The team behind MetaProViz, led by researchers at Heidelberg University and the University of Cologne, including Christina Schmidt, Christian Frezza and Julio Saez-Rodriguez, identified two stubborn obstacles that have long plagued metabolomics analysis. The first is the lack of standardised workflows. Although dozens of tools exist for individual steps, most are available only as web applications whose user-friendly interfaces hide critical parameter settings, undermining the reproducibility standards that modern science demands. The second obstacle is more subtle and arguably more damaging: metabolite annotations are frequently ambiguous. Mass spectrometry cannot reliably distinguish fine structural details such as stereoisomers or constitutional isomers, meaning a detected feature does not correspond to one fully defined chemical structure. Databases compound the problem by storing metabolites at different levels of specificity, so assigning a single unambiguous identifier to every measured peak is often impossible.

MetaProViz tackles these problems with a modular architecture consisting of five interlocking components: pre-processing, differential metabolite analysis, prior knowledge access and integration, functional analysis, and visualisation. The package operates on annotated intensity matrices, the format typically delivered by metabolomics facilities after peak assembly and annotation, and each module can be used interactively or as a building block within larger pipelines. Built-in vignettes with example datasets, automatic logging of parameters, warnings and errors, and publication-ready figures based on ggplot2 make the tool approachable for researchers with minimal coding experience while satisfying the audit trails demanded by experienced bioinformaticians. Automated checks and informative messages guide users through decisions that would otherwise require specialist knowledge, from normalisation strategy to statistical test selection, including Shapiro-Wilk tests that report whether the chosen statistical approach is appropriate for the data at hand.

At the heart of the package lies a curated knowledge resource called MetSigDB, the Metabolism Signature Database. This collection of annotated metabolite sets unifies pathway-metabolite sets from sources such as KEGG, Reactome and WikiPathways, chemical class-metabolite sets, cancer-metabolite sets from MACdb, and metabolite-protein interactions from MetalinksDB covering receptors, transporters and enzymes. MetSigDB also enables conversion of gene sets into metabolite sets using metabolic reactions drawn from a filtered version of the Recon-3D model. Crucially, MetaProViz systematically excludes non-specific metabolites such as water, carbon dioxide and xenobiotics, and its prior knowledge is accessed through the OmniPath ecosystem, giving the tool a sustainable backend that can evolve with the field.

The most technically innovative part of MetaProViz is its handling of metabolite identifier ambiguity, a problem the authors illustrate vividly with the amino acid alanine. When assigning identifiers from the Human Metabolome Database, both D-alanine and L-alanine must be attached because no unspecific HMDB entry exists, whereas ChEBI offers only a single generic identifier that fails to map to most standard pathway databases. Because enzyme stereoselectivity is homochiral, with living systems dominated by L-amino acids and D-sugars, these distinctions matter biologically. MetaProViz addresses the mismatch through an eight-step workflow: quality control of the feature identifier space, counting identifiers per feature, detecting identifier mismatches, translating identifiers from one database to another, traversing a metabolite identifier graph to harvest all connected identifiers, adding equivalent identifiers based on chirality, and then quantifying and flagging mapping ambiguities for manual review.

Applied to a published clear-cell renal cell carcinoma patient dataset, this workflow delivered striking results. Initially only 43 percent of features carried complete assignments across HMDB, KEGG and PubChem, and a quarter of features had nothing but a PubChem identifier. After MetaProViz cleaning, translation and graph traversal, the proportion of fully annotated features rose to 54 percent, dramatically expanding the overlap between measured data and prior knowledge. The team also catalogued the mapping scenarios that can silently distort enrichment analysis, from many-to-one mappings that deflate pathways to one-to-many mappings that inflate them, and flagged sixteen cases in the renal cancer data that required manual review. Without this systematic accounting, over-representation analysis could produce misleading or outright nonsensical conclusions, a known hazard when genomic-style pathway tools are naïvely applied to metabolites.

The package also breaks new ground in exometabolomics, the measurement of metabolites consumed from and released into the culture medium by cells, a capability the authors note is not offered by any other publicly available tool. MetaProViz normalises these data against media blanks and growth factors, yielding negative values for consumed metabolites and positive values for released ones, and replaces classical log2 fold changes with a log2 distance measure that captures the direction of exchange between conditions. Its biological regulatory clustering method, bioRCM, then integrates extracellular consumption-release patterns with intracellular metabolomics through logical regulatory rules, producing clusters of metabolites that carry clear biological meaning.

When the team applied this machinery to renal cancer cell lines, the results were biologically compelling. Primary and metastatic clear-cell renal cell carcinoma cells predominantly consumed amino acids from the medium while healthy renal epithelial cells released them. One cluster of particular interest contained metabolites that were consumed by the cancer cells and simultaneously depleted inside them. Querying this cluster against MetalinksDB revealed methionine as a standout: cancer cells consumed methionine from the media and showed reduced intracellular levels, and tumour tissue from patients similarly exhibited decreased methionine compared with adjacent normal tissue. The authors connect this increased methionine usage to the elevated DNA-hypermethylation landscape characteristic of clear-cell renal cell carcinoma, and the associated enzymes and transporters, including BHMT and SLC43A2, have previously been linked to patient survival. This is precisely the kind of mechanistic hypothesis, linking an extracellular metabolic exchange to an intracellular epigenetic mechanism, that the package was designed to surface.

Beyond cell lines, MetaProViz introduces a metadata analysis method for heterogeneous patient cohorts. By relating principal components of the metabolomics data to clinical variables through analysis of variance, the method identifies which patient characteristics drive metabolic variation. Applied to 138 matched tumour-normal pairs, it confirmed that tissue type and tumour stage explained the largest share of variance, but also revealed that a small percentage of variance in the fourth principal component separated patients by age. Subsequent multi-condition clustering comparing young and old patient subsets uncovered an age-specific signature of dipeptides, upregulated in older patients and downregulated in younger ones, accompanied by altered aminoacyl-tRNA biosynthesis and protein digestion and absorption pathways. The authors suggest such dipeptide patterns could hold promise as biomarkers, and demonstrate that metadata-driven analysis can extract metabolic patterns invisible to classical pathway enrichment alone.

The developers have positioned MetaProViz for the future as well as the present. The package is available through Bioconductor, ensuring reproducible and scalable installation, and the team anticipates integration into workflow management systems such as nf-core modules in Nextflow, which would embed it directly into the pipelines of metabolomics core facilities. As artificial intelligence accelerates metabolite identification through language-model-guided annotation and self-supervised learning of mass spectra, the number of confidently identified metabolites will grow sharply, making rigorous tools for functional interpretation ever more valuable. By combining curated prior knowledge, ambiguity-aware identifier handling, exometabolomics analysis and safeguarded statistics in one flexible framework, MetaProViz offers the metabolomics community what genomics has long enjoyed: a standardised path from raw data to biological understanding, and a systematic way to ask not just which metabolites change, but why it matters.

The statistical machinery underlying MetaProViz reflects a broader lesson about the distinct nature of metabolites as data objects. Unlike genes, whose counts lend themselves to established negative binomial models, metabolite intensities span wide dynamic ranges and their distributions can vary substantially across features, which is why the package evaluates normality per feature and warns users when a chosen test may be inappropriate. The background set problem in over-representation analysis is treated with similar care: because the measured metabolite list is shaped by the extraction method and instrumentation rather than by biology alone, the package allows users to define the statistical universe explicitly, a detail that genomic enrichment tools often take for granted but that can materially alter results in metabolomics.

The methionine findings in clear-cell renal cell carcinoma also sit within a well-documented biological context. Renal cancer cells are known to rely heavily on amino acid uptake, and methionine metabolism feeds directly into the one-carbon cycle that supplies methyl groups for DNA methylation. The observed combination of methionine consumption from the medium, intracellular depletion in tumours, and hypermethylation signatures is therefore internally coherent, and the survival associations reported for the linked enzymes and transporters suggest the pattern may have clinical relevance rather than being a mere artefact of cell culture. The authors are careful to frame these connections as hypotheses for experimental validation, which is precisely the intended output of the functional analysis modules.

The age-specific dipeptide signature uncovered in the patient cohort illustrates another strength of the metadata-driven approach. Dipeptides are products of protein turnover, and altered aminoacyl-tRNA biosynthesis and protein digestion pathways accompanying the age separation point toward shifts in protein metabolism with ageing. Because the analysis of variance framework relates principal components to clinical variables directly, such associations emerge without preselecting metabolites of interest, reducing the risk of confirmation bias. For laboratories adopting the package, the modular design means these analyses can be adopted incrementally, starting with quality control and differential analysis before progressing to prior knowledge integration, making the transition to reproducible metabolomics workflows manageable even for groups without dedicated bioinformatics support.

Subject of Research: An open-source metabolomics data analysis package, MetaProViz, that integrates prior knowledge to generate mechanistic hypotheses.

Article Title: Integrated metabolomics data analysis to generate mechanistic hypotheses with MetaProViz

Article References: Schmidt, C., Franken, J., Turei, D., Prymidis, D., Daley, M., Frezza, C., & Saez-Rodriguez, J. (2026). Integrated metabolomics data analysis to generate mechanistic hypotheses with MetaProViz. Molecular Systems Biology. https://doi.org/10.1038/s44320-026-00231-8

Image Credits: AI Generated

DOI: 10.1038/s44320-026-00231-8

Keywords: metabolomics, MetaProViz, bioinformatics, mass spectrometry, exometabolomics, pathway enrichment, MetSigDB, metabolite identifiers, clear-cell renal cell carcinoma, methionine metabolism, R package, prior knowledge integration

Novel psychoactive substances represent an ever-expanding class of designer drugs engineered to mimic the effects of traditional controlled substances while evading legislative controls. Bromazolam, a benzodiazepine analog boasting potent anxiolytic and sedative properties, and 2-MMC, a synthetic cathinone stimulant with profound psychoactive effects, have emerged as substances of concern in recent years. Their combined presence in a single fatality amplifies the risks associated with unregulated poly-drug use, where synergistic effects can lead to unpredictable and often lethal outcomes.

The detailed forensic examination presented by the researchers from France delves into the biochemical intricacies of the case through extensive sample analysis. Employing nuclear magnetic resonance spectroscopy, a technique renowned for its capacity to provide detailed molecular structure insights, enabled the precise identification of bromazolam and 2-MMC biomarkers within biological specimens. This method was critical in differentiating these compounds from structurally similar analogs, an essential step given the chemical diversity and rapid evolution of NPS.

Complementing the NMR findings, liquid chromatography coupled with high-resolution mass spectrometry was utilized to quantify the substances with exceptional sensitivity and specificity. LC-HRMS facilitated the detection and accurate measurement of trace levels of both bromazolam and 2-MMC metabolites, revealing a complex pharmacokinetic profile suggestive of recent poly-consumption. The integration of these two advanced analytical platforms marks a significant advancement in forensic toxicology, representing a powerful toolkit against the backdrop of escalating NPS abuse.

The case itself involved a young adult whose unexpected death raised immediate concerns among mortuary and forensic teams. Toxicological screening identified the co-presence of bromazolam and 2-MMC in blood and tissue samples at concentrations consistent with fatal intoxication. Importantly, the examination excluded other common substances, emphasizing the lethality of this novel drug combination. The researchers advocate that this case embodies a cautionary tale of the inherent dangers posed by emerging synthetic drugs when consumed concurrently.

From a pharmacological perspective, the study delineates how bromazolam’s potent central nervous system depressant effects can be dramatically exacerbated when combined with the stimulant properties of 2-MMC. This paradoxical combination can induce profound respiratory depression and cardiac instability, leading rapidly to fatal outcomes. Understanding these pharmacodynamic interactions is vital for medical professionals and toxicologists when confronted with unexplained intoxications or overdoses in clinical or forensic scenarios.

Beyond the medical implications, the research underscores a pressing need for the development of more comprehensive monitoring programs for novel psychoactive substances in Europe and worldwide. Conventional drug screening protocols often fail to detect these emerging compounds due to their structural novelty. The adoption of cutting-edge NMR and LC-HRMS methodologies holds promise for expanding detection capabilities and improving both diagnostic and investigative accuracy.

The fatality also poses significant public health concerns. The accessibility of NPS through illicit online markets and their perceived lower risk create a dangerous misperception among users. This study sends a stark warning message: the combination of powerful synthetic sedatives and stimulants, each with their complicated metabolic and toxic profiles, can readily culminate in death. Raising awareness through forensic science and medical research is crucial to curb the proliferation of such substances.

Legally, this investigation sets a precedent in forensic jurisprudence by thoroughly documenting the first French death attributable to bromazolam and 2-MMC poly-consumption. It highlights the challenges law enforcement agencies and statutory bodies encounter in classifying and controlling fast-evolving new substances, whose structures are routinely altered to circumvent regulations. The scientific findings presented could serve as key evidence in legal frameworks aimed at curbing NPS dissemination.

The authors articulate how the utilization of NMR is not merely confirmatory but pivotal in the differentiation of isomeric and closely related chemical entities often encountered in NPS investigations. This meticulous chemical profiling can aid forensic chemists in the development of substance-specific analytical standards, which are instrumental for both criminal investigations and public health strategies.

Significantly, the research details the metabolic fate of bromazolam and 2-MMC in the human body, noting unique biomarkers identified via HRMS spectra analysis. This metabolic fingerprinting may facilitate the design of targeted antidotes or treatments, although currently, therapeutic options remain limited. Clinical understanding of such NPS combinations remains embryonic; hence, this study fills crucial knowledge gaps.

This case also raises ethical questions within the scientific community regarding the dissemination of information about NPS detection, as widespread knowledge could inadvertently aid clandestine chemists in crafting even more elusive substances. However, transparency is fundamental to advancing scientific comprehension and enhancing public safety measures.

Furthermore, the research team recommends increased training for forensic and clinical toxicologists in contemporary analytical techniques. The dual analytical workflow combining NMR and LC-HRMS should be considered a gold standard for future fatalities involving novel psychoactive substances, promoting accuracy in cause-of-death determinations and aiding in epidemiological tracking.

The implications for emergency medicine are profound. First responders and healthcare professionals must recognize the signs of poly-drug intoxication involving sedatives and stimulants, tailoring interventions accordingly. Rapid deployment of LC-HRMS and NMR in hospital toxicology labs could become pivotal in timely diagnosis and treatment, potentially reducing mortality rates.

Finally, this pioneering French case exemplifies the multifaceted potency of interdisciplinary collaboration encompassing analytical chemistry, forensic science, medicine, and law enforcement. Through innovative scientific inquiry, this tragic incident serves as a clarion call to intensify global efforts in combating the rising tide of lethal novel psychoactive substances. The integration of state-of-the-art technologies and comprehensive toxicological profiles will undoubtedly reshape our approach to drug-related fatalities in years to come.

Subject of Research: Fatal poly-consumption of novel psychoactive substances involving bromazolam and 2-MMC analyzed through NMR and LC-HRMS

Article Title: First French case of fatal NPS poly-consumption involving bromazolam and 2-MMC: insights from NMR and LC-HRMS

Article References:
Alexandre, M., Pelletier, R., Daré, B.L. et al. First French case of fatal NPS poly-consumption involving bromazolam and 2-MMC: insights from NMR and LC-HRMS. Int J Legal Med (2025). https://doi.org/10.1007/s00414-025-03689-7

Image Credits: AI Generated

DOI: https://doi.org/10.1007/s00414-025-03689-7

The complexity inherent to oxylipin profiles, marked by their structural diversity and dynamic biological functions, has historically presented significant challenges for analytical chemists and biochemists aiming to quantitatively characterize these molecules with accuracy and reproducibility. The recently published framework, painstakingly developed under the leadership of Valerie O’Donnell from Cardiff University and Nils Helge Schebb of the University of Wuppertal, represents the first global consensus effort to harmonize analytical strategies for oxylipin quantification. This milestone initiative integrated perspectives from academia, industry stakeholders, and clinical laboratories to forge a comprehensive and robust methodological blueprint.

Brazil’s contribution, spearheaded by Sayuri Miyamoto from the University of São Paulo’s Institute of Chemistry and the FAPESP-supported Center for Redox Processes in Biomedicine (Redoxoma), underscored the international essence of the project. The Redoxoma team was instrumental in refining the granular experimental recommendations, ensuring that the guidelines accommodate the varied biological matrices and analytical platforms prevalent in modern lipidomics laboratories. This collaboration exemplifies the power of cross-continental scientific cooperation to solve entrenched methodological bottlenecks.

Liquid chromatography coupled with tandem mass spectrometry (LC-MS/MS) stands at the frontier of lipidomic analyses, offering unmatched sensitivity and selectivity. The technique leverages chromatographic separation of complex mixtures followed by mass-selective detection, enabling researchers to parse highly similar oxylipin isomers amid an intricate biological background. Despite its enormous potential, the absence of universally accepted protocols has contributed to significant interlaboratory variability, hampering comparative studies and slowing translational applications. The new guidelines directly address these concerns, advocating rigorous calibration strategies, sample handling procedures, and data acquisition parameters.

As Miyamoto emphasized, the standardization achieved by these guidelines is more than a technical triumph; it is a critical enabler for advancing clinical research. Robust, reproducible quantitative data on oxylipin dynamics can illuminate novel biomarkers for disease states and therapeutic responses, enhancing personalized medicine approaches. It also improves confidence in multi-center studies where uniformity in analytical procedures is paramount to drawing valid conclusions across patient cohorts.

The history of oxylipin research stretches back nearly a century, with initial identifications dating to the 1930s. Their role as biochemical mediators is exemplified by the mechanism of aspirin and other nonsteroidal anti-inflammatory drugs, which exert effects by modulating specific oxylipin synthesis pathways. However, comprehensive profiling remained elusive until technological leaps in chromatographic separation and mass spectrometry sensitivity facilitated the detection of hundreds of oxylipin species in single runs. Even so, the field was crippled by inconsistent methodologies that muddled the interpretability of results across labs.

The new technical recommendations cater to the nuanced analytical demands required to detect a broad spectrum of oxylipins, including low-abundance species critical in pathological states. By advocating standardized internal standards, optimized extraction protocols, and harmonized instrument settings, the guidelines ensure that features essential for robust data—accuracy, precision, and reproducibility—are systematically addressed. Consequently, laboratories adopting these recommendations will significantly enhance the comparability and reliability of their findings.

From a broader perspective, this consensus framework propels redox lipidomics into a new era of methodological maturity. It supplies both novice and experienced researchers a definitive roadmap that fosters methodological rigor and data integrity. Such uniformity is vital to unlocking the full biological and clinical potential of oxylipins, facilitating discoveries that may elucidate disease mechanisms or identify new therapeutic targets with unprecedented clarity.

Experts leading this initiative, including O’Donnell and Schebb, have stressed the collaborative nature of the project as central to its success. Through extensive international consultation and peer feedback, the guidelines were refined to meet real-world laboratory constraints while maintaining stringent scientific standards. This process ensured that the recommended practices are practical, adaptable, and poised to set a globally respected standard now recognized as the “gold standard” for oxylipin analyses.

The implications extend beyond academia into pharmaceutical development and clinical diagnostics, where validated assays based on these guidelines can accelerate biomarker validation and precision medicine. Standardized oxylipin measurements promise to enhance the understanding of inflammatory pathways and metabolic dysfunctions and ultimately translate into improved patient outcomes across a spectrum of diseases.

The article detailing these technical recommendations was officially published in the high-impact journal Science Signaling, cementing the relevance and anticipated influence of this work within the molecular biology and analytical chemistry communities. The availability of this resource empowers researchers worldwide to elevate the precision of their oxylipin investigations and harmonize efforts toward shared scientific goals.

This landmark publication not only fills a critical gap in methodological standardization but also galvanizes a growing community of scientists dedicated to unraveling the complex biochemistry of lipid mediators. As research on oxylipins intensifies, these unified analytical guidelines will be indispensable in driving forward the field with robustness, clarity, and collaborative spirit.

Subject of Research: Oxylipin analysis standardization using liquid chromatography–mass spectrometry

Article Title: Technical recommendations for analyzing oxylipins by liquid chromatography–mass spectrometry

News Publication Date: 20-May-2025

Web References:

• Science Signaling Article
• Redoxoma Center

References:
O’Donnell, V., Schebb, N. H., Miyamoto, S., et al. (2025). Technical recommendations for analyzing oxylipins by liquid chromatography–mass spectrometry. Science Signaling. DOI: 10.1126/scisignal.adw12

Image Credits: Felipe Maeda / Agência FAPESP

Keywords:
Mass spectrometry, Chromatography, Quantitative analysis, Inflammation, Cancer, Immunity, Neurodegenerative diseases, Cardiovascular disease