MALDI Mass Spectrometry Imaging Highlights Specific Metabolome and Lipidome Profiles in Salivary Gland Tumor Tissues

Abstract

Salivary gland tumors are relatively uncommon neoplasms that represent less than 5% of head and neck tumors, and about 90% are in the parotid gland. The wide variety of histologies and tumor characteristics makes diagnosis and treatment challenging. In the present study, Matrix-assisted laser desorption/ionization mass spectrometry imaging (MALDI-MSI) was used to discriminate the pathological regions of patient-derived biopsies of parotid neoplasms by metabolomic and lipidomic profiles. Fresh frozen parotid tissues were analyzed by MALDI time-of-flight (TOF) MSI, both in positive and negative ionization modes, and additional MALDI-Fourier-transform ion cyclotron resonance (FT-ICR) MSI was carried out for metabolite annotation. MALDI-TOF-MSI spatial segmentation maps with different molecular signatures were compared with the histologic annotation. To maximize the information related to specific alterations between the pathological and healthy tissues, unsupervised (principal component analysis, PCA) and supervised (partial least squares-discriminant analysis, PLS-DA) multivariate analyses were performed presenting a 95.00% accuracy in cross-validation. Glycerophospholipids significantly increased in tumor tissues, while sphingomyelins and triacylglycerols, key players in the signaling pathway and energy production, were sensibly reduced. In addition, a significant increase of amino acids and nucleotide intermediates, consistent with the bioenergetics request of tumor cells, was observed. These results underline the potential of MALDI-MSI as a complementary diagnostic tool to improve the specificity of diagnosis and monitoring of pharmacological therapies.

Multi-Class Cancer Subtyping in Salivary Gland Carcinomas with MALDI Imaging and Deep Learning

“…Until now, high mass resolution MALDI imaging has been used to discriminate between different pathological regions in SGCs [ 28 ] but not to differentiate between different subtypes of SGCs. In a similar way, deep-learning approaches to high-resolution MALDI imaging are, to the best of our knowledge, limited to binary classification tasks on mouse models [ 29 ], and had not been applied before to tumor subtyping of SGCs.…”

Advanced progress of spatial metabolomics in head and neck cancer research

“… [98] salivary gland tumor MALDI-MSI lysophosphatidylcholine, sphingolipid, fatty acid, phosphatidylcholine, phosphatidyl ethanolamine, glutamine, glutamate. [90] oral cancer MALDI-MSI phosphatidylcholine, sphingolipid, acylglycerol. [92] DESI-MSI Cholesterol sulfate, oleic acid.…”

“…Eduardo Sommella et al. employed MALDI-MSI to distinguish healthy tissue areas from tumor tissue areas in patients with salivary gland tumors, revealing altered metabolites related to the tumor's energetic demands [90] .…”

Applications of spatially resolved omics in the field of endocrine tumors

“…2,5-DHB can be used to image lipids, peptides, and amino acids in the positive ion mode ( 140 – 142 ). 9-AA is preferred to be performed under negative ion mode for the ionization of polar metabolites ( 143 ). The application of matrices is required to assist the ionization and subsequent detection of analytes.…”