Challenges and recent advances in quantitative mass spectrometry-based metabolomics.

The field of metabolomics has gained tremendous interest in recent years. Whether the goal is to discover biomarkers related to certain pathologies or to better understand the impact of a drug or contaminant, numerous studies have demonstrated how crucial it is to understand variations in metabolism. Detailed knowledge of metabolic variabilities can lead to more effective treatments, as well as faster or less invasive diagnostics. Exploratory approaches are often employed in metabolomics, using relative quantitation to look at perturbations between groups of samples. Most metabolomics studies have been based on metabolite profiling using relative quantitation, with very few studies using an approach for absolute quantitation. Using accurate quantitation facilitates the comparison between different studies, as well as enabling longitudinal studies. In this review, we discuss the most widely used techniques for quantitative metabolomics using mass spectrometry (MS). Various aspects will be addressed, such as the use of external and/or internal standards, derivatization techniques, in vivo isotopic labelling, or quantitative MS imaging. The principles, as well as the associated limitations and challenges, will be described for each approach.

A combination of omics-based analyses to elucidate the effect of NaCl concentrations on the metabolites and microbial dynamics during the ripening fermentation of Pixian-Douban.

The study investigated the effect of different sodium chloride (NaCl) concentrations (10%, 15%, and 20%) on the ripening fermentation of Pixian-Douban, a traditional fermented condiment. The results showed that NaCl affected the dynamics of physicochemical parameters, volatile components, fatty acids, amino metabolites, organic acids, and microbial composition, and their dynamic modes were different. After 253 days fermentation, the 10% NaCl Pixian-Douban had significantly (p < 0.05) higher levels of total organic acids (20,308.25 mg/kg), amino metabolites (28,144.96 mg/kg), and volatiles (3.36 mg/kg) compared to 15% and 20% NaCl Pixian-Douban. Notably, the possible health risk associated with high concentration of biogenic amines in 10% NaCl Pixian-Douban is of concern. Moreover, correlation analyses indicated that the effect of NaCl on the quality of Pixian-Douban may be mainly related to bacteria. This study deepens the knowledge about the role of NaCl in ripening fermentation of Pixian-Douban and contributes to develop low-NaCl Pixian-Douban product.

A Cross-Sectional Quantitative Metabolomics Study Evidencing the Metabolic Signature in Six Organs during a 14-Week High-Fat High-Sucrose and Standard Diet in Mice.

Obesity is a risk factor for many diseases, such as type 2 diabetes and cardiovascular diseases. In line with the need for precision medicine, the search for biomarkers reporting the progression of obesity- and diet-associated disorders is urgent. We used NMR to determine the metabolomics profile of key organs (lung, liver, heart, skeletal muscle, kidney, and brain) and serum from male C57Bl/6J mice (5 weeks old) fed for 6, 10, and 14 weeks on a high-fat and high-sucrose diet (HFHSD) vs. a standard diet (STD). We determined metabolite concentrations in the organs at each time point, which allowed us to discriminate age- and diet-related effects as well as the interactions between both, highlighting the need to evaluate the influence of age as a confounding factor on metabolic signatures. Notably, the analysis revealed the influence of time on metabolite concentrations in the STD condition, probably reflecting the juvenile-to-adult transition. Variations impacted the liver and lung metabolites, revealing the strong influence of the HFHS diet on normal metabolism maturation during youth.

Discrimination and prediction of Qingzhuan tea storage year using quantitative chemical profile combined with multivariate analysis: Advantages of MRMHR based targeted quantification metabolomics.

The duration of storage significantly influences the quality and market value of Qingzhuan tea (QZT). Herein, a high-resolution multiple reaction monitoring (MRMHR) quantitative method for markers of QZT storage year was developed. Quantitative data alongside multivariate analysis were employed to discriminate and predict the storage year of QZT. Furthermore, the content of the main biochemical ingredients, catechins and alkaloids, and free amino acids (FAA) were assessed for this purpose. The results show that targeted marker-based models exhibited superior discrimination and prediction performance among four datasets. The R2Xcum, R2Ycum and Q2cum of orthogonal projection to latent structure-discriminant analysis discrimination model were close to 1. The correlation coefficient (R2) and the root mean square error of prediction of the QZT storage year prediction model were 0.9906 and 0.63, respectively. This study provides valuable insights into tea storage quality and highlights the potential application of targeted markers in food quality evaluation.

Metabolic Patterns of High-Invasive and Low-Invasive Oral Squamous Cell Carcinoma Cells Using Quantitative Metabolomics and 13C-Glucose Tracing.

Most current metabolomics studies of oral squamous cell carcinoma (OSCC) are mainly focused on identifying potential biomarkers for early screening and diagnosis, while few studies have investigated the metabolic profiles promoting metastasis. In this study, we aimed to explore the altered metabolic pathways associated with metastasis of OSCC. Here, we identified four OSCC cell models (CAL27, HN6, HSC-3, SAS) that possess different invasive heterogeneity via the transwell invasion assay and divided them into high-invasive (HN6, SAS) and low-invasive (CAL27, HSC-3) cells. Quantitative analysis and stable isotope tracing using [U-13C6] glucose were performed to detect the altered metabolites in high-invasive OSCC cells, low-invasive OSCC cells and normal human oral keratinocytes (HOK). The metabolic changes in the high-invasive and low-invasive cells included elevated glycolysis, increased fatty acid metabolism and an impaired TCA cycle compared with HOK. Moreover, pathway analysis demonstrated significant differences in fatty acid biosynthesis; arachidonic acid (AA) metabolism; and glycine, serine and threonine metabolism between the high-invasive and low-invasive cells. Furthermore, the high-invasive cells displayed a significant increase in the percentages of 13C-glycine, 13C-palmitate, 13C-stearic acid, 13C-oleic acid, 13C-AA and estimated FADS1/2 activities compared with the low-invasive cells. Overall, this exploratory study suggested that the metabolic differences related to the metastatic phenotypes of OSCC cells were concentrated in glycine metabolism, de novo fatty acid synthesis and polyunsaturated fatty acid (PUFA) metabolism, providing a comprehensive understanding of the metabolic alterations and a basis for studying related molecular mechanisms in metastatic OSCC cells.

Human Pancreatic Islets React to Glucolipotoxicity by Secreting Pyruvate and Citrate.

Progressive decline in pancreatic beta-cell function is central to the pathogenesis of type 2 diabetes (T2D). Here, we explore the relationship between the beta cell and its nutritional environment, asking how an excess of energy substrate leads to altered energy production and subsequent insulin secretion. Alterations in intracellular metabolic homeostasis are key markers of islets with T2D, but changes in cellular metabolite exchanges with their environment remain unknown. We answered this question using nuclear magnetic resonance-based quantitative metabolomics and evaluated the consumption or secretion of 31 extracellular metabolites from healthy and T2D human islets. Islets were also cultured under high levels of glucose and/or palmitate to induce gluco-, lipo-, and glucolipotoxicity. Biochemical analyses revealed drastic alterations in the pyruvate and citrate pathways, which appear to be associated with mitochondrial oxoglutarate dehydrogenase (OGDH) downregulation. We repeated these manipulations on the rat insulinoma-derived beta-pancreatic cell line (INS-1E). Our results highlight an OGDH downregulation with a clear effect on the pyruvate and citrate pathways. However, citrate is directed to lipogenesis in the INS-1E cells instead of being secreted as in human islets. Our results demonstrate the ability of metabolomic approaches performed on culture media to easily discriminate T2D from healthy and functional islets.

Animal Metabolite Database: Metabolite Concentrations in Animal Tissues and Convenient Comparison of Quantitative Metabolomic Data.

The Animal Metabolite Database (AMDB, https://amdb.online) is a freely accessible database with built-in statistical analysis tools, allowing one to browse and compare quantitative metabolomics data and raw NMR and MS data, as well as sample metadata, with a focus on the metabolite concentrations rather than on the raw data itself. AMDB also functions as a platform for the metabolomics community, providing convenient deposition and exchange of quantitative metabolomic data. To date, the majority of the data in AMDB relate to the metabolite content of the eye lens and blood of vertebrates, primarily wild species from Siberia, Russia and laboratory rodents. However, data on other tissues (muscle, heart, liver, brain, and more) are also present, and the list of species and tissues is constantly growing. Typically, every sample in AMDB contains concentrations of 60-90 of the most abundant metabolites, provided in nanomoles per gram of wet tissue weight (nmol/g). We believe that AMDB will become a widely used tool in the community, as typical metabolite baseline concentrations in tissues of animal models will aid in a wide variety of fundamental and applied scientific fields, including, but not limited to, animal modeling of human diseases, assessment of medical formulations, and evolutionary and environmental studies.

Ambient 1,2-propanediol exposure accelerates the degradation of lipids and amino acids in milk via allosteric effects and affects the utilization of nutrients containing amide bond.

The scandal of detecting 1, 2-propanediol (PL) in milk brought a crisis to the trust of consumers in the dairy industry, and the potential toxicity of PL has aroused the public concern about dietary exposure. A total of 200 pasteurized milk samples were collected from 15 regions, and the quantity of PL ranged between 0 and 0.31 g kg-1. Pseudo-targeted quantitative metabolomics integrated with proteomics demonstrated that PL enhanced the reduction of κ-casein, ß-casein, and 107 substances (41 amines and 66 amides) containing amide bonds. Pathway enrichment and topological analysis indicated that PL induced the metabolism of lipids, amino acids, oligosaccharide nucleotides, and alkaloids by accelerating the rate of nucleophilic reaction, and acetylcholinesterase, sarcosine oxidase, and prolyl 4-hydroxylase were determined as the vital enzymes related to the degradation of above nutrients. The results of molecular simulation calculation illustrated that the number of hydrogen bonds between acetylcholinesterase, sarcosine oxidase, and substrate increased to 2 and 3, respectively, while the position of hydrogen bonds between prolyl 4-hydroxylase and proline was shifted, indicating the change of conformation and the enhancement of hydrogen bond force were essential factors for the up-regulation of enzyme activity. This study first revealed the mechanism of deposition and transformation of PL in milk, which contributed to the knowledge of the quality control of milk and provided vital indicators to evaluate the adverse risks of PL in dairy products.

Metabolomics and modelling approaches for systems metabolic engineering.

Metabolic engineering involves the manipulation of microbes to produce desirable compounds through genetic engineering or synthetic biology approaches. Metabolomics involves the quantitation of intracellular and extracellular metabolites, where mass spectrometry and nuclear magnetic resonance based analytical instrumentation are often used. Here, the experimental designs, sample preparations, metabolite quenching and extraction are essential to the quantitative metabolomics workflow. The resultant metabolomics data can then be used with computational modelling approaches, such as kinetic and constraint-based modelling, to better understand underlying mechanisms and bottlenecks in the synthesis of desired compounds, thereby accelerating research through systems metabolic engineering. Constraint-based models, such as genome scale models, have been used successfully to enhance the yield of desired compounds from engineered microbes, however, unlike kinetic or dynamic models, constraint-based models do not incorporate regulatory effects. Nevertheless, the lack of time-series metabolomic data generation has hindered the usefulness of dynamic models till today. In this review, we show that improvements in automation, dynamic real-time analysis and high throughput workflows can drive the generation of more quality data for dynamic models through time-series metabolomics data generation. Spatial metabolomics also has the potential to be used as a complementary approach to conventional metabolomics, as it provides information on the localization of metabolites. However, more effort must be undertaken to identify metabolites from spatial metabolomics data derived through imaging mass spectrometry, where machine learning approaches could prove useful. On the other hand, single-cell metabolomics has also seen rapid growth, where understanding cell-cell heterogeneity can provide more insights into efficient metabolic engineering of microbes. Moving forward, with potential improvements in automation, dynamic real-time analysis, high throughput workflows, and spatial metabolomics, more data can be produced and studied using machine learning algorithms, in conjunction with dynamic models, to generate qualitative and quantitative predictions to advance metabolic engineering efforts.

The Application of Quantitative Metabolomics for the Taxonomic Differentiation of Birds.

In the current pilot study, we propose the use of quantitative metabolomics to reconstruct the phylogeny of vertebrates, namely birds. We determined the concentrations of the 67 most abundant metabolites in the eye lenses of the following 14 species from 6 orders of the class Aves (Birds): the Black kite (Milvus migrans), Eurasian magpie (Pica pica), Northern raven (Corvus corax), Eurasian coot (Fulica atra), Godlewski's bunting (Emberiza godlewskii), Great crested grebe (Podiceps cristatus), Great tit (Parus major), Hawfinch (Coccothraustes coccothraustes), Hooded crow (Corvus cornix), House sparrow (Passer domesticus), Rock dove (Columba livia), Rook (Corvus frugilegus), Short-eared owl (Asio flammeus) and Ural owl (Strix uralensis). Further analysis shows that the statistical approaches generally used in metabolomics can be applied for differentiation between species, and the most fruitful results were obtained with hierarchical clustering analysis (HCA). We observed the grouping of conspecific samples independently of the sampling place and date. The HCA tree structure supports the key role of genomics in the formation of the lens metabolome, but it also indicates the influence of the species lifestyle. A combination of genomics-based and metabolomics-based phylogeny could potentially resolve arising issues and yield a more reliable tree of life.

Quantitative metabolomics for dynamic metabolic engineering using stable isotope labeled internal standards mixture (SILIS).

The production of chemicals and fuels from renewable resources using engineered microbes is an attractive alternative for current fossil-dependent industries. Metabolic engineering has contributed to pathway engineering for the production of chemicals and fuels by various microorganisms. Recently, dynamic metabolic engineering harnessing synthetic biological tools has become a next-generation strategy in this field. The dynamic regulation of metabolic flux during fermentation optimizes metabolic states according to each fermentation stage such as cell growth phase and compound production phase. However, it is necessary to repeat the evaluation and redesign of the dynamic regulation system to achieve the practical use of engineered microbes. In this study, we performed quantitative metabolome analysis to investigate the effects of dynamic metabolic flux regulation on engineered Escherichia coli for γ-amino butyrate (GABA) fermentation. We prepared a stable isotope-labeled internal standard mixture (SILIS) for the stable isotope dilution method (SIDM), a mass spectrometry-based quantitative metabolome analysis method. We found multiple candidate bottlenecks for GABA production. Some metabolic reactions in the GABA production pathway should be engineered for further improvement in the direct GABA fermentation with dynamic metabolic engineering strategy.

High-Resolution QTOF-MRM for Highly Accurate Identification and Quantification of Trace Levels of Triterpenoids in Ganoderma lucidum Mycelium.

The accurate quantification of triterpenoids in Ganoderma lucidum mushroom in the mycelium stage is challenging due to their low concentrations, interference from other possible isomers, and the complex matrix. Here, a high-resolution quadrupole-time-of-flight mass spectrometry "multiple reaction monitoring" with target enhancement (HR-QTOF-MRM) method was developed to quantify seven target triterpenoids in G. lucidum. The performance of this method was compared against an optimized QQQ-MRM method. The HR-QTOF-MRM was shown to be capable of distinguishing target triterpenoids from interferent peaks in the presence of matrices. The HR-QTOF-MRM LOD and LLOQ values were found to be one to two times lower than those derived from the QQQ-MRM method. Intraday and interday variabilities of the HR-QTOF-MRM demonstrated better reproducibility than the QQQ-MRM. In addition, excellent recoveries of the analytes ranging from 80 to 117% were achieved. Spiking experiments were carried out to verify and compare the quantitative accuracy of the two methods. The HR-QTOF-MRM method provided better percent accuracy, ranging from 84% to 99% (<3% RSD), compared with the range of 69 to 114% (<4%RSD) given by the QQQ-MRM method. These results demonstrate that the new HR-QTOF-MRM mode is able to improve sensitivity, reproducibility, and accuracy of trace level analysis of triterpenoids in the complex biological samples. The triterpenoid concentrations were in the range of nondetect to 0.06-6.72 mg/g of dried weight in fruiting body and to 0.0009-0.01 mg/g of dried weight in mycelium.

Quantitative metabolome and transcriptome analysis reveals complex regulatory pathway underlying photoinduced fiber color formation in cotton.

As an important plant single cell model and textile application materials, poorly known about fiber color formation in cotton, which is sensitively regulated by environmental signals. Our studies underline the importance of photo signal on sensitive fiber color formation and characterize fiber color early initiation (15 DPA) and late accumulated metabolites (45 DPA) in different lighting condition. The results revealed 236 differential metabolites between control and shading, of which phenylpropanoids metabolites accounted for 20%, including uncharacterized novel metabolites and pathways. Furthermore, the early initiation specific genes respond to the absence of light are highly correlated with phenylpropanoid metabolites related to pigmentation. The current study reveals the complex pathways involving early initiation regulation and late metabolic pathways. In addition, the collection composed of uncharacterized photoinduced metabolites and early initiation signaling/regulatory genes were identified, which are important resources for understanding fiber color formation. This report provides new insight into molecular regulatory and biochemical basis underlying photoinduced fiber color formation in cotton.

Post-mortem changes in metabolomic profiles of human serum, aqueous humor and vitreous humor.

INTRODUCTION: Application of metabolomic methods to forensic studies may expand the limits of the post-mortem interval (PMI) estimation, and improve the accuracy of the estimation. To this end, it is important to determine which tissue is the most suitable for analysis, and which compounds are the most promising candidates for PMI estimation. OBJECTIVES: This work is aimed at the comparison of human serum, aqueous humor (AH), and vitreous humor (VH) as perspective tissues for metabolomic-based PMI estimation, at the determination of most promising PMI biomarkers, and at the development of method of PMI estimation based on the measurement of concentrations of PMI biomarkers. METHODS: Quantitative metabolomic profiling of samples of the human serum, AH, and VH taken at different PMIs has been performed with the use of NMR spectroscopy. RESULTS: It is found that the metabolomic changes in anatomically isolated ocular fluids are slower and smoother than that in blood. A good positive time correlation (Pearson coefficient r > 0.5) was observed for several metabolites, including hypoxanthine, choline, creatine, betaine, glutamate, and glycine. A model for PMI estimation based on concentrations of several metabolites in AH and VH is proposed. CONCLUSIONS: The obtained results demonstrate that the metabolomic analysis of AH and VH is more suitable for the PMI estimation than that of serum. The compounds with good positive time correlation can be considered as potential PMI biomarkers.

Development of a high-coverage metabolome relative quantitative method for large-scale sample analysis.

The development of quantitative metabolomics approaches for future standardized and translational applications has become increasingly important. Data-independent targeted quantitative metabolomics (DITQM) is a newly proposed method providing ion pair information on 1324 metabolites. However, the quantification of more than 1000 metabolites in large sample sizes has still not been implemented. In this study, on the basis of the DITQM concept, scheduled multiple reaction monitoring (MRM) methods for both high-abundant and low-abundant metabolites were established to broaden the quantification coverage, and an open-source program "Quanter_1.0" was coded to facilitate efficient data handling. Our results demonstrated that 1015 metabolites in human plasma met the quantitative requirements and could be relatively determined in an effective manner. The method was then applied to a large-scale sample study of lung cancer consisting of three distinct analytical batches. It was obvious that data quality that originated from quantitative metabolomics was improved, with substantially lower intra- and inter-batch data variation, resulting in a more effective multivariate statistical model. Finally, 26 potential biomarkers of lung cancer were discovered. Collectively, our approach provides a promising tool for quantitative metabolomics research involving large-scale sample sizes and clinical application.

UPLC-ESI-MRM/MS for Absolute Quantification and MS/MS Structural Elucidation of Six Specialized Pyranonaphthoquinone Metabolites From Ventilago harmandiana.

Pyranonaphthoquinones (PNQs) are important structural scaffolds found in numerous natural products. Research interest in these specialized metabolites lies in their natural occurrence and therapeutic activities. Nonetheless, research progress has thus far been hindered by the lack of analytical standards and analytical methods for both qualitative and quantitative analysis. We report here that various parts of Ventilago harmandiana are rich sources of PNQs. We developed an ultraperformance liquid chromatography-electrospray ionization multiple reaction monitoring/mass spectrometry method to quantitatively determine six PNQs from leaves, root, bark, wood, and heartwood. The addition of standards in combination with a stable isotope of salicylic acid-D6 was used to overcome the matrix effect with average recovery of 82% ± 1% (n = 15). The highest concentration of the total PNQs was found in the root (11,902 µg/g dry weight), whereas the lowest concentration was found in the leaves (28 µg/g dry weight). Except for the root, PNQ-332 was found to be the major compound in all parts of V. harmandiana, accounting for ∼48% of the total PNQs quantified in this study. However, PNQ-318A was the most abundant PNQ in the root sample, accounting for 27% of the total PNQs. Finally, we provide novel MS/MS spectra of the PNQs at different collision induction energies: 10, 20, and 40 eV (POS and NEG). For structural elucidation purposes, we propose complete MS/MS fragmentation pathways of PNQs using MS/MS spectra at collision energies of 20 and 40 eV. The MS/MS spectra along with our discussion on structural elucidation of these PNQs should be very useful to the natural products community to further exploring PNQs in V. harmandiana and various other sources.

Evaluation of sample preparation protocols for quantitative NMR-based metabolomics.

INTRODUCTION: Quantification of metabolites in biological fluids and tissues by NMR spectroscopy is challenged by the presence of abundant macromolecules and lipoproteins in samples, which give broad signals in the NMR spectra. To improve the quality of NMR spectra the different protocols for protein and lipid removal from the sample are used. OBJECTIVES: This work is aimed at the evaluation of the effectiveness of various methods of purification of blood serum from proteins and lipids for 1H NMR metabolomic profiling. METHODS: The advantages and limitations of different methods of the sample preparation for NMR-based quantitative metabolomics have been compared, including ultrafiltration, methanol and ethanol extractions with and without additional lipid removal, and methanol-chloroform extraction. RESULTS: The concentrations of 30 abundant metabolites extracted from human blood serum have been measured. It is found that ultrafiltration provides the best lipid removal, but causes significant and inhomogeneous metabolite losses. Ethanol and methanol extractions demonstrate similar performance with the minimal metabolite losses, and are ideal for fluids and tissues with low lipid content. The additional purification of alcohol extracts from lipids allows for the significant improving of NMR spectra, but causes additional metabolite losses. CONCLUSIONS: The methanol-chloroform extraction seems to be an optimal method for tissues with the high lipid content, providing a satisfactory lipid removal and low metabolite losses. The ultrafiltration leads to large losses of metabolites (up to 60%) and for this reason is not suitable for quantitative analysis.

Quantitative Metabolomics Using Isotope Residue Outlier Analysis (IROA®) with Internal Standards.

Various research strategies involving biomarker discovery and mechanistic studies in system biology depend on reproducible and reliable quantification of all metabolites from tissue(s) of interest. Contemporary analytical methods rely on mass spectrometry-based targeted and/or untargeted metabolomics platforms. The robustness of these analyses depends on the cleanliness of the samples, accuracy of the database, resolution of the instrument, and, the most variable of the list, the personal preferences of the researcher and the instrument operator. In this chapter, we introduce a simple method to prepare murine liver samples and carry it through the Isotope Ratio Outlier Analysis (IROA®) pipeline. This pipeline encompasses sample preparation, LC-MS-based peak acquisition, proprietary software-based library creation, normalization, and quantification of metabolites. IROA® offers a unique platform to create and normalize a local library and account for run-to-run variability over years of acquisition using the internal standards (IROA®-IS) and long-term reference standards (IROA®-LTRS).

HILIC-Enabled 13C Metabolomics Strategies: Comparing Quantitative Precision and Spectral Accuracy of QTOF High- and QQQ Low-Resolution Mass Spectrometry.

Dynamic 13C-tracer-based flux analyses of in vivo reaction networks still require a continuous development of advanced quantification methods applying state-of-the-art mass spectrometry platforms. Utilizing alkaline HILIC chromatography, we adapt strategies for a systematic quantification study in non- and 13C-labeled multicomponent endogenous Corynebacterium glutamicum extracts by LC-QTOF high resolution (HRMS) and LC-QQQ tandem mass spectrometry (MS/MS). Without prior derivatization, a representative cross-section of 17 central carbon and anabolic key intermediates were analyzed with high selectivity and sensitivity under optimized ESI-MS settings. In column detection limits for the absolute quantification range were between 6.8-304.7 (QQQ) and 28.7-881.5 fmol (QTOF) with comparable linearities (3-5 orders of magnitude) and enhanced precision using QQQ-MRM detection. Tailor-made preparations of uniformly (U)13C-labeled cultivation extracts for isotope dilution mass spectrometry enabled the accurate quantification in complex sample matrices and extended linearities without effect on method parameters. Furthermore, evaluation of metabolite-specific m+1-to-m+0 ratios (ISR1:0) in non-labeled extracts exhibited sufficient methodical spectral accuracies with mean deviations of 3.89 ± 3.54% (QTOF) and 4.01 ± 3.01% (QQQ). Based on the excellent HILIC performance, conformity analysis of time-resolved isotopic enrichments in 13C-tracer experiments revealed sufficient spectral accuracy for QQQ-SIM detection. However, only QTOF-HRMS ensures determination of the full isotopologue space in complex matrices without mass interferences.

Quantitative metabolomic analysis of changes in the lens and aqueous humor under development of age-related nuclear cataract.

INTRODUCTION: Metabolites are essential for the proper functioning of the eye lens, they either enter the lens from the aqueous humor (AH), or are synthesized in the lens epithelium. Antioxidants, osmolytes and UV filters are especially important for the lens protection, and their lack may cause the development of ophthalmic diseases. OBJECTIVES: Comparison of the metabolomic compositions of lenses and AH taken from cataract patients with that taken from human cadavers without cataract can shed light onto molecular mechanisms underlying onset of age-related nuclear cataract. METHODS: Combined use of 1H nuclear magnetic resonance and high performance liquid chromatography with optical and high-resolution mass spectrometric detection for the identification and quantification of metabolites in the lens and AH extracts. RESULTS: The concentrations of 86 metabolites were determined for four groups of samples, including lenses and AH from cataract patients and from human cadavers. In cataractous lens the most abundant metabolites are (in descending order): myo-inositol, lactate, acetate, glutamate, glutathione; in AH-lactate, glucose, glutamine, alanine, valine. The concentrations of the majority of metabolites in normal post-mortem samples of both lens and AH are higher than that in samples from the cataract patients. CONCLUSIONS: Comparison of metabolite concentrations in lens and corresponding AH reveal that the most important for the lens protection metabolites are synthesized in the lens epithelial cells. The reduced levels of antioxidants, UV filters, and osmolytes were found in the cataractous lenses what cannot be explained by post-mortem changes in normal lens; that indicates that the age-related nuclear cataract development may originate from the dysfunction of the lens epithelial cells.