1. Introduction
Cancer is the top two cause of death among people under the age of 70 in most countries, which has long been a serious threat to human life and health [1]. Cancer is caused by spontaneous or environmental factors induced genetic mutations which involves abnormal cell growth with the potential to invade or spread to other parts of the body [2]. As the direct and indirect consequences of carcinogenic mutations, tumorigenesis largely depends on the reprogramming of cell metabolism with metabolic changes into six hallmarks such as dysregulated nutrient uptake kinetics, enhanced opportunistic modes of nutrient acquisition, increased used of central intermediates for biosynthesis and nicotinamide adenine dinucleotide phosphate (NADPH) production, increased demand for nitrogen, altered metabolite-driven gene regulation, and metabolic interactions with the microenvironment [3]. For example, Cairns et al found that the glucose uptake rate of tumor cells could reach about 10 times that of normal cells [4], and Patra et al. found that the expression of hexokinase 2 (HK2) was significantly up-regulated and HK2 ablation inhibited the neoplastic phenotype of human lung and breast cancer cells in vitro and in vivo [5]. Spinelli et al. found that breast cancer cells can recycle waste by-product ”ammonia” as nitrogen source to promote tumor growth [6]. As a result, these specific hallmarks exhibited by an individual tumor can confer a druggable metabolic vulnerability sufficient to open a therapeutic window. For example, the expression of PUMILIO 1/2 (PUM1/2) was increased in colorectal cancer (CRC). Gong et al. found that knockdown of Pum1 and/or Pum2 in human CRC cells resulted in a significant reduction in tumorigenicity and intravenous injection of nanoparticle-encapsulated anti-Pum1 and Pum2 siRNA reduced the growth of CRC cells. These findings revealed the potential of PUM proteins as therapeutic targets for CRC [7]. Non-small-cell lung cancer (NSCLC) patients with activating epidermal growth factor receptor (EGFR) mutations developed resistance to tyrosine kinase inhibitor (TKI). Wang et al. found that EGFR C797S mutation was closely related to AXL through transcriptome and proteome analysis, and inhibition of AXL could effectively slow down the growth of NSCLC cells harboring EGFR C797S. This finding suggested that AXL inhibition might be a potential adjuvant therapy for NSCLC harboring the EGFR C797S mutation [8].
Metabolome defines a set of metabolites in a biological sample, which provides snapshots of tumor cells in response to genetic and/or therapeutic perturbations. Metabolic footprinting has been used to identify tumor biomarkers for cancer diagnosis and prognosis, and such biomarkers can be assayed in non-invasively collected biofluids like blood or serum [9-11]. Recently, the combination of cancer models with metabolomic analysis has been shown to be a powerful approach to generate new hypotheses about predicting drug susceptibility, resistance and mode of action [12]. For example, JAIN et al. found that interfering with glycine metabolism slowed the growth of rapidly proliferating cancer cells and suggested that the increased dependence of rapidly growing cancer cells on glycine might provide a potential target for therapeutic intervention [13]. Newman et al. found that cancer cells could utilize serine as a major source of carbon units and it allowed cancer cells to maintain high proliferation rates [14]. Tao et al. observed 270 dysregulated lipids in serum exosomes between pancreatic cancer (PC) patients and healthy controls through non-targeted lipidomic analysis, which might become diagnostic biomarkers [15]. Through metabolomic analysis, Bermudez et al. assessed proliferation of a collection of cancer cells following inhibition of the mitochondrial electron transport chain (ETC) and found that cell lines least sensitive to ETC inhibition maintained intracellular aspartate levels by importing it. This suggested that aspartate was the limiting metabolite for tumor growth and may be a target for cancer therapy [16].
In order to exploit pivotal intracellular mechanisms associated with tumor growth and drug resistance requires an insightful investigation of intracellular metabolites. To achieve this, sample preparation and analytical protocols should be developed and evaluated in terms of reproducibility and number of metabolite features obtained [17]. Nevertheless, acquiring metabolome changes is experimentally demanding, such that dynamic data in the development, progression and therapeutic treatment of cancer are extremely scarce. Meanwhile, achieving the absolute quantification of global metabolite is further hampered by sample pretreatment with loss and biased mass spectrometry-based analyses. To obtain true snapshots of intracellular metabolites, leakage-free sample pretreatment and unbiased metabolite determination are required. For cell acquisition, suspension cells could be directly centrifuged to obtain cell pellets while the adherent cells must be detached before metabolite extraction. The adherent growth cells can be harvested by scraping or using trypsin. Traditionally, trypsin was often used to harvest cells. However, previous studies have concluded that harvesting cells by trypsinization lead to the loss of a large amount of metabolites [18, 19]. Therefore, direct quenching of cells with physical scraping was used as an alternative. However, this was not conducive to cell counting. To address this, measuring the protein content of cell pellets to correct the number of cells has been reported as a promising method for standardizing cell culture systems [20]. Metabolite quenching is the first critical step in the sample preparation process. The quenching process should meet several requirements: the enzyme activity must be quenched as quickly as possible to avoid metabolite degradation and changes in sample composition. Some metabolites are extremely unstable with a short turnaround time, such as adenosine triphosphate (ATP) and glucose 6-phosphate (G6P) [21], so rapid quenching procedure is essential to ensure the true state of intracellular metabolites [22]. Quenching can usually be achieved by freezing samples in liquid nitrogen [23], or in cold methanol solution [24]. It has been reported that cold methanol quenching caused serious metabolite leakage in cell, resulting in an overall loss of metabolite recovery [25]. However, in cancer cells, a systematic evaluation of the extent of metabolite leakage among different quenchers and the influential factors giving rise to it has rarely been reported.
Besides leakage-free quenching method, a complete extraction of intracellular metabolite is required. In previous studies, a lot of efforts have been devoted to the development of extraction methods for intracellular metabolites in different mammalian cells. For instance, Sellick et al. used 100% methanol extraction twice and then water once to extract the metabolites, which could recover the largest range of intracellular metabolites from Chinese hamster ovary (CHO) cells [26]. However, at the same time, other extraction method should be required to maximize the non-polar metabolites recovered. Dettmer et al. used methanol/water to scrap cells harvest and extract metabolites from adherently growing SW480 cells [18]. Wamelink et al. used 50% acetonitrile to extract intermediates in pentose phosphate pathway in fibroblasts and lymphoblasts [27]. However, these extraction methods have not yet been evaluated together with quenching procedures to develop a generalized protocol for acquiring intracellular metabolome in mammalian cells. Furthermore, obtaining accurate concentrations of intracellular metabolites were of great significance for the validation of metabolic fluxes and the establishment of metabolic models [22, 28, 29]. Mass spectrometry such as Gas Chromatography-Mass Spectrometry (GC-MS) and Liquid Chromatography tandem Mass Spectrometry (LC-MS/MS) has been widely used in the determination of metabolites due to its high accuracy and high resolution [30, 31]. However, sample recovery needs to be determined experimentally for each analyte. To circumvent this, the stable isotope internal standard of the analyte was the best choice, because the substance could be distinguished by mass spectrometry, and IDMS could be used to track and correct the loss of the sample during pretreatment and analysis [32]. The stable isotope internal standards could be cultivated on a medium with uniformly labeled 13C-glucose as the sole carbon source, and the13C-labeled internal standard of intermediate metabolites and target metabolites could be obtained through the self-metabolism of microbial cells [33]. By combining GC-MS/LC-MS/MS with the IDMS method, it is possible to track losses during sample pretreatment and analytical process [32, 33] and greatly increase the accuracy of metabolite quantitation. Furthermore, previously developed sample preparation methods were merely developed for 2D adherent cells, which cannot be directly applied to 3D tumor model without further evaluation. As is known, the 2D cells cannot simulate the complex structure and heterogeneity of tumor tissue in vivo [34, 35]. 3D multicellular tumor spheroids (3D MTSs) are a tumor model between 2D monolayer cell culture and animal models [36]. Compared with traditional 2D model, the internal concentration gradient of nutrients and oxygen led to outer proliferation of living cells, inner accumulation of necrotic cells and living quiescent cells in the transition zone [37]. This inhomogeneity is an important feature of solid tumors in vivo. Many anticancer drugs exert cytotoxic effects on proliferating cells, while quiescent cells escape treatment [38, 39]. Therefore, model systems such as 3D MTS involving quiescent cells are critical for preclinical drug screening and drug resistance investigations.
In this study, we systematically evaluated 12 sample preparation protocols for accurate determination of intracellular metabolites in both 2D and 3D tumor cell models. Further, based on the optimal sample preparation protocol, quantitative metabolomics analysis was used to discover the differential metabolome features of both 2D and 3D tumor models in response to DOX treatment.