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Systems Medical Diagnostics by In-vivo Ambient Mass Spectrometric Profiling of Tissue Lipidome (MASSLIP)
Date du début: 1 juin 2014, Date de fin: 31 mai 2019 PROJET  TERMINÉ 

"The objective of the proposal is the development of ambient mass spectrometric methods for the characterisation of mucosal metabolome and lipidome. While recent advent of ambient MS provided new means for in-situ and imaging analyses and led to the development of real-time, in-vivo MS characterisation of tissues, there are no methods available for minimally invasive testing of mucosal surfaces including the associated microflora. Human mucosa-associated microbiome (with special emphasis on the gastrointestinal microbiota) has been recently demonstrated to play a key role in the pathogenesis of localised (cancer, chronic inflammatory disease) and systemic (hypertension, diabetes, obesity) conditions. While the microbiota interacts with the host mostly via production of a variety of metabolites, currently there is no method available for the in-situ metabolic profiling of mucosa. The envisioned methods will presumably fill this gap, by providing a technique for the diagnosis of a wide range of diseases ranging from acute infections through cancer to dysbiotic conditions of the microflora leading to chronic illnesses.In the current proposal we put forward the development of different ambient ionisation setups utilising Jet Desorption Ionisation, Sonic Spray Ionisation and Rapid Evaporation Ionisation MS covering a broad range of invasiveness. We plan to combine the methods with standard endoscopic tools and develop the concept of ´chemically aware´ or intelligent endoscopic device capable of the unambiguous identification of pathological conditions of the mucosa. Since the metabolic profile-based identification approach requires large authentic datasets, we plan to create both histopathological and bacterial spectral databases with histological and 16SrRNA-based validation. The proposal also comprises the development of novel multivariate statistical analysis workflows and data fusion algorithms allowing rapid and accurate identification using multimodal MS datasets."



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