Abstract
Microbial communities often display region-specific properties, which give rise to complex interactions and emergent behaviors that are critical to the homeostasis and stress response of the communities. However, systems-level understanding of these properties still remains elusive. In this study, we established RAINBOW-seq and profiled the transcriptome of Escherichia coli biofilm communities with high spatial resolution and high gene coverage. We uncovered three modes of community-level coordination, including cross-regional resource allocation, local cycling and feedback signaling, which were mediated by strengthened transmembrane transport and spatially specific activation of metabolism. As a consequence of such coordination, the nutrient-limited region of the community maintained an unexpectedly high level of metabolism, enabling it to express many signaling genes and functionally unknown genes with potential sociality functions. Our work provides an extended understanding of the metabolic interplay in biofilms and presents a new approach of investigating complex interactions in bacterial communities on the systems level.
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Data availability
The raw data were deposited to the Gene Expression Omnibus (GSE197541 for RAINBOW-seq data and GSE213531 for RNA-seq ΔsapD biofilm periphery and pyrimidine biosynthesis heterogeneity). Other materials that support the findings of this study are available upon reasonable request. Source data are provided with this paper.
Code availability
MATLAB codes for RAINBOW-seq (image and FACS data processing) have been deposited to GitHub (https://github.com/Shenpinggg/RainbowSeq.git). The core scripts to process raw NGS data in this work have also been deposited to GitHub (https://github.com/wtmbiohacker/miniBac-seq.git).
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Acknowledgements
We thank M. Asally, Y. Wang, G. Liang, J. Liu and X. Tan for helpful discussions; R. Chai for help with RNA extraction and library preparation; and B. Yu for assistance with cell sorting. J.L. was supported by the National Natural Science Foundation of China (32170099), the Tsinghua-Peking Center for Life Sciences and the Tsinghua University Independent Research Program (20197030008). T.W. was supported by the National Natural Science Foundation of China (32100071), the Chinese Postdoctoral Science Foundation (BX20190176 and 2020M670288) and the Tsinghua-Peking Center for Life Sciences. Y.Z. was supported by the National Natural Science Foundation of China (21908129), the Chinese Postdoctoral Science Foundation (2018M631481) and the Tsinghua-Peking Center for Life Sciences.
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T.W., P.S. and J.L. designed the research. T.W. and P.S. performed all experiments. Y.H. participated in validation experiments. Y.Z. contributed to the microfluidic aspect of the experiments. T.W., P.S., Y.H. and J.L. analyzed the data. T.W., P.S. and J.L. wrote the manuscript. All authors discussed the manuscript.
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Extended data
Extended Data Fig. 1 Molecular structures and fluorescent spectrums of FDAAs used in spatially resolved labeling of biofilm.
Molecular structures and fluorescent spectrums of FDAAs (Fluorescent D-alanine) used in spatially resolved labeling of biofilm. Shown in (a), (b) and (c) are molecular structures of HADA, NADA and TADA, respectively. The fluorescent spectrums of these three FDAAs are shown in (d); here dotted lines represent excitation spectrums and solid lines represent emission spectrums; the spectrum was measured by a microplate reader system (Tecan, Spark); FDAAs were at working concentration in M63B1 medium. (e) Labeling of FDAAs onto actively growing and dormant E. coli cells; here the seed culture was transferred to M63B1 medium with and without glucose to modulate the growth of the bacteria; and the FDAAs were added to stain the bacteria for three hours; shown is the merged image of phase contrast, CFP, GFP and mCherry channels under a 40× objective len; scale bar, 10 μm. Two biologically independent experiments were repeated with similar results.
Extended Data Fig. 2 Perturbation of FDAAs and cell sorting on bacterial transcriptome.
Perturbation of FDAAs and cell sorting on bacterial transcriptome. (a) Workflow to test the impact of FDAAs on E. coli transcriptome. (b) Comparison of transcriptomes when treated with control (DMSO) or FDAAs at working concentrations; shown here is the consistency of transcriptomes in terms of expression (log2(TPM + 1)) correlation (n = 4,508; Pearson’s r = 0.97 for 10 µM HADA, Pearson’s r = 0.95 for 10 µM NADA, Pearson’s r = 0.96 for 25 µM TADA, Pearson’s r = 0.97 for 25 µM CADA). (c) Workflow to test whether cell sorting perturbs E. coli transcriptome. (d) Agreement between control (non-sorting) and sorting group, shown is the consistency of transcriptomes in terms of expression (log2(TPM + 1)) correlation (n = 4,508; Pearson’s r = 0.98).
Extended Data Fig. 3 Sequential staining of the biofilm and the subsequent spatial segmentation according to the final fluorescent image.
Sequential staining of the biofilm and the subsequent spatial segmentation according to the final fluorescent image. (a) Fluorescence images of a biofilm after labeling at different stages. Shown are composite images of the biofilm (phase contrast; mCherry, red; GFP, green; CFP, blue) right after the sequential labeling by TADA, NADA and HADA; Scale bar is 200 µm. Note that TADA molecules were absorbed by the microfluidic chamber (PDMS) during labeling, giving rise to the background signal; Further cultivation using medium without TADA washed out these absorbed dyes and reduced the background signal to the normal level. Actually, that is the main reason we used TADA as the first dye in the sequential labeling. (b) Extracting the fluorescent signals for each pixel of the biofilm in microscopic image; the pixel-fluorescence matrix is presented in the three-dimensional fluorescent space. Next, k-means clustering is executed to assign each pixel to one particular group according to its fluorescent pattern; shown are clustering of all pixels into (c) 4, (d) 8, (e) 12, (f) 20 and (g) 30 groups. According to the clustering result for all pixels, we segmented the biofilm into (h) 4, (i) 8, (j) 12, (k) 20 and (l) 30 groups (scale bar: 500 μm). Note that the number of groups to be clustered (N) is a manual parameter in k-means clustering; when it is set below a threshold (N ≤ 20), the biofilm can be perfectly segmented into multiple concentric rings from the periphery to interior of biofilm—the major direction of spatial heterogeneity (as shown in (h), (i), (j) and (k). However, when N is set too big, the rainbow structure in biofilm segmentation will be disrupted, as shown in (l) N = 30. This gives rise to an upper limit (N = 20) of computational segment of stained biofilm, which further limits the spatial resolution of current version of RAINBOW-seq.
Extended Data Fig. 4 Overall workflow to sort bacterial cells.
Overall workflow to sort bacterial cells. (a) The framework of the experiment (orange step) and computation (green step) during cell sorting stage in RAINBOW-seq; here, the schematics of two core algorithms in automatic gating calculation is presented in (b) the optimal transport and (c) CART tree algorithm to partition the 3D fluorescent space. (b) In optimal transport algorithm, a distance matrix is firstly computed based on the distance between each cell and each pixel in high-dimensional fluorescent space; another assignment matrix is subsequently calculated to assign one cell to one and only one pixel in biofilm image, via minimizing the overall transport distance based on the abovementioned distance matrix. (c) Shown here is a simplified 2D space partition via CART tree algorithm; in RAINBOW-seq experiments reported in this work, 3D space is partitioned via CART tree algorithm based on the group label of each cell mapped from the previous optimal transport step. Shown in (d), (e), (f) and (g) are four real examples of final gates, each of them represents a gate for cells from a particular region in biofilm. In each example, two rectangle gates are combined to generate a final gate. The cells to be sorted in each example are highlighted by corresponding colors of the gate, while all other cells are labeled with grey. Counter-clockwise rotation of the gates and cells are presented for each example.
Extended Data Fig. 5 Overview of spatially resolved gene expressions participating in energy metabolism, biosynthesis, ribosome, pili and curli biogenesis.
Overview of spatially resolved gene expressions participating in (a) energy metabolism, (b) biosynthesis, (c) ribosome, (d) pili and (e) curli biogenesis. Shown is log2(fold change) relative to average of each profile; abbreviations: TCA, tricarboxylic acid cycle; glycerol-3P, glycerol-3-phosphate; PPP, pentose phosphate pathway (provides NADPH for biosynthesis).
Extended Data Fig. 6 Overview of spatially resolved gene expressions participating in amino acid catabolism, fatty acid catabolism, stress response and two-component signaling systems.
Overview of spatially resolved gene expressions participating in (a) amino acid catabolism, (b) fatty acid catabolism, (c) stress response and (d) two-component signaling systems. Shown is log2(fold change) relative to average of each profile.
Extended Data Fig. 7 Spatial profiles of AI-2-QS-related genes in biofilm.
Spatial profiles of AI-2-QS-related genes in biofilm. (a) AI-2-mediated QS in E. coli. (b) The log2(fold change) between biofilm periphery (n = 5) and exponential phase of planktonic culture (n = 2) are presented as heatmaps for AI-2 related genes. The spatial profile (n = 5; solid line and shaded area indicate mean ± std) of AI-2 (c) biosynthesis, (d) import and (e) processing in biofilm. (f) Utilization of secondary metabolites at intermediate region within biofilm. Shown is log2(fold change) relative to average of each profile; abbreviations: GABA, gamma-aminobutyric acid. See ‘Signaling mediated by AI-2 QS and c-di-GMP in biofilms uncovered by RAINBOW-seq’ subsection in Methods for more discussions about AI-2 quorum sensing in biofilm learned from RAINBOW-seq data.
Extended Data Fig. 8 Signaling mediated by c-di-GMP in E. coli biofilm.
Signaling mediated by c-di-GMP in E. coli biofilm. (a) Differential expression of genes encoding c-di-GMP processing enzymes in biofilm relative to planktonic bacteria; Shown is log2(fold change) between the maximum in biofilm (n = 24) and the maximum in planktonic culture (n = 14). (b) Bulk profiling cannot identify the local activation of c-di-GMP processing genes in biofilm. Shown is the log2(fold change) of genes encoding c-di-GMP processing enzymes in biofilm relative to planktonic lifestyle; a computationally synthetic biofilm transcriptome by averaging all 24 spatially resolved samples was generated to mimic a bulk biofilm sample, which was compared with the maximum from planktonic culture (n = 14). (c) Regulatory logic of PdeR/DgcM cascade via local c-di-GMP signaling to modulate MlrA activity, which drives the transcription of csgD; note that PdeR inhibits MlrA, either directly or indirectly via DgcM; and DgcM activates MlrA, either directly or indirectly via PdeR. (d) Gene expression levels of csgD at the interior of biofilm (n = 5) and the stationary phase of planktonic culture (n = 2). Data are presented as mean ± std (P = 0.0009, two tailed Mann-Whitley U test). (e) Different responses of DGCs and PDEs between biofilm and planktonic culture when stepping into famine from feast; x axis presents the log2(fold change) between famine (interior) and feast (periphery) in biofilm; y axis presents the log2(fold change) between famine (stationary stage) and feast (exponential stage) in planktonic culture; blue dots are genes encoding DGCs and red for PDEs; diagonal line highlights the equivalent feast-to-famine expression change between biofilm and planktonic culture; the region between y = x + 1 and y = x − 1 is shaded to highlight those outlier genes; these genes exhibit different expression change from feast to famine between biofilm and planktonic culture. (f) Above are the spatial expression profiles (log2(TPM + 1)) of dgcM and pdeR in biofilm; below are the temporal profiles of these two genes in planktonic culture. See ‘Signaling mediated by AI-2 QS and c-di-GMP in biofilms uncovered by RAINBOW-seq’ subsection in Methods for more discussions about c-di-GMP signaling in biofilm learned from RAINBOW-seq data.
Extended Data Fig. 9 Activation of y-genes in biofilm.
Activation of y-genes in biofilm. (a) Ratio of y-genes in four spatial patterns as shown in Fig. 2j. Y-genes are labeled as dark grey while others kept light grey; pattern 1: 162 y-genes out of 962 genes; pattern 2: 586 y-genes out of 1,702 genes; pattern 3: 61 y-genes out of 184 genes; pattern 4: 304 y-genes out of 823 genes. (b) The spatial expression profiles of 10 representative y-genes with ascending spatial patterns (n = 5; red line and shaded area indicate mean ± std); blue dot is the expression level at the stationary phase of planktonic culture (n = 2, error bar: mean ± std). (c) Activation of sociality protein domains carried by y-genes in biofilm; the fold of activation of the associated y-genes in biofilm relative to planktonic culture are presented as circles; The colors encode cellular locations; Genes previously shown to be related to biofilm formation are highlighted as squares. (d) Occurrence of the activated sociality Pfam-A domains of (c) in prokaryotic genomes. Occurrence are counted based on the distribution of these Pfam-A domains in relevant phyla of bacteria and archaea. Red color indicates that a given domain is present in at least one genome of the relevant class. (e) and (f) Genomic neighborhoods between representative y-genes activated in biofilm and genes of known sociality functions, aligned with the phylogenetic tree of relevant bacteria. For (e), 1: Betaproteobacteria, 2: Alphaproteobacteria, 3: Gammaproteobacteria, 3.1: Aeromonadales, 3.2: Pseudomonadales, 3.3: Enterobacterales, 3.3.1: Enterobacteriaceae; For (f), 1: Firmicutes, 1.1: Clostridia, 1.2: Bacilli, 1.3: Lactobacillales, 2: Proteobacteria, 2.1: Alphaproteobacteria, 2.2: Gammaproteobacteria. Genomic neighborhoods are identified from STRING database according to gene interaction scores. The homologues of y-genes are shown in red and neighborhood genes with known sociality functions are shown in blue; The color bars indicate the degree of identity between homologues in relevant bacteria and in E. coli K-12 MG1655, as calculated by BLASTP. Note that the dgcN homologue in S. flexneri is a pseudogene due to a premature stop codon; and yfcC’ in (f) indicates a paralog of yfcC in (e) encoded by E. coli K12 BW25113.
Extended Data Fig. 10 Polyamine metabolism and the spatial profiles of the relevant genes in biofilm.
Polyamine metabolism and the spatial profiles of the relevant genes in biofilm. (a) Schematics of intracellular metabolism and transmembrane transport for polyamines (putrescine and spermidine) in E. coli. Spatial profiles for (b) polyamine biosynthesis, (c) putrescine degradation and (d) spermidine export. Solid line and shaded area indicate mean ± std (n = 5).
Supplementary information
Supplementary Information
Supplementary Figs. 1–13.
Supplementary Data
Supplementary Table 1: Biofilm information. Supplementary Table 2: y-genes list. Supplementary Table 3: Genes involved in amino acid utilization and synthesis. Supplementary Table 4: Oligos for rRNA depletion. Supplementary Table 5: Fitted spatial expression profiles of biofilm (log2(TPM + 1)). Supplementary Table 6: Transcriptome of biofilm and planktonic bacteria (TPM). Supplementary Table 7: Pattern of spatial expression profile. Supplementary Table 8: Transporter-coding genes list. Supplementary Table 9: Pfam-A domains of protein-coding y-genes. Supplementary Table 10: Sociality Pfam-A domain in biofilm. Supplementary Table 11: y-genes upregulated in biofilm. Supplementary Table 12: Genome and protein homology information used in phylogenetic analysis. Supplementary Table 13: Pattern-enriched Pfam-A domain of y-genes.
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Wang, T., Shen, P., He, Y. et al. Spatial transcriptome uncovers rich coordination of metabolism in E. coli K12 biofilm. Nat Chem Biol 19, 940–950 (2023). https://doi.org/10.1038/s41589-023-01282-w
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DOI: https://doi.org/10.1038/s41589-023-01282-w
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