Tailored Mass Spectral Data Exploration Using the SpecXplore Interactive Dashboard.

Untargeted metabolomics promises comprehensive characterization of small molecules in biological samples. However, the field is hampered by low annotation rates and abstract spectral data. Despite recent advances in computational metabolomics, manual annotations and manual confirmation of in-silico annotations remain important in the field. Here, exploratory data analysis methods for mass spectral data provide overviews, prioritization, and structural hypothesis starting points to researchers facing large quantities of spectral data. In this research, we propose a fluid means of dealing with mass spectral data using specXplore, an interactive Python dashboard providing interactive and complementary visualizations facilitating mass spectral similarity matrix exploration. Specifically, specXplore provides a two-dimensional t-distributed stochastic neighbor embedding embedding as a jumping board for local connectivity exploration using complementary interactive visualizations in the form of partial network drawings, similarity heatmaps, and fragmentation overview maps. SpecXplore makes use of state-of-the-art ms2deepscore pairwise spectral similarities as a quantitative backbone while allowing fast changes of threshold and connectivity limitation settings, providing flexibility in adjusting settings to suit the localized node environment being explored. We believe that specXplore can become an integral part of mass spectral data exploration efforts and assist users in the generation of structural hypotheses for compounds of interest.

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

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-ß-D-glucuronide, genistein-7-sulfate, and daidzein-7-ß-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.

Exposomic Biomonitoring of Polyphenols by Non-Targeted Analysis and Suspect Screening.

Polyphenols, prevalent in plants and fungi, are investigated intensively in nutritional and clinical settings because of their beneficial bioactive properties. Due to their complexity, analysis with untargeted approaches is favorable, which typically use high-resolution mass spectrometry (HRMS) rather than low-resolution mass spectrometry (LRMS). Here, the advantages of HRMS were evaluated by thoroughly testing untargeted techniques and available online resources. By applying data-dependent acquisition on real-life urine samples, 27 features were annotated with spectral libraries, 88 with in silico fragmentation, and 113 by MS1 matching with PhytoHub, an online database containing >2000 polyphenols. Moreover, other exogenous and endogenous molecules were screened to measure chemical exposure and potential metabolic effects using the Exposome-Explorer database, further annotating 144 features. Additional polyphenol-related features were explored using various non-targeted analysis techniques including MassQL for glucuronide and sulfate neutral losses, and MetaboAnalyst for statistical analysis. As HRMS typically suffers a sensitivity loss compared to state-of-the-art LRMS used in targeted workflows, the gap between the two instrumental approaches was quantified in three spiked human matrices (urine, serum, plasma) as well as real-life urine samples. Both instruments showed feasible sensitivity, with median limits of detection in the spiked samples being 10-18 ng/mL for HRMS and 4.8-5.8 ng/mL for LRMS. The results demonstrate that, despite its intrinsic limitations, HRMS can readily be used for comprehensively investigating human polyphenol exposure. In the future, this work is expected to allow for linking human health effects with exposure patterns and toxicological mixture effects with other xenobiotics.

Insights into the early-life chemical exposome of Nigerian infants and potential correlations with the developing gut microbiome.

Early-life exposure to natural and synthetic chemicals can impact acute and chronic health conditions. Here, a suspect screening workflow anchored on high-resolution mass spectrometry was applied to elucidate xenobiotics in breast milk and matching stool samples collected from Nigerian mother-infant pairs (n = 11) at three time points. Potential correlations between xenobiotic exposure and the developing gut microbiome, as determined by 16S rRNA gene amplicon sequencing, were subsequently explored. Overall, 12,192 and 16,461 features were acquired in the breast milk and stool samples, respectively. Following quality control and suspect screening, 562 and 864 features remained, respectively, with 149 of these features present in both matrices. Taking advantage of 242 authentic reference standards measured for confirmatory purposes of food bio-actives and toxicants, 34 features in breast milk and 68 features in stool were identified and semi-quantified. Moreover, 51 and 78 features were annotated with spectral library matching, as well as 416 and 652 by in silico fragmentation tools in breast milk and stool, respectively. The analytical workflow proved its versatility to simultaneously determine a diverse panel of chemical classes including mycotoxins, endocrine-disrupting chemicals (EDCs), antibiotics, plasticizers, perfluorinated alkylated substances (PFAS), and pesticides, although it was originally optimized for polyphenols. Spearman rank correlation of the identified features revealed significant correlations between chemicals of the same classification such as polyphenols. One-way ANOVA and differential abundance analysis of the data obtained from stool samples revealed that molecules of plant-based origin elevated as complementary foods were introduced to the infants' diets. Annotated compounds in the stool, such as tricetin, positively correlated with the genus Blautia. Moreover, vulgaxanthin negatively correlated with Escherichia-Shigella. Despite the limited sample size, this exploratory study provides high-quality exposure data of matched biospecimens obtained from mother-infant pairs in sub-Saharan Africa and shows potential correlations between the chemical exposome and the gut microbiome.

Quantifying up to 90 polyphenols simultaneously in human bio-fluids by LC-MS/MS.

Establishing a method for human biomonitoring (HBM) of polyphenols enables the assessment of internal concentrations of these food bio-actives and the correlation with potential health effects such as antioxidant or anti-inflammatory properties. Thus, a targeted LC-MS/MS method for quantifying up to 90 analytes, representing the main polyphenol classes including flavanones, isoflavones, stilbenes, and phenolic acids, was developed for human urine, serum, and plasma. The method was established for low sample volumes and with a cost and time efficient sample preparation protocol for high-throughput, which is critical for its application in large cohort and exposome-wide association studies. On average, the sample preparation yielded extraction efficiencies of 98% for urine, 98% for serum, and 87% for plasma. Limits of detection were between 0.11 ng mL-1 and 300 ng mL-1 for urine, 0.12 ng mL-1 and 190 ng mL-1 for serum, and 0.12 ng mL-1 and 340 ng mL-1 for plasma, excluding one analyte. In-house validation revealed that 66, 49, and 64 analytes for urine, serum, and plasma, respectively, fulfilled all stringent requirements, that are usually utilized for tailored single analyte methods, at all evaluated concentration levels. After validation, this method was applied in a proof-of-principle study that detected 39 polyphenols in urine. Changes in the concentrations of the analytes after the ingestion of a high polyphenol smoothie was examined over 24 h. The study further confirmed that the majority of polyphenols detected were phenolic acids, and phase II conjugated metabolites were more abundant than their respective non-conjugated forms.

Polyphenol Exposure, Metabolism, and Analysis: A Global Exposomics Perspective.

Polyphenols are generally known for their health benefits and estimating actual exposure levels in health-related studies can be improved by human biomonitoring. Here, the application of newly available exposomic and metabolomic technology, notably high-resolution mass spectrometry, in the context of polyphenols and their biotransformation products, is reviewed. Comprehensive workflows for investigating these important bioactives in biological fluids or microbiome-related experiments are scarce. Consequently, this new era of nontargeted analysis and omic-scale exposure assessment offers a unique chance for better assessing exposure to, as well as metabolism of, polyphenols. In clinical and nutritional trials, polyphenols can be investigated simultaneously with the plethora of other chemicals to which we are exposed, i.e., the exposome, which may interact abundantly and modulate bioactivity. This research direction aims at ultimately eluting into atrue systems biology/toxicology evaluation of health effects associated with polyphenol exposure, especially during early life, to unravel their potential for preventing chronic diseases.