Mar Garcia-Aloy

Untargeted metabolomics frequently utilizes molecular networking (MN) to map mass spectrometry data, enhancing compound annotation workflows. However, identifying which fragmentation features (fragment ions/neutral losses) can be considered characteristics of a molecular families rather than representing generic, non-specific fragments, remains a major challenge. Here, we report a data-driven framework that facilitates MN interpretation by highlighting the fragmentation features that act as signatures for MN subnetworks. Specifically, we apply a network modularity approach coupled with Quantile Generalised Additive Models (QGAM) to identify fragment ions and neutral losses shared across nodes that exhibit higher topological cohesiveness than expected for their level of occurrence. By isolating the chemical signatures typical of an MN region, we provide an objective foundation for annotation propagation. This approach successfully prioritized class-specific diagnostic reporters in the Alternaria metabolome, distinguishing ubiquitous fragmentation patterns from diagnostic tricycloalternarene reporters. Our method provides a data-dependent strategy to guide annotation propagation and accelerate structural elucidation in complex untargeted datasets.

Grapevine (Vitis spp.) is one of the world’s most economically, culturally, and biologically important fruit crops, with remarkable diversity spanning thousands of V. vinifera cultivars together with rootstocks and wild relatives. Yet grapevine biodiversity has never been systematically explored at the metabolome scale, despite its importance for chemotaxonomy, breeding, and sustainable viticulture. Here, we present the Grape Metabolome (GM) Project, the first systematic liquid chromatography–mass spectrometry (LC–MS) survey of grapevine biodiversity, comprising 462 samples from 126 accessions collected across four vintages. Advanced and well-established chemometric tools resolved the major grape chemotypes together with finer cultivar-related substructure, while annotation-guided interpretation linked this organization to distinctive patterns in anthocyanins, flavonols, aroma-related compounds, and tannin-associated phenolics. This data set provides a reference framework for chemotaxonomy, breeding, authenticity, precision viticulture and enology.

High-quality data preprocessing is essential for untargeted metabolomics experiments, where increasing data set scale and complexity demand adaptable, robust, and reproducible software solutions. Modern preprocessing tools must evolve to integrate seamlessly with downstream analysis platforms, ensuring efficient and streamlined workflows. Since its introduction in 2005, the xcms R package has become one of the most widely used tools for LC-MS data preprocessing. Developed through an open-source, community-driven approach, xcms maintains long-term stability while continuously expanding its capabilities and accessibility. We present recent advancements that position xcms as a central component of a modular and interoperable software ecosystem for metabolomics data analysis. Key improvements include enhanced scalability, enabling the processing of large-scale experiments with thousands of samples on standard computing hardware. These developments empower users to build comprehensive, customizable, and reproducible workflows tailored to diverse experimental designs and analytical needs. An expanding collection of tutorials, documentation, and teaching materials further supports both new and experienced users in leveraging broader R and Bioconductor ecosystems. These resources facilitate the integration of statistical modeling, visualization tools, and domain-specific packages, extending the reach and impact of xcms workflows. Together, these enhancements solidify xcms as a cornerstone of modern metabolomics research.

Despite growing evidence linking diet to neurodegenerative diseases, the connection remains unclear. Tomatoes, a widely consumed food, have been proposed as potential sources of neuroactive compounds. Using LC-MS/MS, we profiled organic and conventional “datterini,” plump, and processed tomatoes. Six carotenoids were quantified, with phytoene and lycopene being the most abundant. Multivariate analysis revealed that processing and variety, rather than organic vs. conventional methods, drove dataset variability. Seventy neuroactive compounds were identified, some distinguishing tomato variety, processing, and/or production methods. Processed tomatoes generally showed higher abundance of neuroactive compounds than fresh tomatoes, and “datterini” tomatoes contained more neuroprotective compounds than plump tomatoes. Organic “datterini” did not have higher neuroprotective compound levels than conventional ones. These findings suggest thermal processing may alter the compositional quality of tomatoes, potentially enhancing the levels of certain bioactive constituents, while organic cultivation does not inherently increase the abundance of neuroprotective compounds. Overall, tomatoes represent a complex source of both neuroprotective and neuro-disrupting compounds, warranting further research on their bioaccessibility and physiological relevance.

If life ever existed on Mars, it may have developed survival strategies similar to those adopted by extremophiles living in terrestrial Martian analogs, such as the cryptoendolithic communities found in the rocky substrates of the McMurdo Dry Valleys or other ice-free areas of continental Antarctica. Nearly thirty years of research on these super-adapted organisms laid the foundation for the CRYPTOMARS project, which aims to disclose the genomic and phenotypic features allowing these microbial communities to withstand specific physico-chemical stresses that may be encountered on the Red Planet. This information will provide tools to outline, in terms of diversity and competences, a putative microbial community able to survive, adapt or even perpetuate under early or present Martian conditions. The project and the background information are here presented.

The postprandial period is an opportunity window to assess metabolic phenotype, and its study is gaining popularity due to the wealth of information that can be uncovered when a dietary challenge is associated with the application of metabolomics approaches. Bile acids (BA) were recently identified as signaling molecules that display major changes in circulating levels following food intake. In this regard, a gap of information remains linking BA postprandial kinetics with their possible metabolic effects. This study aimed to characterizing a murine model for investigating postprandial metabolism and inflammation. Changes in plasma and hepatic markers of metabolism, inflammation and BA levels were assessed in male Sprague‐Dawley rats before and after the ingestion of an energy‐dense meal. Rats display postprandial alterations in circulating BA levels, with cholic acid constituting the predominant species (36%). These changes are accompanied by shifts in intermediates of energy metabolism and inflammatory markers, as demonstrated by a four‐fold increase in hepatic NF‐κB protein content, a key inflammatory transcription factor, two hours after food intake. Despite inherent species‐specific differences, this murine model represents a promising tool for studying postprandial modulation energy metabolism, establishing a pioneering framework for future investigations into the role of BA in postprandial metabolic responses.

In mass spectrometry, fragmentation spectra play a central role in compound identification. However, noise in MS/MS spectra can significantly impact similarity scores and molecular network (MN) reliability, leading to inaccurate compound annotation in untargeted metabolomics. This work investigates the influence of noise on MS/MS similarity scores and molecular network structure. Noise elimination increased similarity scores for homologous spectra, enhancing match affordability. In MNs, effective noise management improved network structure, resulting in more interpretable networks with fewer edges and enhanced clustering, decreasing false-positive connections. To quantitatively assess these improvements, a minimum spanning tree (MST) analysis was performed, revealing denser regions in the denoised MNs. An increasing cutoff of noise threshold can lead to an overlay between two or more different compound spectra. A data-specific workflow was developed to identify the optimal threshold for denoising, balancing spectra quality and network integrity during noise elimination, by incorporating statistics calculated on the distribution of the MST distances and the number of fragment ions, which could be explained by an in-silico fragmentation algorithm. Finally, a faster-tailored denoising method, based solely on the intensity of individual spectral ions, demonstrated performance comparable to the previously cited fixed threshold approaches.

High-quality data preprocessing is essential for untargeted metabolomics experiments, where increasing dataset scale and complexity demand adaptable, robust, and reproducible software solutions. Modern preprocessing tools must evolve to integrate seamlessly with downstream analysis platforms, ensuring efficient and streamlined workflows. Since its introduction in 2005, the xcms R package has become one of the most widely used tools for LC-MS data preprocessing. Developed through an open-source, community-driven approach, xcms has maintained long-term stability while continuously expanding its capabilities and accessibility. We present recent advancements that position xcms as a central component of a modular and interoperable software ecosystem for metabolomics data analysis. Key improvements include enhanced scalability, enabling the processing of large-scale experiments with thousands of samples on standard computing hardware. These developments empower users to build comprehensive, customizable, and reproducible workflows tailored to diverse experimental designs and analytical needs. An expanding collection of tutorials, documentation, and teaching materials further supports both new and experienced users in leveraging the broader R and Bioconductor ecosystems. These resources facilitate the integration of statistical modeling, visualization tools, and domain-specific packages, extending the reach and impact of xcms workflows. Together, these enhancements solidify xcms as a cornerstone of modern metabolomics research.