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

Salivary gland carcinomas (SGC) are a heterogeneous group of tumors. The prognosis varies strongly according to its type, and even the distinction between benign and malign tumor is challenging. Adenoid cystic carcinoma (AdCy) is one subgroup of SGCs that is prone to late metastasis. This makes accurate tumor subtyping an important task. Matrix-assisted laser desorption/ionization (MALDI) imaging is a label-free technique capable of providing spatially resolved information about the abundance of biomolecules according to their mass-to-charge ratio. We analyzed tissue micro arrays (TMAs) of 25 patients (including six different SGC subtypes and a healthy control group of six patients) with high mass resolution MALDI imaging using a 12-Tesla magnetic resonance mass spectrometer. The high mass resolution allowed us to accurately detect single masses, with strong contributions to each class prediction. To address the added complexity created by the high mass resolution and multiple classes, we propose a deep-learning model. We showed that our deep-learning model provides a per-class classification accuracy of greater than 80% with little preprocessing. Based on this classification, we employed methods of explainable artificial intelligence (AI) to gain further insights into the spectrometric features of AdCys.

Refinement of Compound Aromaticity in Complex Organic Mixtures by Stable Isotope Label Assisted Ultrahigh-Resolution Mass Spectrometry.

Fourier transform ion cyclotron resonance mass spectrometry (FTICR MS) provides a unique opportunity for molecular analysis of natural complex mixtures. In many geochemical and environmental studies structure-propertry relations are based solely on the elemental compositional information. Several calculated parameters were proposed to increase reliability of structural attribution, among which aromaticity indices (AI and AImod) are widely used. Herein, we applied a combination of selective labeling reactions in order to obtain direct structural information on the individual components of lignin-derived polyphenolic material. Carboxylic (COOH), carbonyl (C═O), and hydroxyl (OH) groups were enumerated by esterification, reducing, and acetylation reactions, respectively, followed by FTICR MS analyses. Obtained information was enabled to constrain aromaticity accounting for the carbon skeleton only. We found that actual aromaticity of components may be both higher or lower than approximated values depending on the abundance of COOH, C═O, and OH groups. The results are of importance for the geochemical community studying terrestrial NOM with structural gradients.