or normalization.
Polar metabolites analyzed by NMR and/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.
 

54 protein determinations

54 protein samples analyzed by RPPA, 16 target antibodies (1 slide)-864 analyses
Total analyses = 1728
(ii) Mouse xenograft

Same cell lines as orthotopic xenograft in NSG mice (10 mice/group), two tracers = 120 mice.
Tumor tissue + nontumor tissue + 2 blood samples per mouse = 240 tissue samples, 240 blood samples.
120 blood analyses by MS and NMR = 480 analytical samples.
Tissue polar and non polar = 480 analyses

240 protein extracts
Total mouse sample analyses = 1200
Grand total = 2928 analyses.
The number of analytes including isotopomers and isotopologues is > 300,000 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 (at least) 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 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.

• IC-MS takes 1 h per sample to run. QC/standard samples must be run in interleaved mode.
• NMR spectra take 1.25 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 may be used.  Consultation with the Biostatisticians is advised.
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 may be appropriate (e.g. dry weight, total protein, total DNA).
9. For fractional enrichment, no further normalization is required.
    

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 necessarily 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).  Protein analysis of small samples may be imprecise.

Sometimes, internal normalization (i.e. ratio to one or more metabolites) is possible, but will be sample and problem specific.

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.
3. Flux analysis is complex and requires a sufficiently detailed quantifiable metabolic network model that can be numerically either at steady state or under non steady state conditions (cf. Zhang, X., Su, Y., Lane, A.N., Stromberg, A., Fan, T. W-M. and Wang, C. (2023).  Bayesian Kinetic Modeling for Tracer-Based Metabolomic Data. BMC Bioinformatics  24:108).