1-Methylnicotinamide

By writing the four English letters “PNMK” on white paper with a marker pen, and then scanning with a DESI ion source to obtain the scanning result. The signal of the chemical substances on the marker pen used appears on the channel with an m/z value of 322.1918, 323.1953, 546.4010, and etc, from the single cell deconvolution sampling layer class_4. This test data was tested by chuxiaoping from PANOMIX’s R&D laboratory.

By writing the four English letters “PNMK” on white paper with a marker pen, and then scanning with a DESI ion source to obtain the scanning result. The signal of the chemical substances on the marker pen used appears on the channel with an m/z value of 322.1918, 323.1953, 546.4010, and etc, from the single cell deconvolution sampling layer class_4. This test data was tested by chuxiaoping from PANOMIX’s R&D laboratory.

A more complete and holistic view on host–microbe interactions is needed to understand the physiological and cellular barriers that affect the efficacy of drug treatments and allow the discovery and development of new therapeutics. Here, we developed a multimodal imaging approach combining histopathology with mass spectrometry imaging (MSI) and same section imaging mass cytometry (IMC) to study the effects of Salmonella Typhimurium infection in the liver of a mouse model using the S. Typhimurium strains SL3261 and SL1344. This approach enables correlation of tissue morphology and specific cell phenotypes with molecular images of tissue metabolism. IMC revealed a marked increase in immune cell markers and localization in immune aggregates in infected tissues. A correlative computational method (network analysis) was deployed to find metabolic features associated with infection and revealed metabolic clusters of acetyl carnitines, as well as phosphatidylcholine and phosphatidylethanolamine plasmalogen species, which could be associated with pro-inflammatory immune cell types. By developing an IMC marker for the detection of Salmonella LPS, we were further able to identify and characterize those cell types which contained S. Typhimurium. [dataset] Nicole Strittmatter. Holistic Characterization of a Salmonella Typhimurium Infection Model Using Integrated Molecular Imaging, metabolights_dataset, V1; 2022. https://www.ebi.ac.uk/metabolights/MTBLS2671.