scholarly journals Development of an Accurate Mass Retention Time Database for Untargeted Metabolomic Analysis and Its Application to Plasma and Urine Pediatric Samples

Molecules ◽  
2021 ◽  
Vol 26 (14) ◽  
pp. 4256
Author(s):  
Chiara Lavarello ◽  
Sebastiano Barco ◽  
Martina Bartolucci ◽  
Isabella Panfoli ◽  
Emanuele Magi ◽  
...  
Keyword(s):  
Retention Time ◽  
High Resolution Mass Spectrometry ◽  
Reversed Phase ◽  
Urine Samples ◽  
Accurate Mass ◽  
Metabolomic Analysis ◽  
Orbitrap Mass Spectrometer ◽  
Starting Point ◽  
Thermo Fisher Scientific ◽  
Plasma And Urine Samples

Liquid-chromatography coupled to high resolution mass spectrometry (LC-HRMS) is currently the method of choice for untargeted metabolomic analysis. The availability of established protocols to achieve a high confidence identification of metabolites is crucial. The aim of this work is to describe the workflow that we have applied to build an Accurate Mass Retention Time (AMRT) database using a commercial metabolite library of standards. LC-HRMS analysis was carried out using a Vanquish Horizon UHPLC system coupled to a Q-Exactive Plus Hybrid Quadrupole-Orbitrap Mass Spectrometer (Thermo Fisher Scientific, Milan, Italy). The fragmentation spectra, obtained with 12 collision energies, were acquired for each metabolite, in both polarities, through flow injection analysis. Several chromatographic conditions were tested to obtain a protocol that yielded stable retention times. The adopted chromatographic protocol included a gradient separation using a reversed phase (Waters Acquity BEH C18) and a HILIC (Waters Acquity BEH Amide) column. An AMRT database of 518 compounds was obtained and tested on real plasma and urine samples analyzed in data-dependent acquisition mode. Our AMRT library allowed a level 1 identification, according to the Metabolomics Standards Initiative, of 132 and 124 metabolites in human pediatric plasma and urine samples, respectively. This library represents a starting point for future metabolomic studies in pediatric settings.

Interpretable machine learning model to detect chemically adulterated urine samples analyzed by high resolution mass spectrometry

2021 ◽  
Vol 0 (0) ◽  
Author(s):  
Gabriel L. Streun ◽  
Andrea E. Steuer ◽  
Lars C. Ebert ◽  
Akos Dobay ◽  
Thomas Kraemer
Keyword(s):  
Machine Learning ◽  
Mass Spectrometry ◽  
Liquid Chromatography ◽  
High Resolution ◽  
Retention Time ◽  
Drug Testing ◽  
High Resolution Mass Spectrometry ◽  
Urine Samples ◽  
Machine Learning Model ◽  
Resolution Mass

Abstract Objectives Urine sample manipulation including substitution, dilution, and chemical adulteration is a continuing challenge for workplace drug testing, abstinence control, and doping control laboratories. The simultaneous detection of sample manipulation and prohibited drugs within one single analytical measurement would be highly advantageous. Machine learning algorithms are able to learn from existing datasets and predict outcomes of new data, which are unknown to the model. Methods Authentic human urine samples were treated with pyridinium chlorochromate, potassium nitrite, hydrogen peroxide, iodine, sodium hypochlorite, and water as control. In total, 702 samples, measured with liquid chromatography coupled to quadrupole time-of-flight mass spectrometry, were used. After retention time alignment within Progenesis QI, an artificial neural network was trained with 500 samples, each featuring 33,448 values. The feature importance was analyzed with the local interpretable model-agnostic explanations approach. Results Following 10-fold cross-validation, the mean sensitivity, specificity, positive predictive value, and negative predictive value was 88.9, 92.0, 91.9, and 89.2%, respectively. A diverse test set (n=202) containing treated and untreated urine samples could be correctly classified with an accuracy of 95.4%. In addition, 14 important features and four potential biomarkers were extracted. Conclusions With interpretable retention time aligned liquid chromatography high-resolution mass spectrometry data, a reliable machine learning model could be established that rapidly uncovers chemical urine manipulation. The incorporation of our model into routine clinical or forensic analysis allows simultaneous LC-MS analysis and sample integrity testing in one run, thus revolutionizing this field of drug testing.

scholarly journals A Comprehensive LC-QTOF-MS Metabolic Phenotyping Strategy: Application to Alkaptonuria

2019 ◽  
Vol 65 (4) ◽  
pp. 530-539 ◽  
Author(s):  
Brendan P Norman ◽  
Andrew S Davison ◽  
Gordon A Ross ◽  
Anna M Milan ◽  
Andrew T Hughes ◽  
...  
Keyword(s):  
Mass Spectrometry ◽  
Liquid Chromatography ◽  
Retention Time ◽  
Metabolic Pathways ◽  
Reversed Phase ◽  
Normal Phase ◽  
Accurate Mass ◽  
Metabolic Phenotyping ◽  
Flight Mass Spectrometry ◽  
Targeted Profiling

Abstract BACKGROUND Identification of unknown chemical entities is a major challenge in metabolomics. To address this challenge, we developed a comprehensive targeted profiling strategy, combining 3 complementary liquid chromatography quadrupole time-of-flight mass spectrometry (LC-QTOF-MS) techniques and in-house accurate mass retention time (AMRT) databases established from commercial standards. This strategy was used to evaluate the effect of nitisinone on the urinary metabolome of patients and mice with alkaptonuria (AKU). Because hypertyrosinemia is a known consequence of nitisinone therapy, we investigated the wider metabolic consequences beyond hypertyrosinemia. METHODS A total of 619 standards (molecular weight, 45–1354 Da) covering a range of primary metabolic pathways were analyzed using 3 liquid chromatography methods—2 reversed phase and 1 normal phase—coupled to QTOF-MS. Separate AMRT databases were generated for the 3 methods, comprising chemical name, formula, theoretical accurate mass, and measured retention time. Databases were used to identify chemical entities acquired from nontargeted analysis of AKU urine: match window theoretical accurate mass ±10 ppm and retention time ±0.3 min. RESULTS Application of the AMRT databases to data acquired from analysis of urine from 25 patients with AKU (pretreatment and after 3, 12, and 24 months on nitisinone) and 18 HGD−/− mice (pretreatment and after 1 week on nitisinone) revealed 31 previously unreported statistically significant changes in metabolite patterns and abundance, indicating alterations to tyrosine, tryptophan, and purine metabolism after nitisinone administration. CONCLUSIONS The comprehensive targeted profiling strategy described here has the potential of enabling discovery of novel pathways associated with pathogenesis and management of AKU.

scholarly journals Metabolomic Analysis via Reversed-Phase Ion-Pairing Liquid Chromatography Coupled to a Stand Alone Orbitrap Mass Spectrometer

2010 ◽  
Vol 82 (8) ◽  
pp. 3212-3221 ◽  
Author(s):  
Wenyun Lu ◽  
Michelle F. Clasquin ◽  
Eugene Melamud ◽  
Daniel Amador-Noguez ◽  
Amy A. Caudy ◽  
...  
Keyword(s):  
Liquid Chromatography ◽  
Mass Spectrometer ◽  
Ion Pairing ◽  
Reversed Phase ◽  
Metabolomic Analysis ◽  
Orbitrap Mass Spectrometer

A Fast and Sensitive Method Combining Reversed-Phase Chromatography with High Resolution Mass Spectrometry to Quantify 2-Fluoro-2-Deoxyglucose and Its Phosphorylated Metabolite for Determining Glucose Uptake

2019 ◽  
Author(s):  
Ashley Williams ◽  
Deborah Muoio ◽  
Guofang Zhang
Keyword(s):  
Glucose Uptake ◽  
Biological Samples ◽  
High Resolution Mass Spectrometry ◽  
Reversed Phase ◽  
Sodium Adduct ◽  
Negative Mode ◽  
Glucose Analogues ◽  
Positive Mode ◽  
Q Exactive ◽  
Glucose Analogue

Quantative measurements of the glucose analogue, 2-deoxyglucose (2DG), and its phosphorylated metabolite (2-deoxyglucose-6-phosphate (2DG-6-P)) are critical for the measurement of glucose uptake. While the field has long identified the need for sensitive and reliable assays that deploy non-radiolabled glucose analogues to assess glucose uptake, no analytical MS-based methods exist to detect trace amounts in complex biological samples. In the present work, we show that 2DG is poorly suited for MS-based methods due to interfering metabolites. We therefore developed and validated an alternative C18-based LC-Q-Exactive-Orbitrap-MS method using 2-fluoro-2-deoxyglucose (2FDG) to quantify both 2FDG and 2FDG-6-P by measuring the sodium adduct of 2FDG in the positive mode and deprotonation of 2FDG-6-P in the negative mode. The low detection limit of this method can reach 81.4 and 48.8 fmol for both 2FDG and 2FDG-6-P, respectively. The newly developed method was fully validated via calibration curves in the presence and absence of biological matrix. The present work is the first successful LC-MS method that can quantify trace amounts of a nonradiolabeled glucose analogue and its phosphorylated metabolite and is a promising analytical method to determine glucose uptake in biological samples.

scholarly journals A combined flow injection/reversed phase chromatography – high resolution mass spectrometry workflow for accurate absolute lipid quantification with 13C- internal standards

The Analyst ◽  
2021 ◽  
Author(s):  
Harald Schoeny ◽  
Evelyn Rampler ◽  
Yasin El Abiead ◽  
Felina Hildebrand ◽  
Olivia Zach ◽  
...  
Keyword(s):  
Mass Spectrometry ◽  
High Resolution ◽  
Flow Injection ◽  
High Resolution Mass Spectrometry ◽  
Reversed Phase ◽  
Internal Standards ◽  
Lipid Quantification ◽  
High Resolution Mass ◽  
Resolution Mass

We propose a fully automated novel workflow for lipidomics based on flow injection- followed by liquid chromatography high resolution mass spectrometry (FI/LC-HRMS). The workflow combined in-depth characterization of the lipidome...

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