M/Z: 410.0641

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

Fig. 4 MALDI-MSI data of mouse lung tissue after administration with D9-choline and U13C-DPPC–containing Poractant alfa surfactant (labels administered 12 h prior to tissue collection). Ion images of (A) m/z 796.6856 ([U13C-DPPC+Na]+), (B) m/z 756.5154 [PC32:0+Na]+), and (C) m/z 765.6079 ([D9-PC32:0+Na]+). D: Overlay image of [U13C-PC32:0+Na]+ (red) and [D9-PC32:0+Na]+ (green). 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. MSI, mass spectrometry imaging; PC, phosphatidylcholine; U13C-DPPC, universally 13C-labeled dipalmitoyl PC.

Fig 6c Fig. 6 MALDI-MSI of U13C-PC16:0/16:0 acyl chain remodeling. A: Averaged MALDI mass spectrum from lung tissue collected from mice euthanized 12 h after administration of D9-choline and U13C-DPPC–containing Poractant alfa surfactant. The ion at m/z 828.6321 is assigned as the [M+Na]+ ion of 13C24-PC16:0_20:4 formed by acyl remodeling of U13C-PC16:0/16:0. The “NL” value refers to the intensity of the base peak in the full range MS1 spectrum. B: MS/MS spectrum of precursor ions at m/z 828.5 ± 0.5 with fragment ions originating from [13C24-PC16:0_20:4+Na]+ annotated. Part-per-million (ppm) mass errors are provided in parentheses. C, D: MALDI-MSI data of [U13C-DPPC+Na]+ (blue), [PC36:4+Na]+ (green) and [13C24-PC16:0_20:4+Na]+ (red) in lung tissue collected from mice (C) 12 h and (D) 18 h after label administration. All images were visualized using total-ion-current normalization and hotspot removal (high quantile = 99%). MS/MS, tandem mass spectrometry; MSI, mass spectrometry imaging; PC, phosphatidylcholine; U13C-DPPC, universally 13C-labeled dipalmitoyl PC.

Supplementary Figure S8. MALDI-MSI data of mouse lung tissue administered with D9-choline and U 13C-DPPC–containing Poractant alfa surfactant (labels administered 18 h prior to sacrifice). Ion images of (a) m/z 796.6856 ([U13C-DPPC+Na]+), (b) m/z 756.5154 [PC32:0+Na]+ and (c) m/z 765.6079 ([D9-PC32:0+Na]+). (d) Overlay image of [U13C-DPPC+Na]+ (red) and [D9-PC32:0+Na]+ (green). Parts per million (ppm) mass errors are indicated in parentheses. All images were visualised using totalion-current normalisation and using hotspot removal (high quantile = 99%). DPPC = PC16:0/16:0.

Supplementary Figure S6. Ion distribution images for (a) [PC36:4+Na]+ (m/z 804.5514) and (b) [PC38:6+Na]+ (m/z 828.5515) obtained from mouse lung tissue collected 6 h after administration of D9- choline and U13C-DPPC–containing CHF5633. Parts-per-million (ppm) mass errors are indicated in parentheses. (c) Magnification of the boxed region in (a) with selected bronchiolar regions outlined in white boxes. (d) The corresponding H&E-stained tissue section with the same selected bronchiolar regions outlined in black boxes. These data demonstrate the co-localisation of the polyunsaturated lipids PC36:4 and PC38:6 with the bronchiolar regions of the lung. All MSI images were visualised using total ion current normalisation and hotspot removal (high quantile = 99%).

Seagrasses are among the most efficient sinks of carbon dioxide on Earth. While carbon sequestration in terrestrial plants is linked to the microorganisms living in their soils, the interactions of seagrasses with their rhizospheres are poorly understood. Here, we show that the seagrass, Posidonia oceanica excretes sugars, mainly sucrose, into its rhizosphere. These sugars accumulate to µM concentrations—nearly 80 times higher than previously observed in marine environments. This finding is unexpected as sugars are readily consumed by microorganisms. Our experiments indicated that under low oxygen conditions, phenolic compounds from P. oceanica inhibited microbial consumption of sucrose. Analyses of the rhizosphere community revealed that many microbes had the genes for degrading sucrose but these were only expressed by a few taxa that also expressed genes for degrading phenolics. Given that we observed high sucrose concentrations underneath three other species of marine plants, we predict that the presence of plant-produced phenolics under low oxygen conditions allows the accumulation of labile molecules across aquatic rhizospheres.

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.