This provides the gold standard that provides information on the overall behaviour of the tissue as a whole, in its natural environment.  Stable isotope precursors are infused into patients and relevant biofluids (e.g. blood and urine) and both tumor and non tumorous tissue are taken in the operating theater ^2-4.  Separate pieces of freshly resected tissue is flash frozen, cut into thin slices for metabolic analysis under controlled conditions (see below), as well as implanted into recipient NSG mice (PDX model, see below) and portions preserved in formalin for histochemical analyses.  The overall procedure including assembling the teams needed for such patient work, IRB and IACUC considerations have been treated in detail ^5,6.  

Isotope tracer studies with human subjects is very complex, and is subjected to a large number of experimental limitations that restrict control of the sample (e.g. a tumor in situ).  A process that retains all of the original cell content  and tissue architecture while decoupling it from the system as a whole (i.e. transfer of compounds from various sites in the organism via the blood and lymphatic systems) is to use thin tissue slices, as originally described by. O. Warburg ⁷. We have adapted and greatly extended this approach to keep thin slices of resected tumor and non tumorous tissue from the same patient in a defined medium for up to 48 h incorporating stable isotope-enriched precursors ^3,4,8,9 This medium can be controlled in composition as desired, as can other environmental factors such as oxygen levels. Because the medium is under complete experimental control and can be sequentially sampled,  the acute effects of agents or pharmaceuticals can be tested using detailed metabolic and histochemical readouts ⁹. Further, by keeping track of adjacent thin slices, gross scale heterogeneity can be addressed from the metabolic and histochemical readouts.

We use both PDX MSG mouse models and transgenic mouse models of lung cancer.  PDX mice are propagated through multiple generations with the tumors frozen down at each generation for subsequent implantation and for metabolic studies.  For metabolic labeling of the mice in vivo we have used either repeated tail vein injections (bolus) for high level, transient labeling for rapidly turning over metabolic pools ^4,10,11, or via liquid diet for longer term labeling in slowly turning over metabolic pools such as lipids ¹²

NSG mice have very little immune system, and are thus not well suited for analyzing the all-important tumor-immune systems interactions. We therefore also work with transgenic mice harboring particular (human) genes that can be turned on and generate tissue specific tumors, such as in the lung.  These tumors, while fundamentally mouse in a mouse background, do include the full complement of the immune infiltrating cells. These immune cells can also be manipulated and the effects determined in vivo, as in a collaboration with Dr. Ruoning Wang at Nationwide Childrens, Columbus OH ¹³.

2D cell cultures, especially with established cell lines, are widely regarded as poor models of human tissues for a variety of reasons, including the absence of homologous and heterologous 3D interactions ¹.  There are now numerous techniques for growing 3D cultures in various matrices, each with their own advantages and disadvantages.  It is possible to grow 3D culture of purified patient-derived cells using magnetic beads- which are fully compatible with any desired medium and tracer study; the resulting cultures can be directly compared with the metabolic and other functional behaviors in 2D culture, which shows how one aspect of the tissue microenvironment influences function ¹⁴. Another commonly used matrix is Matrigel, a less well defined medium, which usually requires assistance from other cell types such as endothelial cells. In the case of dissociated tissue grown in Matrigel (“organoids”), many of the types of cells in tumor (e.g. endothelial cells, fibroblasts, macrophages and TILs) may be present, albeit in ill defined proportions. With the magnetic beads, it is possible to grow spheroids and allow other cell types such as macrophages or T cells to infiltrate, creating a 3D culture wit a more defined ratio of cancer and stromal cell types. We are using this system to generate patient-derived lung cancer organoids with macrophages and T cells for analyzing response t immune modulating agents such as PD-1 inhibitors and the macrophage repolarizer beta-glucan ^9,15.  The co-cultures metabolic and transcriptional readouts can be compared with those of the 3D cultures of the pure cells, to determine the consequences of the interactions within the 3D microenvironment.  Furthermore, in contrast to tissue slices or PDX models, the cell types can also be genetically manipulated, adding a further level of control.

The fraction of a tumor that is cancer cells is highly variable, and most solid tumors usually contain numerous cell types such as fibroblasts, endothelial cells and different kinds of immune cells.  The cancer cells and stromal cells compete for nutrients and space, and considerable reprogramming occurs as the tumor grows, leading to regions of extreme hypoxia and poor nutrient supply and induction of altered behavior of the stromal cells via direct cell-cell contacts and excretion of bioactive agents, that for example suppress the anticancer activity of both macrophages and T-cells.  We have been using fresh paired tissue slices and 3D cell cultures to assess the consequences and mechanisms of interactions in the tumor microenvironment.^1,9,15

For some studies, samples can be very limited, such as human tissue or some mouse samples, or for in vivo studies such that it is difficult to cover wide patches of metabolism with a single tracer. Furthermore, even with cell culture, multiple experiments with different tracers have lower power than having all the tracers added in the same sample.  We have therefore been investigating the use of two or more simultaneous tracers in our various systems, with detection by NMR (the only fully quantitative and structure determining form of spectroscopy known to human and non-human kind) and mass spectrometry. 

Common tracers that probe different parts of the metabolic network are commercially available, for example various isotopomers of glucose, ¹³C,¹⁵N Gln and other amino acids as well as ¹³C or ²H fatty acids ^17-19.  NMR is especially adept at discriminating ¹³C from ¹⁵N using the isotope editing capability of the technique, and which provides the isotopomer distributions ^18-21.  In contrast, mass spectrometry can distinguish different isotope containing metabolites only if there is sufficient resolution ^17,19,22.  Under these conditions however, it is possible to multiplex several different isotope-containing precursors ²³.  For mass spectrometry the procedure for accounting for natural abundance becomes much more complex than for a single label ^24,25, but we have a program that efficiently and accurately strips three isotopes simultaneously from large data sets (manuscript in preparation). We have coined the term multiplexed SIRM (mSIRM for this approach ²³.  Thus it is possible to trace simultaneously three (or potentially) more stable isotopes in the same or different precursors, such as ¹³C glucose + ²H serine + ¹⁵N amino acids chosen to probe as much of the metabolic network as possible for the system under study ²³.

RPPA is a microarray of samples such as cell or tissue extracts spotted on a plane membrane.  A large number of spots can be automatically deposited from multiple samples, and at several amounts for each sample to obtain a concentration curve. We use an ArrayJet Marathon Classic Inkjet Microarray printer. The device is a 126-nozzle inkjet printer that reproducibly deposits liquid spots of volume 0.1 to 0.6 nL on 100 slides per run with high positional registration accuracy.  After drying, the protein amounts in each spot are treated with very specific fluorescent antibodies (usually one per slide, but multiplexing s possible), that are visualized and analyzed using an Innopsys Innoscan 710 AL with infrared detection for higher SNR.  As there is no electrophoretic transfer (blotting), the quantification is expected to be superior to Western Blots, and uses far less material thereby enabling quantification of a large number of proteins from very small sample sizes ²⁶.

1.   Lane, A.N., Higashi, R.M. & Fan, T.W.-M. Preclinical models for interrogating drug action in human cancers using Stable Isotope Resolved Metabolomics (SIRM) Metabolomics 12, 1-15 (2016).

2.   Fan, T.W., Lane, A.N., Higashi, R.M., Farag, M.A., Gao, H., Bousamra, M. & Miller, D.M. Altered Regulation of Metabolic Pathways in Human Lung Cancer Discerned by 13C Stable Isotope-Resolved Metabolomics (SIRM)) Molecular Cancer 8  41 (2009).

3.   Xie, H., Hanai, J., Ren, J.-G., Kats, L., Burgess , K., Bhargava, P., Signoretti, S., Billiard, J., Duffy, K.J., Grant, A., Wang, X., Lorkiewicz, P.K., Schatzman, S., Bousamra II, M., Lane, A.N., Higashi, R.M., Fan, T.W.-M., Pandolfi, P.P.P., Sukhatme, V.P. & Seth, P. Targeting lactate dehydrogenase-A (LDH-A) inhibits tumorigenesis and tumor progression in mouse models of lung cancer and impacts tumor initiating cells. Cell Metabolism 19, 795–809 (2014).

4.   Sellers, K., Fox, M.P., Bousamra, M., Slone, S., Higashi, R.M., Miller, D.M., Wang, Y., Yan, J., Yuneva, M.O., Deshpande, R., Lane, A.N. & Fan, T.W.-M. Pyruvate carboxylase is critical for non-small-cell lung cancer proliferation. J. Clin. Invest. 125, 687-698 (2015).

5.   Bousamra, M., Day, J., Fan, T.W.-M., Higashi, R.M., Kloecker, G., Lane, A.N. & Miller, D.M. “Clinical aspects of metabolomics “ in The Handbook of Metabolomics., Vol. 17 (Humana, Totoya, 2012).

6.   Lane, A.N., Fan, T.W.-M., Bousamra II, M., Higashi, R.M., Yan, J. & Miller, D.M. Clinical Applications of Stable Isotope-Resolved Metabolomics (SIRM) in Non-Small Cell Lung Cancer. Omics 15, 173-182 (2011).

7.   Warburg, O. Versuche an überlebendem Carcinomgewebe (Methoden). Biochem. Zeitschr. 142, 317-333 (1923).

8.   Fan, T.W.-M., Lane, A.N. & Higashi, R.M. Stable Isotope Resolved Metabolomics Studies in ex vivo Tissue Slices. Bio-protocol 6, e1730 (2016).

9.   Fan, T.W.-M., Warmoes, M.O., Sun, Q., Song, H., Turchan-Cholewo, J., Martin, J.T., Mahan, A.L., Higashi, R.M. & Lane, A.N. Distinctly perturbed metabolic networks underlie differential tumor tissue damages induced by immune modulator β-glucan in a two-case ex vivo non-small cell lung cancer study. . CSH Molec. Case Studies 2, a000893 (2016).

10. Fan, T.W.-M., Lane, A.N., Higashi, R.M. & Yan, J. Stable Isotope Resolved Metabolomics of Lung Cancer in a SCID Mouse Model Metabolomics 7 257-269 (2011).

11. Lane, A.N., Yan, J. & Fan, T.W.-M. 13C Tracer Studies of Metabolism in Mouse Tumor Xenografts. Bio-protocol 5, e1650 (2015).

12. Sun, R.C., Fan, T.W.-M., Deng, P., Higashi, R.M., Lane, A.N., Scott, T.L., Sun, Q., Warmoes, M.O. & Yang, Y. Noninvasive liquid diet delivery of stable isotopes into mouse models for deep metabolic network tracing. Nature Communications 8, 1646 (2017).

13. Lian, G., Gnanaprakasam, J.N.R., Wang, T., Wu, R., Chen, X., Liu, L., Shen, Y., Yang, M., Yang, J., Chen, Y., Vasiliou, V., Cassel, T.A., Green, D.R., Liu, Y., Fan, T.W.-M. & Wang, R. Glutathione de novo synthesis but not recycling process coordinates with glutamine catabolism to control redox homeostasis and directs murine T cell differentiation. eLife  7, e36158 (2018).

14. Fan, T.W.-M., El-Amouri, S.S., Macedo, J.K.A., Wang, Q.J., Song, H., Cassel, T.A. & Lane, A.N. Stable Isotope-Resolved Metabolomics shows metabolic resistance to anti-cancer selenite in 3D spheroids versus 2D cell cultures. Metabolites 8, 40 (2018).

15. Liu, M., Luo, F., Ding, C., Albeituni, S., Hu, X., Ma, Y., Cai, Y., McNally, L., Sanders, M.A., Jain, D., Kloecker, G., Bousamra, M., Zhang, H., Higashi, R.M., Lane, A.N., Fan, T.W.M. & Yan, J. Dectin-1 Activation by a Natural Product beta-Glucan Converts Immunosuppressive Macrophages into an M1-like Phenotype. Journal of Immunology 195, 5055-5065 (2015).

16. Fan, T.W.-M., El-Amouri, S.S., Macedo, J.K.A., Wang, Q.J., Cassel, T.A. & Lane, A.N. Mapping Metabolic Networks in 3D Spheroids Using Stable Isotope-Resolved Metabolomics. Metabolites  8, 40 (2018).

17. Bruntz, R.C., Higashi, R.M., Lane, A.N. & Fan, T.W.-M. Exploring Cancer Metabolism using Stable Isotope Resolved Metabolomics (SIRM). J. Biol. Chem. 292, 11601-11609 (2017).

18. Lane, A.N. & Fan, T.W.-M. NMR-based Stable Isotope Resolved Metabolomics in systems biochemistry. Arch. Biochem. Biophys. 628, 123-131 (2017).

19. Lane, A.N., Higashi, R.M. & Fan, T.W.-M. (2019) NMR and MS-based Stable Isotope-Resolved Metabolomics and Applications in Cancer Metabolism. Trends Anal. Chem. 120, 115322.

20. Lane, A.N., Tan, J., Wang, Y., Yan, J., Higashi, R.M. & Fan, T.W.-M. Probing the metabolic phenotype of breast cancer cells by multiple tracer Stable Isotope Resolved Metabolomics. Metabolic Eng. 43, 125-136 (2017).

21. Fan, T.W.-M. & Lane, A.N. Applications of NMR to Systems Biochemistry. Prog. NMR Spectrosc. 92, 18-53 (2016).

22. Higashi, R.M., Fan, T.W.-M., Lorkiewicz, P.K., Moseley, H.N.B. & Lane, A.N. Stable Isotope Labeled Tracers for Metabolic Pathway Elucidation by GC-MS and FT-MS. in Mass Spectrometry Methods in Metabolomics, Vol. 1198 (ed. Raftery, D.) 147-167 (Humana Press USA, 2014).

23. Yang, Y., Fan, W.W.-M., Lane, A.N. & Higashi, R.M. Chloroformate Derivatization for Tracing the Fate of Amino Acids in Cells by Multiple Stable Isotope Resolved Metabolomics (mSIRM). Anal. Chim. Acta  976, 63-73 (2017).
24. Moseley, H. Correcting for the effects of natural abundance in stable isotope resolved metabolomics experiments involving ultra-high resolution mass spectrometry. BMC Bioinformatics 11, 139 (2010).
25. Lane, A.N., Fan, T.W.-M., Xie, X., Moseley, H.N. & Higashi, R.M. Stable isotope analysis of lipid biosynthesis by high resolution mass spectrometry and NMR Anal. Chim. Acta 651, 201-208 (2009).
26. Fan, T. W-M., Bruntz, R.C., Yang. Y., Song, H., Chernyavskaya, Y., Deng, P., Zhang. Y., Shah, P.P. Beverly, L.J., Qi, Z., Mahan, A.L., Higashi, R.M., Dang, C.V., Lane, A.N. (2019) De novo synthesis of serine and glycine fuels purine nucleotide biosynthesis in human lung cancer tissues.  J. Biol. Chem. 294, 13464-13477