M/Z: 489.0023

The Bathymodiolus puteoserpentis specimen used for high resolution AP-MALDI-MSI was collected during the RV Meteor M126 cruise in 2016 at the Logatchev hydrothermal vent field on the Mid-Atlantic Ridge. The specimen was retrieved with the MARUM-Quest remotely operated vehicle (ROV) at the Irina II vent site at 3038 m depth, 14°45’11.01”N and 44°58’43.98”W, and placed in an insulated container to prevent temperature changes during recovery. Gills were dissected from the mussel as soon as brought on board after ROV retrieval, submerged in precooled 2% w/v carboxymethyl cellulose gel (CMC, Mw ~ 700,000, Sigma-Aldrich Chemie GmbH) and snap-frozen in liquid N2. Samples were stored at -80 °C until use.
The CMC-embedded gills were cross-sectioned at 10 µm thickness with a cryostat (Leica CM3050 S, Leica Biosystems Nussloch GmbH) at a chamber temperature of -35 °C and object holder at -22 °C. Individual sections were thaw-mounted onto coated Polysine slides (Thermo Scientific) and subsequently frozen in the cryostat chamber. Slides with tissue sections were stored in slide containers with silica granules, to prevent air moisture condensation on the tissue upon removal from the freezer. Before AP-MALDI matrix application, the sample was warmed to room temperature under a dry atmosphere in a sealed slide container (LockMailer microscope slide jar, Sigma-Aldrich, Steinheim, Germany), filled with silica granules (Carl Roth GmbH) to avoid condensation on the cold glass slide. The sample glass slide was marked with white paint around the tissue for orientation during image acquisition as previously described[1]. Additionally, optical images of the tissue section were acquired with a digital microscope (VHX-5000 Series, Keyence, Neu-Isenburg, Germany) prior to matrix application. To apply the matrix, we used an ultrafine pneumatic sprayer system with N2 gas (SMALDIPrep, TransMIT GmbH, Giessen, Germany)[2], to deliver 100 μl of a 30 mg/ml solution of 2,5-dihydroxybenzoic acid (DHB; 98% 574 purity, Sigma-Aldrich, Steinheim, Germany) dissolved in acetone/water (1:1 v/v) containing 0.1% trifluoroacetic acid (TFA). To locate the field of view and facilitate laser focusing, a red marker was applied adjacent to the matrix-covered tissue section. Ref: [1] Kaltenpoth M, Strupat K, Svatoš A Linking metabolite production to taxonomic identity in environmental samples by (MA)LDI-FISH. ISME J. 2016 Feb;10(2):527-31. doi: 10.1038/ismej.2015.122. PMID:26172211 [2] Kompauer M, Heiles S, Spengler B. Atmospheric pressure MALDI mass spectrometry imaging of tissues and cells at 1.4-μm lateral resolution. Nat Methods. 2017 Jan;14(1):90-96. doi: 10.1038/nmeth.4071. PMID:27842060
High-resolution AP-MALDI-MSI measurements were carried out at an experimental ion source setup [1][2], coupled to a Fourier transform orbital trapping mass spectrometer (Q Exactive HF, Thermo Fisher Scientific GmbH, Bremen, Germany). The sample was rastered with 233 x 233 laser spots with a step size of 3 µm without oversampling, resulting in an imaged area of 699 x 699 µm. AP-MALDI-MSI measurements were performed in positive mode for a mass detection range of 400–1200 Da and a mass resolving power of 240,000 (at 200 m/z). After AP-MALDI-MSI, the measured sample surface was recorded using a stereomicroscope (SMZ25, Nikon, Düssedorf, Germany). Ref: [1] Kompauer M, Heiles S, Spengler B. Atmospheric pressure MALDI mass spectrometry imaging of tissues and cells at 1.4-μm lateral resolution. Nat Methods. 2017 Jan;14(1):90-96. doi: 10.1038/nmeth.4071. PMID:27842060 [2] Kompauer M, Heiles S, Spengler B. Autofocusing MALDI mass spectrometry imaging of tissue sections and 3D chemical topography of nonflat surfaces. Nat Methods. 2017 Dec;14(12):1156-1158. doi:10.1038/nmeth.4433. PMID:28945703

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 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 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.

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.

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