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Proteomes 2016, 4(4), 32; doi:10.3390/proteomes4040032

A Proof of Concept to Bridge the Gap between Mass Spectrometry Imaging, Protein Identification and Relative Quantitation: MSI~LC-MS/MS-LF

1
Institut National de la Recherche Agronomique (INRA), Plateforme d’Exploration du Métabolisme (PFEM), F-63122 Saint Genès Champanelle, France
2
INRA, UMR 1019, Unité de Nutrition Humaine, CRNH Auvergne, F-63122 Saint Genès Champanelle, France
3
Clermont Université, Université d’Auvergne, F-63000 Clermont-Ferrand, France
4
INRA Clermont-Ferrand Theix, UR370 Qualité des Produits Animaux, F-63122 Saint-Genès-Champanelle, France
5
PRES Lyon, SNA-EPIS Research Unit, Exercise and Clinical Physiology Laboratory, University Hospital and Jean Monnet University, F-42023 Saint-Etienne, France
6
Unité de Myologie, LPE-EA4338, UJM/CHU, F-42055 Saint-Etienne, France
These authors contributed equally to this work.
*
Author to whom correspondence should be addressed.
Academic Editors: Shenheng Guan and Jacek R. Wisniewski
Received: 15 July 2016 / Revised: 4 October 2016 / Accepted: 17 October 2016 / Published: 26 October 2016
(This article belongs to the Special Issue Computational Proteomics)
View Full-Text   |   Download PDF [2945 KB, uploaded 26 October 2016]   |  

Abstract

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. View Full-Text
Keywords: MALDI mass spectrometry imaging; protein identification; label-free quantitation; skeletal muscle MALDI mass spectrometry imaging; protein identification; label-free quantitation; skeletal muscle
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This is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited. (CC BY 4.0).

Supplementary materials

  • Supplementary File 1:

    Supplementary (XLSX, 516 KB)

  • Externally hosted supplementary file 1
    Link: http://Theronetal_SupplementaryData.xlsx
    Description: Supplementary Materials: Supplementary Data 1: Table of identified proteins in the 8 individuals within the three conditions: ‘On tissue’, ‘SA-coated’, and ‘Post-MSI’ extractions, and their main cellular compartment. Supplementary Data 2: Classification of proteins according to their main and intermediate cellular compartment. Supplementary Data 3: Comparison of proteins identified by ‘Muscle homogenate’ proteomic workflow. Supplementary Data 4: Label-free quantitation analysis performed on proteins extracted in situ ‘Post-MSI’ and MALDI-MSI intensities.

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MDPI and ACS Style

Théron, L.; Centeno, D.; Coudy-Gandilhon, C.; Pujos-Guillot, E.; Astruc, T.; Rémond, D.; Barthelemy, J.-C.; Roche, F.; Feasson, L.; Hébraud, M.; Béchet, D.; Chambon, C. A Proof of Concept to Bridge the Gap between Mass Spectrometry Imaging, Protein Identification and Relative Quantitation: MSI~LC-MS/MS-LF. Proteomes 2016, 4, 32.

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