= Emerging. More than 5 years before clinical availability. (24.37%, 2023)
= Expected to be clinically available in 1 to 4 years. (39.50%, 2023)
= Clinically available now. (36.13%, 2023)
MSACL 2023 : Wishart

MSACL 2023 Abstract

Self-Classified Topic Area(s): Metabolomics > Assays Leveraging MS > Precision Medicine

Podium Presentation in Steinbeck 2 on Thursday at 8:45 (Chair: William Perry / Erika Dorado)

A Quantitative Assay for Measuring 1000 Metabolites in Serum, Urine and Fecal Samples

David S. Wishart (1,2), Lun Zhang (1), Jiamin Zheng (1), Mathew Johnson (1), Rupasri Mandal (1)
Departments of Biological Sciences (1) and Computing Science (2), University of Alberta, Edmonton, AB, Canada T6G 2R3

David Wishart, PhD (Presenter)
University of Alberta

Presenter Bio: Dr. David Wishart (PhD Yale, 1991) was born and raised in Edmonton and identifies as Metis. He is Fellow of the Royal Society of Canada. Since 1995, he has been a professor at the University of Alberta. Currently, he is a Distinguished University Professor in the Departments of Biological Sciences and Computing Science with adjunct appointments in the Faculty of Pharmaceutical Sciences and the Department of Pathology and Laboratory Medicine. His research interests are broad and include metabolomics, analytical chemistry, food chemistry, natural product chemistry, molecular biology, protein chemistry and neuroscience. He has developed several widely used techniques using NMR spectroscopy, mass spectrometry, liquid chromatography and gas chromatography to characterize the structures of both large and small molecules. He has led the “Human Metabolome Project” (HMP), a multi-university, multi-investigator project that catalogued >250,000 human metabolites in human tissues and biofluids. This information has been archived on a freely accessible web-resource called the Human Metabolome Database (HMDB). More recently, his efforts have focused on characterizing the chemical constituents in various foods (through the FooDB database) and food-associated biomarkers. His lab has used machine learning and artificial intelligence to create >60 useful databases and software tools to help characterize and identify microbial metabolites, drugs, pesticides, and natural products. During his career, Dr. Wishart has published >500 papers in high impact journals on many subject areas and has been cited >100,000 times.


Metabolomics involves the comprehensive characterization of small molecule metabolites in specific biological specimens, such as blood, urine, feces and saliva. Metabolomics has traditionally been divided into two complementary but somewhat different approaches: untargeted and targeted metabolomics. Untargeted metabolomics is a discovery-based approach that aims to “qualitatively” measure all detectable spectral features over a large number of biospecimens. Targeted metabolomics studies are aimed at quantitatively measuring a small number of targeted compounds that can be readily used in clinical chemistry, medical diagnoses, and disease biomarker discovery studies. While untargeted methods can generate data on up to 1000 compounds, targeted methods usually generate data only on 100-200 compounds.

In an effort to allow targeted metabolomics to achieve the same coverage as untargeted metabolomics, we have developed a custom made, comprehensive, quantitative LC-MS/MS-based assay for targeted metabolomics analysis of biospeimens such as serum, urine and fecal extracts. This assay allows for the identification of up to 1000 metabolites along with another 400 metabolite sums and ratios.

Our method uses a combination of direct injection (DI) mass spectrometry with a reverse-phase LC-MS/MS. It combines the derivatization and extraction of analytes, and the selective mass-spectrometric detection using multiple reaction monitoring (MRM) pairs. Two separate panels involving two different precolumn derivatization reactions were developed for this assay: Panel A - Phenylisothiocyanate (PITC) derivatization targeting amine-containing compounds and Panel B - 3-nitrophenylhydrazine (3-NPH) derivatization targeting keto- and carboxyl-containing compounds. Isotopically-labeled internal standards are used for metabolite quantification. Calibration of metabolite concentration ranges in both panels was adjusted for different biofluid types.

For panel A, a 96 deep-well plate with a filter plate attached via sealing tape, containing the required reagents and solvents, was used to prepare the plate assay. The first 14 wells of each plate are used for calibration and quality control purposes. For all metabolites, except organic acids, samples are first thawed on ice and then vortexed and centrifuged at 13,000x g. 10 µL of each sample are loaded onto the center of the filter on the upper 96-well plate and dried in a stream of nitrogen. Subsequently, PITC is added for derivatization. After incubation, the filter spots are dried again using an evaporator. Extraction of the metabolites is then achieved by adding 300 µL of extraction solvent. The extracts are obtained by centrifugation into the lower 96-deep well plate, followed by a dilution step with MS running solvent.

For organic acid analysis (panel B), 90 µL of ice-cold methanol is added to 30 µL of each sample for overnight protein precipitation. The sample is centrifuged at 13000x g for 20 min. 50 µL of supernatant was loaded into the center of wells of a 96-deep well plate, followed by the addition of NPH. After incubation for 2h, isotope-labeled internal standards, BHT stabilizer and water are added before LC-MS injection.

Mass spectrometric analysis was performed on Sciex 5500 QTrap® tandem MS instrument equipped with an Agilent 1290 series UHPLC system. The samples are delivered to the MS by a standard LC method followed by a direct injection (DI) method. Data analysis was done using Analyst 1.6.2. Calibration regression, accuracy and precision of QC standards, and spiked recovery of each targeted metabolite were used for method validation.

This custom assay can be used for the targeted identification and quantification of up to 1000 metabolites across 21 chemical classes including amino acids and derivatives, biogenic amines, organic acids, nucleotides/nucleosides, ketone and keto acids, indole derivatives, vitamins and derivatives, sulfates, dipeptides and 11 different classes of lipids. In addition, 400 biomedically related metabolite sums and ratios can be calculated from concentrations of these 1000 targeted metabolites. In total, this custom assay can measure up to 1400 metabolites and ratios/sums. The accuracy of QC standards with 3 different concentration levels are in the range of 80% to 120% with satisfactory precision values of less than 20%. The recovery rates of spiked serum, urine and fecal extract samples with three different concentration levels are in the range of 80% to 120% with satisfactory precision values of less than 20%. More than 1500 biological samples such as serum, urine, and fecal extracts have been analyzed in our laboratory variations of this assay since 2021. The analysis time is approximately 1 sample/hour on a single LC-MS/MS instrument or about 100 samples over 4 days.

We have developed a comprehensive, sensitive, high-throughput, low-volume, quantitative targeted LC-MS/MS assay for the analysis of up to 1400 metabolites and ratios, across 21 chemical classes for serum, urine, and fecal extracts. Only 40 µL of a given sample is required for the entire analysis. We have adapted this assay into a 96-well plate format to enable high-throughput analysis. This targeted assay will be able to deliver the same metabolite coverage with far more precise quantification, over much shorter periods of time at much lower costs than the most comprehensive untargeted assays. We believe that targeted metabolomics assays can now exceed the performance of untargeted assays and will continue to pull away from untargeted methods in terms of both performance and utility over the coming years.

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