Multimodal mass spectrometry imaging (MSI) is a critical technique used for deeply investigating biological systems by combining multiple MSI platforms in order to gain the maximum molecular information about a sample that would otherwise be limited by a single analytical technique. The aim of this work was to create a multimodal MSI approach that measures metabolomic and proteomic data from a single biological organ by combining infrared matrix-assisted laser desorption electrospray ionization (IR-MALDESI) for metabolomic MSI and nanodroplet processing in one pot for trace samples (nanoPOTS) LC-MS/MS for spatially resolved proteome profiling. Adjacent tissue sections of rat brain were analyzed by each platform and each dataset was individually analyzed using previously optimized workflows. IR-MALDESI datasets were annotated by accurate mass and spectral accuracy using HMDB, METLIN, and LipidMaps databases, while nanoPOTS-LC-MS/MS datasets were searched against the rat proteome using the Sequest HT algorithm and filtered with a 1% FDR. The combined data revealed complementary molecular profiles distinguishing the corpus collosum against other sampled regions of the brain. A multiomic pathway integration showed a strong correlation between the two datasets when comparing average abundances of metabolites and corresponding enzymes in each brain region. This work demonstrates the first steps in the creation of a multimodal MSI technique that combines two highly sensitive and complementary imaging platforms.
[doi:10.25345/C5ZW05]
[dataset license: CC0 1.0 Universal (CC0 1.0)]
Keywords: Multimodal MSI ; Multiomic ; IR-MALDESI ; nanoPOTS-LC-MS/MS ; Proteomics ; Metabolomics
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David C. Muddiman, North Carolina State University, United States of America |
Submitting User: | clgunth2 |
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