M/Z: 712.3932

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

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 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.

Seagrasses are one of the most efficient natural sinks of carbon dioxide (CO2) on Earth. Despite covering less than 0.1% of coastal regions, they have the capacity to bury up to 10% of marine organic matter and can bury the same amount of carbon 35 times faster than tropical rainforests. On land, the soil’s ability to sequestrate carbon is intimately linked to microbial metabolism. Despite the growing attention to the link between plant production, microbial communities, and the carbon cycle in terrestrial ecosystems, these processes remain enigmatic in the sea. Here, we show that seagrasses excrete organic sugars, namely in the form of sucrose, into their rhizospheres. Surprisingly, the microbial communities living underneath meadows do not fully use this sugar stock in their metabolism. Instead, sucrose piles up in the sediments to mM concentrations underneath multiple types of seagrass meadows. Sediment incubation experiments show that microbial communities living underneath a meadow use sucrose at low metabolic rates. Our metagenomic analyses revealed that the distinct community of microorganisms occurring underneath meadows is limited in their ability to degrade simple sugars, which allows these compounds to persist in the environment over relatively long periods of time. Our findings reveal how seagrasses form blue carbon stocks despite the relatively small area they occupy. Unfortunately, anthropogenic disturbances are threatening the long-term persistence of seagrass meadows. Given that these sediments contain a large stock of sugars that heterotopic bacteria can degrade, it is even more important to protect these ecosystems from degradation.

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.

Sample information Organism: Homo sapiens (human) Organism part: Pancreas Condition: N/A Sample preparation Sample stabilisation: Paraformaldehyde fixed Tissue modification: None MALDI matrix: CHCA MALDI matrix application: quantification Solvent: none MS analysis Polarity: Positive Ionisation source: MALDI Analyzer: timsTOF fleX Pixel size: 0.1μm × 0.1μm Annotation settings m/z tolerance (ppm): 3 Analysis version: Original MSM Pixel count: 43035 Imzml file size: 94.45 MB Ibd file size: 2.41 GB