Application of Metabolomics to Renal and Cardiometabolic Diseases

Bifunctional nanomaterial enabled high-specific isolation of urinary exosomes for cervical cancer metabolomics analysis and biomarker discovery

Cervical cancer (CC) remains a critical public health issue, highlighting the importance of early detection. However, current methods such as cytological and HPV testing face challenges of invasiveness and low patient compliance. Exosomes, emerging as crucial in cancer diagnosis, offer promise due to their noninvasive, highly specificity, and abundant biomarkers. However, isolating exosomes efficiently remains challenging. In this study, we designed and synthesized a bifunctional affinity nanomaterial Fe3O4 @CD63-CLIKKPF, based on the synergistic interaction between its modified aptamer CD63 and peptide CLIKKPF, and CD63 protein and PS of exosomes which can achieve high specificity and high yield separation of urinary exosomes. Notably, the co-modified aptamer CD63 and peptide CLIKKPF not only enable efficient exosome isolation by leveraging dual-affinity mechanisms through a synergistic "AND" logic analysis, but also could be achieved on the Fe3O4 in one-step reaction at room temperature via Fe-S bonding. Combined with LC-MS/MS, we conducted exosome metabolomics analysis in healthy individuals and CC patients across various stages, and machine learning models demonstrated accurate classification (accuracy >0.822) and prediction capabilities for CC. Furthermore, six key metabolites indicative of CC progression were identified and validated in additional patient samples, highlighting their potential as biomarkers. Overall, this study establishes a novel method for exosome metabolomics in CC, offering insights for non-invasive early diagnosis and progression prediction on a large scale.

Metabolomics for personalized medicine: the input of analytical chemistry from biomarker discovery to point-of-care tests

Metabolomics refers to the large-scale detection, quantification, and analysis of small molecules (metabolites) in biological media. Although metabolomics, alone or combined with other omics data, has already demonstrated its relevance for patient stratification in the frame of research projects and clinical studies, much remains to be done to move this approach to the clinical practice. This is especially true in the perspective of being applied to personalized/precision medicine, which aims at stratifying patients according to their risk of developing diseases, and tailoring medical treatments of patients according to individual characteristics in order to improve their efficacy and limit their toxicity. In this review article, we discuss the main challenges linked to analytical chemistry that need to be addressed to foster the implementation of metabolomics in the clinics and the use of the data produced by this approach in personalized medicine. First of all, there are already well-known issues related to untargeted metabolomics workflows at the levels of data production (lack of standardization), metabolite identification (small proportion of annotated features and identified metabolites), and data processing (from automatic detection of features to multi-omic data integration) that hamper the inter-operability and reusability of metabolomics data. Furthermore, the outputs of metabolomics workflows are complex molecular signatures of few tens of metabolites, often with small abundance variations, and obtained with expensive laboratory equipment. It is thus necessary to simplify these molecular signatures so that they can be produced and used in the field. This last point, which is still poorly addressed by the metabolomics community, may be crucial in a near future with the increased availability of molecular signatures of medical relevance and the increased societal demand for participatory medicine.

Bridging Targeted and Untargeted Mass Spectrometry-Based Metabolomics via Hybrid Approaches

This mini-review aims to discuss the development and applications of mass spectrometry (MS)-based hybrid approaches in metabolomics. Several recently developed hybrid approaches are introduced. Then, the overall workflow, frequently used instruments, data handling strategies, and applications are compared and their pros and cons are summarized. Overall, the improved repeatability and quantitative capability in large-scale MS-based metabolomics studies are demonstrated, in comparison to either targeted or untargeted metabolomics approaches alone. In summary, we expect this review to serve as a first attempt to highlight the development and applications of emerging hybrid approaches in metabolomics, and we believe that hybrid metabolomics approaches could have great potential in many future studies.