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SAMPL-seq reveals micron-scale spatial hubs in the human gut microbiome

Nature Microbiology volume 10, pages 527–540 (2025)Cite this article

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A Publisher Correction to this article was published on 11 February 2025

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Abstract

The local arrangement of microbes can profoundly impact community assembly, function and stability. However, our understanding of the spatial organization of the human gut microbiome at the micron scale is limited. Here we describe a high-throughput and streamlined method called Split-And-pool Metagenomic Plot-sampling sequencing (SAMPL-seq) to capture spatial co-localization in a complex microbial consortium. The method obtains microbial composition of micron-scale subcommunities through split-and-pool barcoding. SAMPL-seq analysis of the healthy human gut microbiome identified bacterial taxa pairs that consistently co-occurred both over time and across multiple individuals. These co-localized microbes organize into spatially distinct groups or ‘spatial hubs’ dominated by Bacteroidaceae, Ruminococcaceae and Lachnospiraceae families. Using inulin as a dietary perturbation, we observed reversible spatial rearrangement of the gut microbiome where specific taxa form new local partnerships. Spatial metagenomics using SAMPL-seq can unlock insights into microbiomes at the micron scale.

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Fig. 1: Spatial metagenomics of thousands of micron-sized communities using SAMPL-seq.
Fig. 2: SAMPL-seq performance using mixing experiments.
Fig. 3: Co-localization analysis using SAMPL-seq data.
Fig. 4: A longitudinal profile of gut microbiota spatial co-associations from one person.
Fig. 5: Consistent spatial hubs of gut microbiota found across humans.
Fig. 6: Spatial reorganization of the human gut microbiome in response to inulin supplementation.

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Data availability

Raw sequencing reads are available from PRJNA1196417. The Refseq 16S database (https://www.ncbi.nlm.nih.gov/refseq/targetedloci/16S_process/) was used for species-level taxonomic identification, and SILVA 138.1 (https://www.arb-silva.de/documentation/release-1381/) was used for taxonomic assignment of all ASVs. Source data are provided with this paper.

Code availability

Scripts for read processing are implemented in BASH and R. They are available from GitHub at https://github.com/wanglabcumc/SAMPL-seq (ref. 61).

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Acknowledgements

We thank P. Sims, D. Vitkup, L. Dietrich, K. Tchourine for immensely helpful insights and discussions; G. Gerber and T. Gibson for strengthening the computational rigour of the method; and members of the Wang Laboratory for providing a supportive environment. H.H.W. acknowledges funding support from the NSF (MCB-2025515), NIH (2R01AI132403, 1R01DK118044, 1R01EB031935, 1R21AI146817), ONR (N00014-18-1-2237), Burroughs Wellcome Fund (1016691), ARO (W911NF-22-2-0210), DARPA (HR0011-23-2-0001), Irma T. Hirschl Trust, and the Schaefer Research Award. M.R., R.U.S. and F.V.-C. were supported by the NSF Graduate Research Fellowship Program (DGE-1644869). R.U.S. was supported by the Fannie and John Hertz Foundation Fellowship. This study was supported in part by the Columbia University Digestive and Liver Disease Research Center (funded by NIH grant 5P30DK132710) through use of its bioinformatic and single-cell analysis core.

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  1. Ravi U. Sheth

    Present address: Kingdom Supercultures, Brooklyn, NY, USA

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  1. Department of Systems Biology, Columbia University, New York, NY, USA

    Miles Richardson, Shijie Zhao, Liyuan Lin, Ravi U. Sheth, Yiming Qu, Jeongchan Lee, Thomas Moody, Deirdre Ricaurte, Yiming Huang, Florencia Velez-Cortes, Guillaume Urtecho & Harris H. Wang

  2. Integrated Program in Cellular, Molecular and Biomedical Studies, Columbia University, New York, NY, USA

    Miles Richardson, Yiming Qu, Thomas Moody, Deirdre Ricaurte, Yiming Huang & Florencia Velez-Cortes

  3. Department of Pathology and Cell Biology, Columbia University, New York, NY, USA

    Harris H. Wang

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Contributions

M.R., R.U.S. and H.H.W. conceived the project. R.U.S., M.R. and T.M. developed and validated the protocol. M.R., R.U.S., D.R., Y.H., L.L. J.L. and G.U. performed experiments. M.R., S.Z., Y.Q. and F.V.-C. analysed the data. M.R. and H.H.W. generated and edited figures. H.H.W. supervised the overall project. M.R., H.H.W. and S.Z. wrote the manuscript with input from co-authors. All authors reviewed and approved the manuscript.

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Correspondence to Harris H. Wang.

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Competing interests

H.H.W. is a scientific advisor of SNIPR Biome, Kingdom Supercultures, Fitbiomics, VecX Biomedicines, Genus PLC, and a scientific co-founder of Aclid and Foli Bio, all of which are not involved in the study. R.U.S. is a co-founder of Kingdom Supercultures. The remaining authors declare no competing interests.

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Extended data

Extended Data Fig. 1 Characterization of SAMPL-seq steps and comparison with other methods.

(a) Histograms showing the effect of the number of in situ PCR cycles on both the ASVs per particle and reads per particle based on different number of PCR cycles. (b) Plot summarizing the overall ease of use and performance of various microbial spatial analysis methods. (c) Table comparing the performance of different spatial analysis methods.

Source data

Extended Data Fig. 2 Homogenized fecal mixing experiment (M1).

(a) Histograms of particle sizes for the replicates. (b,c) Correlation of ASV prevalence by SAMPLE-seq and ASV relative abundance by bulk 16S sequencing for Core 1 at particle size 15-30 μm (b) and Core 2 (c). SAMPL-seq abundances are averaged between replicates (excluding Spike-in). (d) Histogram of the ASV per particle distribution by size (excluding Spike-in). (e) Barplot of the singlet rate of each replicate, grouped by particle size.

Source data

Extended Data Fig. 3 Defined community mixing experiment (M2).

(a) An outline of the process for producing the fecal mixing library. Zymo Gut Microbial Standard and Zymo High Concentration Spike-in are separately embedded at equal cell ratios at 1x or 3x concentration replicates. They are then combined during the cryofracturing step, and are then size sorted, amplified and sequenced in aliquots of 10,000 particles, of average size ~50μm, which were used for further analysis (b) Histograms of particle sizes for the replicates. (c) Scatterplots of technical (amplification) and biological (concentration) replicates, using both the relative abundance based on summed reads, and ASV prevalence among particles, which is the percentage of particles an ASV is found (excluding the Spike-in). (d) Scatterplot of ASV relative abundance and prevalence compared to absolute reference provided by the manufacturer. SAMPL-seq abundances are averaged between replicates (excluding Spike-in). (e) Histograms of the ASV per particle distribution by concentration (excluding Spike-in). (f) Barplot of the multiplet rate of each replicate, grouped by concentration. (g) Plot showing mixing rates of two defined communities (M1A and M1B), with each colored dot corresponding to a classified particle. (h) Heatmap of particles clustered by Bray-Curtis similarity and the Ward’s method.

Source data

Extended Data Fig. 4 Correlation between ASVs from different murine gut compartments.

(a) Clustered heatmap of prevalent mouse ASVs grouped by gut compartments, with summed abundances at the end of the row. ASVs are clustered by Jaccard overlap across the dataset. (b, c) Correlation between ASV relative abundance (b) and prevalence among particles (c) between mouse gut compartments. Colon and feces samples showed the highest correlation among samples. (d) Principal Coordinate Analysis (PCoA) plot of particles derived from different mouse gut compartments using Simpson distance, colored by gut compartments. Dashed circles correspond to the 95% confidence interval for each compartment using the multivariate t-distribution.

Source data

Extended Data Fig. 5 Particle-level data of the human gut microbiome from stool profiling.

(a, b) Using filters for 20-40 μm, distributions of particle sizes (a) and ASVs per particle (b) for longitudinal human stool samples from H11 are shown. (c, d) Using filters for 20-40 μm, distributions of particle size (c) and ASVs per particle (d) for interpersonal samples are shown. (e,f) Particles from longitudinal (e) or interpersonal (f) are clustered within each day using the Simpson overlap, and ASVs are clustered using their Jaccard overlap across all days. (g) Rarefaction plot for longitudinal samples of unique ASV-particle pairs for prevalent ASVs (>1% prevalence in particles). (h, i) Rarefaction plots for interpersonal samples of unique ASV-particle pairs (h) for prevalent ASVs (>1% particle prevalence) or unique ASV-ASV co-presence (i) in a particle (observed >3 times).

Source data

Extended Data Fig. 6 Technical validations of H11 Day 4 SAMPL-seq libraries.

(a) Scatterplots of amplification (technical) replicates showed high correlation. Correlation between spatial (biological) replicates also showed high correlation. Homogenized sample showed high correlation, but increased particle prevalence relative to intact libraries. (b, c) Correlation of bulk ASV relative abundance (b) or ASV prevalence (c) between longitudinal SAMPL-seq libraries. (d, e) Correlation of bulk ASV relative abundance (d) or ASV prevalence (e) between interpersonal SAMPL-seq libraries. (f, g) Correlation of ASV relative abundance between interpersonal (f) or H11 longitudinal (g) samples with their corresponding bulk measurements.

Source data

Extended Data Fig. 7 Large scale ASV compositional patterns.

(a) Heatmap of overall ASV abundance in the dataset of prevalent ASVs (>1%), clustered by Jaccard overlap. (b) Heatmap of overall ASV abundance of prevalent ASVs (>1%) across 5 humans (H1, H10, H11, H18, H19), clustered by Bray-Curtis distance. (c) Heatmap of family-level relative abundance of human fecal samples. Families are clustered using the Jaccard overlap, and families conserved across all individuals are indicated in green.

Source data

Extended Data Fig. 8 Pairwise ASV colocalization analysis.

(a) Barplot of the number of significant ASV pairs found in each sample. (b) Scatterplot between ASV-pair Z-Scores between intact and disrupted samples. (c) Scatterplots of ASV-Pair Z-scores between fresh and frozen samples. (d) Pairwise ASV spatial associations in five people. Each heatmap shows all statistically significant spatial associations between pairs of ASVs for each individual (H1, H10, H11, H18, H19). Colors in the heatmap correspond to Z-scores and stars correspond to statistical significance (p < 0.05 BH FDR Corrected). ASVs are labeled in 3 possible colors (red, green, blue) if they belong to a conserved spatial cluster group (P1, P2, P3) found across 3 or more individuals. Common taxonomic families are labeled next to each ASV label on the y-axis. (e) Heatmap of significant co-associations found on 2 or more days in H11. (f) Heatmap of significant co-associations found in 3 or more people.

Source data

Extended Data Fig. 9 Phylogenetic Distance Distributions and Relationships Between Clusters.

Histograms of simulated MPD distributions for L1-L4 (a) or P1-P3 (b) spatial hubs. The red line indicates the observed MPD in the cluster, while blue dashed lines indicate the 95% confidence interval around the mean of simulations. P-value was calculated using the two-sided Z-score. (c) Scatterplot of Z-score for associations found in both for longitudinal and interpersonal samples with the corresponding correlation. (d) Contingency table of the sign of longitudinal versus interpersonal associations. (e) Contingency table of ASV presence across the clusters. Chi-squared test of independence (χ2 = 22.4, df = 6, p = 0.001).

Source data

Extended Data Fig. 10 Inulin supplementation.

Heatmaps of bulk relative abundance, Z-score sum, and change in total Z-score for ASVs that had a total Z-score change >10 over the course of inulin supplementation.

Source data

Supplementary information

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Richardson, M., Zhao, S., Lin, L. et al. SAMPL-seq reveals micron-scale spatial hubs in the human gut microbiome. Nat Microbiol 10, 527–540 (2025). https://doi.org/10.1038/s41564-024-01914-4

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