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.
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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.
All code is available on GitHub at https://github.com/proudquartz/hiprfish.
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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.
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.
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
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.
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.
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.
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.
Classification accuracy as a function of Hamming distance for each species of bacteria measured using different barcodes.
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.
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.
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.
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.
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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