Strategy of using microsome-based metabolite production to facilitate the identification of endogenous metabolites by liquid chromatography mass spectrometry

Quantitative metabolomics of photoreceptor degeneration and the effects of stem cell-derived retinal pigment epithelium transplantation

Photoreceptor degeneration is characteristic of vision-threatening diseases including age-related macular degeneration. Photoreceptors are metabolically demanding cells in the retina, but specific details about their metabolic behaviours are unresolved. The quantitative metabolomics of retinal degeneration could provide valuable insights and inform future therapies. Here, we determined the metabolomic 'fingerprint' of healthy and dystrophic retinas in rat models using optimized metabolite extraction techniques. A number of classes of metabolites were consistently dysregulated during degeneration: vitamin A analogues, fatty acid amides, long-chain polyunsaturated fatty acids, acyl carnitines and several phospholipid species. For the first time, a distinct temporal trend of several important metabolites including DHA (4Z,7Z,10Z,13Z,16Z,19Z-docosahexaenoic acid), all-trans-retinal and its toxic end-product N-retinyl-N-retinylidene-ethanolamine were observed between healthy and dystrophic retinas. In this study, metabolomics was further used to determine the temporal effects of the therapeutic intervention of grafting stem cell-derived retinal pigment epithelium (RPE) in dystrophic retinas, which significantly prevented photoreceptor atrophy in our previous studies. The result revealed that lipid levels such as phosphatidylethanolamine in eyes were restored in those animals receiving the RPE grafts. In conclusion, this study provides insight into the metabolomics of retinal degeneration, and further understanding of the efficacy of RPE transplantation.This article is part of the themed issue 'Quantitative mass spectrometry'.

Evaluation of steroidomics by liquid chromatography hyphenated to mass spectrometry as a powerful analytical strategy for measuring human steroid perturbations

This review presents the evolution of steroid analytical techniques, including gas chromatography coupled to mass spectrometry (GC-MS), immunoassay (IA) and targeted liquid chromatography coupled to mass spectrometry (LC-MS), and it evaluates the potential of extended steroid profiles by a metabolomics-based approach, namely steroidomics. Steroids regulate essential biological functions including growth and reproduction, and perturbations of the steroid homeostasis can generate serious physiological issues; therefore, specific and sensitive methods have been developed to measure steroid concentrations. GC-MS measuring several steroids simultaneously was considered the first historical standard method for analysis. Steroids were then quantified by immunoassay, allowing a higher throughput; however, major drawbacks included the measurement of a single compound instead of a panel and cross-reactivity reactions. Targeted LC-MS methods with selected reaction monitoring (SRM) were then introduced for quantifying a small steroid subset without the problems of cross-reactivity. The next step was the integration of metabolomic approaches in the context of steroid analyses. As metabolomics tends to identify and quantify all the metabolites (i.e., the metabolome) in a specific system, appropriate strategies were proposed for discovering new biomarkers. Steroidomics, defined as the untargeted analysis of the steroid content in a sample, was implemented in several fields, including doping analysis, clinical studies, in vivo or in vitro toxicology assays, and more. This review discusses the current analytical methods for assessing steroid changes and compares them to steroidomics. Steroids, their pathways, their implications in diseases and the biological matrices in which they are analysed will first be described. Then, the different analytical strategies will be presented with a focus on their ability to obtain relevant information on the steroid pattern. The future technical requirements for improving steroid analysis will also be presented.

Rapid metabolite discovery, identification, and accurate comparison of the stereoselective metabolism of metalaxyl in rat hepatic microsomes

Metabolite identification and quantitation impose great challenges on risk assessment of agrochemicals, as many metabolite standards are generally unavailable. In this study, metalaxyl metabolites were identified by time-of-flight mass spectrometry and semiquantified by triple quadrupole tandem mass spectrometry with self-prepared (13)C-labeled metalaxyl metabolites as internal standards. Such methodology was employed to characterize the stereoselective metabolism of metalaxyl in rat hepatic microsomes successfully. Metabolites derived from hydroxylation, demethylation, and didemethylation were identified and semiquantified. The results indicated that (+)-S-metalaxyl eliminated preferentially as the enantiomer fraction was 0.32 after 60 min incubation. The amounts of hydroxymetalaxyl and demethylmetalaxyl derived from (-)-R-metalaxyl were 1.76 and 1.82 times higher than that of (+)-S-metalaxyl, whereas didemethylmetalaxyl derived from (+)-S-metalaxyl was 1.44 times larger than that from (-)-R-metalaxyl. This study highlights a new quantitation approach for stereoselective metabolism of chiral agrochemicals and provides more knowledge on metalaxyl risk assessment.

Early prediction of macrosomia based on an analysis of second trimester amniotic fluid by capillary electrophoresis

AIM

To identify, using capillary electrophoresis and chemometrics, early biomarkers in human amniotic fluid of large-for-gestational-age (LGA) infants.

MATERIALS & METHODS

Second trimester amniotic fluid samples, obtained from mothers undergoing age-related amniocentesis, were analyzed by capillary electrophoresis. Electropherogram data were aligned using correlation-optimized warping. A genetic algorithm using a Bayesian evaluation function and a leave-one-out cross-validation strategy for two birth outcomes: appropriate-for-gestational-age (AGA) versus LGA infants.

RESULTS

LGA (n = 23) was differentiated from AGA (n = 86) with a sensitivity of 100% and a specificity of 98% using only two wavelets. The first wavelet is associated with albumin and the second wavelet with an unknown small molecule.

CONCLUSION

The approach developed herein allows LGA fetuses to be metabolically distinguished from AGA fetuses early in pregnancy and indicates that the birth of a LGA infant is already associated with an altered biochemical profile by the second trimester.

Trace analysis in complex mixtures using a high-component filtering strategy with liquid chromatography-mass spectrometry

Trace constituents are widely present in complex mixtures, and trace analysis is challenging because of the unpredictable matrix. In this work, a high-component filtering strategy was developed for improved analysis of trace constituents in complex sample by liquid chromatography-mass spectrometry (LC-MS). Using a specifically designed chromatographic apparatus, the high-abundant fractions were filtered prior to LC-MS analysis. The samples complexity was reduced and the sample-loading amount for the rest low-level fractions can be considerably increased. The application of this approach was illustrated with an analytically challenging sample, a traditional Chinese herbal medicine named Compound Danshen Sample. We observed that the loss rate for 12 analytes during the filtering procedure ranged from 6.54 to 26.11%, but showed a stable repeatability with RSD<3.79%. The proposed filtering method with quadrupole time-of-flight mass spectrometritry (Q-TOF/MS) enhanced the detection capacity, offering a comprehensive characterization of 133 compounds in Compound Danshen Samples. The quantification sensitivity was also improved in trace analysis, allowing six low compounds that cannot be quantified by the traditional methods to be tested by the filtering method. It can be predicted that the qualitative and quantitative trace analysis will be greatly improved when the loading samples is increased resulting from the filtration of high-level targets. The proposed strategy is promising to monitor trace constituents in diverse complex mixtures in the analytical field of pharmaceutics, metabonomics and environments.

Integrated rapid resolution liquid chromatography-tandem mass spectrometric approach for screening and identification of metabolites of the potential anticancer agent 3,6,7-trimethoxyphenanthroindolizidine in rat urine

An integrated approach combining data acquisition using MS(E) and multi-period product ion scan (mpMS/MS), with high-resolution characteristic extracted ion chromatograms (hcXIC) as a data mining method, was developed for in vivo drug metabolites screening and identification. This approach is illustrated by analyzing metabolites of a potential anticancer agent, 3,6,7-trimethoxyphenanthroindolizidine (CAT) in rat urine based on rapid resolution liquid chromatography combined with tandem mass spectrometry (RRLC-MS/MS). Untargeted full-scan MS(E) enabled the high-throughput acquisition of potential metabolites, and targeted mpMS/MS contributed to the sensitivity and specificity of the acquisition of molecules of interest. The data processing method hcXIC, based on the structure of CAT, was shown to be highly effective for the metabolite discovery. Through the double-filtering effect of the characteristic ion and accurate mass, conventional extracted ion chromatograms that contained a substantial number of false-positive peaks were simplified into chromatograms essentially free of endogenous interferences. As a result, 21 metabolites were detected in rat urine after oral administration of CAT. Based on the characteristic fragmentation patterns of the phenanthroindolizidine alkaloid, the structures of 9 metabolites were identified. Furthermore, the interpretation of the MS/MS spectra of these metabolites enabled the determination of demethylation position as well as the differentiation between N-oxidized and hydroxylated metabolites.

Advances in metabolite identification

One of the central challenges to metabolomics is metabolite identification. Regardless of whether one uses so-called 'targeted' or 'untargeted' metabolomics, eventually all paths lead to the requirement of identifying (and quantifying) certain key metabolites. Indeed, without metabolite identification, the results of any metabolomic analysis are biologically and chemically uninterpretable. Given the chemical diversity of most metabolomes and the character of most metabolomic data, metabolite identification is intrinsically difficult. Consequently a great deal of effort in metabolomics over the past decade has been focused on making metabolite identification better, faster and cheaper. This review describes some of the newly emerging techniques or technologies in metabolomics that are making metabolite identification easier and more robust. In particular, it focuses on advances in metabolite identification that have occurred over the past 2 to 3 years concerning the technologies, methodologies and software as applied to NMR, MS and separation science. The strengths and limitations of some of these approaches are discussed along with some of the important trends in metabolite identification.