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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
| Content Provider | Semantic Scholar |
|---|---|
| Author | Chekmeneva, Elena Correia, Gonçalo Dos Santos Gómez-Romero, María Stamler, Jeremiah Chan, Queenie Elliott, Paul Nicholson, Jeremy K. Holmes, Elaine |
| Copyright Year | 2018 |
| Abstract | 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. |
| Starting Page | 3492 |
| Ending Page | 3502 |
| Page Count | 11 |
| File Format | PDF HTM / HTML |
| PubMed reference number | 30183320v1 |
| Alternate Webpage(s) | https://doi.org/10.1021/acs.jproteome.8b00413 |
| DOI | 10.1021/acs.jproteome.8b00413 |
| Journal | Journal of proteome research |
| Volume Number | 17 |
| Language | English |
| Access Restriction | Open |
| Subject Keyword | Brain Diseases, Metabolic Cost Effectiveness Diabetes Insipidus Epidemiology Liquid Chromatography Metabolic Process, Cellular Metabolic Profile Metabolite Metabolomics Ploidies Spectrometry |
| Content Type | Text |
| Resource Type | Article |
