Spatial proteomic alterations detected <i>via</i> MALDI-MS imaging implicate neuronal loss in a Huntington's disease mouse (YAC128) brain.

Karayel-Basar M., Uras I., Kiris I., Sahin B., Akgun E., Baykal A. T.

Molecular omics, 2022 (Journal Indexed in SCI) identifier identifier

  • Publication Type: Article / Article
  • Volume:
  • Publication Date: 2022
  • Doi Number: 10.1039/d1mo00440a
  • Title of Journal : Molecular omics


Huntington's disease (HD) is an autosomal dominant neurodegenerative disorder that occurs with the increase of CAG trinucleotide repeats in the huntingtin gene. To understand the mechanisms of HD, powerful proteomics techniques, such as liquid chromatography-tandem mass spectrometry (LC-MS/MS) were employed. However, one major drawback of these methods is loss of the region-specific quantitative information of the proteins due to analysis of total tissue lysates. Matrix-assisted laser desorption/ionization mass spectrometry imaging (MALDI-MSI) is a MS-based label-free technique that works directly on tissue sections and gathers m/z values with their respective regional information. In this study, we established a data processing protocol that includes several software programs and methods to determine spatial protein alterations between the brain samples of a 12 month-old YAC128 HD mouse model and their non-transgenic littermates. 22 differentially expressed proteins were revealed with their respective regional information, and possible relationships of several proteins were discussed. As a validation of the MALDI-MSI analysis, a differentially expressed protein (GFAP) was verified using immunohistochemical staining. Furthermore, since several proteins detected in this study have previously been associated with neuronal loss, neuronal loss in the cortical region was demonstrated using an anti-NeuN immunohistochemical staining method. In conclusion, the findings of this research have provided insights into the spatial proteomic changes between HD transgenic and non-transgenic littermates and therefore, we suggest that MALDI-MSI is a powerful technique to determine spatial proteomic alterations between biological samples, and the data processing that we present here can be employed as a complementary tool for the data analysis.