M/Z: 689.3943

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 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.

All MSI experiments were performed on a hybrid linear ion trap 21 T FT-ICR mass spectrometer at the National High Magnetic Field Laboratory (NHMFL) at Florida State University (Tallahassee, FL). A Velos Pro linear ion trap (Thermo Scientific, San Jose, CA) was combined with NHMFL-designed external linear quadrupole ion trap, quadrupole ion transfer optics and a novel dynamically harmonized ICR cell, which is operated at 7.5 V trapping potential[1]. Briefly, the cell uses 120° cell segments for ion excitation and detection, for improved excitation electric field, detection sensitivity and reduced third harmonic signals[2][3]. The commercial ion source and stacked ring ion guide were replaced with an elevated-pressure MALDI ion source incorporating a dual-ion funnel interface (Spectroglyph LLC, Kennewick, WA) as has been described previously[4]. Voltages within the funnels were 625 kHz, 150 V peak-to-peak (first, high-pressure ion funnel) and 1.2 MHz, 90 V peak-to-peak (second, low-pressure ion funnel). An electric field gradient of ∼10 V/cm was maintained within the dual-funnel system, with a gradient of 100 V/cm between the sample and the funnel inlet. The system was equipped with a Q-switched, frequency-tripled Nd:YLF laser emitting 349 nm light (Explorer One, Spectra Physics, Mountain View, CA). The laser was operated at a repetition rate of 1 kHz and pulse energy of ∼1.2 μJ. Pressure within the ion source was set to 10 mbar in the first ion funnel and 2 mbar in the second ion funnel. MALDI stage motion was synchronized with ion accumulation using the Velos trigger signal indicating commencement of the ion trap injection event, as previously described[4]. The mass spectrometer was operated with an ion injection time of 250 ms and automatic gain control (AGC) was turned off. A transient duration of 3.1 s was used for ultrahigh mass resolving power analyses, resulting in a total time of 4s per pixel. Spectra were obtained in both positive and negative mode, at 100 μm spatial resolution. Total number of pixels per brain section were approximately 22 000 and 24 h of experimental time. A Predator data station was used for ion excitation and detection[5]. Refs: [1] Hendrickson CL, Quinn JP, Kaiser NK, Smith DF, Blakney GT, Chen T, Marshall AG, Weisbrod CR, Beu SC. 21 Tesla Fourier Transform Ion Cyclotron Resonance Mass Spectrometer: A National Resource for Ultrahigh Resolution Mass Analysis. J Am Soc Mass Spectrom. 2015 Sep;26(9):1626-32. doi:10.1007/s13361-015-1182-2. Epub 2015 Jun 20. PMID:26091892. [2] Hendrickson CL, Beu SC, Blakney GT, Kaiser NK, McIntosh DG, Quinn JP, Marshall AG. In Optimized cell geometry for Fourier transform ion cyclotron resonance mass spectrometry, Proceedings of the 57th ASMS Conference on Mass Spectrometry and Allied Topics, Philadelphia, PA, May 31 to June 4; Philadelphia, PA, 2009. [3] Chen T, Beu SC, Kaiser NK, Hendrickson CL. Note: Optimized circuit for excitation and detection with one pair of electrodes for improved Fourier transform ion cyclotron resonance mass spectrometry. Rev Sci Instrum. 2014 Jun;85(6):066107. doi:10.1063/1.4883179. PMID:24985871. [4] Belov ME, Ellis SR, Dilillo M, Paine MRL, Danielson WF, Anderson GA, de Graaf EL, Eijkel GB, Heeren RMA, McDonnell LA. Design and Performance of a Novel Interface for Combined Matrix-Assisted Laser Desorption Ionization at Elevated Pressure and Electrospray Ionization with Orbitrap Mass Spectrometry. Anal Chem. 2017 Jul 18;89(14):7493-7501. doi:10.1021/acs.analchem.7b01168. Epub 2017 Jun 28. PMID:28613836. [5] Blakney GT, Hendrickson CL, Marshall AG. Predator data station: A fast data acquisition system for advanced FT-ICR MS experiments. Int. J. Mass Spectrom. 2011;306 (2-3), 246- 252. doi:10.1016/j.ijms.2011.03.009.

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.