AI Article Synopsis

  • Mass spectrometry imaging (MSI) is used to visualize how molecules are distributed in tissue samples, but it struggles with directly identifying proteins.
  • This study developed a workflow combining MALDI-MSI with LC-MS/MS and label-free quantitation to improve protein identification and analysis from a single tissue section.
  • The results indicated this new approach effectively linked nine peaks from MALDI-MSI to identified proteins, demonstrating it could analyze muscle proteomes with minimal impact from how samples are prepared.

Article 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 / 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.

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http://www.ncbi.nlm.nih.gov/pmc/articles/PMC5260965PMC
http://dx.doi.org/10.3390/proteomes4040032DOI Listing

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