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Spatial metabolomics of in situ host–microbe interactions at the micrometre scale

Abstract

Spatial metabolomics describes the location and chemistry of small molecules involved in metabolic phenotypes, defence molecules and chemical interactions in natural communities. Most current techniques are unable to spatially link the genotype and metabolic phenotype of microorganisms in situ at a scale relevant to microbial interactions. Here, we present a spatial metabolomics pipeline (metaFISH) that combines fluorescence in situ hybridization (FISH) microscopy and high-resolution atmospheric-pressure matrix-assisted laser desorption/ionization mass spectrometry to image host–microbe symbioses and their metabolic interactions. The metaFISH pipeline aligns and integrates metabolite and fluorescent images at the micrometre scale to provide a spatial assignment of host and symbiont metabolites on the same tissue section. To illustrate the advantages of metaFISH, we mapped the spatial metabolome of a deep-sea mussel and its intracellular symbiotic bacteria at the scale of individual epithelial host cells. Our analytical pipeline revealed metabolic adaptations of the epithelial cells to the intracellular symbionts and variation in metabolic phenotypes within a single symbiont 16S rRNA phylotype, and enabled the discovery of specialized metabolites from the host–microbe interface. metaFISH provides a culture-independent approach to link metabolic phenotypes to community members in situ and is a powerful tool for microbiologists across fields.

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Fig. 1: Combining spatial metabolomics and taxon-specific labelling in a correlative imaging and analysis pipeline (metaFISH).
Fig. 2: Intracellular microbial communities and their spatial metabolomes in the symbiotic gills of B. puteoserpentis.
Fig. 3: Spatial metabolome binning based on FISH signals.
Fig. 4: Spatial metabolic heterogeneity of MOX symbionts in the gill.
Fig. 5: Metabolic landscape of the symbiotic organ.
Fig. 6: metaFISH metabolite screening reveals a group of metabolites specific to the mussel–MOX symbiont interaction as candidates for structural elucidation.

Data availability

The data generated during and/or analysed in the current study are all publicly available. The microscopy data have been deposited on figshare: stereomicroscopy data at https://doi.org/10.6084/m9.figshare.6887180.v1; wide-field microscopy data at https://doi.org/10.6084/m9.figshare.6887231.v2; and CLSM data at https://doi.org/10.6084/m9.figshare.6887315.v2, https://doi.org/10.6084/m9.figshare.10298111.v1. The image stack of the micro computed tomography data, used for illustrating the three-dimensional anatomy of the mussel, can be accessed at https://doi.org/10.6084/m9.figshare.5458234.v1. The MS data generated have been deposited into the EMBL-EBI MetaboLights repository86 under accession numbers MTBLS744, MTBLS805, MTBLS746 and MTBLS811. Annotations of the two high-resolution AP-MALDI-MSI datasets can be publicly browsed at and downloaded from the online annotation platform METASPACE (https://metaspace2020.eu) using the dataset identifiers MPIMM_054_QE_P_BP_CF and MPIMM_039_QE_P_BP_CF. The results generated using the molecular LC–MS/MS networking platform GNPS (https://gnps.ucsd.edu) are publicly available and can be accessed via https://gnps.ucsd.edu/ProteoSAFe/status.jsp?task=c9bc4fae716c45dcbff19619b30090d2 and https://gnps.ucsd.edu/ProteoSAFe/status.jsp?task=5b9eb8be34c141d48688065d8081bf44.

Code availability

Image registration and alignment of the AP-MALDI-MSI and FISH data in Matlab and the R scripts for Cardinal Data analysis are available in the Supplementary Information and on Github (R scripts: https://github.com/esogin/miniature-octo-fiesta; Matlab: https://github.com/BenediktSenorDingDong/MALDI-FISHregistration).

References

  1. Cleary, J. L., Condren, A. R., Zink, K. E. & Sanchez, L. M. Calling all hosts: bacterial communication in situ. Chem. 2, 334–358 (2017).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  2. Song, C. X. et al. Molecular and chemical dialogues in bacteria–protozoa interactions. Sci. Rep. 5, 12837 (2015).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  3. Garg, N. et al. Spatial molecular architecture of the microbial community of a peltigera lichen. mSystems 1, e00139-16 (2016).

    Article  PubMed  PubMed Central  Google Scholar 

  4. Chagas, F. O., Pessotti, R. D., Caraballo-Rodriguez, A. M. & Pupo, M. T. Chemical signaling involved in plant–microbe interactions. Chem. Soc. Rev. 47, 1652–1704 (2018).

    Article  CAS  PubMed  Google Scholar 

  5. Dubilier, N., Bergin, C. & Lott, C. Symbiotic diversity in marine animals: the art of harnessing chemosynthesis. Nat. Rev. Microbiol. 6, 725–740 (2008).

    Article  CAS  PubMed  Google Scholar 

  6. Belin, B. J. et al. Hopanoid lipids: from membranes to plant–bacteria interactions. Nat. Rev. Microbiol. 16, 304–315 (2018).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  7. Kroiss, J. et al. Symbiotic streptomycetes provide antibiotic combination prophylaxis for wasp offspring. Nat. Chem. Biol. 6, 261–263 (2010).

    Article  CAS  PubMed  Google Scholar 

  8. Login, F. H. et al. Antimicrobial peptides keep insect endosymbionts under control. Science 334, 362–365 (2011).

    Article  CAS  PubMed  Google Scholar 

  9. Finlay, B. B. & McFadden, G. Anti-immunology: evasion of the host immune system by bacterial and viral pathogens. Cell 124, 767–782 (2006).

    Article  CAS  PubMed  Google Scholar 

  10. Nyholm, S. V. & Graf, J. Knowing your friends: invertebrate innate immunity fosters beneficial bacterial symbioses. Nat. Rev. Microbiol. 10, 815–827 (2012).

    Article  CAS  PubMed  Google Scholar 

  11. Dunham, S. J. B., Ellis, J. F., Li, B. & Sweedler, J. V. Mass spectrometry imaging of complex microbial communities. Accounts Chem. Res. 50, 96–104 (2017).

    Article  CAS  Google Scholar 

  12. Watrous, J. D. & Dorrestein, P. C. Imaging mass spectrometry in microbiology. Nat. Rev. Microbiol. 9, 683–694 (2011).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  13. Brunetti, A. E. et al. An integrative omics perspective for the analysis of chemical signals in ecological interactions. Chem. Soc. Rev. 47, 1574–1591 (2018).

    Article  CAS  PubMed  Google Scholar 

  14. Ackermann, M. A functional perspective on phenotypic heterogeneity in microorganisms. Nat. Rev. Microbiol. 13, 497–508 (2015).

    Article  CAS  PubMed  Google Scholar 

  15. Phelan, V. V., Liu, W. T., Pogliano, K. & Dorrestein, P. C. Microbial metabolic exchange—the chemotype-to-phenotype link. Nat. Chem. Biol. 8, 26–35 (2012).

    Article  CAS  Google Scholar 

  16. Shank, E. A. Considering the lives of microbes in microbial communities. mSystems 3, e00155-17 (2018).

    Article  PubMed  PubMed Central  Google Scholar 

  17. Kaltenpoth, M., Strupat, K. & Svatos, A. Linking metabolite production to taxonomic identity in environmental samples by (MA)LDI-FISH. ISME J. 10, 527–531 (2016).

    Article  PubMed  Google Scholar 

  18. Dorrestein, P. C., Mazmanian, S. K. & Knight, R. Finding the missing links among metabolites, microbes, and the host. Immunity 40, 824–832 (2014).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  19. Tropini, C., Earle, K. A., Huang, K. C. & Sonnenburg, J. L. The gut microbiome: connecting spatial organization to function. Cell Host Microbe 21, 433–442 (2017).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  20. Passarelli, M. K. et al. The 3D OrbiSIMS-label-free metabolic imaging with subcellular lateral resolution and high mass-resolving power. Nat. Methods 14, 1175–1183 (2017).

    Article  CAS  PubMed  Google Scholar 

  21. Amann, R. I. et al. Combination of 16S rRNA-targeted oligonucleotide probes with flow cytometry for analyzing mixed microbial populations. Appl. Environ. Microbiol. 56, 1919–1925 (1990).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  22. Welch, J. L. M., Hasegawa, Y., McNulty, N. P., Gordon, J. I. & Borisy, G. G. Spatial organization of a model 15-member human gut microbiota established in gnotobiotic mice. Proc. Natl Acad. Sci. USA 114, E9105–E9114 (2017).

    Article  CAS  Google Scholar 

  23. Musat, N. et al. A single-cell view on the ecophysiology of anaerobic phototrophic bacteria. Proc. Natl Acad. Sci. USA 105, 17861–17866 (2008).

    Article  PubMed  PubMed Central  Google Scholar 

  24. Dekas, A. E., Poretsky, R. S. & Orphan, V. J. Deep-sea archaea fix and share nitrogen in methane-consuming microbial consortia. Science 326, 422–426 (2009).

    Article  CAS  PubMed  Google Scholar 

  25. Soltwisch, J. et al. Mass spectrometry imaging with laser-induced postionization. Science 348, 211–215 (2015).

    Article  CAS  PubMed  Google Scholar 

  26. Zavalin, A., Yang, J., Hayden, K., Vestal, M. & Caprioli, R. M. Tissue protein imaging at 1 µm laser spot diameter for high spatial resolution and high imaging speed using transmission geometry MALDI TOF MS. Anal. Bioanal. Chem. 407, 2337–2342 (2015).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  27. Kompauer, M., Heiles, S. & Spengler, B. Atmospheric pressure MALDI mass spectrometry imaging of tissues and cells at 1.4-µm lateral resolution. Nat. Methods 14, 90–96 (2017).

    Article  CAS  PubMed  Google Scholar 

  28. Kompauer, M., Heiles, S. & Spengler, B. Autofocusing MALDI mass spectrometry imaging of tissue sections and 3D chemical topography of nonflat surfaces. Nat. Methods 14, 1156–1158 (2017).

    Article  CAS  PubMed  Google Scholar 

  29. Spengler, B., Hubert, M. & Kaufmann, R. Maldi ion imaging and biological ion imaging with a new scanning UV-laser microprobe. J. Am. Soc. Mass Spectr. 42, abstr. 1041 (1994).

  30. Caprioli, R. M., Farmer, T. B. & Gile, J. Molecular imaging of biological samples: localization of peptides and proteins using MALDI–TOF MS. Anal. Chem. 69, 4751–4760 (1997).

    Article  CAS  PubMed  Google Scholar 

  31. Spengler, B. & Hubert, M. Scanning microprobe matrix-assisted laser desorption ionization (SMALDI) mass spectrometry: instrumentation for sub-micrometer resolved LDI and MALDI surface analysis. J. Am. Soc. Mass. Spectrom. 13, 735–748 (2002).

    Article  CAS  PubMed  Google Scholar 

  32. Lackner, G., Peters, E. E., Helfrich, E. J. N. & Piel, J. Insights into the lifestyle of uncultured bacterial natural product factories associated with marine sponges. Proc. Natl Acad. Sci. USA 114, E347–E356 (2017).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  33. Gould, A. L. et al. Microbiome interactions shape host fitness. Proc. Natl Acad. Sci. USA 115, E11951–E11960 (2018).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  34. Duperron, S. et al. A dual symbiosis shared by two mussel species, Bathymodiolus azoricus and Bathymodiolus puteoserpentis (Bivalvia: Mytilidae), from hydrothermal vents along the northern Mid-Atlantic ridge. Environ. Microbiol. 8, 1441–1447 (2006).

    Article  CAS  PubMed  Google Scholar 

  35. Petersen, J. M. et al. Hydrogen is an energy source for hydrothermal vent symbioses. Nature 476, 176–180 (2011).

    Article  CAS  PubMed  Google Scholar 

  36. Goodwin, R. J. A. Sample preparation for mass spectrometry imaging: small mistakes can lead to big consequences. J. Proteomics 75, 4893–4911 (2012).

    Article  CAS  PubMed  Google Scholar 

  37. Spengler, B., Kompauer, M. & Heiles, S. AP-MALDI MSI of Lipids in Mouse Brain Tissue Sections https://protocolexchange.researchsquare.com/article/nprot-5227/v1 (2017).

  38. Geier, B. et al. Spatial metabolomics of in situ, host-microbe interactions (practical guide for combining MALDI-MSI and FISH microscopy on the same section) protocols.io https://www.protocols.io/view/spatial-metabolomics-of-in-situ-host-microbe-inter-6jchciw (2019).

  39. Spengler, B., Kompauer, M. & Heiles, S. Chemical and Topographical 3D Surface Profiling Using Atmospheric Pressure LDI and MALDI MS Imaging. Protocol Exchange https://protocolexchange.researchsquare.com/article/nprot-6131/v1 (2017).

  40. Bemis, K. D. et al. Probabilistic segmentation of mass spectrometry (MS) images helps select important ions and characterize confidence in the resulting segments. Mol. Cell Proteomics 15, 1761–1772 (2016).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  41. Alexandrov, T. & Bartels, A. Testing for presence of known and unknown molecules in imaging mass spectrometry. Bioinformatics 29, 2335–2342 (2013).

    Article  CAS  PubMed  Google Scholar 

  42. Romero Picazo, D. et al. Horizontally transmitted symbiont populations in deep-sea mussels are genetically isolated. ISME J. 13, 2954–2968 (2019).

    Article  PubMed  PubMed Central  Google Scholar 

  43. Ansorge, R. et al. Functional diversity enables multiple symbiont strains to coexist in deep-sea mussels. Nat. Microbiol. 4, 2487–2497 (2019).

    Article  CAS  PubMed  Google Scholar 

  44. Burgess, K. E. V., Borutzki, Y., Rankin, N., Daly, R. & Jourdan, F. MetaNetter 2: a cytoscape plugin for ab initio network analysis and metabolite feature classification. J. Chromatogr. B 1071, 68–74 (2017).

    Article  CAS  Google Scholar 

  45. Kharbush, J. J., Ugalde, J. A., Hogle, S. L., Allen, E. E. & Aluwihare, L. I. Composite bacterial hopanoids and their microbial producers across oxygen gradients in the water column of the California current. Appl. Environ. Microbiol. 79, 7491–7501 (2013); erratum 80, 3283 (2014).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  46. Szafranski, K. M., Piquet, B., Shillito, B., Lallier, F. H. & Duperron, S. Relative abundances of methane- and sulfur-oxidizing symbionts in gills of the deep-sea hydrothermal vent mussel Bathymodiolus azoricus under pressure. Deep Sea Res. Pt I 101, 7–13 (2015).

    Article  CAS  Google Scholar 

  47. Assie, A. et al. A specific and widespread association between deep-sea Bathymodiolus mussels and a novel family of Epsilonproteobacteria. Env. Microbiol. Rep. 8, 805–813 (2016).

    Article  Google Scholar 

  48. Alexandrov, T. et al. METASPACE: a community-populated knowledge base of spatial metabolomes in health and disease. Preprint at https://www.biorxiv.org/content/10.1101/539478v1 (2019).

  49. Geiger, O., Lopez-Lara, I. M. & Sohlenkamp, C. Phosphatidylcholine biosynthesis and function in bacteria. Biochim. Biophys. Acta 1831, 503–513 (2013).

    Article  CAS  PubMed  Google Scholar 

  50. Alvarez, H. M. & Steinbuchel, A. Triacylglycerols in prokaryotic microorganisms. Appl. Microbiol. Biotechnol. 60, 367–376 (2002).

    Article  CAS  PubMed  Google Scholar 

  51. Yoon, K., Han, D. X., Li, Y. T., Sommerfeld, M. & Hu, Q. Phospholipid:diacylglycerol acyltransferase is a multifunctional enzyme involved in membrane lipid turnover and degradation while synthesizing triacylglycerol in the unicellular green microalga Chlamydomonas reinhardtii. Plant Cell 24, 3708–3724 (2012).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  52. Barry, J. P. et al. Methane-based symbiosis in a mussel, Bathymodiolus platifrons, from cold seeps in Sagami Bay, Japan. Invertebr. Biol. 121, 47–54 (2002).

    Article  Google Scholar 

  53. Villarreal-Chiu, J. F., Quinn, J. P. & McGrath, J. W. The genes and enzymes of phosphonate metabolism by bacteria, and their distribution in the marine environment. Front. Microbiol. 3, 19 (2012).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  54. Martinez, A., Tyson, G. W. & DeLong, E. F. Widespread known and novel phosphonate utilization pathways in marine bacteria revealed by functional screening and metagenomic analyses. Environ. Microbiol. 12, 222–238 (2010).

    Article  CAS  PubMed  Google Scholar 

  55. Kellermann, M. Y. et al. Symbiont–host relationships in chemosynthetic mussels: a comprehensive lipid biomarker study. Org. Geochem. 43, 112–124 (2012).

    Article  CAS  Google Scholar 

  56. Assie, A. et al. Horizontal acquisition of a patchwork Calvin cycle by symbiotic and free-living Campylobacterota (formerly Epsilonproteobacteria). ISME J. 14, 104–122 (2020).

    Article  PubMed  Google Scholar 

  57. Simmons, T. L. et al. Biosynthetic origin of natural products isolated from marine microorganism–invertebrate assemblages. Proc. Natl Acad. Sci. USA 105, 4587–4594 (2008).

    Article  PubMed  PubMed Central  Google Scholar 

  58. Esquenazi, E. et al. Visualizing the spatial distribution of secondary metabolites produced by marine cyanobacteria and sponges via MALDI–TOF imaging. Mol. Biosyst. 4, 562–570 (2008).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  59. Thubaut, J., Puillandre, N., Faure, B., Cruaud, C. & Samadi, S. The contrasted evolutionary fates of deep-sea chemosynthetic mussels (Bivalvia, Bathymodiolinae). Ecol. Evol. 3, 4748–4766 (2013).

    Article  PubMed  PubMed Central  Google Scholar 

  60. Tavormina, P. L. et al. Methyloprofundus sedimenti gen. nov., sp nov., an obligate methanotroph from ocean sediment belonging to the ‘deep sea-1’ clade of marine methanotrophs. Int. J. Syst. Evol. Microbiol. 65, 251–259 (2015).

    Article  CAS  PubMed  Google Scholar 

  61. Wang, M. X. et al. Sharing and community curation of mass spectrometry data with global natural products social molecular networking. Nat. Biotechnol. 34, 828–837 (2016).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  62. Barrero-Canosa, J., Moraru, C., Zeugner, L., Fuchs, B. M. & Amann, R. Direct-geneFISH: a simplified protocol for the simultaneous detection and quantification of genes and rRNA in microorganisms. Environ. Microbiol. 19, 70–82 (2017).

    Article  CAS  PubMed  Google Scholar 

  63. Yamaguchi, T. et al. In situ DNA-hybridization chain reaction (HCR): a facilitated in situ HCR system for the detection of environmental microorganisms. Environ. Microbiol. 17, 2532–2541 (2015).

    Article  CAS  PubMed  Google Scholar 

  64. Stewart, G. R., Robertson, B. D. & Young, D. B. Tuberculosis: a problem with persistence. Nat. Rev. Microbiol. 1, 97–105 (2003).

    Article  CAS  PubMed  Google Scholar 

  65. Folkesson, A. et al. Adaptation of Pseudomonas aeruginosa to the cystic fibrosis airway: an evolutionary perspective. Nat. Rev. Microbiol. 10, 841–851 (2012).

    Article  CAS  PubMed  Google Scholar 

  66. Duperron, S. et al. Dual symbiosis in a Bathymodiolus sp mussel from a methane seep on the Gabon continental margin (southeast Atlantic): 16S rRNA phylogeny and distribution of the symbionts in gills. Appl. Environ. Microbiol. 71, 1694–1700 (2005).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  67. Pernthaler, A., Pernthaler, J. & Amann, R. Fluorescence in situ hybridization and catalyzed reporter deposition for the identification of marine bacteria. Appl. Environ. Microbiol. 68, 3094–3101 (2002).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  68. Stoecker, K., Dorninger, C., Daims, H. & Wagner, M. Double labeling of oligonucleotide probes for fluorescence in situ hybridization (DOPE-FISH) improves signal intensity and increases rRNA accessibility. Appl. Environ. Microbiol. 76, 922–926 (2010).

    Article  CAS  PubMed  Google Scholar 

  69. Wallner, G., Amann, R. & Beisker, W. Optimizing fluorescent insitu hybridization with rRNA-targeted oligonucleotide probes for flow cytometric identification of microorganisms. Cytometry 14, 136–143 (1993).

    Article  CAS  PubMed  Google Scholar 

  70. Rueden, C. T. et al. ImageJ2: ImageJ for the next generation of scientific image data. BMC Bioinformatics 18, 529 (2017).

    Article  PubMed  PubMed Central  Google Scholar 

  71. Verbeeck, N. et al. Connecting imaging mass spectrometry and magnetic resonance imaging-based anatomical atlases for automated anatomical interpretation and differential analysis. Biochim. Biophys. Acta 1865, 967–977 (2017).

    Article  CAS  Google Scholar 

  72. Chambers, M. C. et al. A cross-platform toolkit for mass spectrometry and proteomics. Nat. Biotechnol. 30, 918–920 (2012).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  73. Race, A. M., Styles, I. B. & Bunch, J. Inclusive sharing of mass spectrometry imaging data requires a converter for all. J. Proteomics 75, 5111–5112 (2012).

    Article  CAS  PubMed  Google Scholar 

  74. Bemis, K. D. et al. Cardinal: an R package for statistical analysis of mass spectrometry-based imaging experiments. Bioinformatics 31, 2418–2420 (2015).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  75. Dixon, P. VEGAN, a package of R functions for community ecology. J. Veg. Sci. 14, 927–930 (2003).

    Article  Google Scholar 

  76. Shannon, P. et al. Cytoscape: a software environment for integrated models of biomolecular interaction networks. Genome Res. 13, 2498–2504 (2003).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  77. Breitkopf, S. B. et al. A relative quantitative positive/negative ion switching method for untargeted lipidomics via high resolution LC-MS/MS from any biological source. Metabolomics 13, 30 (2017).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  78. Sumner, L. W. et al. Proposed minimum reporting standards for chemical analysis. Metabolomics 3, 211–221 (2007).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  79. Viant, M. R., Kurland, I. J., Jones, M. R. & Dunn, W. B. How close are we to complete annotation of metabolomes? Curr. Opin. Chem. Biol. 36, 64–69 (2017).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  80. Wishart, D. S. et al. HMDB 4.0: the human metabolome database for 2018. Nucleic Acids Res. 46, D608–D617 (2018).

    Article  CAS  PubMed  Google Scholar 

  81. Hastings, J. et al. ChEBI in 2016: improved services and an expanding collection of metabolites. Nucleic Acids Res. 44, D1214–D1219 (2016).

    Article  CAS  PubMed  Google Scholar 

  82. Smith, C. A. et al. METLIN: a metabolite mass spectral database. Ther. Drug Monit. 27, 747–751 (2005).

    Article  CAS  PubMed  Google Scholar 

  83. Palmer, A. et al. FDR-controlled metabolite annotation for high-resolution imaging mass spectrometry. Nat. Methods 14, 57–60 (2017).

    Article  CAS  PubMed  Google Scholar 

  84. Fernandez, R., Kvist, S., Lenihan, J., Giribet, G. & Ziegler, A. Sine systemate chaos? A versatile tool for earthworm taxonomy: non-destructive imaging of freshly fixed and museum specimens using micro-computed tomography. PLoS ONE 9, e96617 (2014).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  85. Limaye, A. Drishti: a volume exploration and presentation tool. In Proc. of SPIE 8506, Developments in X-Ray Tomography VIII 85060X (2012).

  86. Haug, K. et al. MetaboLights-an open-access general-purpose repository for metabolomics studies and associated meta-data. Nucleic Acids Res. 41, D781–D786 (2013).

    Article  CAS  PubMed  Google Scholar 

Download references

Acknowledgements

We would like to thank the crew and captains of the scientific vessels Meteor (M64 M114 M126), Nautilus (Na 58), Sonne (SO253) and Atlantis (AT26–10, AT21-02) and their ROV pilots that helped us collect our extensive sample set of mussel species. We thank M. Á. González Porras for advice during FISH experiments, M. Ücker for support in the laboratory, C. Borowski for sample collection and S. Markert for providing the Bathymodiolus thermophilus samples used for LC–MS/MS. We thank B. Ruthensteiner for providing access to the micro computed tomography set-up at the Bavarian State Collection of Zoology in Munich, Germany. We thank M. Witt from Bruker Daltonik for the exact mass measurements using scimaX MRMS. We thank J. Tebben (University of Bremen) for help with attempts to purify the group of specialized metabolites. We thank D. Tasdemir (GEOMAR Helmholtz-Zentrum für Ozeanforschung Kiel) for searches in MarinLit and the laboratory of L. Sanchez (University of Illinois at Chicago) for constructive feedback on the preprint. We would also like to thank R. Naisbit for editing and commenting on the manuscript. This work was funded by the Max Planck Society, the DFG Cluster of Excellence ‘The Ocean in the Earth System’ at MARUM (University of Bremen), a Gordon and Betty Moore Foundation Marine Microbiology Initiative Investigator Award through grant GBMF3811 to N.D., and a European Research Council Advanced Grant (BathyBiome, grant 340535). For instrumental development, financial support by the Deutsche Forschungsgemeinschaft, DFG under grant Sp314/13-1, is gratefully acknowledged.

Author information

Authors and Affiliations

Authors

Contributions

B.G., N.D. and M.L. conceived the study. B.G developed the correlative imaging pipeline and compiled the Matlab script for image processing and alignment. E.M.S., M.L. and B.G. conceived the correlative analysis. E.M.S. wrote and implemented the bioinformatics tools for the correlative MSI and FISH data analysis and contributed to the methods section. M.K. and B.S. enabled MSI at the experimental ion source, and M.K. provided expertise in sample preparation and measurements. D.M. acquired and analysed the LC–MS/MS data and contributed to the methods section. M.J. refined sample preparation and conducted on-tissue MALDI-MS/MS measurements for metabolite identification. B.G. and M.L. wrote the manuscript. E.M.S., B.S. and N.D. contributed to the writing and editing of the manuscript.

Corresponding authors

Correspondence to Benedikt Geier or Manuel Liebeke.

Ethics declarations

Competing interests

B.S. is a consultant at and M.K. is an employee of TransMIT GmbH, Giessen, Germany. All other authors declare no conflicts of interest.

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Publisher’s note Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.

Extended data

Extended Data Fig. 1 HR AP-MALDI-MSI dataset of 233 × 233 pixels at 3 μm resolution (699 × 699 μm) of the gill filaments.

a, Total ion count (TIC) heat-map image showing full intensity range of recorded ions (TIC counts: 1.248 e3 - 4.928 e6). b, TIC image after narrowing the threshold of the histogram (TIC counts: 4.928 e3 - 2.332 e5) which revealed the low intensity ions/ tissue metabolites. c, Bright-field microscopy of the same tissue region, measured with AP-MALDI-MSI and the MALDI-matrix was washed off d, TIC spectrum recorded at 240.000 mass resolution at m/z 200 for a mass range of m/z 400 – 1200. For panels ad a parallel imaging experiment of a second sample showed similar results (see methods and data availability). Visualization was done in ImageQuest v. 1.1.0 (Thermo Fisher Scientific™). a,b, Color scheme of ion maps ‘physics’, scale bar in a-c: 200 μm.

Extended Data Fig. 2 Confocal laser scanning microscopy (CLSM) of the tissue sections after MALDI MSI and FISH.

Symbiotic bacteria in red (methane oxidizers) and green (sulfur oxidizers) and DNA in blue (DAPI stain, mainly host nuclei). Yellow dashed line indicates the area measured with high-resolution AP-MALDI-MSI before FISH. a, z 1–z 4, CLSM layers along the z-axis (5.72 µm each); layer z2 and z3, together 11.48 µm in depth covered the 10 µm tissue section and were used for further processing. Notably, in z2, the fluorescent signals appear slightly diminished yet remain clearly visible in the MALDI-MSI-measured area. This indicates that the top layer of the tissue (1-3 µm) was destructed to a point where FISH was not possible or it was even ablated. b, Each layer stitched from 25 tiles (5 × 5 white dashed lines) of which 6 tiles (blue dashed line) contained the area measured with HR MALDI-MS (yellow dashed line). c, RGB overlay image of 6-tiles (blue square in b) after corrected montage and channel specific histogram adjustments, based on the individual channels in grayscale with corresponding labeled histogram (d: Green/sulfur oxidizers (SOX), e: Red/ methane oxidizers (MOX), f: Blue/DAPI DNA stain). For panels af a parallel imaging experiment of a second sample showed similar results. Scale bars: 300 μm.

Extended Data Fig. 3 Spatial clustering of the AP-MALDI-MSI data.

Spatial clustering results from Cardinal for each of the seven clusters (1-7), column a shows the segmentation maps of each cluster, column b is the shrunken mean spectrum over m/z values, column c the shrunken t-statistics over m/z values and column d shows three ion maps, assigned to the spatial cluster, sorted after significance from left (high) to right (low). Ion maps of cluster 1 and cluster 3 show inverse tissue signals, leaving the area where the gill filaments are black. Scale of x and y in column a represents pixel counts of the dataset (233 × 233) of which each pixel is 3 µm × 3 µm. The ion images in column d are in the color scheme ‘viridis’, scale bars: 100 µm.

Extended Data Fig. 4 Image registration/alignment of the AP-MALDI-MSI and FISH data in Matlab.

a, Maximum intensity projection of four ion images merged on top of each (b) resulted in an image of the gill filaments. This image was used as visual reference for the alignment between AP-MALDI-MSI and FISH imaging data. c, Alignment, based on 18 corresponding reference points (landmarks) in FISH and MALDI-MS images, selected via ‘cpselect’. d, Control of alignment precision through the overlay of the MALDI-MS maximum intensity image, colored in green on top of the aligned FISH image, colored in purple. The precise alignment is reflected by the low amount of purple FISH pixels covered by the green MALDI-MSI pixels. e, Final, aligned and cropped FISH image, showing symbiotic MOX in red and SOX in green and DNA in blue (DAPI stain, mainly host nuclei); f. Aligned and cropped bright-field image (left) after AP-MALDI-MSI and segmented “on-tissue” (black) and “off tissue” (white) signals for background removal; color scheme of ion maps ‘physics’, scale bars: 150 µm.and cropped bright-field image (left) after AP-MALDI-MSI and segmented “on-tissue” (black) and “off tissue” (white) signals for background removal; color scheme of ion maps ‘physics’, scale bars: 150 µm.

Extended Data Fig. 5 Spatial metabolic heterogeneity of methanotrophic symbionts in gill tissue based on hopanoids and 16S rRNA distributions.

a, Molecular transformation subnetwork using MSI m/z values showing the potential mass shifts between the four metaspace-annotated hopanoids. b, Ion maps and chemical structures (Lipid Maps, see methods) of the four detected and metaspace-annotated hopanoids: 35-aminobacteriohopane-32,33,34-triol (m/z 546.4886, C35H64NO3+H+), 35-aminobacteriohopane-31,32,33,34-tetrol (m/z 562.4833, C35H64NO4+H+), bacteriohopane-32.33.34.35-tetrol (m/z 547.4731, C35H63O4+H+) and 31-hydroxy-32,35-anhydro-bacteriohopane-tetrol (m/z 545.4578, C35H61O4+H+). c, On-tissue MALDI-MS/MS identification of 35-aminobacteriohopane-31,32,33,34-tetrol (m/z 562.4791, C35H64NO4+H+) in positive-ion mode at NCE 70, showing loss of two H2O (2 × 18 Da) and characteristic MS/MS hopanoid ring fragment ion (191.1785 Da). For panel a a parallel imaging experiment of a second sample showed similar results. Experiments in panel b were repeated three times with similar results. Color scheme of ion maps in ‘viridis’ and scale bars: 100 µm.

Extended Data Fig. 6 Visual colocalization of metabolites (1) and (6) with the colonized or bacteria-free gill tissue.

a, CMY FISH image (see above); b, FISH-based outline of the ciliated edge (bacteria-free, white) and bacteriocyte region (bacterial symbionts, red). Panels c to e show an overlay of the metabolite images (left column) and the FISH-based outlines in b, of bacteria-free and bacteria-rich regions (right column). c, metabolite (1) m/z 869.5374 d, colocalized with bacteriocyte region (red); e, metabolite (6) m/z 577.2604 f, colocalized with the bacteria-free ciliated edge and general gill tissue. A parallel imaging experiment of a second sample showed similar results. Overlays were generated with the layer function in Adobe Photoshop CS5. Color scheme of ion maps in ‘viridis’, scale bars: 100 µm.

Extended Data Fig. 7 Spatial correlation analysis in SCiLS Lab v.2018b.

Ranking of ion images after co-localization to m/z 869.5381, sorted from most similar at the top right to least similar at the bottom left of the panel. The correlation values are given in Supplementary Table 4. (Mass deviations between Cardinal and SCiLS are due to preprocessing differences). Ions linked to m/z 869.5375 through chemical transformations like fatty acids or changes in their alkane length are ranked within the top ten (m/z 577.2627, 813.4752, 815.4915, 843.5219) and top 100 (m/z 841.5056, 1105.7507) ion images in SCiLS. The strong spatial correlations between the metabolites are paralleling the theoretical molecular transformations in the MS1 networks in Extended Data Fig. 8. A parallel imaging experiment of a second sample showed similar results. Color scheme of ion maps in ‘viridis’, scale bars: 300 µm.

Extended Data Fig. 8 MS1-based subnetwork around m/z 869.5375 (1) and the structurally unidentified metabolites (2)-(6).

Detection of further unknown metabolites linked to m/z 869.5375 (blue node, cluster 2) in the MS1 network of all measured metabolites (Supplementary Fig. 15). Subnetwork around m/z 869.5375 (magenta nodes in a) enlarged below to visualize precursor mass (node) and assigned transformations (edge). Ion m/z 869.5375 is directly linked to m/z 577.2604 through the loss of eicosenoic acid (-H2O) or the addition of palmitoleic acid (-H2O) resulting in m/z 1105.7525. Other ions are either directly (m/z 841.5062, 843.5215) or indirectly (m/z 813.4736, 815.4902) linked to m/z 869.5375 through changes in the length and saturation of alkane chains.

Extended Data Fig. 9 LC-MS/MS identification of m/z 869.5368 (1) in positive-ion mode.

a, Overall chromatogram; b, Chromatogram for m/z 869.5368 (m/z 869.5300-869.5400) eluting at 14.32 min.; c, Chromatogram for the parent ion m/z 869.5363. d, Shows a full MS at 14.32 min. and e, a MS/MS spectrum of ion m/z 869.5368. The mass difference of 132.0417 Da between the precursor ion m/z 869.5363 and the fragment ion m/z 737.4946 corresponds to the loss of a pentose. The fragment ion m/z 541.2402 represents the core molecule with pentose but after the loss of a fatty acid (eicosenoic acid, C20H38O2. 310.2867 Da) and H2O (18.0106 Da). The experiments in panels ae were repeated independently three times with similar results.

Extended Data Fig. 10 Schematic molecular composition of metabolites (1)-(6), based on MS/MS and accurate mass measurements.

The general fragment of C21H25N6O4 (m/z 409.1982) is part of (1)-(6) and does not match any databse entry of metabolite fragments. The fragments of a pentose and a fatty acid are characteristic parts of (1)-(5), whereas the length and saturation of the attached fatty acid can vary. Metabolite (6) only consist of the pentose and the C21H25N6O4 core.

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Supplementary Information

Supplementary Notes 1–3, Supplementary Figs. 1–33 and Supplementary Tables 1–9 (excluding 2, 4 and 8).

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Supplementary Tables 2, 4 and 8.

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Geier, B., Sogin, E.M., Michellod, D. et al. Spatial metabolomics of in situ host–microbe interactions at the micrometre scale. Nat Microbiol 5, 498–510 (2020). https://doi.org/10.1038/s41564-019-0664-6

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