Short communication: Distribution of phospholipids in parotid cancer by matrix-assisted laser desorption/ionization imaging mass spectrometry

Background Parotid cancer is relatively rare, and malignancy varies; therefore, novel markers are needed to predict prognosis. Recent advances in matrix-assisted laser desorption/ionization imaging mass spectrometry (MALDI-IMS), useful for visualization of lipid molecules, have revealed the relationship between cancer and lipid metabolism, indicating the potential of lipids as biomarkers. However, the distribution and importance of phospholipids in parotid cancer remain unclear. Objective This study aimed to use MALDI-IMS to comprehensively investigate the spatial distribution of phospholipids characteristically expressed in human parotid cancer tissues. Methods Tissue samples were surgically collected from two patients with parotid cancer (acinic cell carcinoma and mucoepidermoid carcinoma). Frozen sections of the samples were assessed using MALDI-IMS in both positive and negative ion modes, with an m/z range of 600–1000. The mass spectra obtained in the tumor and non-tumor regions were compared and analyzed. Ion images corresponding to the peak characteristics of the tumor regions were visualized. Results Several candidate phospholipids with significantly different expression levels were detected between the tumor and non-tumor regions. The number of unique lipid peaks with significantly different intensities between the tumor and non-tumor regions was 95 and 85 for Cases 1 and 2, respectively, in positive ion mode, and 99 and 97 for Cases 1 and 2, respectively, in negative ion mode. Imaging differentiated the characteristics that phospholipids were heterogeneously distributed in the tumor regions. Conclusion Phospholipid candidates that are characteristically expressed in human parotid cancer tissues were found, demonstrating the localization of their expression. These findings are notable for further investigation of alterations in lipid metabolism of parotid cancer and may have potential for the development of phospholipids as biomarkers.

Proteomic Distributions in CD34+ Microvascular Niche Patterns of Glioblastoma

The poor clinical prognosis and microvascular patterns of glioblastoma (GBM) are of serious concern to many clinicians and researchers. However, very few studies have examined the correlation between microvascular niche patterns (MVNPs) and proteomic distribution. In this study, CD34 immunofluorescence staining and matrix-assisted laser desorption ionization-mass spectrometry imaging (MALDI-IMS) technology were used to investigate the protein distributions in MVNPs. CD34+ microvascular phenotype could be divided into four types: microvascular sprouting (MS), vascular cluster (VC), vascular garland (VG), and glomeruloid vascular proliferation (GVP). Based on such characteristics, MVNPs were divided into two types by cluster analysis, namely, type I, comprising primarily MS and VC, and type II, comprising many VGs and GVPs. Survival analysis indicated the type of MVNPs to be an independent prognostic factor for progression-free and overall survival in GBM. MALDI-IMS results showed the peaks at m/z 1037 and 8960 to exhibit stronger ion signals in type II, while those at m/z 3240 and 3265 exhibited stronger ion signals in type I. The findings may assist future research on therapy and help predict prognosis in GBM. However, due to the limited number of studies, more well-designed studies are warranted to further verify our results.

MALDI TIMS IMS of Disialoganglioside Isomers – GD1a and GD1b in Murine Brain Tissue

Gangliosides are classified as acidic glycosphingolipids, containing ceramide moieties and oligosaccharide chains with one or multiple sialic acid residue(s). The presence of multiple sialylation sites gives rise to highly diverse isomeric structures with distinct biological roles. Matrix-assisted laser desorption/ionization imaging mass spectrometry (MALDI IMS) enables the untargeted spatial analysis of gangliosides, among other biomolecules, directly from tissue sections. Integrating trapped ion mobility mass spectrometry (TIMS), a gas-phase separation technology, with MALDI IMS allows for the investi-gation of isomeric lipid structures in situ. Here we demonstrate the gas-phase separation of disialoganglioside isomers GD1a and GD1b that differ in the position of a sialic acid residue, in a standard mixture of both isomers, a total ganglioside extract, and directly from thin tissue sections. The unique spatial distributions of GD1a/b (d36:1) and GD1a/b (d38:1) were deter-mined from rat hippocampus, as well as in a spinal cord tissue section.

Optimized Workflow for High Spatial Resolution MALDI Imaging Mass Spectrometry of Fresh-Frozen Bone

Bone and bone marrow are vital to mammalian structure, movement, and immunity. These tissues are also commonly subjected to pathological alterations giving rise to debilitating diseases like rheumatoid arthritis, osteoporosis, osteomyelitis, and cancer. Technologies such as matrix-assisted laser desorption/ionization (MALDI) imaging mass spectrometry (IMS) enable the discovery of spatially resolved chemical information in biological tissue samples to help elucidate the complex molecular processes underlying pathology. Traditionally, preparation of native osseous tissue for MALDI IMS has been difficult due to the mineralized composition and heterogenous morphology of the tissue, and compensation for these challenges with decalcification and fixation protocols can remove or delocalize molecular species. Here, sample preparation methods were advanced to enable multimodal MALDI IMS of undecalcified, fresh-frozen murine femurs allowing the distribution of endogenous lipids to be linked to specific tissue structures and cell types. Adhesive-bound bone sections were mounted onto ITO coated glass slides with a microscopy-compatible glue and freeze-dried to minimize artificial bone marrow damage. Subliming matrix does not induce further bone marrow cracks, and recrystallizing the deposited matrix improves lipid signal. High spatial resolution (10 μm) MALDI IMS was leveraged to characterize lipid distributions in fresh-frozen bone, and complementary microscopy modalities aided tissue and cell assignments. For example, various phosphatidylcholines localize to bone marrow, adipose tissue, marrow adipose tissue, and muscle. Furthermore, we discovered that [sphingomyelin(42:1) + H]+ was abundant in megakaryocytes, whereas [sphingomyelin(42:2) + H]+ was diminished in this cell type. These data reflect the vast molecular and cellular heterogeneity indicative of the bone marrow and the soft tissue surrounding the femur. Therefore, this application of multimodal MALDI IMS has the potential to advance bone-related biomedical research by offering deep molecular coverage in a preserved native bone microenvironment.

Classification of molecular subtypes of high-grade serous ovarian cancer by MALDI-Imaging.

e17544 Background: High-grade serous ovarian cancer (HGSOC) can be separated by gene expression profiling into four molecular subtypes with clear correlation of the clinical outcome. However, these gene signatures have not been implemented in clinical practice to stratify patients for targeted therapy. This is mainly due to a lack of easy, cost-effective and reproducible methods, as well as the high heterogeneity of HGSOC. Hence, we aimed to examine the potential of unsupervised matrix assisted laser desorption/ionization imaging mass spectrometry (MALDI-IMS) to stratify patients, which might benefit from targeted therapeutic strategies. Methods: Molecular subtyping of paraffin-embedded tissue samples from 279 HGSOC patients was performed by NanoString analysis (ground truth labeling). Next, we applied MALDI-IMS, a novel technology to identify distinct mass profiles on the same paraffin-embedded tissue sections paired with machine learning algorithms to identify HGSOC subtypes by proteomic signature. Finally, we devised a novel strategy to annotate spectra of stromal origin. Results: We elucidated a MALDI-derived proteomic signature (135 peptides) able to classify HGSOC subtypes. Random forest classifiers achieved an area under the curve (AUC) of 0.983. Furthermore, we demonstrated that the exclusion of stroma associated spectra provides tangible improvements to classification quality (AUC = 0.988). False discovery rates (FDR) were reduced from 10.2% to 8.0%. Finally, novel MALDI-based stroma annotation achieved near-perfect classifications (AUC = 0.999, FDR < 1.0%). Conclusions: Here, we present a concept integrating MALDI-IMS with machine learning algorithms to classify patients according to distinct molecular subtypes of HGSOC. This has great potential to assign patients for targeted therapies.

Tissue fixation effects on human retinal lipid analysis by MALDI imaging and LC-MS/MS technologies

Imaging mass spectrometry (IMS) allows the location and abundance of lipids to be mapped across tissue sections of human retina. For reproducible and accurate information, sample preparation methods need to be optimized. Paraformaldehyde fixation of a delicate multilayer structure like human retina facilitates the preservation of tissue morphology by forming methylene bridge cross-links between formaldehyde and amine/ thiols in biomolecules; however, retina sections analyzed by IMS are typically fresh-frozen. To determine if clinically significant inferences could be reliably based on fixed tissue, we evaluated the effect of fixation on analyte detection, spatial localization, and introduction of artefactual signals. Hence, we assessed the molecular identity of lipids generated by matrix-assisted laser desorption ionization (MALDI-IMS) and liquid chromatography coupled tandem mass spectrometry (LC-MS/MS) for fixed and fresh-frozen retina tissues in positive and negative ion modes. Based on MALDI-IMS analysis, more lipid signals were observed in fixed compared to fresh-frozen retina. More potassium adducts were observed in fresh-frozen tissues than fixed as the fixation process caused displacement of potassium adducts to protonated and sodiated species in ion positive ion mode. LC-MS/MS analysis revealed an overall decrease in lipid signals due to fixation that reduced glycerophospholipids and glycerolipids and conserved most sphingolipids and cholesteryl esters. The high quality and reproducible information from untargeted lipidomics analysis of fixed retina informs on all major lipid classes, similar to fresh-frozen retina, and serves as a steppingstone towards understanding of lipid alterations in retinal diseases.

Classification of Molecular Subtypes of High-Grade Serous Ovarian Cancer by MALDI-Imaging

Despite the correlation of clinical outcome and molecular subtypes of high-grade serous ovarian cancer (HGSOC), contemporary gene expression signatures have not been implemented in clinical practice to stratify patients for targeted therapy. Hence, we aimed to examine the potential of unsupervised matrix-assisted laser desorption/ionization imaging mass spectrometry (MALDI-IMS) to stratify patients who might benefit from targeted therapeutic strategies. Molecular subtyping of paraffin-embedded tissue samples from 279 HGSOC patients was performed by NanoString analysis (ground truth labeling). Next, we applied MALDI-IMS paired with machine-learning algorithms to identify distinct mass profiles on the same paraffin-embedded tissue sections and distinguish HGSOC subtypes by proteomic signature. Finally, we devised a novel approach to annotate spectra of stromal origin. We elucidated a MALDI-derived proteomic signature (135 peptides) able to classify HGSOC subtypes. Random forest classifiers achieved an area under the curve (AUC) of 0.983. Furthermore, we demonstrated that the exclusion of stroma-associated spectra provides tangible improvements to classification quality (AUC = 0.988). Moreover, novel MALDI-based stroma annotation achieved near-perfect classifications (AUC = 0.999). Here, we present a concept integrating MALDI-IMS with machine-learning algorithms to classify patients according to distinct molecular subtypes of HGSOC. This has great potential to assign patients for personalized treatment.