Chapter Four - Metabolomic approaches to study the tumor microenvironment

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Abstract

Tumors are characterized by metabolic dysregulation, reprogramming, and the presence of metabolites, which can act both as energy mediators and signaling messengers. Measuring the concentration and composition of metabolites in the tumor microenvironment can help to better understand the tumor pathology and might improve therapeutic treatments. Metabolomics can provide a description of the physiological and pathological status, as well as help to identify biomarkers of the disease. Additionally, mass spectrometry-based tissue imaging techniques can show the spatial distribution of metabolites. In this chapter we present protocols for the extraction and analysis of metabolites and lipids, with emphasis on liquid chromatography-mass spectrometry and mass spectrometry imaging.

Introduction

The tumor microenvironment (TME) is characterized by the dynamic interaction between malignant and non-transformed cells. Immune cells, vascular endothelial cells, cytokines acting in an autocrine or paracrine fashion, and alterations of cell elasticity create a highly active environment that can also be characterized by pro-inflammatory responses. In addition, small molecules < 1 kDa (metabolites, lipids) and alterations in cellular metabolism have been closely linked to cancer progression and activation of signaling pathways, either in neighboring cells or intracellularly. Significant knowledge has been acquired on cancer cell metabolism and reprogramming over the years, with the classical Warburg effect being at the forefront of cancer metabolism. This theory defines cancer cells as relying on glycolysis for adenosine triphosphate (ATP) production, unlike normal cells that depend on the tricarboxylic acid (TCA) cycle (Kim, 2019). However, cancer energy metabolism may depend on cytosolic availability of molecules. Additionally, signaling based on small molecules through the TME may further dysregulate such processes, with heterocellular crosstalk between tumor and non-tumor cells being a hallmark of TME. Most notably, nutrient availability, oxidative stress, and pH can result in perturbations of biochemical processes followed by adaptive rewiring of microenvironment metabolomics (Hanahan & Weinberg, 2011; Makohon-Moore & Iacobuzio-Donahue, 2016). Furthermore, within the TME metabolites are being recognized to play a critical role in cellular signaling and serve as substrates to enzymes that regulate the epigenome (Lorendeau, Christen, Rinaldi, & Fendt, 2015; Pavlova & Thompson, 2016). Insights into the role metabolites play in the tumor development and progression are key to developing targeted cancer therapies and designing new drugs (Anastasiou, 2017; Murray, 2016).

In the recent years, metabolomics has been the major analytical tool to investigate metabolism in health and disease. There are two main metabolomics approaches: (1) the untargeted approach to screen in an unbiased fashion the metabolite profiles and (2) the targeted approach to monitor specific pathways or metabolites of choice. Such metabolomics approaches have allowed to describe both static and dynamic metabolic differences of the TME (Badur & Metallo, 2018; Zasada & Kempa, 2016).

Liquid chromatography (LC) coupled to mass spectrometry (MS) is the technique of choice for metabolomics. Although additional analytical approaches including gas chromatography (GC) and nuclear magnetic resonance (NMR) have been used for metabolomics, LC-MS has gained popularity given its sensitivity and selectivity, which allows to cover multiple metabolite classes with various degrees of concentration. Additionally, state-of-the-art MS-based techniques, such as MS imaging, allow to visualize spatial distribution of select metabolites in biological tissues, providing important information for better understanding TME. In this chapter we will discuss protocols regarding sample preparation for downstream metabolomic analysis using LC-MS or MS imaging approaches.

Section snippets

Untargeted metabolomics

Untargeted metabolomics aims to analyze a wide range of polar and nonpolar molecules providing a snapshot of the metabolic status of a tissue. Generally, tissues are rapidly collected and snap-frozen in liquid nitrogen. Metabolite composition is altered during tissue thawing at room temperature. Thus, to avoid cellular degeneration, tissue samples are cut and weighed while still frozen. The use of ultra high performance LC in combination with high resolution MS instruments can increase the

Targeted metabolomics

Targeted approaches are more sensitive and quantitative compared to an untargeted approach, primarily because of the instrument used for analysis (triple or tandem quadrupole MS instruments). Panels of metabolites of interest can be designed to encompass multiple targets from different metabolic pathways, or alternatively an assay can be designed to quantitate all the intermediates in a selected pathway. Storage and handling of tissue samples are critical steps to control for the precision of

MS imaging

MS imaging provides spatial distributions of specific lipid ions in the biological tissues (Amstalden van Hove, Smith, & Heeren, 2010; Gode & Volmer, 2013). Lipids can be ionized, detected, and mapped by several imaging ionization modalities, e.g., matrix-assisted laser desorption ionization (MALDI) (Berry et al., 2011; Jackson et al., 2007), laser ablation electrospray ionization (Shrestha et al., 2010), and desorption electrospray ionization (DESI) (Dill, Ifa, Manicke, Ouyang, & Cooks, 2009;

Summary

Investigators who wish to assess the metabolic profile of the TME will find themselves able to select from a myriad of options. LC-MS has proven to be a sensitive method for metabolomic profiling and for quantitative assessment of whole pathways or select biomarkers. MS imaging can highlight the spatial distribution of a metabolite of interest in biological tissues, allowing for multiple metabolite assessment and evaluation of co-localization, which can be used for assessment of metabolic

Acknowledgments

This work was supported by the National Institutes of Health National Institute of Allergy and Infectious Diseases (NIAID) grant U19 AI067773 (P.I. David J. Brenner, performed as part of Columbia University Center for Medical Countermeasures against Radiation) and pilot award AWD-7773347 (P.I. Evagelia C. Laiakis), NIAID R01AI101798 (P.I. Albert J. Fornace, Jr), and National Cancer Institute R01CA184168 (P.I. Albert J. Fornace, Jr, former P.I. Eliot M. Rosen).

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