CHDWB human plasma exposomics analysis - 1
Ontology highlight
ABSTRACT:
ORGANISM(S): Homo Sapiens
TISSUE(S): Blood
PROVIDER: ST001801 | MetabolomicsWorkbench |
REPOSITORIES: MetabolomicsWorkbench
Dataset's files
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| mwtab | Other |
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Project description:The challenge of chemical exposomics in human plasma is the 1000-fold concentration gap between endogenous substances and environmental pollutants. Phospholipids are the major endogenous small molecules in plasma, thus we validated a chemical exposomics protocol with an optimized phospholipid-removal step prior to targeted and non-targeted liquid chromatography high-resolution mass spectrometry. Increased injection volume with negligible matrix effect permitted sensitive multiclass targeted analysis of 77 priority analytes; median MLOQ = 0.05 ng/mL for 200 μL plasma. In non-targeted acquisition, mean total signal intensities of non-phospholipids were enhanced 6-fold in positive (max 28-fold) and 4-fold in negative mode (max 58-fold) compared to a control method without phospholipid removal. Moreover, 109 and 28% more non-phospholipid molecular features were detected by exposomics in positive and negative mode, respectively, allowing new substances to be annotated that were non-detectable without phospholipid removal. In individual adult plasma (100 μL, n = 34), 28 analytes were detected and quantified among 10 chemical classes, and quantitation of per- and polyfluoroalkyl substances (PFAS) was externally validated by independent targeted analysis. Retrospective discovery and semi-quantification of PFAS-precursors was demonstrated, and widespread fenuron exposure is reported in plasma for the first time. The new exposomics method is complementary to metabolomics protocols, relies on open science resources, and can be scaled to support large studies of the exposome.
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Project description:For comprehensive chemical exposomics in blood, analytical workflows are evolving through advances in sample preparation and instrumental methods. We hypothesized that gas chromatography-high-resolution mass spectrometry (GC-HRMS) workflows could be enhanced by minimizing lipid coextractives, thereby enabling larger injection volumes and lower matrix interference for improved target sensitivity and nontarget molecular discovery. A simple protocol was developed for small plasma volumes (100-200 μL) by using isohexane (H) to extract supernatants of acetonitrile-plasma (A-P). The HA-P method was quantitative for a wide range of hydrophobic multiclass target analytes (i.e., log Kow > 3.0), and the extracts were free of major lipids, thereby enabling robust large-volume injections (LVIs; 25 μL) in long sequences (60-70 h, 70-80 injections) to a GC-Orbitrap HRMS. Without lipid removal, LVI was counterproductive because method sensitivity suffered from the abundant matrix signal, resulting in low ion injection times to the Orbitrap. The median method quantification limit was 0.09 ng/mL (range 0.005-4.83 ng/mL), and good accuracy was shown for a certified reference serum. Applying the method to plasma from a Swedish cohort (n = 32; 100 μL), 51 of 103 target analytes were detected. Simultaneous nontarget analysis resulted in 112 structural annotations (12.8% annotation rate), and Level 1 identification was achieved for 7 of 8 substances in follow-up confirmations. The HA-P method is potentially scalable for application in cohort studies and is also compatible with many liquid-chromatography-based exposomics workflows.
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2021-09-03 | MSV000088054 | MassIVE
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Project description:Exposome analysis in humans. Negative ion mode data. Reverse phase chromatography.
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Project description:Liquid chromatography coupled to high-resolution mass spectrometry (LC-HRMS) and untargeted metabolomics are increasingly used in exposome studies to study the interactions between nongenetic factors and the blood metabolome. To reliably and efficiently link detected compounds to exposures and health phenotypes in such studies, it is important to understand the variability in metabolome measures. We assessed the within- and between-subject variability of untargeted LC-HRMS measurements in 298 nonfasting human serum samples collected on two occasions from 157 subjects. Samples were collected ca. 107 (IQR: 34) days apart as part of the multicenter EXPOsOMICS Personal Exposure Monitoring study. In total, 4294 metabolic features were detected, and 184 unique compounds could be identified with high confidence. The median intraclass correlation coefficient (ICC) across all metabolic features was 0.51 (IQR: 0.29) and 0.64 (IQR: 0.25) for the 184 uniquely identified compounds. For this group, the median ICC marginally changed (0.63) when we included common confounders (age, sex, and body mass index) in the regression model. When grouping compounds by compound class, the ICC was largest among glycerophospholipids (median ICC 0.70) and steroids (0.67), and lowest for amino acids (0.61) and the O-acylcarnitine class (0.44). ICCs varied substantially within chemical classes. Our results suggest that the metabolome as measured with untargeted LC-HRMS is fairly stable (ICC > 0.5) over 100 days for more than half of the features monitored in our study, to reflect average levels across this time period. Variance across the metabolome will result in differential measurement error across the metabolome, which needs to be considered in the interpretation of metabolome results.
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