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Single-cell RNA-sequencing reports growth-condition-specific global transcriptomes of individual bacteria

Abstract

Bacteria respond to changes in their environment with specific transcriptional programmes, but even within genetically identical populations these programmes are not homogenously expressed1. Such transcriptional heterogeneity between individual bacteria allows genetically clonal communities to develop a complex array of phenotypes1, examples of which include persisters that resist antibiotic treatment and metabolically specialized cells that emerge under nutrient-limiting conditions2. Fluorescent reporter constructs have played a pivotal role in deciphering heterogeneous gene expression within bacterial populations3 but have been limited to recording the activity of single genes in a few genetically tractable model species, whereas the vast majority of bacteria remain difficult to engineer and/or even to cultivate. Single-cell transcriptomics is revolutionizing the analysis of phenotypic cell-to-cell variation in eukaryotes, but technical hurdles have prevented its robust application to prokaryotes. Here, using an improved poly(A)-independent single-cell RNA-sequencing protocol, we report the faithful capture of growth-dependent gene expression patterns in individual Salmonella and Pseudomonas bacteria across all RNA classes and genomic regions. These transcriptomes provide important reference points for single-cell RNA-sequencing of other bacterial species, mixed microbial communities and host–pathogen interactions.

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Fig. 1: Overview of bacterial single-cell RNA-seq workflow.
Fig. 2: Characterization of transcriptomes down to the single-bacterium level under different growth conditions.
Fig. 3: Single-bacterium RNA-seq reveals specific transcriptional signatures associated with growth conditions.

Data availability

All RNA-seq data have been deposited in NCBI’s Gene Expression Omnibus under accession no. GSE119888. Source data for Figs. 2 and 3 and Extended Data Figs. 2 and 47 are provided with the paper.

Code availability

All codes used to perform the analysis and reproduce the figures are available on GitHub: https://github.com/saliba-lab/single-bacterium-transcriptome-profiling.

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Acknowledgements

We thank S. Gorski for critical comments on the manuscript; K. Secener and X. Hartmann for performing preliminary experiments; C. Michaux for advice on bacterial culture; L. Barquist and F. Erhard for advice on statistical analysis; and P. Arampatzi, T. Heckel (SysMed Würzburg) and R. Geffers (Genome Analytics, HZI) for conducting the sequencing.

Author information

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Authors

Contributions

F.I. and C.H. conducted experiments. E.V. performed data analysis. A.-E.S. designed research. J.V and A.-E.S. directed research. A.-E.S. and J.V. wrote the manuscript.

Corresponding authors

Correspondence to Antoine-Emmanuel Saliba or Jörg Vogel.

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The authors declare no competing interests.

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Extended data

Extended Data Fig. 1 Single-bacteria sorting.

Evaluation of single-bacteria sorting efficiency. On a LB agar molded in a 96 well format dish, GFP-fluorescent and non-fluorescent Salmonella 100, 10, 2 and 1 bacteria are sorted systematically in alternative manner. After overnight growth colonies are observed (upper bright field image) and absent colonies are indicated with a red circle. Observing the plate under fluorescent reader allows to count the sorting mismatches (indicated by an orange square) and define the sorting precision.

Extended Data Fig. 2 Comparison of library size and number of detected genes between different growth conditions.

a, Violin plots represent the size of libraries cultured in the different growth conditions for 10-pooled and single bacteria. b, Scatter plots depicting the relation between the number of detected genes and library size. All libraries labelled as outliers have been removed from the analysis (Methods, Supplementary Table 1).

Source data

Extended Data Fig. 3 Coverage plot and read densities.

a, Read densities of structural genes with a high expression in 10- and single-cell libraries. b, Gene coverage of selected differentially expressed genes in the respective conditions in 10-cell libraries. c, Read densities of csrA in 10-pooled libraries and respective conditions overlapping to 5’ UTR. Libraries have been automatically log scaled by Integrative Genomics Viewer.

Extended Data Fig. 4 Technical noise assessment of 10-pooled bacteria and single‐bacteria RNA‐seq data.

a, b, Coefficient of variation is plotted against the log2 (average of normalized read count) for (a) 10-pooled bacteria and (b) single bacteria conditions. Color code refers to salt (NaCl) shock (green), anaerobic shock (red) and late stationary phase (blue). Such analysis are routinely conducted when analyzing mammalian single-cell RNA-seq data (see ref. 23). Note that upper bound corresponds to CV=√n with n the number of samples analyzed.

Source data

Extended Data Fig. 5 Correlations between matching growth conditions.

a, Scatter plots show the correlation between the matching pooled (10-pooled) and single bacteria in the respective conditions with the associated Spearman’s correlation coefficient p<2.2e10-16 in all three growth conditions. b, Scatter plots show the correlation between 10-pooled and single bacteria (this study) and bulk RNA-seq in the respective conditions with the associated Spearman’s correlation coefficient (ρ) with the associated p-values.

Source data

Extended Data Fig. 6 Technical parameters associated to the Principal Component Analysis (PCA) of 10-pooled and single bacteria transcriptomes.

a, b, For single-bacteria (a) and 10-pooled bacteria transcriptomes (b) library size (left), number of detected genes (middle) and genes associated with PC loading 1 and 2 are overlaid on top of the PCA. The top 15 genes with the highest contribution to PC were selected and their related loading vectors are shown on the PCA plot. The vectors show how the original variables contribute to creating the principal component. c, Scree plots give the variance associated to each PC loading.

Source data

Extended Data Fig. 7 Application of MATQ-seq to Pseudomonas aeruginosa.

a, Scatter plots depicting the relation between the number of detected genes versus library size for single bacteria libraries (blue) and 10-pooled bacteria libraries (red). b, Violin plots representing the number genes detected for 10-pooled and single bacteria. c, Average proportions of transcript categories after removal of unmapped reads obtained for 10-pooled and single bacteria. CDS: coding sequences; IGR, intergenic region; ncRNA: non-coding RNA; sRNA: small RNA; other: all other RNA classes (Supplementary Tables 7 and 8). d, Representative reads aligning to the reference sequence of tmRNA-encoding ssrA gene (±300 bp upstream and downstream the CDS) across 10-pooled and single bacteria.

Source data

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Imdahl, F., Vafadarnejad, E., Homberger, C. et al. Single-cell RNA-sequencing reports growth-condition-specific global transcriptomes of individual bacteria. Nat Microbiol 5, 1202–1206 (2020). https://doi.org/10.1038/s41564-020-0774-1

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