N-Linked glycans are structurally diverse polysaccharides that represent significant biological relevance due to their involvement in disease progression and cancer. Due to their complex nature, N-linked glycans pose many analytical challenges requiring the continued development of analytical technologies. Infrared matrix-assisted laser desorption electrospray ionization (IR-MALDESI) is a hybrid ionization technique commonly used for mass spectrometry imaging (MSI) applications. Previous work demonstrated IR-MALDESI to significantly preserve sialic acid containing N-linked glycans that otherwise require chemical derivatization prior to detection. Here, we demonstrate the first analysis of N-linked glycans in situ by IR-MALDESI MSI. A formalin-fixed paraffin-embedded human prostate tissue was analyzed in negative ionization mode after tissue washing, antigen retrieval, and pneumatic application of PNGase F for enzymatic digestion of N-linked glycans. Fifty-three N-linked glycans were confidently identified in the prostate sample where more than 60% contained sialic acid residues. This work demonstrates the first steps in N-linked glycan imaging of biological tissues by IR-MALDESI MSI. Raw data files are available in MassIVE (identifier: MSV000088414).

Glycan analysis by mass spectrometry has rapidly progressed due to the interest in understanding the role of glycans in disease and tumor progression. Glycans are complex molecules that pose analytical challenges due to their isomeric compositions, labile character, and ionization preferences. This study sought to demonstrate infrared matrix-assisted laser desorption electrospray ionization (IR-MALDESI) as a novel approach for the direct analysis of N-linked glycans. The glycoprotein bovine fetuin was chosen for this analysis as its glycome is well-characterized and heavily composed of sialylated glycans. Native N-linked glycans produced by enzymatic cleavage (via PNGase F) of bovine fetuin were analyzed directly by IR-MALDESI in both positive and negative ionization mode. In this study, we detected 12 N-linked glycans in negative mode and 4 N-linked glycans in positive mode, a significant increase in the amount of underivatized glycans detected by other ionization sources. Importantly, all N-linked glycans detected contained at least one sialic acid residue, which are known to be labile. This work represents a critical first step for N-linked glycan analysis by IR-MALDESI with future efforts directed at mass spectrometry imaging.

We report the spatially resolved metabolic profiling of cherry tomatoes using infrared matrix-assisted laser desorption electrospray ionization (IR-MALDESI); an ambient mass spectrometry imaging (MSI) technique that requires no sample derivatization.

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 data set was individually analyzed using previously optimized workflows. IR-MALDESI data sets were annotated by accurate mass and spectral accuracy using HMDB, METLIN, and LipidMaps databases, while nanoPOTS-LC-MS/MS data sets 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 callosum against other sampled regions of the brain. A multiomic pathway integration showed a strong correlation between the two data sets 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. Raw data files are available in METASPACE (https://metaspace2020.eu/project/ pace-2021) and MassIVE (identifier: MSV000088211).

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