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ProBac-seq, a bacterial single-cell RNA sequencing methodology using droplet microfluidics and large oligonucleotide probe sets

Nature Protocols volume 19, pages 2939–2966 (2024)Cite this article

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Abstract

Methods that measure the transcriptomic state of thousands of individual cells have transformed our understanding of cellular heterogeneity in eukaryotic cells since their introduction in the past decade. While simple and accessible protocols and commercial products are now available for the processing of mammalian cells, these existing technologies are incompatible with use in bacterial samples for several fundamental reasons including the absence of polyadenylation on bacterial messenger RNA, the instability of bacterial transcripts and the incompatibility of bacterial cell morphology with existing methodologies. Recently, we developed ProBac sequencing (ProBac-seq), a method that overcomes these technical difficulties and provides high-quality single-cell gene expression data from thousands of bacterial cells by using messenger RNA-specific probes. Here we provide details for designing large oligonucleotide probe sets for an organism of choice, amplifying probe sets to produce sufficient quantities for repeated experiments, adding unique molecular indexes and poly-A tails to produce finalized probes, in situ probe hybridization and single-cell encapsulation and library preparation. This protocol, from the probe amplification to the library preparation, requires ~7 d to complete. ProBac-seq offers several advantages over other methods by capturing only the desired target sequences and avoiding nondesired transcripts, such as highly abundant ribosomal RNA, thus enriching for signal that better informs on cellular state. The use of multiple probes per gene can detect meaningful single-cell signals from cells expressing transcripts to a lesser degree or those grown in minimal media and other environmentally relevant conditions in which cells are less active. ProBac-seq is also compatible with other organisms that can be profiled by in situ hybridization techniques.

Key points

  • This protocol describes ProBac sequencing, a bacterial single-cell RNA sequencing method using droplet microfluidics and large oligonucleotide probe sets.

  • ProBac sequencing offers advantages over other methods. By capturing only the desired target sequences and avoiding nondesired transcripts such as highly abundant ribosomal RNA, it better informs on cellular state. Using multiple probes per gene allows meaningful single-cell signals to be detected from less active cells or those expressing transcripts to a lesser degree.

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Fig. 1: An overview of the ProBac-seq protocol.
Fig. 2: Preparation of oligonucleotide probe library.
Fig. 3: Crucial steps in ProBac-seq.

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Data availability

The main data discussed in this protocol are available in the supporting primary research paper19. The raw datasets are available for research purposes upon reasonable request from the corresponding author.

References

  1. Subramanian, A. et al. Gene set enrichment analysis: a knowledge-based approach for interpreting genome-wide expression profiles. Proc. Natl Acad. Sci. USA 102, 15545–15550 (2005).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  2. Tang, F., Lao, K. & Surani, M. A. Development and applications of single-cell transcriptome analysis. Nat. Methods 8, S6–S11 (2011).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  3. Ozsolak, F. & Milos, P. M. RNA sequencing: advances, challenges and opportunities. Nat. Rev. Genet. 12, 87–98 (2011).

    Article  CAS  PubMed  Google Scholar 

  4. Fan, H. C., Fu, G. K. & Fodor, S. P. A. Expression profiling. Combinatorial labeling of single cells for gene expression cytometry. Science 347, 1258367 (2015).

    Article  PubMed  Google Scholar 

  5. Klein, A. M. et al. Droplet barcoding for single-cell transcriptomics applied to embryonic stem cells. Cell 161, 1187–1201 (2015).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  6. Macosko, E. Z. et al. Highly parallel genome-wide expression profiling of individual cells using nanoliter droplets. Cell 161, 1202–1214 (2015).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  7. Cao, J. et al. Comprehensive single-cell transcriptional profiling of a multicellular organism. Science 357, 661–667 (2017).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  8. Zheng, G. X. Y. et al. Massively parallel digital transcriptional profiling of single cells. Nat. Commun. 8, 14049 (2017).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  9. Rosenberg, A. B. et al. Single-cell profiling of the developing mouse brain and spinal cord with split-pool barcoding. Science 360, 176–182 (2018).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  10. Rodriques, S. G. et al. Slide-seq: a scalable technology for measuring genome-wide expression at high spatial resolution. Science 363, 1463–1467 (2019).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  11. Hashimshony, T., Wagner, F., Sher, N. & Yanai, I. CEL-seq: single-cell RNA-seq by multiplexed linear amplification. Cell Rep. 2, 666–673 (2012).

    Article  CAS  PubMed  Google Scholar 

  12. Jovic, D. et al. Single-cell RNA sequencing technologies and applications: a brief overview. Clin. Transl. Med. 12, e694 (2022).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  13. Sheng, K., Cao, W., Niu, Y., Deng, Q. & Zong, C. Effective detection of variation in single-cell transcriptomes using MATQ-seq. Nat. Methods 14, 267–270 (2017).

    Article  CAS  PubMed  Google Scholar 

  14. Imdahl, F., Vafadarnejad, E., Homberger, C., Saliba, A. E. & Vogel, J. Single-cell RNA-sequencing reports growth-condition-specific global transcriptomes of individual bacteria. Nat. Microbiol. 5, 1202–1206 (2020).

    Article  CAS  PubMed  Google Scholar 

  15. Blattman, S. B., Jiang, W., Oikonomou, P. & Tavazoie, S. Prokaryotic single-cell RNA sequencing by in situ combinatorial indexing. Nat. Microbiol. 5, 1192–1201 (2020).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  16. Kuchina, A. et al. Microbial single-cell RNA sequencing by split-pool barcoding. Science 371, eaba5257 (2021).

    Article  CAS  PubMed  Google Scholar 

  17. Wang, B. et al. Single-cell massively-parallel multiplexed microbial sequencing (M3-seq) identifies rare bacterial populations and profiles phage infection. Nat. Microbiol. https://doi.org/10.1038/s41564-023-01462-3 (2023).

  18. Ma, P. et al. Bacterial droplet-based single-cell RNA-seq reveals antibiotic-associated heterogeneous cellular states. Cell 186, 877–891.e14 (2023).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  19. McNulty, R. et al. Probe-based bacterial single-cell RNA sequencing predicts toxin regulation. Nat. Microbiol. 8, 934–945 (2023).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  20. Xu, Z. et al. Droplet-based high-throughput single microbe RNA sequencing by smRandom-seq. Nat. Commun. 14, 5130 (2023).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  21. Phillips, R. M. Cell Biology by the Numbers (Garland Science, 2015).

  22. Régnier, P. & Marujo, P. E. Polyadenylation and degradation of RNA in Prokaryotes. In Madame Curie Bioscience Database (Landes Bioscience, 2013).

  23. Dar, D., Dar, N., Cai, L. & Newman, D. K. Spatial transcriptomics of planktonic and sessile bacterial populations at single-cell resolution. Science 373, eabi4882 (2021).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  24. Raj, A. Single-molecule RNA FISH. in Encyclopedia of Biophysics (ed. Roberts, G. C. K.) 2340–2343 (Springer, 2013).

  25. Taniguchi, Y. et al. Quantifying E. coli proteome and transcriptome with single-molecule sensitivity in single cells. Science 329, 533–538 (2010).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  26. Jia, Z., Dong, Y., Xu, H. & Wang, F. Optimizing the hybridization chain reaction–fluorescence in situ hybridization (HCR–FISH) protocol for detection of microbes in sediments. Mar. Life Sci. Technol. 3, 529–541 (2021).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  27. Pavlekovic, M. et al. Optimization of three FISH procedures for in situ detection of anaerobic ammonium oxidizing bacteria in biological wastewater treatment. J. Microbiol. Methods 78, 119–126 (2009).

    Article  CAS  PubMed  Google Scholar 

  28. Glöckner, F. O. et al. An in situ hybridization protocol for detection and identification of planktonic bacteria. Syst. Appl. Microbiol. 19, 403–406 (1996).

    Article  Google Scholar 

  29. Potapov, V. & Ong, J. L. Examining sources of error in PCR by single-molecule sequencing. PloS ONE 12, e0169774 (2017).

    Article  PubMed  PubMed Central  Google Scholar 

  30. Dragosits, M. & Mattanovich, D. Adaptive laboratory evolution—principles and applications for biotechnology. Microb. Cell Fact. 12, 64 (2013).

    Article  PubMed  PubMed Central  Google Scholar 

  31. Lenski, R. E. Experimental evolution and the dynamics of adaptation and genome evolution in microbial populations. ISME J. 11, 2181–2194 (2017).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  32. Bastian, F. et al. Eukaryotic cell capture by amplified magnetic in situ hybridization using yeast as a model. Front. Microbiol. 12, 759478 (2021).

    Article  PubMed  PubMed Central  Google Scholar 

  33. Chen, S.-H., Lo, C.-Z., Tsai, M.-C., Hsiung, C. A. & Lin, C.-Y. The unique probe selector: a comprehensive web service for probe design and oligonucleotide arrays. BMC Bioinforma. 9, S8 (2008).

    Article  Google Scholar 

  34. Schmidt, T. L. et al. Scalable amplification of strand subsets from chip-synthesized oligonucleotide libraries. Nat. Commun. 6, 8634 (2015).

    Article  CAS  PubMed  Google Scholar 

  35. Rosenthal, A. Z. et al. Metabolic interactions between dynamic bacterial subpopulations. eLife 7, e33099 (2018).

    Article  PubMed  PubMed Central  Google Scholar 

  36. Tsoi, R. et al. Metabolic division of labor in microbial systems. Proc. Natl Acad. Sci. USA 115, 2526–2531 (2018).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  37. Mridha, S. & Kümmerli, R. Coordination of siderophore gene expression among clonal cells of the bacterium Pseudomonas aeruginosa. Commun. Biol. 5, 1–12 (2022).

    Article  Google Scholar 

  38. Armbruster, C. R. et al. Heterogeneity in surface sensing suggests a division of labor in Pseudomonas aeruginosa populations. eLife 8, e45084 (2019).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  39. Akerley, B. J., Cotter, P. A. & Miller, J. F. Ectopic expression of the flagellar regulon alters development of the bordetella–host interaction. Cell 80, 611–620 (1995).

    Article  CAS  PubMed  Google Scholar 

  40. Lyu, Z., Yang, A., Villanueva, P., Singh, A. & Ling, J. Heterogeneous flagellar expression in single salmonella cells promotes diversity in antibiotic tolerance. mBio 12, e0237421 (2021).

    Article  PubMed  Google Scholar 

  41. Zuo, W. & Wu, Y. Dynamic motility selection drives population segregation in a bacterial swarm. Proc. Natl Acad. Sci. USA 117, 4693–4700 (2020).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  42. Garrett, E. M. et al. Phase variation of a signal transduction system controls Clostridioides difficile colony morphology, motility, and virulence. PLoS Biol. 17, e3000379 (2019).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  43. Adiciptaningrum, A. M., Blomfield, I. C. & Tans, S. J. Direct observation of type 1 fimbrial switching. EMBO Rep. 10, 527–532 (2009).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  44. Yu, Q. et al. The Agr-like quorum sensing system is required for pathogenesis of necrotic enteritis caused by Clostridium perfringens in poultry. Infect. Immun. 85, e00975–16 (2017).

    Article  PubMed  PubMed Central  Google Scholar 

  45. Arnoldini, M. et al. Bistable expression of virulence genes in salmonella leads to the formation of an antibiotic-tolerant subpopulation. PLoS Biol. 12, e1001928 (2014).

    Article  PubMed  PubMed Central  Google Scholar 

  46. Hockenberry, A. M. et al. Microbiota-derived metabolites inhibit Salmonella virulent subpopulation development by acting on single-cell behaviors. Proc. Natl Acad. Sci. USA 118, e2103027118 (2021).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  47. Penaranda, C. & Hung, D. T. Single-cell RNA sequencing to understand host–pathogen interactions. ACS Infect. Dis. 5, 336–344 (2019).

    Article  CAS  PubMed  Google Scholar 

  48. Fuchs, B. M. et al. Flow cytometric analysis of the in situ accessibility of Escherichia coli 16S rRNA for fluorescently labeled oligonucleotide probes. Appl. Environ. Microbiol. 64, 4973–4982 (1998).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  49. Singh, L. & Jones, K. W. The use of heparin as a simple cost-effective means of controlling background in nucleic acid hybridization procedures. Nucleic Acids Res. 12, 5627–5638 (1984).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  50. Takei, N. et al. High-sensitivity and high-resolution in situ hybridization of coding and long non-coding RNAs in vertebrate ovaries and testes. Biol. Proced. Online 20, 6 (2018).

    Article  PubMed  PubMed Central  Google Scholar 

  51. Hutton, J. R. Renaturation kinetics and thermal stability of DNA in aqueous solutions of formamide and urea. Nucleic Acids Res. 4, 3537–3555 (1977).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  52. Beliveau, B. J. et al. OligoMiner provides a rapid, flexible environment for the design of genome-scale oligonucleotide in situ hybridization probes. Proc. Natl Acad. Sci. USA 115, E2183–E2192 (2018).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  53. Rouillard, J.-M., Zuker, M. & Gulari, E. OligoArray 2.0: design of oligonucleotide probes for DNA microarrays using a thermodynamic approach. Nucleic Acids Res. 31, 3057–3062 (2003).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  54. Srivastava, G. P. & Xu, D. Genome-scale PCR primer design. In PCR Primer Design (ed. Yuryev, A.) 159–175 (Humana Press, 2007).

  55. Friedrich, T. et al. High-throughput microarray technology in diagnostics of enterobacteria based on genome-wide probe selection and regression analysis. BMC Genomics 11, 591 (2010).

    Article  PubMed  PubMed Central  Google Scholar 

  56. Zhang, T., Liu, G., Zhao, H., Braz, G. T. & Jiang, J. Chorus2: design of genome-scale oligonucleotide-based probes for fluorescence in situ hybridization. Plant Biotechnol. J. 19, 1967–1978 (2021).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  57. Srinivasan, M., Sedmak, D. & Jewell, S. Effect of fixatives and tissue processing on the content and integrity of nucleic acids. Am. J. Pathol. 161, 1961–1971 (2002).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  58. Thomsen, E. R. et al. Fixed single-cell transcriptomic characterization of human radial glial diversity. Nat. Methods 13, 87–93 (2016).

    Article  CAS  PubMed  Google Scholar 

  59. Alles, J. et al. Cell fixation and preservation for droplet-based single-cell transcriptomics. BMC Biol. 15, 44 (2017).

    Article  PubMed  PubMed Central  Google Scholar 

  60. Ku, W.-C., Lau, W. K., Tseng, Y.-T., Tzeng, C.-M. & Chiu, S.-K. Dextran sulfate provides a quantitative and quick microarray hybridization reaction. Biochem. Biophys. Res. Commun. 315, 30–37 (2004).

    Article  CAS  PubMed  Google Scholar 

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Acknowledgements

A.R. acknowledges funding from the University of North Carolina School of Medicine and the CGIBD institute at the University of North Carolina including by a grant from the National Institutes of Health, P30 DK034987. S.H. acknowledges funding from the National Institutes of Health National Institute of General Medical Sciences grant no. R00GM118910; National Institutes of Health National Heart, Lung, and Blood Institute grant no. R01HL158269; U19 Systems Immunology Pilot Project Grant at Harvard University and the Harvard University William F. Milton Fund. Portions of this research were conducted on the O2 High Performance Compute Cluster, supported by the Research Computing Group, at Harvard Medical School (see http://rc.hms.harvard.edu for more information).

Author information

Authors and Affiliations

  1. Department of Microbiology and Immunology, University of North Carolina, Chapel Hill, NC, USA

    Prosenjit Samanta, Samuel F. Cooke & Adam Rosenthal

  2. Lewis-Sigler Institute for Integrative Genomics, Princeton University, Princeton, NJ, USA

    Ryan McNulty

  3. Department of Data Science, Dana-Farber Cancer Institute, Boston, MA, USA

    Sahand Hormoz

  4. Department of Systems Biology, Harvard Medical School, Boston, MA, USA

    Sahand Hormoz

  5. Broad Institute of MIT and Harvard, Cambridge, MA, USA

    Sahand Hormoz

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Contributions

A.R. and P.S. conceived the manuscript. R.M., S.F.C., P.S. and A.R. performed the laboratory experiments. S.H. and R.M. created the data analysis pipeline. P.S. and A.R. wrote the paper with input from all other authors. A.R. and S.H. supervised the project.

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Correspondence to Adam Rosenthal.

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Nature Protocols thanks Kuanwei Sheng and the other, anonymous, reviewer(s) for their contribution to the peer review of this work.

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Key reference using this protocol

McNulty, R. et al. Nat. Microbiol. 8, 934–945 (2023).

Supplementary information

Supplementary Information

Supplementary Figs. 1–6.

Supplementary Table 1

B. subtilis probes ordered from Twist Bioscience and shown here as a representation of an initial probe sequence

Supplementary Table 2

Primers for probe amplification and library generation. RCA primers shown here are specific for B. subtilis

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Samanta, P., Cooke, S.F., McNulty, R. et al. ProBac-seq, a bacterial single-cell RNA sequencing methodology using droplet microfluidics and large oligonucleotide probe sets. Nat Protoc 19, 2939–2966 (2024). https://doi.org/10.1038/s41596-024-01002-1

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