Sample preparation strategies for mass spectrometry analysis in human exposome research: Current status and future perspectives

Gu, Yu; Peach, Jesse T.; Warth, Benedikt

doi:10.1016/j.trac.2023.117151

Cited by 12 publications

(13 citation statements)

References 93 publications

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“…A summary of the validation results is listed in Table 1 and the detailed results are reported in Tables S4–S5 . In comprehensive multi-analyte human biomonitoring assays, pragmatic compromises are essential to keep a fine balance between covering as many analytes as possible while ensuring high sensitivity and minimal matrix interferences [ 48 ]. Consequently, it was not expected that all 86 analytes will perform ideally applying this method.…”

Section: Resultsmentioning

confidence: 99%

Polyphenol exposure of mothers and infants assessed by LC–MS/MS based biomonitoring in breast milk

Berger,

Oesterle,

Ayeni

et al. 2024

Anal Bioanal Chem

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Exposure to polyphenols is relevant throughout critical windows of infant development, including the breastfeeding phase. However, the quantitative assessment of polyphenols in human breast milk has received limited attention so far, though polyphenols may positively influence infant health. Therefore, a targeted LC–MS/MS assay was developed to investigate 86 analytes representing different polyphenol classes in human breast milk. The sample preparation consisted of liquid extraction, salting out, freeze-out, and a dilution step. Overall, nearly 70% of the chemically diverse polyphenols fulfilled all strict validation criteria for full quantitative assessment. The remaining analytes did not fulfill all criteria at every concentration level, but can still provide useful semi-quantitative insights into nutritional and biomedical research questions. The limits of detection for all analyzed polyphenols were in the range of 0.0041–87 ng*mL−1, with a median of 0.17 ng*mL−1. Moreover, the mean recovery was determined to be 82% and the mean signal suppression and enhancement effect was 117%. The developed assay was applied in a proof-of-principle study to investigate polyphenols in breast milk samples provided by twelve Nigerian mothers at three distinct time points post-delivery. In total, 50 polyphenol analytes were detected with almost half being phenolic acids. Phase II metabolites, including genistein-7-β-D-glucuronide, genistein-7-sulfate, and daidzein-7-β-D-glucuronide, were also detected in several samples. In conclusion, the developed method was demonstrated to be fit-for-purpose to simultaneously (semi-) quantify a wide variety of polyphenols in breast milk. It also demonstrated that various polyphenols including their biotransformation products were present in breast milk and therefore likely transferred to infants where they might impact microbiome development and infant health. Graphical abstract

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Section: Resultsmentioning

confidence: 99%

Polyphenol exposure of mothers and infants assessed by LC–MS/MS based biomonitoring in breast milk

Berger,

Oesterle,

Ayeni

et al. 2024

Anal Bioanal Chem

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“…In 2005, the concept of “exposome” was coined, which comprehends every exposure an individual suffers throughout their lifetime, involving internal processes and external interactions [ 7 , 8 ]. An approach to evaluate the latter is by linking the potential exposures to the presence of endogenous and/or exogenous substances (i.e., xenobiotics) in the human body [9] . Nonetheless, the scope of targeted biomonitoring assays is limited to a few hundred compounds at a time [10] , which contrasts with the over 193 million compounds registered in the Chemical Abstracts Service (CAS) by 2023 [11] .…”

Section: Methods Detailsmentioning

confidence: 99%

“…Still, samples need to be pre-processed to remove interferences or pre-concentrate the potential suspect xenobiotics, among others. In that sense, the chemical coverage aimed at exposomic studies is reduced, in pursuit of minimizing matrix effects [9] .…”

Section: Methods Detailsmentioning

confidence: 99%

Sample preparation for suspect and non-target screening of xenobiotics in human biofluids by liquid chromatography—High resolution tandem mass spectrometry

Baciero-Hernández,

Musatadi,

Olivares

et al. 2024

MethodsX

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“…With the small size of PM 2.5 , these particulates can penetrate deeply into the lungs and bloodstream, creating adverse health effects related to the respiratory system [3], increased mortality [4], heart disease [5], inflammatory responses, and adverse birth-related effects [6]. The pollutants we considered are exemplars of the wider human exposome [7][8][9], which refers to the comprehensive accumulation of all environmental exposures that an individual encounters throughout their lifetime, including chemicals and biological agents. The exposome encompasses exposures to both gases and particulates, and appropriate care should be taken to include the often ignored ultrafine particulates [10].…”

Section: Introductionmentioning

confidence: 99%

Quantifying Inhaled Concentrations of Particulate Matter, Carbon Dioxide, Nitrogen Dioxide, and Nitric Oxide Using Observed Biometric Responses with Machine Learning

Ruwali,

Talebi,

Fernando

et al. 2024

BioMedInformatics

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Introduction: Air pollution has numerous impacts on human health on a variety of time scales. Pollutants such as particulate matter—PM1 and PM2.5, carbon dioxide (CO2), nitrogen dioxide (NO2), and nitric oxide (NO) are exemplars of the wider human exposome. In this study, we adopted a unique approach by utilizing the responses of human autonomic systems to gauge the abundance of pollutants in inhaled air. Objective: To investigate how the human body autonomically responds to inhaled pollutants in microenvironments, including PM1, PM2.5, CO2, NO2, and NO, on small temporal and spatial scales by making use of biometric observations of the human autonomic response. To test the accuracy in predicting the concentrations of these pollutants using biological measurements of the participants. Methodology: Two experimental approaches having a similar methodology that employs a biometric suite to capture the physiological responses of cyclists were compared, and multiple sensors were used to measure the pollutants in the air surrounding them. Machine learning algorithms were used to estimate the levels of these pollutants and decipher the body’s automatic reactions to them. Results: We observed high precision in predicting PM1, PM2.5, and CO2 using a limited set of biometrics measured from the participants, as indicated with the coefficient of determination (R2) between the estimated and true values of these pollutants of 0.99, 0.96, and 0.98, respectively. Although the predictions for NO2 and NO were reliable at lower concentrations, which was observed qualitatively, the precision varied throughout the data range. Skin temperature, heart rate, and respiration rate were the common physiological responses that were the most influential in predicting the concentration of these pollutants. Conclusion: Biometric measurements can be used to estimate air quality components such as PM1, PM2.5, and CO2 with high degrees of accuracy and can also be used to decipher the effect of these pollutants on the human body using machine learning techniques. The results for NO2 and NO suggest a requirement to improve our models with more comprehensive data collection or advanced machine learning techniques to improve the results for these two pollutants.

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Sample preparation strategies for mass spectrometry analysis in human exposome research: Current status and future perspectives

Cited by 12 publications

References 93 publications

Polyphenol exposure of mothers and infants assessed by LC–MS/MS based biomonitoring in breast milk

Polyphenol exposure of mothers and infants assessed by LC–MS/MS based biomonitoring in breast milk

Sample preparation for suspect and non-target screening of xenobiotics in human biofluids by liquid chromatography—High resolution tandem mass spectrometry

Quantifying Inhaled Concentrations of Particulate Matter, Carbon Dioxide, Nitrogen Dioxide, and Nitric Oxide Using Observed Biometric Responses with Machine Learning

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