Ultra-Performance Liquid Chromatography-High-Resolution Mass Spectrometry and Direct Infusion-High-Resolution Mass Spectrometry for Combined Exploratory and Targeted Metabolic Profiling of Human Urine.
Elena ChekmenevaGonçalo Dos Santos CorreiaMaría Gómez-RomeroJeremiah StamlerQueenie ChanPaul ElliottJeremy K NicholsonElaine HolmesPublished in: Journal of proteome research (2018)
The application of metabolic phenotyping to epidemiological studies involving thousands of biofluid samples presents a challenge for the selection of analytical platforms that meet the requirements of high-throughput precision analysis and cost-effectiveness. Here direct infusion-nanoelectrospray (DI-nESI) was compared with an ultra-performance liquid chromatography (UPLC)-high-resolution mass spectrometry (HRMS) method for metabolic profiling of an exemplary set of 132 human urine samples from a large epidemiological cohort. Both methods were developed and optimized to allow the simultaneous collection of high-resolution urinary metabolic profiles and quantitative data for a selected panel of 35 metabolites. The total run time for measuring the sample set in both polarities by UPLC-HRMS was 5 days compared with 9 h by DI-nESI-HRMS. To compare the classification ability of the two MS methods, we performed exploratory analysis of the full-scan HRMS profiles to detect sex-related differences in biochemical composition. Although metabolite identification is less specific in DI-nESI-HRMS, the significant features responsible for discrimination between sexes were mostly the same in both MS-based platforms. Using the quantitative data, we showed that 10 metabolites have strong correlation (Pearson's r > 0.9 and Passing-Bablok regression slope of 0.8-1.3) and good agreement assessed by Bland-Altman plots between UPLC-HRMS and DI-nESI-HRMS and thus can be measured using a cheaper and less sample- and time-consuming method. A further twenty metabolites showed acceptable correlation between the two methods with only five metabolites showing weak correlation (Pearson's r < 0.4) and poor agreement due to the overestimation of the results by DI-nESI-HRMS.
Keyphrases
- high resolution mass spectrometry
- liquid chromatography
- mass spectrometry
- high resolution
- simultaneous determination
- ultra high performance liquid chromatography
- tandem mass spectrometry
- ms ms
- gas chromatography
- high throughput
- endothelial cells
- biofilm formation
- solid phase extraction
- multiple sclerosis
- low dose
- single cell
- computed tomography
- machine learning
- induced pluripotent stem cells
- deep learning
- staphylococcus aureus
- magnetic resonance
- high speed
- artificial intelligence
- cancer therapy
- case control