Thioguanosine 5'-diphosphate

Mass spectrometry imaging of phospholipids in mouse urinary bladder (imzML dataset)
The spatial distribution of phospholipids in a tissue section of mouse urinary bladder was analyzed by MALDI MS imaging at 10 micrometer pixel size with high mass resolution (using an LTQ Orbitrap mass spectrometer).

R, ö, mpp A, Guenther S, Schober Y, Schulz O, Takats Z, Kummer W, Spengler B, Histology by mass spectrometry: label-free tissue characterization obtained from high-accuracy bioanalytical imaging. Angew Chem Int Ed Engl, 49(22):3834-8(2010)

Fig. S2: Single ion images of compounds shown in Fig. 1A-B : (upper left to lower right) m/z = 743.5482 (unknown), m/z = 741.5307 (SM (16:0), [M+K]+), m/z = 798.5410 (PC (34:1), [M+K]+), m/z = 616.1767 (heme b, M+), m/z = 772.5253 (PC (32:0), [M+K]+).

Stability of determined mass values was in the range of +/- 1 ppm over 22 hours of measurement (Fig. S4), with a standard deviation of 0.56 ppm. Accuracy data were obtained during tissue scanning experiments by monitoring the mass signal at nominal mass 798. The internal lock mass function of the Orbitrap instrument was used for automatic calibration during imaging measurements, using the known matrix-related ion signals at m/z = 137.0233, m/z = 444.0925 and m/z = 716.1246.

Fig. 2 MALDI-MSI data from the same mouse lung tissue analyzed in Fig. 1. A: Optical image of the post-MSI, H&E-stained tissue section. B–D, F–G: Ion images of (B) m/z 796.6855 ([U13C-DPPC+Na]+), (C) m/z 756.5514 ([PC32:0+Na]+), (D) m/z 765.6079 ([D9-PC32:0+Na]+), (F) m/z 754.5359 ([PC32:1+Na]+), and (G) m/z 763.5923 ([D9-PC32:1+Na]+). E, H: Ratio images of (E) [D9-PC32:0+Na]+:[PC32:0+Na]+ and (H) [D9-PC32:1+Na]+:[PC32:1+Na]+. Part-per-million (ppm) mass errors are indicated in parentheses. All images were visualized using total-ion-current normalization and using hotspot removal (high quantile = 99%). DPPC = PC16:0/16:0. U13C-DPPC, universally 13C-labeled dipalmitoyl PC; PC, phosphatidylcholine; MSI, mass spectrometry imaging; H&E, hematoxylin and eosin. Fig 1-3, Fig S1-S3, S5

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.

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.

single cell layer class_4 is the gill structure cells, metabolite ion 534.2956 is the top representive ion of this type of cell

single cell layer class_4 is the gill structure cells, metabolite ion 534.2956 is the top representive ion of this type of cell