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.

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.

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.

Flavor is the main factor driving consumers acceptance of food products. However, tracking the biochemistry of flavor is a formidable challenge due to the complexity of food composition. Current methodologies for linking individual molecules to flavor in foods and beverages are expensive and time-consuming. Predictive models based on machine learning (ML) are emerging as an alternative to speed up this process. Nonetheless, the optimal approach to predict flavor features of molecules remains elusive. In this work we present FlavorMiner, an ML-based multilabel flavor predictor. FlavorMiner seamlessly integrates different combinations of algorithms and mathematical representations, augmented with class balance strategies to address the inherent class of the input dataset. Notably, Random Forest and K-Nearest Neighbors combined with Extended Connectivity Fingerprint and RDKit molecular descriptors consistently outperform other combinations in most cases. Resampling strategies surpass weight balance methods in mitigating bias associated with class imbalance. FlavorMiner exhibits remarkable accuracy, with an average ROC AUC score of 0.88. This algorithm was used to analyze cocoa metabolomics data, unveiling its profound potential to help extract valuable insights from intricate food metabolomics data. FlavorMiner can be used for flavor mining in any food product, drawing from a diverse training dataset that spans over 934 distinct food products.

Flavor is the main factor driving consumers acceptance of food products. However, tracking the biochemistry of flavor is a formidable challenge due to the complexity of food composition. Current methodologies for linking individual molecules to flavor in foods and beverages are expensive and time-consuming. Predictive models based on machine learning (ML) are emerging as an alternative to speed up this process. Nonetheless, the optimal approach to predict flavor features of molecules remains elusive. In this work we present FlavorMiner, an ML-based multilabel flavor predictor. FlavorMiner seamlessly integrates different combinations of algorithms and mathematical representations, augmented with class balance strategies to address the inherent class of the input dataset. Notably, Random Forest and K-Nearest Neighbors combined with Extended Connectivity Fingerprint and RDKit molecular descriptors consistently outperform other combinations in most cases. Resampling strategies surpass weight balance methods in mitigating bias associated with class imbalance. FlavorMiner exhibits remarkable accuracy, with an average ROC AUC score of 0.88. This algorithm was used to analyze cocoa metabolomics data, unveiling its profound potential to help extract valuable insights from intricate food metabolomics data. FlavorMiner can be used for flavor mining in any food product, drawing from a diverse training dataset that spans over 934 distinct food products.

Flavor is the main factor driving consumers acceptance of food products. However, tracking the biochemistry of flavor is a formidable challenge due to the complexity of food composition. Current methodologies for linking individual molecules to flavor in foods and beverages are expensive and time-consuming. Predictive models based on machine learning (ML) are emerging as an alternative to speed up this process. Nonetheless, the optimal approach to predict flavor features of molecules remains elusive. In this work we present FlavorMiner, an ML-based multilabel flavor predictor. FlavorMiner seamlessly integrates different combinations of algorithms and mathematical representations, augmented with class balance strategies to address the inherent class of the input dataset. Notably, Random Forest and K-Nearest Neighbors combined with Extended Connectivity Fingerprint and RDKit molecular descriptors consistently outperform other combinations in most cases. Resampling strategies surpass weight balance methods in mitigating bias associated with class imbalance. FlavorMiner exhibits remarkable accuracy, with an average ROC AUC score of 0.88. This algorithm was used to analyze cocoa metabolomics data, unveiling its profound potential to help extract valuable insights from intricate food metabolomics data. FlavorMiner can be used for flavor mining in any food product, drawing from a diverse training dataset that spans over 934 distinct food products.

Cis-(+)-12-oxophytodienoic acid (cis-(+)-OPDA) is a bioactive jasmonate, a precursor of jasmonic acid, which also displays signaling activity on its own. Modulation of cis-(+)-OPDA actions may be carried out via biotransformation leading to metabolites of various functions, similar to other phytohormones. This work introduces a methodology for the synthesis of racemic cis-OPDA conjugates with amino acids (OPDA-aa) and their deuterium-labeled analogs, which enables the identification and accurate quantification of these compounds in plants. We have developed a highly sensitive liquid chromatography-tandem mass spectrometry-based method for the reliable determination of seven OPDA-aa (OPDA-Alanine, OPDA-Aspartate, OPDA-Glutamate, OPDA-Glycine, OPDA-Isoleucine, OPDA-Phenylalanine, and OPDA-Valine) from minute amount of plant material. The extraction from 10 mg of fresh plant tissue by 10% aqueous methanol followed by single-step sample clean-up on hydrophilic–lipophilic balanced columns prior to final analysis was optimized. The method was validated in terms of accuracy and precision, and the method parameters such as process efficiency, recovery and matrix effects were evaluated. In mechanically wounded 30-day-old Arabidopsis thaliana leaves, five endogenous (+)-OPDA-aa were identified and their endogenous levels reached a maximum of pmol/g. The time-course accumulation revealed a peak 60 min after the wounding, roughly corresponding to the accumulation of cis-(+)-OPDA. Current synthetic and analytical methodologies support studies on cis-(+)-OPDA conjugation with amino acids and research into the biological significance of these metabolites in plants.

Cis-(+)-12-oxophytodienoic acid (cis-(+)-OPDA) is a bioactive jasmonate, a precursor of jasmonic acid, which also displays signaling activity on its own. Modulation of cis-(+)-OPDA actions may be carried out via biotransformation leading to metabolites of various functions, similar to other phytohormones. This work introduces a methodology for the synthesis of racemic cis-OPDA conjugates with amino acids (OPDA-aa) and their deuterium-labeled analogs, which enables the identification and accurate quantification of these compounds in plants. We have developed a highly sensitive liquid chromatography-tandem mass spectrometry-based method for the reliable determination of seven OPDA-aa (OPDA-Alanine, OPDA-Aspartate, OPDA-Glutamate, OPDA-Glycine, OPDA-Isoleucine, OPDA-Phenylalanine, and OPDA-Valine) from minute amount of plant material. The extraction from 10 mg of fresh plant tissue by 10% aqueous methanol followed by single-step sample clean-up on hydrophilic–lipophilic balanced columns prior to final analysis was optimized. The method was validated in terms of accuracy and precision, and the method parameters such as process efficiency, recovery and matrix effects were evaluated. In mechanically wounded 30-day-old Arabidopsis thaliana leaves, five endogenous (+)-OPDA-aa were identified and their endogenous levels reached a maximum of pmol/g. The time-course accumulation revealed a peak 60 min after the wounding, roughly corresponding to the accumulation of cis-(+)-OPDA. Current synthetic and analytical methodologies support studies on cis-(+)-OPDA conjugation with amino acids and research into the biological significance of these metabolites in plants.

Cis-(+)-12-oxophytodienoic acid (cis-(+)-OPDA) is a bioactive jasmonate, a precursor of jasmonic acid, which also displays signaling activity on its own. Modulation of cis-(+)-OPDA actions may be carried out via biotransformation leading to metabolites of various functions, similar to other phytohormones. This work introduces a methodology for the synthesis of racemic cis-OPDA conjugates with amino acids (OPDA-aa) and their deuterium-labeled analogs, which enables the identification and accurate quantification of these compounds in plants. We have developed a highly sensitive liquid chromatography-tandem mass spectrometry-based method for the reliable determination of seven OPDA-aa (OPDA-Alanine, OPDA-Aspartate, OPDA-Glutamate, OPDA-Glycine, OPDA-Isoleucine, OPDA-Phenylalanine, and OPDA-Valine) from minute amount of plant material. The extraction from 10 mg of fresh plant tissue by 10% aqueous methanol followed by single-step sample clean-up on hydrophilic–lipophilic balanced columns prior to final analysis was optimized. The method was validated in terms of accuracy and precision, and the method parameters such as process efficiency, recovery and matrix effects were evaluated. In mechanically wounded 30-day-old Arabidopsis thaliana leaves, five endogenous (+)-OPDA-aa were identified and their endogenous levels reached a maximum of pmol/g. The time-course accumulation revealed a peak 60 min after the wounding, roughly corresponding to the accumulation of cis-(+)-OPDA. Current synthetic and analytical methodologies support studies on cis-(+)-OPDA conjugation with amino acids and research into the biological significance of these metabolites in plants.

Directed evolution requires the screening of enzyme libraries in biological matrices. Available assays are mostly substrate or enzyme specific. Chromatographic techniques like LC and GC overcome this limitation, but require long analysis times. The herein developed multiple injections in a single experimental run (MISER) using GC coupled to MS allows the injection of samples every 33 s resulting in 96-well microtiter plate analysis within 50 min. This technique is implementable in any GC-MS system with autosampling. Since the GC-MS is far less prone to ion suppression than LCMS, no chromatographic separation is required. This allows the utilisation of an internal standards and the detection of main and side-product. To prove the feasibility of the system in enzyme screening, two libraries were assessed: i) YfeX library in an E. coli whole cell system for the carbene-transfer reaction on indole revealing the novel axial ligand tryptophan, ii) a library of 616 chimeras of fungal unspecific peroxygenase (UPO) in S. cerevisiae supernatant for hydroxylation of tetralin resulting in novel constructs. The data quality and representation are automatically assessed by a new R-script.