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Updated: May 9, 2026

A Streamlined Approach for Mass Spectrometry-Based Proteomics Using Selected Tissue Regions
Published on: April 18, 2025
Quantification-based mass spectrometry imaging of proteins by parafilm assisted microdissection
Julien Franck1, Jusal Quanico, Maxence Wisztorski
1Laboratoire de Spectrométrie de Masse Biologique Fondamentale et Appliquée-EA 4550, Université de Lille 1, Bât SN3, 1er étage, F-59655 Villeneuve D'Ascq, France.
Abstract:
MALDI mass spectrometry imaging (MALDI-MSI) was presented as a good strategy to highlight regions presenting specific phenotypes based on molecular content. The proteins present in the different areas can be identified by MALDI MSI; however, the number of protein identifications remains low in comparison with classical MS-based proteomics approaches. To overcome this, a new strategy, involving the microdissection of tissue sections mounted on parafilm M-covered glass slides, is presented. Extraction and fractionation of proteins from a specific region of interest were investigated, leading to the identification of more than 1000 proteins from each microdissected piece. The strength of this cheap technique lies in the facile excision of millimeter-sized portions from the tissue allowing for the identification of proteins from cells of a specific phenotype obtained from the MALDI MS imaging-based molecular classification using hierarchical clustering. This approach can be extended to whole tissue sections in order to generate images of the section based on label-free quantification obtained from identification data. As a proof of concept, we have studied a tissue mounted on a parafilm M-covered glass slide, cut it into regular pieces, and submitted each piece to identification and quantification according to the developed parafilm-assisted microdissection (PAM) method. Images were then reconstructed by relative quantification of identified proteins based on spectral counting of the peptides analyzed by nanoLC-MS and MS/MS. This strategy of quantification-based MSI offers new possibilities for mapping a large number of high and low abundance proteins.
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