Patients with metabolic diseases are affected by a loss of microbiome species and a reduced number of metabolites detected in their blood, which hints that the missed metabolites may be top mechanistic mediator candidates of the human-gut microbiome interaction. For a better understanding of this interaction, improved metabolomics strategies must be developed, capable of exhaustive characterizations and of delving into the previously undetected metabolites (the co-called “dark metabolome”). Our objective is to illuminate the blood “dark metabolome” with the development of an “all-in-all” framework combining state-of-the-art instrumentation with analytical methods using computational approaches (such as chemoinformatics and chemometrics) for addressing key roadblocks in the field of liquid chromatography coupled to high-resolution mass spectrometry metabolomics and, in doing so, bringing altogether next generation metabolomics. This analytical framework will be developed at 3 levels, corresponding to my 3 work tasks: Task 1, for maximizing the number of mass fragmentation spectra obtained; Task 2, for increasing the number of annotations and the quality of these annotations; and Task 3, for the design of data-driven methods to evaluate the role of the new detected compounds. I will set up this framework with plasma samples from the Euroepan cohort study METACARDIS, descriptive of individuals with cardiometabolic diseases, and validate the biological findings with samples of the Lille cohort study Heart&Brain (comprising diabetic patients without initial diabetes-related complications). To sum up, this project will enhance our understanding of the underexplored microbiome-derived metabolites present in the human blood, as well as their roles in metabolism and physiology, that could ultimately lead to new diagnostic tools and novel treatment and prevention strategies.