Purpose Most cancer-related deaths worldwide are associated with lung cancer. Subtyping of non-small cell lung cancer (NSCLC) into adenocarcinoma (AC) and squamous cell carcinoma (SqCC) is of importance, as therapy regimes differ. However, conventional staining and immunohistochemistry have their limitations. Therefore, a spatial metabolomics approach was aimed to detect differences between subtypes and to discriminate tumor and stroma regions in tissues. Methods Fresh-frozen NSCLC tissues (n = 35) were analyzed by matrix-assisted laser desorption/ionization-mass spectrometry imaging (MALDI-MSI) of small molecules (< m/z 1000). Measured samples were subsequently stained and histopathologically examined. A differentiation of subtypes and a discrimination of tumor and stroma regions was performed by receiver operating characteristic analysis and machine learning algorithms. Results Histology-guided spatial metabolomics revealed differences between AC and SqCC and between NSCLC tumor and tumor microenvironment. A diagnostic ability of 0.95 was achieved for the discrimination of AC and SqCC. Metabolomic contrast to the tumor microenvironment was revealed with an area under the curve of 0.96 due to differences in phospholipid profile. Furthermore, the detection of NSCLC with rarely arising mutations of the isocitrate dehydrogenase (IDH) gene was demonstrated through 45 times enhanced oncometabolite levels. Conclusion MALDI-MSI of small molecules can contribute to NSCLC subtyping. Measurements can be performed intraoperatively on a single tissue section to support currently available approaches. Moreover, the technique can be beneficial in screening of IDH-mutants for the characterization of these seldom cases promoting the development of treatment strategies.

Urachal adenocarcinomas (UrC) are rare but aggressive. Despite being of profound therapeutic relevance, UrC cannot be differentiated by histomorphology alone from other adenocarcinomas of differential diagnostic importance. As no reliable tissue-based diagnostic biomarkers are available, we aimed to detect such by integrating mass-spectrometry imaging-based metabolomics and digital pathology, thus allowing for a multimodal approach on the basis of spatial information. To achieve this, a cohort of UrC (n = 19) and colorectal adenocarcinomas (CRC, n = 27) as the differential diagnosis of highest therapeutic relevance was created, tissue micro-arrays (TMAs) were constructed, and pathological data was recorded. Hematoxylin and eosin (H&E) stained tissue sections were scanned and annotated, enabling an automized discrimination of tumor and non-tumor areas after training of an adequate algorithm. Spectral information within tumor regions, obtained via matrix-assisted laser desorption/ionization (MALDI)-Orbitrap-mass spectrometry imaging (MSI), were subsequently extracted in an automated workflow. On this basis, metabolic differences between UrC and CRC were revealed using machine learning algorithms. As a result, the study demonstrated the feasibility of MALDI-MSI for the evaluation of FFPE tissue in UrC and CRC with the potential to combine spatial metabolomics data with annotated histopathological data from digitalized H&E slides. The detected Area under the curve (AUC) of 0.94 in general and 0.77 for the analyte taurine alone (diagnostic accuracy for taurine: 74%) makes the technology a promising tool in this differential diagnostic dilemma situation. Although the data has to be considered as a proof-of-concept study, it presents a new adoption of this technology that has not been used in this scenario in which reliable diagnostic biomarkers (such as immunohistochemical markers) are currently not available.

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Herbivores face a broad range of defences when feeding on plants. By mixing diets, polyphagous herbivores are assumed to benefit during their development by gaining a better nutritional balance and reducing the intake of toxic compounds from individual plant species. Nevertheless, they also show strategies to metabolically cope with plant defences. In this study, we investigated the development of the polyphagous tansy leaf beetle, Galeruca tanaceti (Coleoptera: Chrysomelidae), on mono diets consisting of one plant species [cabbage (Brassica rapa), Brassicaceae; lettuce (Lactuca sativa), or tansy (Tanacetum vulgare), Asteraceae] vs. two mixed diets, both containing tansy. Leaves of the three species were analysed for contents of water, carbon and nitrogen, the specific leaf area (SLA) and trichome density. Furthermore, we studied the insect metabolism of two glucosinolates, characteristic defences of Brassicaceae. Individuals reared on cabbage mono diet developed fastest and showed the highest survival, while the development was slowest for individuals kept on tansy mono diet. Cabbage had the lowest water content, while tansy had the highest water content, C/N ratio and trichome density and the lowest SLA. Lettuce showed the lowest C/N ratio, highest SLA and no trichomes. Analysis of insect samples with UHPLC-DAD-QTOF-MS/MS revealed that benzyl glucosinolate was metabolised to N-benzoylglycine, N-benzoylalanine and N-benzoylserine. MALDI-Orbitrap-MS imaging revealed the localisation of these metabolites in the larval hindgut region. 4-Hydroxybenzyl glucosinolate was metabolised to N-(4-hydroxybenzoyl)glycine. Our results highlight that G. tanaceti deals with toxic hydrolysis products of glucosinolates by conjugation with different amino acids, which may enable this species to develop well on cabbage. The high trichome density and/or specific plant chemistry may lower the accessibility and/or digestibility of tansy leaves, leading to a poorer beetle development on pure tansy diet or diet mixes containing tansy. Thus, diet mixing is not necessarily beneficial, if one of the plant species is strongly defended.