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.

Blood microsampling (BμS) has recently emerged as an interesting approach in the analysis of endogenous metabolites but also in metabolomics applications. Their non-invasive way of use and the simplified logistics that they offer renders these technologies highly attractive in large-scale studies, especially the novel quantitative microsampling approaches such as VAMs or qDBS. Objectives: Herein, we investigate the potential of BµS devices compared to the conventional plasma samples used in global untargeted mass spectrometry-based metabolomics of blood. Methods: Two novel quantitative devices, namely, Mitra, Capitainer, and the widely used Whatman cards, were selected for comparison with plasma. Venous blood was collected from 10 healthy, overnight-fasted individuals and loaded on the devices; plasma was also collected from the same venous blood. An extraction solvent optimization study was first performed on the three devices before the main study, which compared the global metabolic profiles of the four extracts (three BµS devices and plasma). Analysis was conducted using reverse phase LC-TOF MS in positive mode. Results: BµS devices, especially Mitra and Capitainer, provided equal or even superior information on the metabolic profiling of human blood based on the number and intensity of features and the precision and stability of some annotated metabolites compared to plasma. Despite their rich metabolic profiles, BµS did not capture metabolites associated with biological differentiation of sexes. Conclusions: Overall, our results suggest that a more in-depth investigation of the acquired information is needed for each specific application, as a metabolite-dependent trend was obvious.

We present the Spectra software, an R/Bioconductor package defining an efficient infrastructure for storing, handling and visualising mass spectrometry data. We describe how Spectra adapts to different data types, sizes, and use cases,addressing scalability while keeping a consistent user interface. We also demonstrate how it can integrate with third party Python tools and follows modern tidyomics principles,seamlessly integrating with more general tidy data science tools.

Chromatographic traces are central to LC-MS analysis, yet most software treats them as transient intermediates inside a fixed pipeline rather than as data objects users can inspect and manipulate directly. In R, xcms provides the established infrastructure for large-scale LC-MS analysis, but the way it represents chromatograms limits what can be done with them once extracted. We present chromatograms, an open-source R/Bioconductor package and a core component of the R for mass spectrometry ecosystem, built on two design principles: interchangeable storage backends, which keep the analysis code independent of where the data reside (in memory, on disk, etc.), and delayed evaluation, which applies queued operations only to the chromatograms actually requested and processed at a time. Chromatograms was fully integrated into xcms hence enabling the efficient extraction of EICs for identified features, exemplified on public data, for which extraction of 4980 features across 4000 samples took only a few hours, against a full day with the legacy infrastructure. For targeted data analysis or inspection, a peak table from any external software, or published with a study, is enough to recover the corresponding chromatograms from the raw files, with annotation columns preserved; four published biomarkers were recovered from all 4063 samples of a cohort even without parallel processing in just under an hour. Chromatograms does not perform peak detection: it provides the infrastructure for working with chromatographic data once peaks are defined, giving a scalable foundation for targeted and untargeted LC-MS analysis in R.