Background: Three-dimensional (3D) imaging mass spectrometry (MS) is an analytical chemistry technique for the 3D molecular analysis of a tissue specimen, entire organ, or microbial colonies on an agar plate. 3D-imaging MS has unique advantages over existing 3D imaging techniques, offers novel perspectives for understanding the spatial organization of biological processes, and has growing potential to be introduced into routine use in both biology and medicine. Owing to the sheer quantity of data generated, the visualization, analysis, and interpretation of 3D imaging MS data remain a significant challenge. Bioinformatics research in this field is hampered by the lack of publicly available benchmark datasets needed to evaluate and compare algorithms. Findings: High-quality 3D imaging MS datasets from different biological systems at several labs were acquired, supplied with overview images and scripts demonstrating how to read them, and deposited into MetaboLights, an open repository for metabolomics data. 3D imaging MS data were collected from five samples using two types of 3D imaging MS. 3D matrix-assisted laser desorption/ionization imaging (MALDI) MS data were collected from murine pancreas, murine kidney, human oral squamous cell carcinoma, and interacting microbial colonies cultured in Petri dishes. 3D desorption electrospray ionization (DESI) imaging MS data were collected from a human colorectal adenocarcinoma. Conclusions: With the aim to stimulate computational research in the field of computational 3D imaging MS, selected high-quality 3D imaging MS datasets are provided that could be used by algorithm developers as benchmark datasets.

The intraoperative assessment of tumor margins of head and neck cancer is crucial for complete tumor resection and patient outcome. The current standard is to take tumor biopsies during surgery for frozen section analysis by a pathologist after H&E staining. This evaluation is time-consuming, subjective, methodologically limited and underlies a selection bias. Optical methods such as hyperspectral imaging (HSI) are therefore of high interest to overcome these limitations. We aimed to analyze the feasibility and accuracy of an intraoperative HSI assessment on unstained tissue sections taken from seven patients with oral squamous cell carcinoma. Afterwards, the tissue sections were subjected to standard histopathological processing and evaluation. We trained different machine learning models on the HSI data, including a supervised 3D convolutional neural network to perform tumor detection. The results were congruent with the histopathological annotations. Therefore, this approach enables the delineation of tumor margins with artificial HSI-based histopathological information during surgery with high speed and accuracy on par with traditional intraoperative tumor margin assessment (Accuracy: 0.76, Specificity: 0.89, Sensitivity: 0.48). With this, we introduce HSI in combination with ML hyperspectral imaging as a potential new tool for intraoperative tumor margin assessment.

Purpose: The heterogeneity of squamous cell carcinoma tissue greatly complicates diagnosis and individualized therapy. Therefore, characterizing the heterogeneity of tissue spatially and identifying appropriate biomarkers is crucial. MALDI–MS imaging (MSI) is capable of analyzing spatially resolved tissue biopsies on a molecular level.Experimental design: MALDI–MSI is used on snap frozen and formalin‐fixed and paraffin‐embedded (FFPE) tissue samples from patients with head and neck cancer (HNC) to analyze m/z values localized in tumor and nontumor regions. Peptide identification is performed using LC–MS/MS and immunohistochemistry (IHC).Results: In both FFPE and frozen tissue specimens, eight characteristic masses of the tumor's epithelial region are found. Using LC–MS/MS, the peaks are identified as vimentin, keratin type II, nucleolin, heat shock protein 90, prelamin‐A/C, junction plakoglobin, and PGAM1. Lastly, vimentin, nucleolin, and PGAM1 are verified with IHC.Conclusions and Clinical Relevance: The combination of MALDI–MSI, LC‐MS/MS, and subsequent IHC furnishes a tool suitable for characterizing the molecular heterogeneity of tissue. It is also suited for use in identifying new representative biomarkers to enable a more individualized therapy.

Aim was to identify methylated genes with functional involvement in cisplatin‐resistance development of epithelial ovarian cancer (EOC). Genome‐wide analyses of hypermethylated CpG‐islands in resistant cell lines in combination with qRT‐PCR analyses were used to identify epigenetically silenced genes. EOC‐Type‐II tumors were analyzed for gene methylation and expression and TCGA data were interrogated in‐silico. Experiments revealed 37 commonly hypermethylated genes in resistant cells of which Tribbles 2 (TRIB2) showed the most pronounced downregulation on mRNA level and was characterized further. TRIB2 showed a reactivation after 5′‐Aza‐Cytidine treatment in resistant cells but a cisplatin‐dependent, prominent upregulation on mRNA level in sensitive cells, only. Re‐expression in resistant A2780 cells increased the sensitivity to cisplatin and other DNA‐damaging agents, but not taxanes. Contrary, knockdown of TRIB2 increased resistance to cisplatin in sensitive cells. TRIB2 was involved in the induction of a cisplatin‐dependent cell cycle arrest and apoptosis by influencing p21 and survivin expression. An increased Pt‐DNA‐adduct formation in TRIB2 re‐expressing cells did not translate in higher levels of dsDNA damage (yH2AX‐foci). Thus, TRIB2 is potentially involved in the signal transduction from nucleotide excision repair of intrastrand cross links. Importantly, patient stratification of two homogenous cohorts of EOC‐Type‐II patients from Jena (n = 38) and the TCGA (n = 149) by TRIB2 mRNA expression consistently revealed a significantly decreased PFS for patients with low TRIB2 levels (log‐rank p < 0.05). Tumors from resistant patients expressed the lowest levels of TRIB2. Downregulation of TRIB2 contributes to platin‐resistance and TRIB2 expression should be validated as prognostic and predictive marker for EOC.