Recommandations préanalytiques pour les analyses de protéomique clinique des fluides biologiques

International audienc

Prise en compte de la technologie dans la quantification des biomarqueurs

International audiencePart de la variance technologique dans la quantification des biomarqueurs. Exemple en protéomique avec la technologie MALDI-TO

Identification clinique par spectrométrie de masse MALDI-TOF des champignons filamenteux : construction de banques de données pour les dermatophytes et les<em> Fusarium</em>

National audienc

Prise en compte de la technologie dans la quantification des biomarqueurs

International audiencePart de la variance technologique dans la quantification des biomarqueurs. Exemple en protéomique avec la technologie MALDI-TO

Comparative proteomic analysis of the extracellular matrix secreted by two types of skin fibroblasts.

International audienceThe hair follicle dermal papilla is composed primarily of extracellular matrix (ECM) proteins secreted by resident fibroblasts. Dermal papilla is endowed with hair morphogenic properties, yet its composition is poorly characterized. In an attempt to understand its specificity better, we compared the protein composition of ECM secreted by cultured dermal papilla fibroblasts with that of dermal fibroblasts. ECM proteins are generally large, difficult to solubilize, and abundantly post-translationally modified. We thus implemented an original protocol for analyzing them: ECM samples were enzymatically digested directly in the culture flasks and analyzed by LC-MS/MS. Sequencing of proteolytic peptides by MS/MS yielded protein identification. The relative abundance of a given protein in dermal fibroblast versus dermal papilla samples was estimated by comparing proteolytic peptide intensities detected by MS. Using this approach, several matrix proteins were found to be present at markedly different levels in each ECM type; in particular, thrombospondin 1 and fibronectin appeared to be overrepresented in the dermal papilla fibroblast ECM. MS results were supported by Western blot and immunostaining experiments. In addition, peptide intensities were processed in two ways, which proved to favor either the quantification accuracy or the information precision at the sequence level

Prise en compte de la technologie dans la quantification des biomarqueurs

International audiencePart de la variance technologique dans la quantification des biomarqueurs. Exemple en protéomique avec la technologie MALDI-TO

VARIABLE SELECTION FOR NOISY DATA APPLIED IN PROTEOMICS

International audienceThe paper proposes a variable selection method for pro-teomics. It aims at selecting, among a set of proteins, those (named biomarkers) which enable to discriminate between two groups of individuals (healthy and pathological). To this end, data is available for a cohort of individuals: the biological state and a measurement of concentrations for a list of proteins. The proposed approach is based on a Bayesian hierarchical model for the dependencies between biological and instrumental variables. The optimal selection function minimizes the Bayesian risk, that is to say the selected set of variables maximizes the posterior probability. The two main contributions are: (1) we do not impose ad-hoc relationships between the variables such as a logistic regression model and (2) we account for instrumental variability through measurement noise. We are then dealing with indirect observations of a mixture of distributions and it results in intricate probability distributions. A closed-form expression of the posterior distributions cannot be derived. Thus, we discuss several approximations and study the robustness to the noise level. Finally, the method is evaluated both on simulated and clinical data. Index Terms— Model and variable selection, Bayesian approach, biological et technological variability, Gaussian mixture, proteomics

VARIABLE SELECTION FOR NOISY DATA APPLIED IN PROTEOMICS

International audienceThe paper proposes a variable selection method for pro-teomics. It aims at selecting, among a set of proteins, those (named biomarkers) which enable to discriminate between two groups of individuals (healthy and pathological). To this end, data is available for a cohort of individuals: the biological state and a measurement of concentrations for a list of proteins. The proposed approach is based on a Bayesian hierarchical model for the dependencies between biological and instrumental variables. The optimal selection function minimizes the Bayesian risk, that is to say the selected set of variables maximizes the posterior probability. The two main contributions are: (1) we do not impose ad-hoc relationships between the variables such as a logistic regression model and (2) we account for instrumental variability through measurement noise. We are then dealing with indirect observations of a mixture of distributions and it results in intricate probability distributions. A closed-form expression of the posterior distributions cannot be derived. Thus, we discuss several approximations and study the robustness to the noise level. Finally, the method is evaluated both on simulated and clinical data. Index Terms— Model and variable selection, Bayesian approach, biological et technological variability, Gaussian mixture, proteomics