Using the Matrix-Induced Ion Suppression Method for Concentration Normalization in Cellular Metabolomics Studies

Development of an Untargeted Metabolomics Strategy to Study the Metabolic Rewiring of Dendritic Cells upon Lipopolysaccharide Activation

Dendritic cells (DCs) are essential immune cells for defense against external pathogens. Upon activation, DCs undergo profound metabolic alterations whose precise nature remains poorly studied at a large scale and is thus far from being fully understood. The goal of the present work was to develop a reliable and accurate untargeted metabolomics workflow to get a deeper insight into the metabolism of DCs when exposed to an infectious agent (lipopolysaccharide, LPS, was used to mimic bacterial infection). As DCs transition rapidly from a non-adherent to an adherent state upon LPS exposure, one of the leading analytical challenges was to implement a single protocol suitable for getting comparable metabolomic snapshots of those two cellular states. Thus, a thoroughly optimized and robust sample preparation method consisting of a one-pot solvent-assisted method for the simultaneous cell lysis/metabolism quenching and metabolite extraction was first implemented to measure intracellular DC metabolites in an unbiased manner. We also placed special emphasis on metabolome coverage and annotation by using a combination of hydrophilic interaction liquid chromatography and reverse phase columns coupled to high-resolution mass spectrometry in conjunction with an in-house developed spectral database to identify metabolites at a high confidence level. Overall, we were able to characterize up to 171 unique meaningful metabolites in DCs. We then preliminarily compared the metabolic profiles of DCs derived from monocytes of 12 healthy donors upon in vitro LPS activation in a time-course experiment. Interestingly, the resulting data revealed differential and time-dependent activation of some particular metabolic pathways, the most impacted being nucleotides, nucleotide sugars, polyamines pathways, the TCA cycle, and to a lesser extent, the arginine pathway.

Cell metabolomics analyses revealed a role of altered fatty acid oxidation in neurotoxicity pattern difference between nab-paclitaxel and solvent-based paclitaxel

Peripheral neuropathy (PN) is a dose-limiting, painful adverse reaction associated with the use of paclitaxel. This common side effect was often partially attributed to the solvent used for solubilization of the highly hydrophobic drug substance. Therefore, the development of alternative formulations thrived, which included that of Abraxane® containing nanoparticle albumin-bound paclitaxel (nab-paclitaxel). However, studies demonstrated inconsistent conclusions regarding the mitigation of PN in comparison with the traditional formulation. The mass spectrometry-based cell metabolomics approach was used in the present study to explore the potentially associated mechanisms. Although no significant difference in the effects on cell viability was observed, fold changes in carnitine, several acylcarnitines and long-chain fatty acid(s) were significantly different between treatment groups in differentiated and undifferentiated SH-SY5Y cells. The most prominent difference observed was the significant increase of octanoylcarnitine in cells treated with solvent-based paclitaxel, which was found to be associated with significant decrease of medium-chain acyl-CoA dehydrogenase (MCAD). The findings suggested the potential role of altered fatty acid oxidation in the different neurotoxicity patterns observed, which may be a possible target for therapeutic interventions worth further investigation.

Differences in Fatty Acid Oxidation between Nab-Paclitaxel- and Solvent-Based Paclitaxel-Treated A549 Cells Based on Metabolomics

The pharmacokinetics, safety, and anticancer efficacy profiles of nanoparticle albumin-bound (nab)-paclitaxel formulations are superior to those of solvent-based paclitaxel formulations. The aims of the present study were to study the effects of nab-paclitaxel and solvent-based paclitaxel formulations on the metabolic profiles of the model cell line (A549) and attempt to elucidate the associated metabolic pathways. A mass spectrometry-based cell metabolomics approach and viability evaluation were used to explore the potential difference. Western blotting was utilized to measure the levels of relevant proteins, and carnitine palmitoyltransferase 1 (CPT1) activities were quantified. Fold changes normalized to controls in levels of carnitine and several acylcarnitines were significantly different (p < 0.05) between A549 cells treated with nab-paclitaxel and those treated with solvent-based paclitaxel. Relative to the controls, there were also significant fold change differences in palmitic and linoleic acid levels in the cell lysates, mitochondrial CPT1 activities, and mitochondrial medium-chain acyl-CoA dehydrogenase (MCAD) protein levels in the A549 cells subjected to the nab-paclitaxel and solvent-based paclitaxel formulations. Results suggested that the two formulations differentially modulated fatty acid oxidation in the A549 cells. While cell viability results did not reveal significant differences, the findings implied that a mass spectrometry-based cell metabolomics approach could be a sensitive tool to explore the differences caused by formulation changes without using animals. Since uncertainties of products containing nanomaterials warrant holistic screening to address safety concerns, the aforementioned approach may be of regulatory importance and is worth further investigation.

Using matrix-induced ion suppression combined with LC-MS/MS for quantification of trimethylamine-N-oxide, choline, carnitine and acetylcarnitine in dried blood spot samples

Recently, there has been significant interest in the influences of the human gut microbiota on many diseases, such as cardiovascular disease (CVD) and metabolic disorders. Trimethylamine N-oxide (TMAO) is one of the most frequently discussed gut-derived metabolites. Dried blood spot (DBS) sampling has been regarded as an attractive alternative sampling strategy for clinical studies and offers many advantages. For DBS sample processing, whole-spot analysis could minimize hematocrit-related bias, but it requires blood volume calibration. This study developed a method combining matrix-induced ion suppression (MIIS) with liquid chromatography-electrospray ionization-tandem mass spectrometry (LC-ESI-MS/MS) to estimate blood volume and quantify TMAO and its precursors and derivatives, including choline, carnitine and acetylcarnitine, in DBSs. The MIIS method used an ion suppression indicator (ISI) to measure the extent of ion suppression caused by the blood matrix, which was related to the blood volume. The results showed that the volume estimation accuracy of the MIIS method was within 91.7-109.7%. The combined MIIS and LC-MS/MS method for quantifying TMAO, choline, carnitine and acetylcarnitine was validated in terms of linearity, precision and accuracy. The quantification accuracy was within 91.2-113.2% (with LLOQ <119%), and the imprecision was below 8.0% for all analytes. A stability study showed that the analytes in DBSs were stable at all evaluated temperatures for at least 30 days. The validated method was applied to quantify DBS samples (n = 56). Successful application of the new method demonstrated the potential of this method for real-world DBS samples and to facilitate our understanding of the gut microbiota in human health.

Postprandial Metabolomics Response to Various Cooking Oils in Humans

Lipids account for a high proportion of dietary calories, which greatly affect human health. As a result of differences in composition of fatty acid of individual cooking oils, certain biological effects of these oils may vary. This study aimed to compare postprandial metabolomic profiles of six commonly consumed cooking oils/fats. Adopting a switch-over experimental design ( n = 15), we carried out a human feeding study with six groups (control without oils, soybean oil, olive oil, palm oil, camellia oil, and tallow) and collected fasting and postprandial serum samples. The metabolomic profile was measured by ultra-high-pressure liquid chromatography-quadrupole time of flight. We observed significant differences between the control group and experimental groups for 33 serum metabolites (false discovery rate; p < 0.05), which take part in lipid digestion, fatty acid metabolism, metabolism of pyrimidines and pyrimidine nucleosides, amino acid metabolism, neurobiology, and antioxidation. Sparse partial least squares discriminant analysis revealed distinct metabolomics patterns between monounsaturated fatty acid (MUFA) and saturated fatty acid oils, between soybean oil, olive oil, and palm oil, and between two MUFA-rich oils (olive and camellia oils). The present metabolomics study suggests shared and distinct metabolisms of various cooking oils/fats.

Quality assurance procedures for mass spectrometry untargeted metabolomics. a review

Untargeted metabolomics, as a global approach, has already proven its great potential and capabilities for the investigation of health and disease, as well as the wide applicability for other research areas. Although great progress has been made on the feasibility of metabolomics experiments, there are still some challenges that should be faced and that includes all sources of fluctuations and bias affecting every step involved in multiplatform untargeted metabolomics studies. The identification and reduction of the main sources of unwanted variation regarding the pre-analytical, analytical and post-analytical phase of metabolomics experiments is essential to ensure high data quality. Nowadays, there is still a lack of information regarding harmonized guidelines for quality assurance as those available for targeted analysis. In this review, sources of variations to be considered and minimized along with methodologies and strategies for monitoring and improvement the quality of the results are discussed. The given information is based on evidences from different groups among our own experiences and recommendations for each stage of the metabolomics workflow. The comprehensive overview with tools presented here might serve other researchers interested in monitoring, controlling and improving the reliability of their findings by implementation of good experimental quality practices in the untargeted metabolomics study.

Sample normalization methods in quantitative metabolomics

To reveal metabolomic changes caused by a biological event in quantitative metabolomics, it is critical to use an analytical tool that can perform accurate and precise quantification to examine the true concentration differences of individual metabolites found in different samples. A number of steps are involved in metabolomic analysis including pre-analytical work (e.g., sample collection and storage), analytical work (e.g., sample analysis) and data analysis (e.g., feature extraction and quantification). Each one of them can influence the quantitative results significantly and thus should be performed with great care. Among them, the total sample amount or concentration of metabolites can be significantly different from one sample to another. Thus, it is critical to reduce or eliminate the effect of total sample amount variation on quantification of individual metabolites. In this review, we describe the importance of sample normalization in the analytical workflow with a focus on mass spectrometry (MS)-based platforms, discuss a number of methods recently reported in the literature and comment on their applicability in real world metabolomics applications. Sample normalization has been sometimes ignored in metabolomics, partially due to the lack of a convenient means of performing sample normalization. We show that several methods are now available and sample normalization should be performed in quantitative metabolomics where the analyzed samples have significant variations in total sample amounts.