A Proof of Concept to Bridge the Gap between Mass Spectrometry Imaging, Protein Identification and Relative Quantitation: MSI~LC-MS/MS-LF
Laëtitia Théron,
Delphine Centeno,
Cécile Coudy-Gandilhon,
Estelle Pujos-Guillot,
Thierry Astruc,
Didier Rémond,
Jean-Claude Barthelemy,
Frédéric Roche,
Léonard Feasson,
Michel Hébraud,
Daniel Béchet,
Christophe Chambon
Affiliations
Laëtitia Théron
Institut National de la Recherche Agronomique (INRA), Plateforme d’Exploration du Métabolisme (PFEM), F-63122 Saint Genès Champanelle, France
Delphine Centeno
Institut National de la Recherche Agronomique (INRA), Plateforme d’Exploration du Métabolisme (PFEM), F-63122 Saint Genès Champanelle, France
Cécile Coudy-Gandilhon
INRA, UMR 1019, Unité de Nutrition Humaine, CRNH Auvergne, F-63122 Saint Genès Champanelle, France
Estelle Pujos-Guillot
Institut National de la Recherche Agronomique (INRA), Plateforme d’Exploration du Métabolisme (PFEM), F-63122 Saint Genès Champanelle, France
Thierry Astruc
INRA Clermont-Ferrand Theix, UR370 Qualité des Produits Animaux, F-63122 Saint-Genès-Champanelle, France
Didier Rémond
INRA, UMR 1019, Unité de Nutrition Humaine, CRNH Auvergne, F-63122 Saint Genès Champanelle, France
Jean-Claude Barthelemy
PRES Lyon, SNA-EPIS Research Unit, Exercise and Clinical Physiology Laboratory, University Hospital and Jean Monnet University, F-42023 Saint-Etienne, France
Frédéric Roche
PRES Lyon, SNA-EPIS Research Unit, Exercise and Clinical Physiology Laboratory, University Hospital and Jean Monnet University, F-42023 Saint-Etienne, France
Léonard Feasson
Unité de Myologie, LPE-EA4338, UJM/CHU, F-42055 Saint-Etienne, France
Michel Hébraud
Institut National de la Recherche Agronomique (INRA), Plateforme d’Exploration du Métabolisme (PFEM), F-63122 Saint Genès Champanelle, France
Daniel Béchet
INRA, UMR 1019, Unité de Nutrition Humaine, CRNH Auvergne, F-63122 Saint Genès Champanelle, France
Christophe Chambon
INRA, UMR 1019, Unité de Nutrition Humaine, CRNH Auvergne, F-63122 Saint Genès Champanelle, France
Mass spectrometry imaging (MSI) is a powerful tool to visualize the spatial distribution of molecules on a tissue section. The main limitation of MALDI-MSI of proteins is the lack of direct identification. Therefore, this study focuses on a MSI~LC-MS/MS-LF workflow to link the results from MALDI-MSI with potential peak identification and label-free quantitation, using only one tissue section. At first, we studied the impact of matrix deposition and laser ablation on protein extraction from the tissue section. Then, we did a back-correlation of the m/z of the proteins detected by MALDI-MSI to those identified by label-free quantitation. This allowed us to compare the label-free quantitation of proteins obtained in LC-MS/MS with the peak intensities observed in MALDI-MSI. We managed to link identification to nine peaks observed by MALDI-MSI. The results showed that the MSI~LC-MS/MS-LF workflow (i) allowed us to study a representative muscle proteome compared to a classical bottom-up workflow; and (ii) was sparsely impacted by matrix deposition and laser ablation. This workflow, performed as a proof-of-concept, suggests that a single tissue section can be used to perform MALDI-MSI and protein extraction, identification, and relative quantitation.