Extracellular flux assay (Seahorse assay): Diverse applications in metabolic research across biological disciplines.

Metabolic networks are fundamental to cellular processes, driving energy production, biosynthesis, redox regulation, and cellular signaling. Recent advancements in metabolic research tools have provided unprecedented insights into cellular metabolism. Among these tools, the extracellular flux analyzer stands out for its real-time measurement of key metabolic parameters: glycolysis, mitochondrial respiration, and fatty acid oxidation, leading to its widespread use. This review provides a comprehensive summary of the basic principles and workflow of the extracellular flux assay (the Seahorse assay) and its diverse applications. We highlight the assay's versatility across various biological models, including cancer cells, immunocytes, Caenorhabditis elegans, tissues, isolated mitochondria, and three-dimensional structures such as organoids, and summarize key considerations for using extracellular flux assay in these models. Additionally, we discuss the limitations of the Seahorse assay and propose future directions for its development. This review aims to enhance the understanding of extracellular flux assay and its significance in biological studies.

Combining Gas Exchange and Rapid Quenching of Leaf Tissue for Mass Spectrometry Analysis Directly in Gas Exchange Cuvette.

Isotopically nonstationary metabolic flux analysis (INST-MFA) is a powerful technique for studying plant central metabolism, which involves introducing a 13CO2 tracer to plant leaves and sampling the labeled metabolic intermediates during the transient period before reaching an isotopic steady state. The metabolic intermediates involved in the C3 cycle have exceptionally fast turnover rates, with some intermediates turning over many times a second. As a result, it is necessary to rapidly introduce the label and then rapidly quench the plant tissue to determine concentrations in the light or capture the labeling kinetics of these intermediates at early labeling time points. Here, we describe a rapid quenching (0.1-0.5 s) system for 13CO2 labeling experiments in plant leaves to minimize metabolic changes during labeling and quenching experiments. This system is integrated into a commercially available gas exchange analyzer to measure initial rates of gas exchange, precisely control ambient conditions, and monitor the conversion from 12CO2 to 13CO2.

Isotope-assisted metabolic flux analysis as an equality-constrained nonlinear program for improved scalability and robustness.

Stable isotope-assisted metabolic flux analysis (MFA) is a powerful method to estimate carbon flow and partitioning in metabolic networks. At its core, MFA is a parameter estimation problem wherein the fluxes and metabolite pool sizes are model parameters that are estimated, via optimization, to account for measurements of steady-state or isotopically-nonstationary isotope labeling patterns. As MFA problems advance in scale, they require efficient computational methods for fast and robust convergence. The structure of the MFA problem enables it to be cast as an equality-constrained nonlinear program (NLP), where the equality constraints are constructed from the MFA model equations, and the objective function is defined as the sum of squared residuals (SSR) between the model predictions and a set of labeling measurements. This NLP can be solved by using an algebraic modeling language (AML) that offers state-of-the-art optimization solvers for robust parameter estimation and superior scalability to large networks. When implemented in this manner, the optimization is performed with no distinction between state variables and model parameters. During each iteration of such an optimization, the system state is updated instead of being calculated explicitly from scratch, and this occurs concurrently with improvement in the model parameter estimates. This optimization approach starkly contrasts with traditional "shooting" methods where the state variables and model parameters are kept distinct and the system state is computed afresh during each iteration of a stepwise optimization. Our NLP formulation uses the MFA modeling framework of Wiechert et al. [1], which is amenable to incorporation of the model equations into an NLP. The NLP constraints consist of balances on either elementary metabolite units (EMUs) or cumomers. In this formulation, both the steady-state and isotopically-nonstationary MFA (inst-MFA) problems may be solved as an NLP. For the inst-MFA case, the ordinary differential equation (ODE) system describing the labeling dynamics is transcribed into a system of algebraic constraints for the NLP using collocation. This large-scale NLP may be solved efficiently using an NLP solver implemented on an AML. In our implementation, we used the reduced gradient solver CONOPT, implemented in the General Algebraic Modeling System (GAMS). The NLP framework is particularly advantageous for inst-MFA, scaling well to large networks with many free parameters, and having more robust convergence properties compared to the shooting methods that compute the system state and sensitivities at each iteration. Additionally, this NLP approach supports the use of tandem-MS data for both steady-state and inst-MFA when the cumomer framework is used. We assembled a software, eiFlux, written in Python and GAMS that uses the NLP approach and supports both steady-state and inst-MFA. We demonstrate the effectiveness of the NLP formulation on several examples, including a genome-scale inst-MFA model, to highlight the scalability and robustness of this approach. In addition to typical inst-MFA applications, we expect that this framework and our associated software, eiFlux, will be particularly useful for applying inst-MFA to complex MFA models, such as those developed for eukaryotes (e.g. algae) and co-cultures with multiple cell types.

Contribution of branched chain amino acids to energy production and mevalonate synthesis in cancer cells.

Leucine, isoleucine and valine, known as branched chain amino acids (BCAAs), have been reported to be degraded by different cancer cells, and their biodegradation pathways have been suggested as anticancer targets. However, the mechanisms by which the degradation of BCAAs could support the growth of cancer cells remains unclear. In this work, 13C experiments have been carried out in order to elucidate the metabolic role of BCAA degradation in two breast cancer cell lines (MCF-7 and BCC). The results revealed that up to 36% of the energy production via respiration by MCF-7 cells was supported by the degradation of BCAAs. Also, 67% of the mevalonate (the precursor of cholesterol) synthesized by the cells was coming from the degradation of leucine. The results were lower for BCC cells (14 and 30%, respectively). The non-tumorigenic epythelial cell line MCF-10A was used as a control, showing that 10% of the mitochondrial acetyl-CoA comes from the degradation of BCAAs and no mevalonate production. Metabolic flux analysis around the mevalonate node, also revealed that significant amounts of acetoacetate are being produced from BCAA derived carbon, which could be at the source of lipid synthesis. From these results we can conclude that the degradation of BCAAs is an important energy and carbon source for the proliferation of some cancer cells and its therapeutic targeting could be an interesting option.

Investigating the K+ sensitivity of cellular metabolism by extracellular flux analysis.

We have recently demonstrated that the activity of hexokinase 2 is dependent on the intracellular potassium ion (K+) concentration ([K+]). To analyze the K+ dependency of the cell metabolism in cell populations, we used an extracellular flux analyzer to assess oxygen consumption and acidification rates as well-established measures of oxidative- and glycolytic metabolic activities. This protocol describes in detail how a potential K+ sensitivity of the cell metabolism can be elucidated by extracellular flux analysis. For complete details on the use and execution of this protocol, please refer to Bischof et al. (2021).

Maternal metabolic health drives mesenchymal stem cell metabolism and infant fat mass at birth.

Exposure to maternal obesity may promote metabolic dysfunction in offspring. We used infant mesenchymal stem cells (MSCs) to experimentally examine cellular mechanisms of intergenerational health transmission. Our earlier reports show MSCs collected from infants of mothers with obesity had a dichotomous distribution in metabolic efficiency; they were either efficient (Ef-Ob) or inefficient (In-Ob) with respect to fatty acid oxidation (FAO). Here, we sought to determine if this was due to a primary defect in FAO. Accordingly, we measured FAO in myogenic differentiating MSCs under 3 conditions: (a) myogenesis alone, (b) excess fatty acid exposure, and (c) excess fatty acid exposure plus a chemical uncoupler to increase metabolic rate. Compared with normal weight and Ef-Ob MSCs, In-Ob displayed lower FAO in myogenesis alone and after fatty acid plus uncoupler, indicating In-Ob were less metabolically flexible after increasing lipid availability and metabolic rate, demonstrating a primary deficit in FAO. MSC FAO was negatively associated with fasting maternal glucose and insulin and positively associated with fasting HDL-cholesterol. MSC FAO was negatively associated with infant fat mass. These data indicate a less favorable maternal metabolic milieu, independent of maternal BMI, reduces intrinsic MSC FAO and is linked to higher infant adiposity as early as birth.

Metabolic analysis of mouse bone-marrow-derived dendritic cells using an extracellular flux analyzer.

Dendritic cell (DC) maturation induced by Toll-like receptor (TLR) agonists requires the activation of downstream metabolic changes. Here, we provide a detailed protocol to measure glycolysis, mitochondrial respiration, and fatty acid oxidation in mouse bone-marrow-derived DCs with the Seahorse XF24 extracellular flux (XF) analyzer. XF analysis with the Seahorse bioanalyzer has become a standard method to measure bioenergetic functions in cells, and this protocol can be adapted to other immune cells. For complete information on using this protocol, please refer to Gotoh et al. (2018).

Quantifying H+ exchange from muscle cytosolic energy catabolism using metabolite flux and H+ coefficients from multiple competitive cation binding: New evidence for consideration in established theories.

The purpose of this investigation was to present calculations of fractional H+ exchange (~H+e ) from the chemical reactions of non-mitochondrial energy catabolism. Data of muscle pH and metabolite accumulation were based on published research for intense exercise to contractile failure within ~3 min, from which capacities and time profiles were modeled. Data were obtained from prior research for multiple competitive cation dissociation constants of metabolites and the chemical reactions of non-mitochondrial energy catabolism, and pH dependent calculations of ~H+e from specific chemical reactions. Data revealed that the 3 min of intense exercise incurred a total ATP turnover of 142.5 mmol L-1 , with a total intramuscular ~H+ exchange (-'ve = release) of -187.9 mmol L-1 . Total ~H+ metabolic consumption was 130.6 mmol L-1 , revealing a net total ~H+e (~H+te ) of -57.3 mmol L-1 . Lactate production had a ~H+te of 44.2 mmol L-1 (for a peak accumulation = 45 mmol L-1 ). The net ~H+te for the sum of the CK, AK, and AMPD reactions was 36.33 mmol L-1 . The ~H+te from ATP turnover equaled -47.5 mmol L-1 . The total ~H+ release to lactate ratio was 4.3 (187.9/44). Muscle ~H+ release during intense exercise is up to ~4-fold larger than previously assumed based on the lactic acid construct.

Measurement of Metabolites from Migrating Cells.

Metastasis is a multistep process that involves responses to extrinsic and intrinsic signals at every step. It is thus only truly appreciated in the context of a whole organism. Nevertheless, in vitro studies can be used to facilitate understanding of the possible factors contributing to any phenotype that is associated with metastatic competence. The use of migration assays-where monolayers of cells migrate to cover gaps or "wounds"-has been described for decades to identify signaling pathways that regulate motile competence and to screen for ways of interfering with this ability. Here we depict the combination of such an assay with assessment of indicators of carbon metabolism using commercially available assays. This enables identification of changes in cellular metabolism associated with actively migrating cells.

Fatty Acid-Driven Polarization of Suppressive Bone Marrow-Derived Macrophages Including Metabolic and Functional Analysis.

Macrophages represent not only the first line of defense against pathogens and are the main drivers of inflammation but are also involved in the initiation, immune evasion as well as metastasis of tumors. Therefore, it has been suggested that diminishing the immune regulatory function of macrophages would support the natural immune surveillance or antitumor therapies, respectively. However, the plasticity of macrophages represents an obstacle in understanding and manipulating the role of macrophages in tumor tissue or the tumor microenvironment. Here, we describe a protocol to differentiate macrophages, based on changing their metabolic environment, from bone marrow precursors to tumor-associated macrophage-like cells of an immune suppressive phenotype. Based on these protocols, the inhibitory functional phenotype of macrophages can be manipulated and therefore further analyzed as described, by interrupting metabolic pathways.

Analyzing Melanoma Cell Oxygen Consumption and Extracellular Acidification Rates Using Seahorse Technology.

Cancer cells have deregulated metabolism that can contribute to the unique metabolic makeup of the tumor microenvironment. This can be variable between patients, and it is important to understand these differences since they potentially can affect therapy response. Here we discuss a method of processing and assaying metabolism from direct ex vivo murine and human tumor samples using seahorse extracellular flux analysis. This provides real-time profiling of oxidative versus glycolytic metabolism and can help infer the metabolic status of the tumor microenvironment.

Application of metabolic modeling for targeted optimization of high seeding density processes.

Process intensification by application of perfusion mode in pre-stage bioreactors and subsequent inoculation of cell cultures at high seeding densities (HSD) has the potential to meet the increasing requirements of future manufacturing demands. However, process development is currently restrained by a limited understanding of the cell's requirements under these process conditions. The goal of this study was to use extended metabolite analysis and metabolic modeling for targeted optimization of HSD cultivations. The metabolite analysis of HSD N-stage cultures revealed accumulation of inhibiting metabolites early in the process and flux balance analysis led to the assumption that reactive oxygen species (ROS) were contributing to the fast decrease in cell viability. Based on the metabolic analysis an optimized feeding strategy with lactate and cysteine supplementation was applied, resulting in an increase in antibody titer of up to 47%. Flux balance analysis was further used to elucidate the surprisingly strong synergistic effect of lactate and cysteine, indicating that increased lactate uptake led to reduced ROS formation under these conditions whilst additional cysteine actively reduced ROS via the glutathione pathway. The presented results finally demonstrate the benefit of modeling approaches for process intensification as well as the potential of HSD cultivations for biopharmaceutical manufacturing.

Mitochondrial respiration of human platelets in young adult and advanced age - Seahorse or O2k?

The objective of the present study was to evaluate platelet mitochondrial oxygen consumption using high-resolution respirometry (HRR) and metabolic flux analysis (MFA) and to verify the effect of advanced age on these parameters. HRR was used to analyze permeabilized and intact platelets, MFA to measure oxygen consumption rates (OCR), extracellular acidification rates (ECAR) and ATP production rate in intact fixed platelets. Two groups of healthy volunteers were included in the study: YOUNG (20-42 years, n=44) and older adults (OLD; 70-89 years; n=15). Compared to YOUNG donors, platelets from group OLD participants displayed significantly lower values of oxygen consumption in the Complex II-linked phosphorylating and uncoupled states and the Complex IV activity in HRR protocols for permeabilized cells and significantly lower resting and uncoupled respirations in intact cells when analyzed by both methods. In addition, mitochondrial ATP production rate was also significantly lower in platelets isolated from older adults. Variables measured by both methods from the same bloods correlated significantly, nevertheless those acquired by MFA were higher than those measured using HRR. In conclusion, the study verifies compromised mitochondrial respiration and oxidative ATP production in the platelets of aged persons and documents good compatibility of the two most widely used methods for determining the global performance of the electron-transporting system, i.e. HRR and MFA.

Studying the Metabolism of Epithelial-Mesenchymal Plasticity Using the Seahorse XFe96 Extracellular Flux Analyzer.

The critical role of metabolism in facilitating cancer cell growth and survival has been demonstrated by a combination of methods including, but not limited to, genomic sequencing, transcriptomic and proteomic analyses, measurements of radio-labelled substrate flux and the high throughput measurement of oxidative metabolism in unlabelled live cells using the Seahorse Extracellular Flux (XF) technology. These studies have revealed that tumour cells exhibit a dynamic metabolic plasticity, using numerous pathways including both glycolysis and mitochondrial oxidative phosphorylation (OXPHOS) to support cell proliferation, energy production and the synthesis of biomass. These advanced technologies have also demonstrated metabolic differences between cancer cell types, between molecular subtypes within cancers and between cell states. This has been exemplified by examining the transitions of cancer cells between epithelial and mesenchymal phenotypes, referred to as epithelial-mesenchymal plasticity (EMP). A growing number of studies are demonstrating significant metabolic alterations associated with these transitions, such as increased use of glycolysis by triple negative breast cancers (TNBC) or glutamine addiction in lung cancer. Models of EMP, including invasive cell lines and xenografts, isolated circulating tumour cells and metastatic tissue have been used to examine EMP metabolism. Understanding the metabolism supporting molecular and cellular plasticity and increased metastatic capacity may reveal metabolic vulnerabilities that can be therapeutically exploited. This chapter describes protocols for using the Seahorse Extracellular Flux Analyzer (XFe96), which simultaneously performs real-time monitoring of oxidative phosphorylation and glycolysis in living cells. As an example, we compare the metabolic profiles generated from two breast cancer sublines that reflect epithelial and mesenchymal phenotypes, respectively. We use this example to show how the methodology described can generate bioenergetic results that in turn can be correlated to EMP phenotypes. Normalisation of bioenergetic studies should be considered with respect to cell number, and to potential differences in mitochondrial mass, itself being an important bioenergetics endpoint.

Comprehensive Isotopic Targeted Mass Spectrometry: Reliable Metabolic Flux Analysis with Broad Coverage.

Metabolic flux analysis (MFA) is highly relevant to understanding metabolic mechanisms of various biological processes. While the pace of methodology development in MFA has been rapid, a major challenge the field continues to witness is limited metabolite coverage, often restricted to a small to moderate number of well-known compounds. In addition, isotopic peaks from an enriched metabolite tend to have low abundances, which makes liquid chromatography tandem mass spectrometry (LC-MS/MS) highly useful in MFA due to its high sensitivity and specificity. Previously we have built large-scale LC-MS/MS approaches that can be routinely used for measurement of up to ∼1,900 metabolite/feature levels [Gu et al. Anal. Chem. 2015, 87, 12355-12362. Shi et al. Anal. Chem. 2019, 91, 13737-13745.]. In this study, we aim to expand our previous studies focused on metabolite level measurements to flux analysis and establish a novel comprehensive isotopic targeted mass spectrometry (CIT-MS) method for reliable MFA analysis with broad coverage. As a proof-of-principle, we have applied CIT-MS to compare the steady-state enrichment of metabolites between Myc(oncogene)-On and Myc-Off Tet21N human neuroblastoma cells cultured with U-13C6-glucose medium. CIT-MS is operationalized using multiple reaction monitoring (MRM) mode and is able to perform MFA of 310 identified metabolites (142 reliably detected, 46 kinetically profiled) selected from >35 metabolic pathways of strong biological significance. Further, we developed a novel concept of relative flux, which eliminates the requirement of absolute quantitation in traditional MFA and thus enables comparative MFA under the pseudosteady state. As a result, CIT-MS was shown to possess the advantages of broad coverage, easy implementation, fast throughput, and more importantly, high fidelity and accuracy in MFA. In principle, CIT-MS can be easily adapted to track the flux of other labeled tracers (such as 15N-tracers) in any metabolite detectable by LC-MS/MS and in various biological models (such as mice). Therefore, CIT-MS has great potential to bring new insights to both basic and clinical metabolism research.

Inferring Metabolic Flux from Time-Course Metabolomics.

The metabolic activity of a mammalian cell changes dynamically over time and is tied to the changing metabolic demands of cellular processes such as cell differentiation and proliferation. While experimental tools like time-course metabolomics and flux tracing can measure the dynamics of a few pathways, they are unable to infer fluxes at the whole network level. To address this limitation, we have developed the Dynamic Flux Activity (DFA) algorithm, a genome-scale modeling approach that uses time-course metabolomics to predict dynamic flux rewiring during transitions between metabolic states. This chapter provides a protocol for applying DFA to characterize the dynamic metabolic activity of various cancer cell lines.

13C metabolic flux analysis on roles of malate transporter in lipid accumulation of Mucor circinelloides.

BACKGROUND: Mitochondrial and cytoplasmic malate transporter proteins are responsible for transmembrane transport of malate, thereby linking malate metabolism in various subcellular regions of the cell. These transporters play an important role in fatty acid biosynthesis of oleaginous microorganisms. Our previous studies have found that lipid content of the recombinant Mucor circinelloides (M. circinelloides) strain with mitochondrial malate transporter (mt) gene overexpression was increased by 70%, while that of strain with mt gene knockout was decreased by 27%. However, the mechanism of malate transporter promoting the transport of mitochondrial malate and citrate related to lipid accumulation is not clear. Therefore, 13C-labeled glucose metabolic flux analysis was carried out to identify the metabolic network topology and estimate intracellular fluxes of genetically engineered M. circinelloides strains for the purpose of better understanding the roles of malate transporters in citrate transport systems and lipid accumulation. RESULTS: The metabolic flux distribution analysis suggested that tricarboxylic acid (TCA) cycle flux ratio of mt-overexpression strains was decreased compared to that of the control strain, but in contrast, glyoxylic acid (GOX) cycle flux ratio was increased. Accordingly, the mt-knockout strain showed an opposite phenomenon with a higher TCA cycle flux ratio and a lower GOX cycle flux ratio than the control strain. GOX cycle might be more effective than TCA cycle in producing malate and oxaloacetate replenishment. Moreover, a relatively higher flux ratio of the pentose phosphate (PP) pathway was obtained in mt-overexpression strains, but no significant difference in the malic enzyme flux between recombinant strains and the control strain. Our results confirmed that PP pathway might play an important role for supplying NADPH and malic enzyme is not a limiting factor for fatty acid synthesis in oleaginous fungus M. circinelloides strains. CONCLUSION: Intracellular metabolic flux information suggested that mt-overexpression strains had higher flux in PP pathway and GOX cycle, lower flux in TCA cycle, and no difference in malic enzyme cycle. Together, the role of malate transporter was assumed to further participate in transporting cycle of acetyl-CoA and drive PP pathway to supply NADPH required for lipid accumulation in recombinant M. circinelloides strains.

Nuclear Magnetic Resonance Measurement of Metabolic Flux Using 13C and 1H Signals.

Metabolic flux modeling is a complementary technique to standard metabolomics/metabonomics as routinely practiced in biological systems. Here we outline carbon-13-based isotopomer analysis using nuclear magnetic resonance as the primary analytical technique. Isotopomer analysis produces relative rates of tricarboxylic acid cycle turnover. If these measures are normalized to O2 consumption, absolute rates can be inferred. The primary biological system targeted in this review is cell culture.

Labeling Cancer Stem Cells with 13C6 Glucose and 13C5 Glutamine for Metabolic Flux Analysis.

Cancer stem cells (CSCs) or tumor-initiating cells (TICs) are a population of cells present within tumor that have increased self-renewal, chemoresistance, and aggressiveness, thereby contributing to tumor relapse. Literature shows that CSCs or TICs typically originate within the hypoxic niches of the tumor, making hypoxia one of the driving factors for generation of this population. Hypoxic stress promotes adaptation to low oxygen tension in the tissues by altering metabolic properties of the CSCs. This leads to a number of altered enzymatic activities in the CSC population that further contribute to the survival of the CSCs leading to resistance to standard therapy. Hence, understanding this altered metabolic pathways as well as targeting key nodes in these may pave the way for cancer management.Glucose and glutamine are the major substrates utilized by cancer cells and feed into multiple biosynthetic pathways. Hence, labeling and tracking these compounds may reveal some novel metabolic pathways exploited by cancer stem cells to acquire survival advantage. In these current book chapters, we elaborately summarized the basic steps required for isolation, characterization, and metabolic labeling (13C6 glucose and 13C5 glutamine) of CSC for flux analysis.

Quantitative Isotopomer Rates in Real-Time Metabolism of Cells Determined by NMR Methods.

Tracer-based metabolism is becoming increasingly important for studying metabolic mechanisms in cells. NMR spectroscopy offers several approaches to measure label incorporation in metabolites, including 13 C- and 1 H-detected spectra. The latter are generally more sensitive, but quantification depends on the proton-carbon 1 JCH coupling constant, which varies significantly between different metabolites. It is therefore not possible to have one experiment optimised for all metabolites, and quantification of 1 H-edited spectra such as HSQCs requires precise knowledge of coupling constants. Increasing interest in tracer-based and metabolic flux analysis requires robust analyses with reasonably small acquisition times. Herein, we compare 13 C-filtered and 13 C-edited methods for quantification and show the applicability of the methods for real-time NMR spectroscopy of cancer-cell metabolism, in which label incorporations are subject to constant flux. We find an approach using a double filter to be most suitable and sufficiently robust to reliably obtain 13 C incorporations from difference spectra. This is demonstrated for JJN3 multiple myeloma cells processing glucose over 24 h. The proposed method is equally well suited for calculating the level of label incorporation in labelled cell extracts in the context of metabolic flux analysis.