Yeast-based reference materials for quantitative metabolomics

We introduce a new concept of yeast-derived biological matrix reference material for metabolomics research relying on in vivo synthesis of a defined biomass, standardized extraction followed by absolute quantification with isotope dilution. The yeast Pichia pastoris was grown using full control- and online monitoring fed-batch fermentations followed by fast cold methanol quenching and boiling ethanol extraction. Dried extracts served for the quantification campaign. A metabolite panel of the evolutionarily conserved primary metabolome (amino acids, nucleotides, organic acids, and metabolites of the central carbon metabolism) was absolutely quantified by isotope dilution utilizing uniformly labeled 13C-yeast-based internal standards. The study involved two independent laboratories employing complementary mass spectrometry platforms, namely hydrophilic interaction liquid chromatography-high resolution mass spectrometry (HILIC-HRMS) and gas chromatography-tandem mass spectrometry (GC–MS/MS). Homogeneity, stability tests (on a panel of >70 metabolites over a period of 6 months), and excellent biological repeatability of independent fermentations over a period of 2 years showed the feasibility of producing biological reference materials on demand. The obtained control ranges proved to be fit for purpose as they were either superior or comparable to the established reference materials in the field.

Plasma Metabolome Profiling for the Diagnosis of Catecholamine Producing Tumors

ContextPheochromocytomas and paragangliomas (PPGL) cause catecholamine excess leading to a characteristic clinical phenotype. Intra-individual changes at metabolome level have been described after surgical PPGL removal. The value of metabolomics for the diagnosis of PPGL has not been studied yet.ObjectiveEvaluation of quantitative metabolomics as a diagnostic tool for PPGL.DesignTargeted metabolomics by liquid chromatography-tandem mass spectrometry of plasma specimens and statistical modeling using ML-based feature selection approaches in a clinically well characterized cohort study.PatientsProspectively enrolled patients (n=36, 17 female) from the Prospective Monoamine-producing Tumor Study (PMT) with hormonally active PPGL and 36 matched controls in whom PPGL was rigorously excluded.ResultsAmong 188 measured metabolites, only without considering false discovery rate, 4 exhibited statistically significant differences between patients with PPGL and controls (histidine p=0.004, threonine p=0.008, lyso PC a C28:0 p=0.044, sum of hexoses p=0.018). Weak, but significant correlations for histidine, threonine and lyso PC a C28:0 with total urine catecholamine levels were identified. Only the sum of hexoses (reflecting glucose) showed significant correlations with plasma metanephrines.By using ML-based feature selection approaches, we identified diagnostic signatures which all exhibited low accuracy and sensitivity. The best predictive value (sensitivity 87.5%, accuracy 67.3%) was obtained by using Gradient Boosting Machine Modelling.ConclusionsThe diabetogenic effect of catecholamine excess dominates the plasma metabolome in PPGL patients. While curative surgery for PPGL led to normalization of catecholamine-induced alterations of metabolomics in individual patients, plasma metabolomics are not useful for diagnostic purposes, most likely due to inter-individual variability.

The Role of Raman Spectroscopy Within Quantitative Metabolomics

Ninety-two years have passed since the discovery of the Raman effect, and there are currently more than 25 different types of Raman-based techniques. The past two decades have witnessed the blossoming of Raman spectroscopy as a powerful physicochemical technique with broad applications within the life sciences. In this review, we critique the use of Raman spectroscopy as a tool for quantitative metabolomics. We overview recent developments of Raman spectroscopy for identification and quantification of disease biomarkers in liquid biopsy, with a focus on the recent advances within surface-enhanced Raman scattering–based methods. Ultimately, we discuss the applications of imaging modalities based on Raman scattering as label-free methods to study the abundance and distribution of biomolecules in cells and tissues, including mammalian, algal, and bacterial cells. Expected final online publication date for the Annual Review of Analytical Chemistry, Volume 14 is June 2021. Please see http://www.annualreviews.org/page/journal/pubdates for revised estimates.

Calibration-Curve-Locking Database for Semi-Quantitative Metabolomics by Gas Chromatography/Mass Spectrometry

Calibration-Curve-Locking Databases (CCLDs) have been constructed for automatic compound search and semi-quantitative screening by gas chromatography/mass spectrometry (GC/MS) in several fields. CCLD felicitates the semi-quantification of target compounds without calibration curve preparation because it contains the retention time (RT), calibration curves, and electron ionization (EI) mass spectra, which are obtained under stable apparatus conditions. Despite its usefulness, there is no CCLD for metabolomics. Herein, we developed a novel CCLD and semi-quantification framework for GC/MS-based metabolomics. All analytes were subjected to GC/MS after derivatization under stable apparatus conditions using (1) target tuning, (2) RT locking technique, and (3) automatic derivatization and injection by a robotic platform. The RTs and EI mass spectra were obtained from an existing authorized database. A quantifier ion and one or two qualifier ions were selected for each target metabolite. The calibration curves were obtained as plots of the peak area ratio of the target compounds to an internal standard versus the target compound concentration. These data were registered in a database as a novel CCLD. We examined the applicability of CCLD for analyzing human plasma, resulting in time-saving and labor-saving semi-qualitative screening without the need for standard substances.

A rapid, simultaneous and quantitative analysis of 26 ginsenosides in white and red Panax ginseng using LC–MS/MS

A quantitative analysis of ginsenoside is very important for ginseng studies because each ginsenoside shows different medical activity and metabolic pathway. In this study, a rapid, simultaneous, and quantitative analysis of 26 ginsenosides (Rb1, Rb2, Rc, Rd, Re, Rf, Rg1, Rg2(R), Rg2(S), Rg3(S), Rg3(R), Rg5, Rg6, Rh1(R), Rh1(S), Rh2(R), Rh2(S), F1, F2, F3, F4, K, Mc, PPT(S), XVII, and Y) in white, and red Panax ginseng was established using multiple reaction monitoring (MRM) mode on ultra-high-performance liquid chromatography-tandem mass spectrometry (UHPLC–MS/MS). The mobile phase of water and methanol containing 0.1% formic acid and HSS T3 C18 analytical column was used for the chromatographic separation. The four sets of stereoisomers were successfully separated within a 26-min run time, eluting the S-isomer faster than the R-isomer with higher concentration. The ginseng extract was diluted by 100, 400 and 8000 times to fit in the calibration range and quantitated by the standard addition method. Matrix matched calibration by mixing 64 µL of the ginseng extract with 16 µL of the standard solution was used for compensating the matrix effect. Such quantitation methodology using dilution, standard addition and matrix matching resulted in precise and unambiguous quantitation of 26 ginsenosides in ginseng products. Major ginsenosides were observed at relatively higher concentrations in red Panax ginseng and the Mc was detected and quantitated for the first time in this study. The comprehensive quantitation system established in this study will contribute to quality evaluation, breeding and culturing, and quantitative metabolomics study of ginseng.