Background Mass spectrometry imaging is increasingly used in biological and translational research because it has the ability to determine the spatial distribution of hundreds of analytes in a sample. Being at the interface of proteomics/metabolomics and imaging, the acquired datasets are large and complex and often analyzed with proprietary software or in-house scripts, which hinders reproducibility. Open source software solutions that enable reproducible data analysis often require programming skills and are therefore not accessible to many mass spectrometry imaging (MSI) researchers. Findings We have integrated 18 dedicated mass spectrometry imaging tools into the Galaxy framework to allow accessible, reproducible, and transparent data analysis. Our tools are based on Cardinal, MALDIquant, and scikit-image and enable all major MSI analysis steps such as quality control, visualization, preprocessing, statistical analysis, and image co-registration. Furthermore, we created hands-on training material for use cases in proteomics and metabolomics. To demonstrate the utility of our tools, we re-analyzed a publicly available N-linked glycan imaging dataset. By providing the entire analysis history online, we highlight how the Galaxy framework fosters transparent and reproducible research. Conclusion The Galaxy framework has emerged as a powerful analysis platform for the analysis of MSI data with ease of use and access, together with high levels of reproducibility and transparency.

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S-adenosylmethionine (SAM) is essential for methyl transfer reactions. All SAM is produced de novo via the methionine cycle. The demethylation of SAM produces S-adenosylhomocysteine (SAH), an inhibitor of methyltransferases and the precursor of homocysteine (Hcy). The measurement of SAM and SAH in plasma has value in the diagnosis of inborn errors of metabolism (IEM) and in research to assess methyl group homeostasis. The determination of SAM and SAH is complicated by the instability of SAM under neutral and alkaline conditions and the naturally low concentration of both SAM and SAH in plasma (nM range). Herein, we describe an optimised LC-MS/MS method for the determination of SAM and SAH in plasma, urine, and cells. The method is based on isotopic dilution and employs 20 µL of plasma or urine, or 500,000 cells, and has an instrumental running time of 5 min. The reference ranges for plasma SAM and SAH in a cohort of 33 healthy individuals (age: 19–60 years old; mean ± 2 SD) were 120 ± 36 nM and 21.5 ± 6.5 nM, respectively, in accordance with independent studies and diagnostic determinations. The method detected abnormal concentrations of SAM and SAH in patients with inborn errors of methyl group metabolism. Plasma and urinary SAM and SAH concentrations were determined for the first time in a randomised controlled trial of 53 healthy adult omnivores (age: 18–60 years old), before and after a 4 week intervention with a vegan or meat-rich diet, and revealed preserved variations of both metabolites and the SAM/SAH index.

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About 50% of colorectal cancer patients develop liver metastases. Patients with metastatic colorectal cancer have 5-year survival rates below 20% despite new therapeutic regimens. Tumor heterogeneity has been linked with poor treatment response and clinical outcome, but was so far mainly studied via bulk genomic analyses. In this study we performed spatial proteomics via MALDI mass spectrometry imaging on six patient-matched CRC primary tumor and liver metastases to characterize interpatient, intertumor and intratumor hetereogeneity. We found several peptide features that were enriched in vital tumor areas of primary tumors and liver metastasis and tentatively derived from tumor cell specific proteins such as annexin A4 and prelamin A/C. Liver metastases of colorectal cancer showed higher heterogeneity between patients than primary tumors while within patients both entities show similar intratumor heterogeneity sometimes organized in zonal pattern. Together our findings give new insights into the spatial proteomic heterogeneity of primary CRC and patient-matched liver metastases.