Lynn Vanhaecke

The placenta is the interface between maternal exposome and fetal development. However, analytical platforms for simultaneous analysis of its (semi-)polar metabolome and lipidome from a single tissue aliquot are lacking. We developed and validated a dual-extraction method for metabolomics and lipidomics analyses of human placenta.,A Design of Experiments approach was based on 54 targeted metabolites and lipids (log P=–3.24-10.59) and untargeted feature count to optimize bead mill homogenization, solvent composition and dilution for sequential extraction, prior to ultra-high performance liquid chromatography coupled to high-resolution mass spectrometry. Method validation was performed on pooled porcine (as a proxy) and human placenta samples. Targeted validation demonstrated a coefficient of variation (CV) ≤20% for repeatability and inter-day precision and a coefficient of determination (R²) ≥0.99 for >26/27 metabolites and >17/27 lipids. Untargeted analysis consistently detected 2,811 and 5,412 features (CV ≤30%) in metabolite and lipid fractions, respectively.,The method was applied to 371 placental samples from the ENVIRONAGE birth cohort to assess metabolic perturbations associated with maternal lifestyle factors and adverse neonatal outcomes (small for gestational age (SGA) status, preterm birth, and low 1-minute Appearance, Pulse, Grimace, Activity and Respiration (APGAR)). Notably, placental cotinine levels showed associations with self-reported maternal smoking behavior (η² =0.14, FDR p <0.2). Pathway enrichment and targeted analyses identified outcome-specific metabolic signatures under basic covariate adjustment, though these were sensitive to extended covariate correction.,Our validated dual-extraction platform enables reproducible, resource-efficient placental phenotyping with demonstrated sensitivity to exposome signals, supporting large-scale birth cohort research into adverse neonatal outcomes.

Metabolomics studies employing liquid chromatography-mass spectrometry are affected by signal drift and batch effects, introducing technical variance that impedes biological knowledge discovery. Quality control (QC) sample-based normalization strategies are widely implemented but remain vulnerable to outliers, thereby reducing normalization performance. We introduce rLOESS, rGAM, and tGAM, three robust normalization methods that improve resistance to outliers by downweighting or accommodating them. Leveraging additive models, the rGAM and tGAM methods allow flexible nonlinear modeling, differential sample weighting, and data-driven QC representativeness evaluation. Implementations of these methods are gathered in the Metanorm R package, integrating robust normalization with visualization for performance verification while supporting efficient parallel processing. In in silico and/or experimental data sets, the robust methods, relative to several popular existing strategies, improved replicate concordance and reduced drift and batch effects. The robust methods, with improved recovery of the underlying signal demonstrated in simulation, produced distinct differential abundance results, highlighting the impact of normalization on downstream statistical inference. Overall, tGAM-based normalization suggested the best performance across scenarios and is proposed as the default choice. Metanorm is versatile, supporting normalization in metabolomics studies across scales and experimental setups. Metanorm is freely available at https://github.com/UGent-LIMET/Metanorm.

Biological regulation emerges from coordinated interactions between genes, proteins, and metabolites; yet, despite their central regulatory potential, metabolites remain largely absent from genome-wide gene regulatory network inference. Current transcriptomics–metabolomics integration approaches are either limited by poor interpretability or constrained by incomplete prior knowledge, preventing the systematic identification of regulatory metabolites. Here, we present Lemonite, a data-driven and interpretable framework for integrating bulk transcriptomics and metabolomics data to uncover regulatory metabolites acting on gene modules. Lemonite extends module network inference to jointly associate transcription factors and metabolites with coexpressed gene programs, without requiring prior differential analysis or complete metabolome annotation. To contextualize predictions, we constructed a comprehensive gene–metabolite knowledge graph integrating over 370 000 metabolite–gene and 2.1 million protein–protein interactions. Applied to glioblastoma and inflammatory bowel disease cohorts (n=99 and n=75 resp), Lemonite identified over 50 functionally coherent gene modules per disease, revealing established and previously uncharacterized metabolite–gene regulatory relationships. In glioblastoma, myo-inositol and phosphatidylcholines, together with IRF6, regulate mesenchymal-like immune programs, which upon integration with single-cell transcriptomics are primarily expressed in tumor-associated macrophages and monocytes. In inflammatory bowel disease, regulatory metabolites were prioritized that change the expression of their predicted target genes in colonic epithelial cells in vitro . Overall, Lemonite provides a principled framework to explore the genome-wide regulatory potential of the metabolome and to generate biologically interpretable, experimentally testable hypotheses from multi-omics data.

DNA adductomics is the study of the whole of DNA adducts in a biological sample and is a valuable asset to exposomics research. To date, a clear view on how to analyze larger sample series is lacking in DNA adductomics, and the preprocessing of untargeted DNA adductomics data is seldom applied. This work aimed to optimize a DNA adductomics data preprocessing workflow (in true untargeted mode). Building upon the xcms R package, we optimized parameters for peak detection, retention time alignment, and peak grouping to reliably detect and integrate putative DNA adduct LC-MS peaks. Next, to ensure reliable downstream data analysis, six sample- and feature-based normalization methods were tested and quantitatively evaluated in two data sets (placental tissue, n = 375, and blood samples, n = 51). As a result, a successful and reproducible procedure for optimization of xcms parameters for DNA adductomics is proposed. Furthermore, evaluation of normalization methods demonstrated the importance and limitations of objective (RSD* and D-ratio) and subjective, i.e., visual (PCA score plot) evaluation. This work supports reproducible and transparent untargeted DNA adductomics data preprocessing to be implemented in large-scale exposomics studies.

Recently, feces has gained increased attention in metabolomics and lipidomics research due to its ability to reflect complex diet-host-microbiome interactions. Traditionally, these analyses rely on separate workflows, resulting in longer analysis times and increased instrument load. To address these limitations, we present a dual ultra-high-performance liquid chromatography coupled to high-resolution mass spectrometry (dual UHPLC-HRMS) approach. As a first step, two previously validated single UHPLC-HRMS methods for metabolomics and lipidomics, each demonstrating robust chromatographic separation of compounds, covering a broad physicochemical range (LogP -5.30 to 21.90), were selected. To integrate both workflows into the dual platform, thirteen critical LC-MS parameters were systematically optimized using a Design of Experiments (DoE). The parameters encompassed ion generation, transmission and detection, injection-related factors, unified column oven conditions, and source geometry. This approach enabled a robust dual workflow, achieving a 21% reduction in analysis time. Targeted evaluation demonstrated consistent detection of 288 metabolites (260 with CV<20%) and 164 lipids (144 with CV<20%), thereby outperforming the single methods, which detected 279 metabolites (232 with CV<20%) and 151 lipids (116 with CV<20%), respectively. Untargeted analysis further demonstrated increased coverage, with an additional 1 652 metabolite and 3 966 lipid features detected with the dual method without compromising repeatability of the feature signal intensities (79.4% vs. 81.2% for metabolomics and 83.1% vs. 82.7% for lipidomics, with CV<30%). Our novel dual UHPLC-HRMS workflow enhances analytical throughput, while also improving fecal metabolome and lipidome coverage and repeatability, offering a robust and cost-effective solution for large-scale studies.

Ambient metabolomics techniques such as laser-assisted rapid evaporative ionization mass spectrometry (LA-REIMS) enable fast, preparation-free fingerprinting of biological samples but are inherently limited by spectral congestion in the absence of chromatographic separation. While ion mobility spectrometry provides additional gas-phase separation, maintaining ion transmission under the transient signals characteristic of laser desorption remains analytically challenging. Here, we define operating conditions for cyclic traveling-wave ion mobility spectrometry (cIMS) that preserve transmission under LA-REIMS duty-cycle constraints and systematically evaluate how mobility integration reshapes biofluid fingerprints and enhances chemical specificity in chromatography-free analysis. Under optimized single-pass conditions, cIMS separation reorganized LA-REIMS spectra into structured mass/mobility feature domains, enabling selective mobility-based filtering of matrix-derived salt cluster ions. This reduced non-biological background contributions by up to 35% of total spectral intensity while preserving over 90% of detected untargeted features. Although cIMS operation introduced a sensitivity penalty relative to time-of-flight-only acquisition, approximately 80% of the total ion current was recovered under optimized conditions. Mobility-resolved data revealed coherent homologous series and class-specific structural trends, particularly for lipids, supporting class-level annotation. Analysis of 101 metabolite and lipid standards covering a broad physicochemical range (logP -5.30 to 19.40) demonstrated comprehensive molecular coverage, high mass accuracy (mean 2.3 ppm), and reproducible mobility behavior (mean CCS deviation 4.5%), with isomer separation observed for biologically important secondary bile acids in extended separation cycles. Collectively, these results establish LA-REIMS-cIMS as a practical analytical strategy for enhancing chemical specificity and spectral interpretability in high-throughput ambient biofluid metabolomics in support of high-throughput large-scale metabolic fingerprinting.

Global population growth in both humans and animals is contributing to a shortage of proteins and fats derived from traditional sources such as meat and fish. As a result, alternative food sources such as insects are gaining increased attention. Edible insects are considered an excellent source of nutrients as they contain high-quality proteins, fats and vitamins. In Europe, entomophagy is relatively new and therefore concerns about its safety, potential beneficial health effects and nutritional value need to be considered. In this work, scientific literature was systematically searched using PubMed, Web of Science and Scopus, resulting in 51 studies that examined the effects of edible insects as dietary ingredients on human and mammalian health. Findings suggest that edible insects such as black soldier fly larvae ( H. illucens ), silkworm ( B. mori ), yellow mealworm ( T. molitor ) and field cricket ( G. bimaculatus ) contain a wide range of bioactive peptides with potential antioxidant, antihypertensive, antimicrobial, immunomodulatory and anti-inflammatory properties. However, entomophagy also entails certain risks, such as allergic reactions and potential contamination with harmful substances that may pose health concerns. A clear understanding of these benefits and risks is crucial for both consumers and industry stakeholders to make informed decisions, guide future research, regulations, and the responsible integration of edible insects into a sustainable food system.