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

Nature Microbiology volume 5, pages 498–510 (2020)Cite this article

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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.

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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).

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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

  1. Max Planck Institute for Marine Microbiology, Bremen, Germany

    Benedikt Geier, Emilia M. Sogin, Dolma Michellod, Moritz Janda, Nicole Dubilier & Manuel Liebeke

  2. Institute of Inorganic and Analytical Chemistry, Justus Liebig University Giessen, Giessen, Germany

    Mario Kompauer & Bernhard Spengler

  3. MARUM, University of Bremen, Bremen, Germany

    Nicole Dubilier

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  1. Benedikt Geier
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  2. Emilia M. Sogin
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  4. Moritz Janda
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  5. Mario Kompauer
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  6. Bernhard Spengler
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  7. Nicole Dubilier
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  8. Manuel Liebeke
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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.

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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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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 a–d 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 a–f 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 a–e 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.

Supplementary information

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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