Spatial segmentation is a critical procedure in mass spectrometry imaging (MSI)-based biochemical analysis. However, the commonly used unsupervised MSI segmentation methods may lead to inappropriate segmentation results as the MSI data is characterized by high dimensionality and low signal-to-noise ratio. This process can be improved by the incorporation of precise prior knowledge, which is hard to obtain in most cases. In this study, we show that the incorporation of partial or coarse prior knowledge from different sources such as reference images or biological knowledge may also help to improve MSI segmentation results. Here, we propose a novel interactive segmentation strategy for MSI data called iSegMSI, which incorporates prior information in the form of scribble-regularization of the unsupervised model to fine-tune the segmentation results. By using two typical MSI data sets (including a whole-body mouse fetus and human thyroid cancer), the present results demonstrate the effectiveness of the iSegMSI strategy in improving the MSI segmentations. Specifically, the method can be used to subdivide a region into several subregions specified by the user-defined scribbles or to merge several subregions into a single region. Additionally, these fine-tuned results are highly tolerant to the imprecision of the scribbles. Our results suggest that the proposed iSegMSI method may be an effective preprocessing strategy to facilitate the analysis of MSI data.

Drug combinations are commonly used to treat various diseases to achieve synergistic therapeutic effects or to alleviate drug resistance. Nevertheless, some drug combinations might lead to adverse effects, and thus, it is crucial to explore the mechanisms of drug interactions before clinical treatment. Generally, drug interactions have been studied using nonclinical pharmacokinetics, toxicology, and pharmacology. Here, we propose a complementary strategy based on metabolomics, which we call interaction metabolite set enrichment analysis, or iMSEA, to decipher drug interactions. First, a digraph-based heterogeneous network model was constructed to model the biological metabolic network based on the Kyoto Encyclopedia of Genes and Genomes (KEGG) database. Second, treatment-specific influences on all detected metabolites were calculated and propagated across the whole network model. Third, pathway activity was defined and enriched to quantify the influence of each treatment on the predefined functional metabolite sets, i.e., metabolic pathways. Finally, drug interactions were identified by comparing the pathway activity enriched by the drug combination treatments and the single drug treatments. A data set consisting of hepatocellular carcinoma (HCC) cells that were treated with oxaliplatin (OXA) and/or vitamin C (VC) was used to illustrate the effectiveness of the iMSEA strategy for evaluation of drug interactions. Performance evaluation using synthetic noise data was also performed to evaluate sensitivities and parameter settings for the iMSEA strategy. The iMSEA strategy highlighted synergistic effects of combined OXA and VC treatments including the alterations in the glycerophospholipid metabolism pathway and glycine, serine, and threonine metabolism pathway. This work provides an alternative method to reveal the mechanisms of drug combinations from the viewpoint of metabolomics.

Matriptase is an epithelia‐specific membrane‐anchored serine protease, and its dysregulation is highly related to the progression of a variety of cancers. Hepatocyte growth factor activator inhibitor‐1 (HAI‐1) inhibits matriptase activity through forming complex with activated matriptase. The balance of matriptase activation and matriptase/HAI‐1 complex formation determines the intensity and duration of matriptase activity. 3‐Cl‐AHPC, 4‐[3‐(1‐adamantyl)‐4‐hydroxyphenyl]‐3‐chlorocinnamic acid, is an adamantly substituted retinoid‐related molecule and a ligand of retinoic acid receptor γ (RARγ). 3‐Cl‐AHPC is of strong anti‐cancer effect but with elusive mechanisms. In our current study, we show that 3‐Cl‐AHPC time‐ and dose‐ dependently induces matriptase/HAI‐1 complex formation, leading to the suppression of activated matriptase in cancer cells and tissues. Furthermore, 3‐Cl‐AHPC promotes matriptase shedding but without increasing the activity of shed matriptase. Moreover, 3‐Cl‐AHPC inhibits matriptase‐mediated cleavage of pro‐HGF through matriptase/HAI‐1 complex induction, resulting in the suppression of pro‐HGF‐stimulated signalling and cell scattering. Although 3‐Cl‐AHPC binds to RARγ, its induction of matriptase/HAI‐1 complex is not RARγ dependent. Together, our data demonstrates that 3‐Cl‐AHPC down‐regulates matriptase activity through induction of matriptase/HAI‐1 complex formation in a RARγ‐independent manner, providing a mechanism of 3‐Cl‐AHPC anti‐cancer activity and a new strategy to inhibit abnormal matriptase activity via matriptase/HAI‐1 complex induction using small molecules.