Metabolomics technologies have matured over the last 20 years to provide broad and robust pictures of a person’s biology. Combined with an appropriate study design, metabolomics can reveal mechanisms and complex biomarkers that will enable a new generation of care. Thanks to the development of standardized metabolomics, the implementation of this tool in routine diagnostic and prognostic procedures is at our fingertips. Metabolomics also integrates very well with other omics (genomics, epigenomics, proteomics, microbiome…) in multiomics data analysis, providing powerful tools for personalized medicine and population health applications. In this talk, Dr. Alice Limonciel, the chief scientific officer of biocrates, will discuss how metabolomics is leveraged in today’s biomedical research, and the added value of including metabolomics in a multiomics approach.

Abstract coming soon

The solvent system used for metabolite extraction is one of the most critical factors in metabolomics as it determines the recovery of the metabolites from biological sample that can be measured effectively. The success of a metabolomics study hinges on an extraction protocol with reproducibly isolates metabolites while minimizing biases. With solvent system column selection plays a critical role in metabolomics because it determines the separation of diverse metabolites, influencing the accuracy and comprehensiveness of the results. A well-chosen column enhances resolution and sensitivity by effectively separating compounds based on properties like polarity, while a poor choice can lead to poor separation, missed metabolites, and flawed data analysis. No “one-size-fits-all” solution: Due to the extreme chemical diversity of the metabolome, no single solvent can provide complete coverage. Purpose-driven selection: The optimal solvent system depends on the study’s objective, the biological sample type, and the target analytes. Critical impact on data: The solvent system has direct consequences for the number and type of metabolites detected, the accuracy of their quantification, and the overall result reproducibility.

Schizophrenia (SCZ) is a serious, debilitating psychiatric disorder. However, the molecular mechanism of SCZ remains largely unknown, and very few studies have examined the effect of antipsychotic treatment response. This study investigates differences in metabolomic signatures among Drug-Naïve (DN) and Risperidone-Treated (RT) SCZ patients, compared to Healthy Controls (HCs). A total of 245 participants were recruited from the clinical services of a large neuropsychiatric hospital, which included DN SCZ (n=80), RT SCZ (n=90) and HCs (n=75). A liquid chromatography mass spectrometry (LC-MS/MS) based metabolomic approach was used to identify potential metabolite biomarkers using the biocrates AbsoluteIDQ p400 HR Kit and Metaboanalyst 6.0.

Retinoblastoma (RB) is a rare ocular cancer seen in children that counts for approximately 3% of all childhood cancers. lt is found
that mutation in RBl, a tumor Suppressor gene on chromosome 13 as the cause of malignancy. We studied lipidomics of two RB cell
lines, one aggressive cell line (NCC-RbC-51) derived from a
metastatic site and one non aggressive cell line (WERI-Rbl) in
comparison with a control cell line (MIO-Ml). Lipid profiles of all the cell lines were studied using high resolution mass
spectrometer coupled to high performance Liquid
Chromatography. Data acquired from all the three cell lines in
positive mode were analyzed to identify differentially expressed metabolites. Several phospholipids and lysophospholipids were
found to be dysregulated. We observed upregulation of hexosyl
ceramides, and down regulation of dihydroceramides and higher order sphingoglycolipids hinting at a hindered sphingolipid
biosynthesis. The results obtained from Liquid Chromatography­Mass Spectrometry are validated by using qPCR and it was
observed that genes involved in ceramide biosynthesis pathway are getting down regulated.

While genomics has revolutionized precision medicine, it remains insufficient on its own to fully elucidate disease biology or to predict therapeutic response. Proteomics is rapidly emerging as a powerful tool to generate complementary data, offering functional insights that enhance our understanding of drug targets, resistance mechanisms, and patient stratification. Thanks to the versatility of mass spectrometry-based proteomics, the platform can be customized to address a wide range of research topics.

Metabolic health, particularly obesity and type 2 diabetes, is increasingly recognized as the outcome of a complex interplay between genetic susceptibility and environmental exposures. Circulating blood biomarkers provide insights into systemic physiological processes, although they often represent composite signals originating from multiple tissues and organs. Numerous small molecules have been associated with metabolic traits and are subject to substantial genetic regulation. To investigate these relationships, we performed multivariable linear and logistic regression analyses, using metabolite levels as predictors and glycemic status as a key indicator of metabolic health, while adjusting for demographic and clinical covariates. Potential causal effects of omics traits on disease outcomes were further assessed using two-sample Mendelian randomization (2SMR). Our results highlight branched-chain amino acids (BCAAs), lipids, lipid metabolism, and inflammatory pathways as critical modulators of metabolic health, underscoring their potential as shared therapeutic targets for improving metabolic health outcomes.

Recent metabolomic and multi-omics investigations provide mechanistic validation for classical Ayurvedic adaptogen-based interventions in enhancing mitochondrial resilience and biogenesis. Targeted LC–MS/MS and NMR studies on Withania somnifera (Ashwagandha), Picrorhiza kurroa (Kutki), Triphala, and Curcumin reveal modulation of key metabolic axes—branched-chain amino acid catabolism, fatty-acid β-oxidation, kynurenine–tryptophan immunometabolism, and ceramide-linked lipid signaling. Integrated proteomic and metabolomic datasets demonstrate restoration of mitochondrial redox homeostasis, improved acylcarnitine turnover, and suppression of inflammatory proteoforms. Collectively, these findings define a systems-level biochemical framework for mitochondrial optimization, positioning Ayurvedic adaptogens as precision metabolic modulators. This approach aligns traditional Rasayana-based nutrition within the 5P medicine paradigm—Predictive, Preventive, Personalized, Participatory, and Precision—bridging classical dietetics with translational metabolomics and integrative systems biology.

At VUGENE we work on various omics: transcriptomics and (epi)genomics, as well as proteomics and metabolomics to enhance a better understanding of disease and treatments. When people struggle to have large numbers of
samples – multiomics can be extremely helpful to robustly inform about important genes, transcripts, proteins or metabolites and the pathways they are involved in. Elevating research into a new multi-dimension understanding of the condition, informing better stratifications and treatment.

Mass spectrometry possesses the advanced capability to provide various benefits to both clinical and experimental sciences. The exceptional capabilities of mass spectrometry have positioned it prominently in comparison to other instrumentation techniques. The introduction of high resolution mass spectrometry has significantly transformed the field. The availability of HRMS and precise quantitative mass spectrometers has enabled the option to proceed with either untargeted or targeted methodologies. This discussion aims to provide clarity on the appropriate circumstances and reasons for selecting either approach.

Metabolomics offers a powerful window into the dynamic biochemical alterations that accompany liver injury and adaptation. In this talk, we will explore how targeted and untargeted metabolomic profiling is being applied to define disease-specific metabolic signatures in metabolic dysfunction-associated steatotic liver disease (MASLD) and acute-on-chronic liver failure (ACLF). Using plasma and tissue-based approaches, metabolomics enables identification of key pathway perturbations-particularly in lipid, bile acid, and amino acid metabolism-that reflect the molecular phenotype of disease. Beyond mechanistic insights, these profiles are being leveraged to develop biomarker panels predictive of progression, organ failure, and outcomes, forming the foundation for translational tools in clinical risk stratification. The presentation will highlight integrative studies combining metabolomics with proteomics and immunological readouts, and discuss how these efforts are converging toward point-of-care assays that link molecular understanding to bedside application.