Skip to main content

Thank you for visiting nature.com. You are using a browser version with limited support for CSS. To obtain the best experience, we recommend you use a more up to date browser (or turn off compatibility mode in Internet Explorer). In the meantime, to ensure continued support, we are displaying the site without styles and JavaScript.

  • Article
  • Published:

Highly multiplexed spatial mapping of microbial communities

Abstract

Mapping the complex biogeography of microbial communities in situ with high taxonomic and spatial resolution poses a major challenge because of the high density1 and rich diversity2 of species in environmental microbiomes and the limitations of optical imaging technology3,4,5,6. Here we introduce high-phylogenetic-resolution microbiome mapping by fluorescence in situ hybridization (HiPR-FISH), a versatile technology that uses binary encoding, spectral imaging and decoding based on machine learning to create micrometre-scale maps of the locations and identities of hundreds of microbial species in complex communities. We show that 10-bit HiPR-FISH can distinguish between 1,023 isolates of Escherichia coli, each fluorescently labelled with a unique binary barcode. HiPR-FISH, in conjunction with custom algorithms for automated probe design and analysis of single-cell images, reveals the disruption of spatial networks in the mouse gut microbiome in response to treatment with antibiotics, and the longitudinal stability of spatial architectures in the human oral plaque microbiome. Combined with super-resolution imaging, HiPR-FISH shows the diverse strategies of ribosome organization that are exhibited by taxa in the human oral microbiome. HiPR-FISH provides a framework for analysing the spatial ecology of environmental microbial communities at single-cell resolution.

This is a preview of subscription content, access via your institution

Access options

Rent or buy this article

Prices vary by article type

from$1.95

to$39.95

Prices may be subject to local taxes which are calculated during checkout

Fig. 1: Working principle of HiPR-FISH.
Fig. 2: Algorithm for single-cell segmentation.
Fig. 3: Antibiotic treatments disrupt the spatial organization of the mouse gut microbiome.
Fig. 4: Biogeography of human oral biofilms.

Similar content being viewed by others

Data availability

PacBio sequencing data are available at the NCBI Sequence Read Archive (SRA) with accession number PRJNA665727. Metagenomic sequencing data of laser-capture-microdissected tissue samples are available at the NCBI SRA with accession number PRJNA665536. All microscopy data have been deposited to Zenodo. A full list of DOIs is provided in the Supplementary Information. Source data are provided with this paper.

Code availability

All code is available on GitHub at https://github.com/proudquartz/hiprfish.

References

  1. Donaldson, G. P., Lee, S. M. & Mazmanian, S. K. Gut biogeography of the bacterial microbiota. Nat. Rev. Microbiol. 14, 20–32 (2016).

    Article  CAS  Google Scholar 

  2. The Human Microbiome Project Consortium. Structure, function and diversity of the healthy human microbiome. Nature 486, 207–214 (2012).

    Article  ADS  Google Scholar 

  3. 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  Google Scholar 

  4. Mark Welch, J. L., Rossetti, B. J., Rieken, C. W., Dewhirst, F. E. & Borisy, G. G. Biogeography of a human oral microbiome at the micron scale. Proc. Natl Acad. Sci. USA 113, E791–E800 (2016).

    Article  ADS  CAS  Google Scholar 

  5. Valm, A. M. et al. Systems-level analysis of microbial community organization through combinatorial labeling and spectral imaging. Proc. Natl Acad. Sci. USA 108, 4152–4157 (2011).

    Article  ADS  CAS  Google Scholar 

  6. Valm, A. M., Mark Welch, J. L. & Borisy, G. G. CLASI-FISH: principles of combinatorial labeling and spectral imaging. Syst. Appl. Microbiol. 35, 496–502 (2012).

    Article  CAS  Google Scholar 

  7. De Weirdt, R. & Van De Wiele, T. Micromanagement in the gut: Microenvironmental factors govern colon mucosal biofilm structure and functionality. npj Biofilms Microbiomes 1, 15026 (2015).

    Article  Google Scholar 

  8. Thaiss, C. A. et al. Microbiota diurnal rhythmicity programs host transcriptome oscillations. Cell 167, 1495–1510 (2016).

    Article  CAS  Google Scholar 

  9. Valm, A. M., Oldenbourg, R. & Borisy, G. G. Multiplexed spectral imaging of 120 different fluorescent labels. PLoS ONE 11, e0158495 (2016).

    Article  Google Scholar 

  10. Mark Welch, J. L., 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 

  11. Earle, K. A. et al. Quantitative imaging of gut microbiota spatial organization. Cell Host Microbe 18, 478–488 (2015).

    Article  CAS  Google Scholar 

  12. Dagher, M., Kleinman, M., Ng, A. & Juncker, D. Ensemble multicolour FRET model enables barcoding at extreme FRET levels. Nat. Nanotechnol. 13, 925–932 (2018).

    Article  ADS  CAS  Google Scholar 

  13. Otsu, N. A threshold selection method from gray-level histograms. IEEE Trans. Syst. Man Cybern. 9, 62–66 (1979).

    Article  Google Scholar 

  14. Sauvola, J. & Pietikäinen, M. Adaptive document image binarization. Pattern Recognit. 33, 225–236 (2000).

    Article  Google Scholar 

  15. Francino, M. P. Antibiotics and the human gut microbiome: dysbioses and accumulation of resistances. Front. Microbiol. 6, 1543 (2016).

    Article  Google Scholar 

  16. Samad, T., Co, J. Y., Witten, J. & Ribbeck, K. Mucus and mucin environments reduce the efficacy of polymyxin and fluoroquinolone antibiotics against Pseudomonas aeruginosa. ACS Biomater. Sci. Eng. 5, 1189–1194 (2019).

    Article  CAS  Google Scholar 

  17. Antonopoulos, D. A. et al. Reproducible community dynamics of the gastrointestinal microbiota following antibiotic perturbation. Infect. Immun. 77, 2367–2375 (2009).

    Article  CAS  Google Scholar 

  18. Panda, S. et al. Short-term effect of antibiotics on human gut microbiota. PLoS ONE 9, e95476 (2014).

    Article  ADS  Google Scholar 

  19. Li, J. et al. Probiotics modulated gut microbiota suppresses hepatocellular carcinoma growth in mice. Proc. Natl Acad. Sci. USA 113, E1306–E1315 (2016).

    Article  CAS  Google Scholar 

  20. Scheiman, J. et al. Meta-omics analysis of elite athletes identifies a performance-enhancing microbe that functions via lactate metabolism. Nat. Med. 25, 1104–1109 (2019).

    Article  CAS  Google Scholar 

  21. Dewhirst, F. E. et al. The human oral microbiome. J. Bacteriol. 192, 5002–5017 (2010).

    Article  CAS  Google Scholar 

  22. Rasiah, I. A., Wong, L., Anderson, S. A. & Sissons, C. H. Variation in bacterial DGGE patterns from human saliva: over time, between individuals and in corresponding dental plaque microcosms. Arch. Oral Biol. 50, 779–787 (2005).

    Article  CAS  Google Scholar 

  23. Costello, E. K. et al. Bacterial community variation in human body habitats across space and time. Science 326, 1694–1697 (2009).

    Article  ADS  CAS  Google Scholar 

  24. Li, K., Bihan, M. & Methé, B. A. Analyses of the stability and core taxonomic memberships of the human microbiome. PLoS ONE 8, e63139 (2013).

    Article  ADS  CAS  Google Scholar 

  25. Zhou, Y. et al. Biogeography of the ecosystems of the healthy human body. Genome Biol. 14, R1 (2013).

    Article  Google Scholar 

  26. Baddour, L. M. et al. Infective endocarditis in adults: diagnosis, antimicrobial therapy, and management of complications: a scientific statement for healthcare professionals from the American Heart Association. Circulation 132, 1435–1486 (2015).

    Article  CAS  Google Scholar 

  27. Hedberg, M. E. et al. Lachnoanaerobaculum gen. nov., a new genus in the Lachnospiraceae: characterization of Lachnoanaerobaculum umeaense gen. nov., sp. nov., isolated from the human small intestine, and Lachnoanaerobaculum orale sp. nov., isolated from saliva, and reclassification of Eubacterium saburreum (Prevot 1966) Holdeman and Moore 1970 as Lachnoanaerobaculum saburreum comb. nov. Int. J. Syst. Evol. Microbiol. 62, 2685–2690 (2012).

    Article  Google Scholar 

  28. Stracy, M. et al. Live-cell superresolution microscopy reveals the organization of RNA polymerase in the bacterial nucleoid. Proc. Natl Acad. Sci. USA 112, E4390–E4399 (2015).

    Article  CAS  Google Scholar 

  29. Sanamrad, A. et al. Single-particle tracking reveals that free ribosomal subunits are not excluded from the Escherichia coli nucleoid. Proc. Natl Acad. Sci. USA 111, 11413–11418 (2014).

    Article  ADS  CAS  Google Scholar 

  30. Smid, M. C., Dotters-Katz, S. K., Plongla, R. & Boggess, K. A. Leptotrichia buccalis: a novel cause of chorioamnionitis. Infect. Dis. Rep. 7, 5801 (2015).

    Article  Google Scholar 

  31. Hou, H., Chen, Z., Tian, L. & Sun, Z. Leptotrichia trevisanii bacteremia in a woman with systemic lupus erythematosus receiving high-dose chemotherapy. BMC Infect. Dis. 18, 661 (2018).

    Article  Google Scholar 

  32. Kraatz, M., Wallace, R. J. & Svensson, L. Olsenella umbonata sp. nov., a microaerotolerant anaerobic lactic acid bacterium from the sheep rumen and pig jejunum, and emended descriptions of Olsenella, Olsenella uli and Olsenella profusa. Int. J. Syst. Evol. Microbiol. 61, 795–803 (2011).

    Article  CAS  Google Scholar 

  33. Moffitt, J. R. & Zhuang, X. RNA imaging with multiplexed error-robust fluorescence in situ hybridization (MERFISH). Methods Enzymol. 572, 1–49 (2016).

    Article  CAS  Google Scholar 

  34. Chen, K. H., Boettiger, A. N., Moffitt, J. R., Wang, S. & Zhuang, X. Spatially resolved, highly multiplexed RNA profiling in single cells. Science 348, aaa6090 (2015).

    Article  Google Scholar 

  35. Singer, E. et al. High-resolution phylogenetic microbial community profiling. ISME J. 10, 2020–2032 (2016).

    Article  Google Scholar 

  36. Manz, W., Amann, R., Ludwig, W., Wagner, M. & Schleifer, K.-H. Phylogenetic oligodeoxynucleotide probes for the major subclasses of proteobacteria: problems and solutions. Syst. Appl. Microbiol. 15, 593–600 (1992).

    Article  Google Scholar 

  37. Randall, J. T. & Jackson, S. F. Fine structure and function in Stentor polymorphus. J. Biophys. Biochem. Cytol. 4, 807–830 (1958).

    Article  CAS  Google Scholar 

Download references

Acknowledgements

We thank R. M. Williams and J. M. Dela Cruz for assistance with microscopy; A. Douglas, J. McMullen, T. Doerr, J. Peters and M. Petassi for providing materials; and P. S. Burnham, A. P. Cheng, T. Lan and E. Michel for discussions and feedback. This work was supported by an instrumentation grant from the Kavli Institute at Cornell and by US National Institutes of Health (NIH) grants 1DP2AI138242 to I.D.V. and 1R33CA235302 to I.D.V., W.Z. and I.L.B. Imaging data were acquired in the Cornell Biotechnology Resource Center Imaging Facility using the shared, NYSTEM (CO29155)- and NIH (S10OD018516)-funded Zeiss LSM880 confocal and multiphoton microscope.

Author information

Authors and Affiliations

Authors

Contributions

H.S. and I.D.V. conceived the study. H.S., W.Z., I.L.B. and I.D.V. contributed to the study design. H.S., performed the E. coli experiments. H.S. and J.L.S performed the multispecies community experiments. Q.S. and I.L.B. performed mouse experiments. H.S. performed PacBio sequencing experiments and analysed data. H.S. and B.G. performed HiPR-FISH experiments in mice. H.S. performed HiPR-FISH experiments on the human oral microbiome. H.S. and I.D.V. analysed the data. H.S., W.Z., I.L.B. and I.D.V. wrote the manuscript.

Corresponding authors

Correspondence to Hao Shi or Iwijn De Vlaminck.

Ethics declarations

Competing interests

H.S. and I.D.V. are inventors on the patent WO 2019/173555, which was filed in September 2019 by Cornell University Center for Technology Licensing and which covers the technical aspects of the manuscript.

Additional information

Peer review information Nature thanks Arjun Raj, Carolina Tropini and the other, anonymous, reviewer(s) for their contribution to the peer review of this work.

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

Extended data figures and tables

Extended Data Fig. 1 Workflow of HiPR-FISH experiments.

Environmental microbial consortia are first split into two samples. One sample is used to generate full-length 16S amplicon sequences using PCR and PacBio sequencing. The resulting sequence file is used to generate a list of probes, which are purchased from a commercial vendor. The other sample is used for imaging experiments. Fixed samples are hybridized using an encoding hybridization buffer containing the amplified complex probes and read out using a readout hybridization buffer containing fluorescently labelled readout probes. Samples are then embedded and imaged on a standard confocal microscope in the spectral imaging mode. Resulting raw images are registered and segmented. The spectra of individual cells are measured using the raw image and the segmentation image and classified using a machine learning algorithm. Finally, classified images are used for downstream quantitative measurements of microbial spatial associations.

Extended Data Fig. 2 Spectra construction and classification.

a, Fluorescence spectra measured using each laser are concatenated for classification. b, Example spectra for two different barcodes. The combined spectra can contain distinct peaks or broad peaks, depending on the fluorophores used for each barcode. c, Spectra for the 10 fluorophores used in this study. d, Classification algorithm for barcode assignment. Concatenated spectra are projected using UMAP before classified using support vector machines.

Extended Data Fig. 3 Classification accuracy for E. coli 1,023-plex barcoding experiment.

a, Classification frequency as a function of Hamming distance for all 1,023 barcodes. Insets show barcodes with detectable error (in orange) and an example of a barcode with no detectable error (in blue). b, c, Photon counting measurements for Alexa 488 for each pixel (b) and across each cell (c). d, Signal-to-noise ratio calculated using Poisson statistics for the E. coli cells under nominal experimental imaging conditions. e, Simulated classification error as a function of ribosomal density within individual cells. In box plots (b, c), the centre lines show the median value, the bounds of the boxes correspond to the 25th and 75th percentiles and the whiskers extend to 1.5 × IQR.

Extended Data Fig. 4 Probe design pipeline and amplification strategy.

a, Full-length 16S sequences are first grouped by taxa. The consensus sequence for each taxon is used to design probes. Each probe within each taxon is then blasted against the database of full-length 16S sequences. Several probe quality metrics are calculated on the basis of the blast results and are used to select probes. All selected probes are conjugated to the appropriate readout sequences and blasted against the database of full-length 16S sequences to remove probes with any potential mis-hybridization sites owing to the conjugation of the readout sequences. b, Schematic for probe synthesis. Complex oligo pools are amplified using limited-cycle PCR. The T7 promoter introduced during PCR allows the templates to be in-vitro-transcribed. Reverse transcription then converts RNA to cDNA. Finally, alkaline hydrolysis removes the RNA strand to generate the final single-stranded DNA probe pool.

Extended Data Fig. 5 Classification accuracy in synthetic communities.

Classification accuracy as a function of Hamming distance for each species of bacteria measured using different barcodes.

Extended Data Fig. 6 Image segmentation workflow and comparison to other methods.

a, A typical raw image of a human plaque biofilm sample averaged along the spectral axis was enhanced using the LNE algorithm before segmentation using the watershed algorithm. Spectra of segmented cells were then used to generate the identification image. Scale bars, 25 μm. b, Examples of segmentation comparisons between LNE and existing methods. Scale bars, 25 μm. c, Enlarged views highlight advantages of LNE over existing methods at segmenting closely packed cells. Scale bars, 5 μm.

Extended Data Fig. 7 LNE can segment objects with diverse shapes in images collected using different modalities.

a, Raw (left) and segmented (right) images of a longitudinal section of a km fibre37 in a partially contracted Stentor coeruleus cell imaged using transmission electron microscopy. b, Raw (left) and segmented (right) images of fluorescently labelled actin bundles from chicken muscle imaged using total internal reflection microscopy. Source images are from the Cell Image Library.

Extended Data Fig. 8 Spatio-spectral deconvolution accuracy of simulated merged objects.

a, Heat map of merger detection rates across all 10-bit barcode combinations. Barcode–neighbour combinations not detected in the 1,023-plex E. coli mixing experiment are shown in orange. The diagonal corresponds to the correct identification of merged objects with the same barcode as single objects. b, Merger detection rate as a function of Hamming distance. The spatio-spectral deconvolution approach can detect 99.6% of all objects with spatially varying barcodes that are more than 1 bit away.

Extended Data Fig. 9 Additional analysis of the gut microbiome images.

a, Heat map of the Pearson correlation between maximum average intensity for all detected barcodes from different FFPE sections of the same mouse gut, with 15 FOVs per FFPE section. b, Heat map of Pearson correlation between maximum average intensity for all detected barcodes from different mice, with 14 to 15 FOVs per mouse. c, Comparison of imaging and sequencing measurements on mouse gut FFPE tissue sections. d, Phylum abundance measurements from images of a clindamycin-treated mouse compared to a control mouse. The clindamycin-treated mouse shows a lower Bacteroidetes to Firmicutes ratio than the control mouse. e, Measured Shannon diversity is lower in the clindamycin-treated mouse than the control mouse. The centre lines show the median value, the bounds of the boxes correspond to the 25th and 75th percentiles and the whiskers extend to 1.5 × IQR. f, β-diversity as a function of patch size shows similar trends in the clindamycin-treated mouse and the control mouse. The boxes correspond to 25th and 75th quartile, and the whiskers extend to the most extreme data points. g, Bray–Curtis dissimilarity increases as a function of intra-patch distance in both the clindamycin-treated and the control mice. h, Volcano plot of significance versus spatial association fold change between ciprofloxacin-treated mice and control mice. Altered spatial associations that are statistically significant after Bonferroni correction are listed.

Extended Data Fig. 10 Reproducible and recurrent microarchitectures in the oral microbiome.

a, Clusters of Lautropia cells observed using different panels of probes. b. Two-dimensional UMAP projections of the physical properties of Lautropia cells observed using different probe panels overlap in the reduced dimensions. c, Additional observed instances of the Pseudopropionibacterium–Cardiobacterium–Schwartzia consortium.

Supplementary information

Supplementary Information

This file contains data access information as well as Supplementary Tables related to fluorescent probes information, cultured cell information, relevant software versions, and image acquisition conditions.

Reporting Summary

Source data

Rights and permissions

Reprints and permissions

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

Shi, H., Shi, Q., Grodner, B. et al. Highly multiplexed spatial mapping of microbial communities. Nature 588, 676–681 (2020). https://doi.org/10.1038/s41586-020-2983-4

Download citation

  • Received:

  • Accepted:

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1038/s41586-020-2983-4

This article is cited by

Comments

By submitting a comment you agree to abide by our Terms and Community Guidelines. If you find something abusive or that does not comply with our terms or guidelines please flag it as inappropriate.

Search

Quick links

Nature Briefing

Sign up for the Nature Briefing newsletter — what matters in science, free to your inbox daily.

Get the most important science stories of the day, free in your inbox. Sign up for Nature Briefing