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Applications of single-cell technology on bacterial analysis

  • Zhixin Ma
  • Pan M. Chu
  • Yingtong Su
  • Yue Yu
  • Hui Wen
  • Xiongfei Fu
  • Shuqiang HuangEmail author
Review
  • 1 Downloads

Abstract

Background

Traditionally, scientists studied microbiology through the manner of batch cultures, to conclude the dynamics or outputs by averaging all individuals. However, as the researches go further, the heterogeneities among the individuals have been proven to be crucial for the population dynamics and fates.

Results

Due to the limit of technology, single-cell analysis methods were not widely used to decipher the inherent connections between individual cells and populations. Since the early decades of this century, the rapid development of microfluidics, fluorescent labelling, next-generation sequencing, and high-resolution microscopy have speeded up the development of single-cell technologies and further facilitated the applications of these technologies on bacterial analysis.

Conclusions

In this review, we summarized the recent processes of single-cell technologies applied in bacterial analysis in terms of intracellular characteristics, cell physiology dynamics, and group behaviors, and discussed how single-cell technologies could be more applicable for future bacterial researches.

Keywords

single-cell technology bacterial analysis fluorescent labelling next-generation sequencing microfluidics 

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Notes

Acknowledgments

This paper was supported by the National Natural Science Foundation of China (Nos. 31770111, 31800083 and 31570095); Shenzhen Science Technology and Innovation Commission (Nos. KQTD2016112915000294, JCYJ20170413153329565, JCYJ20170818160418654 and JCYJ2018030- 2145817753); Instrumental project from Chinese Academy of Science (No. YJKYYQ20170063); China Postdoctoral Science Foundation Grant (Nos. 2017M622832 and 2018M631002).

Compliance with Ethics Guidelines

The authors Zhixin Ma, Pan M. Chu, Yingtong Su, Yue Yu, Hui Wen, Xiongfei Fu and Shuqiang Huang declare that they have no conflict of interests.

This article is a review article and does not contain any studies with human or animal subjects performed by any of the authors.

References

  1. 1.
    Cryan J. F. and Dinan T. G. (2012) Mind-altering microorganisms: the impact of the gut microbiota on brain and behaviour. Nat. Rev. Neurosci., 13, 701–712CrossRefGoogle Scholar
  2. 2.
    Crick F. (1970) Central dogma of molecular biology. Nature, 227, 561–563CrossRefGoogle Scholar
  3. 3.
    Monod J. (1949) The growth of bacterial cultures. Annu. Rev. Microbiol., 3, 371–394CrossRefGoogle Scholar
  4. 4.
    Elowitz M. B., Levine A. J., Siggia E. D. and Swain P. S. (2002) Stochastic gene expression in a single cell. Science, 297, 1183–1186CrossRefGoogle Scholar
  5. 5.
    Ozbudak E. M., Thattai M., Kurtser I., Grossman A. D. and van Oudenaarden A. (2002) Regulation of noise in the expression of a single gene. Nat. Genet., 31, 69–73CrossRefGoogle Scholar
  6. 6.
    Rosenfeld N., Young J.W., Alon U., Swain P. S. and Elowitz M. B. (2005) Gene regulation at the single-cell level. Science, 307, 1962–1965CrossRefGoogle Scholar
  7. 7.
    Wang P., Robert L., Pelletier J., Dang W. L., Taddei F., Wright A. and Jun S. (2010) Robust growth of Escherichia coli. Curr. Biol., 20, 1099–1103CrossRefGoogle Scholar
  8. 8.
    Robert L., Ollion J., Robert J., Song X., Matic I. and Elez M. (2018) Mutation dynamics and fitness effects followed in single cells. Science, 359, 1283–1286CrossRefGoogle Scholar
  9. 9.
    Jones D. L., Leroy P., Unoson C., Fange D., Ćurić V., Lawson M. J. and Elf J. (2017) Kinetics of dCas9 target search in Escherichia coli. Science, 357, 1420–1424CrossRefGoogle Scholar
  10. 10.
    Jones D. L., Brewster R. C. and Phillips R. (2014) Promoter architecture dictates cell-to-cell variability in gene expression. Science, 346, 1533–1536CrossRefGoogle Scholar
  11. 11.
    Golding I., Paulsson J., Zawilski S. M. and Cox E. C. (2005) Realtime kinetics of gene activity in individual bacteria. Cell, 123, 1025–1036CrossRefGoogle Scholar
  12. 12.
    Huh D. and Paulsson J. (2011) Non-genetic heterogeneity from stochastic partitioning at cell division. Nat. Genet., 43, 95–100CrossRefGoogle Scholar
  13. 13.
    Chen Y., Kim J. K., Hirning A. J., Josić K. and Bennett M. R. (2015) Emergent genetic oscillations in a synthetic microbial consortium. Science, 349, 986–989CrossRefGoogle Scholar
  14. 14.
    Ishiura M., Kutsuna S., Aoki S., Iwasaki H., Andersson C. R., Tanabe A., Golden S. S., Johnson C. H. and Kondo T. (1998) Expression of a gene cluster kaiABC as a circadian feedback process in cyanobacteria. Science, 281, 1519–1523CrossRefGoogle Scholar
  15. 15.
    Elowitz M. B. and Leibler S. (2000) A synthetic oscillatory network of transcriptional regulators. Nature, 403, 335–338CrossRefGoogle Scholar
  16. 16.
    Wallden M., Fange D., Lundius E. G., Baltekin Ü. and Elf J. (2016) The synchronization of replication and division cycles in individual E. coli cells. Cell, 166, 729–739Google Scholar
  17. 17.
    Amir A. and Balaban N. Q. (2018) Learning from noise: how observing stochasticity may aid microbiology. Trends Microbiol., 26, 376–385CrossRefGoogle Scholar
  18. 18.
    Prakadan S. M., Shalek A. K. and Weitz D. A. (2017) Scaling by shrinking: empowering single-cell “omics” with microfluidic devices. Nat. Rev. Genet., 18, 345–361CrossRefGoogle Scholar
  19. 19.
    Amann R. and Fuchs B. M. (2008) Single-cell identification in microbial communities by improved fluorescence in situ hybridization techniques. Nat. Rev. Microbiol., 6, 339–348CrossRefGoogle Scholar
  20. 20.
    Kang Y., McMillan I., Norris M. H. and Hoang T. T. (2015) Single prokaryotic cell isolation and total transcript amplification protocol for transcriptomic analysis. Nat. Protoc., 10, 974–984CrossRefGoogle Scholar
  21. 21.
    DeLong E. F., Wickham G. S. and Pace N. R. (1989) Phylogenetic stains: ribosomal RNA-based probes for the identification of single cells. Science, 243, 1360–1363CrossRefGoogle Scholar
  22. 22.
    Manz W., Szewzyk U., Ericsson P., Amann R., Schleifer K. H. and Stenström T. A. (1993) In situ identification of bacteria in drinking water and adjoining biofilms by hybridization with 16S and 23S rRNA-directed fluorescent oligonucleotide probes. Appl. Environ. Microbiol., 59, 2293–2298Google Scholar
  23. 23.
    Wagner M., Schmid M., Juretschko S., Trebesius K. H., Bubert A., Goebel W. and Schleifer K. H. (1998) In situ detection of a virulence factor mRNA and 16S rRNA in Listeria monocytogenes. FEMS Microbiol. Lett., 160, 159–168CrossRefGoogle Scholar
  24. 24.
    Zwirglmaier K., Ludwig W. and Schleifer K. H. (2004) Recognition of individual genes in a single bacterial cell by fluorescence in situ hybridization–RING-FISH. Mol. Microbiol., 51, 89–96CrossRefGoogle Scholar
  25. 25.
    Chong S., Chen C., Ge H. and Xie X. S. (2014) Mechanism of transcriptional bursting in bacteria. Cell, 158, 314–326CrossRefGoogle Scholar
  26. 26.
    Wallner G., Amann R. and Beisker W. (1993) Optimizing fluorescent in situ hybridization with rRNA-targeted oligonucleotide probes for flow cytometric identification of microorganisms. Cytometry, 14, 136–143CrossRefGoogle Scholar
  27. 27.
    Paige J. S., Wu K. Y. and Jaffrey S. R. (2011) RNA mimics of green fluorescent protein. Science, 333, 642–646CrossRefGoogle Scholar
  28. 28.
    Strack, R. L., Disney, M. D. and Jaffrey, S. R. (2013) A superfolding Spinach2 reveals the dynamic nature of trinucleotide repeat-containing RNA. Nat. Methods, 10, 1219–1224CrossRefGoogle Scholar
  29. 29.
    Dolgosheina, E. V., Jeng, S. C., Panchapakesan, S. S. S., Cojocaru R., Chen P. S., Wilson P. D., Hawkins N., Wiggins P. A. and Unrau P. J. (2014) RNA mango aptamer-fluorophore: a bright, highaffinity complex for RNA labeling and tracking. ACS Chem. Biol., 9, 2412–2420CrossRefGoogle Scholar
  30. 30.
    Filonov, G. S., Moon, J. D., Svensen, N. and Jaffrey, S. R. (2014) Broccoli: rapid selection of an RNA mimic of green fluorescent protein by fluorescence-based selection and directed evolution. J. Am. Chem. Soc., 136, 16299–16308CrossRefGoogle Scholar
  31. 31.
    Arora A., Sunbul M. and Jäschke A. (2015) Dual-colour imaging of RNAs using quencher- and fluorophore-binding aptamers. Nucleic Acids Res., 43, e144Google Scholar
  32. 32.
    Shimomura, O., Johnson, F. H. and Saiga, Y. (1962) Extraction, purification and properties of aequorin, a bioluminescent protein from the luminous hydromedusan, Aequorea. J. Cell. Comp. Physiol., 59, 223–239CrossRefGoogle Scholar
  33. 33.
    Stearns T. (1995) Green fluorescent protein. The green revolution. Curr. Biol., 5, 262–264Google Scholar
  34. 34.
    Norman T. M., Lord N. D., Paulsson J. and Losick R. (2013) Memory and modularity in cell-fate decision making. Nature, 503, 481–486CrossRefGoogle Scholar
  35. 35.
    Friedman N., Vardi S., Ronen M., Alon U. and Stavans J. (2005) Precise temporal modulation in the response of the SOS DNA repair network in individual bacteria. PLoS Biol., 3, e238CrossRefGoogle Scholar
  36. 36.
    Ozbudak E. M., Thattai M., Lim H. N., Shraiman B. I. and Van Oudenaarden A. (2004) Multistability in the lactose utilization network of Escherichia coli. Nature, 427, 737–740CrossRefGoogle Scholar
  37. 37.
    Rosenthal A. Z., Qi Y., Hormoz S., Park J., Li S. H.-J. and Elowitz M. B. (2018) Metabolic interactions between dynamic bacterial subpopulations. eLife, 7, 1–18CrossRefGoogle Scholar
  38. 38.
    Taniguchi Y., Choi P. J., Li G. W., Chen H., Babu M., Hearn J., Emili A. and Xie X. S. (2010) Quantifying E. coli proteome and transcriptome with single-molecule sensitivity in single cells. Science, 329, 533–538Google Scholar
  39. 39.
    Tan C., Marguet P. and You L. (2009) Emergent bistability by a growth-modulating positive feedback circuit. Nat. Chem. Biol., 5, 842–848CrossRefGoogle Scholar
  40. 40.
    Cluzel P., Surette M. and Leibler S. (2000) An ultrasensitive bacterial motor revealed by monitoring signaling proteins in single cells. Science, 287, 1652–1655CrossRefGoogle Scholar
  41. 41.
    Uphoff S., Lord N. D., Okumus B., Potvin-Trottier L., Sherratt D. J. and Paulsson J. (2016) Stochastic activation of a DNA damage response causes cell-to-cell mutation rate variation. Science, 351, 1094–1097CrossRefGoogle Scholar
  42. 42.
    Badrinarayanan A., Reyes-Lamothe R., Uphoff S., Leake M. C. and Sherratt D. J. (2012) In vivo architecture and action of bacterial structural maintenance of chromosome proteins. Science, 338, 528–531CrossRefGoogle Scholar
  43. 43.
    Le T. T., Harlepp S., Guet C. C., Dittmar K., Emonet T., Pan T. and Cluzel P. (2005) Real-time RNA profiling within a single bacterium. Proc. Natl. Acad. Sci. USA, 102, 9160–9164CrossRefGoogle Scholar
  44. 44.
    Gawad C., Koh W. and Quake S. R. (2016) Single-cell genome sequencing: current state of the science. Nat. Rev. Genet., 17, 175–188CrossRefGoogle Scholar
  45. 45.
    Podar M., Abulencia C. B., Walcher M., Hutchison D., Zengler K., Garcia J. A., Holland T., Cotton D., Hauser L. and Keller M. (2007) Targeted access to the genomes of low-abundance organisms in complex microbial communities. Appl. Environ. Microbiol., 73, 3205–3214CrossRefGoogle Scholar
  46. 46.
    Rinke C., Schwientek P., Sczyrba A., Ivanova N. N., Anderson I. J., Cheng J. F., Darling A., Malfatti S., Swan B. K., Gies E. A., et al. (2013) Insights into the phylogeny and coding potential of microbial dark matter. Nature, 499, 431–437CrossRefGoogle Scholar
  47. 47.
    Zhang Y., Gao J., Huang Y. and Wang J. (2018) Recent developments in single-Cell RNA-seq of microorganisms. Biophys. J., 115, 173–180CrossRefGoogle Scholar
  48. 48.
    Kang Y., Norris M. H., Zarzycki-Siek J., Nierman W. C., Donachie S. P. and Hoang T. T. (2011) Transcript amplification from single bacterium for transcriptome analysis. Genome Res., 21, 925–935CrossRefGoogle Scholar
  49. 49.
    Avital G., Avraham R., Fan A., Hashimshony T., Hung D. T. and Yanai I. (2017) scDual-seq: mapping the gene regulatory program of Salmonella infection by host and pathogen single-cell RNAsequencing. Genome Biol., 18, 200CrossRefGoogle Scholar
  50. 50.
    Saliba A. E., Li L., Westermann A. J., Appenzeller S., Stapels D. A., Schulte L. N., Helaine S. and Vogel J. (2016) Single-cell RNAseq ties macrophage polarization to growth rate of intracellular Salmonella. Nat. Microbiol., 2, 16206CrossRefGoogle Scholar
  51. 51.
    Gale E. F. (2009) Bacterial Physiology. Vol. 2, 1st edition. ElsevierGoogle Scholar
  52. 52.
    Kjeldgaard N. O., Maaloe O. and Schaechter M. (1958) The transition between different physiological states during balanced growth of Salmonella typhimurium. J. Gen. Microbiol., 19, 607–616CrossRefGoogle Scholar
  53. 53.
    Schaechter M., Maaloe O. and Kjeldgaard N. O. (1958) Dependency on medium and temperature of cell size and chemical composition during balanced grown of Salmonella typhimurium. J. Gen. Microbiol., 19, 592–606CrossRefGoogle Scholar
  54. 54.
    Brehm-Stecher B. F. and Johnson E. A. (2004) Single-cell microbiology: tools, technologies, and applications. Microbiol. Mol. Biol. Rev., 68, 538–559CrossRefGoogle Scholar
  55. 55.
    Young J. W., Locke J. C., Altinok A., Rosenfeld N., Bacarian T., Swain P. S., Mjolsness E. and Elowitz M. B. (2011) Measuring single-cell gene expression dynamics in bacteria using fluorescence time-lapse microscopy. Nat. Protoc., 7, 80–88CrossRefGoogle Scholar
  56. 56.
    Mather W., Mondragón-Palomino O., Danino T., Hasty J. and Tsimring L. S. (2010) Streaming instability in growing cell populations. Phys. Rev. Lett., 104, 208101CrossRefGoogle Scholar
  57. 57.
    Ullman G., Wallden M., Marklund E. G., Mahmutovic A., Razinkov I. and Elf J. (2013) High-throughput gene expression analysis at the level of single proteins using a microfluidic turbidostat and automated cell tracking. Philos. Trans. R Soc. B Biol. Sci., 368Google Scholar
  58. 58.
    Campos M., Surovtsev I. V., Kato S., Paintdakhi A., Beltran B., Ebmeier S. E. and Jacobs-Wagner C. (2014) A constant size extension drives bacterial cell size homeostasis. Cell, 159, 1433–1446CrossRefGoogle Scholar
  59. 59.
    Wehrens M., Ershov D., Rozendaal R., Walker N., Schultz D., Kishony R., Levin P. A. and Tans S. J. (2018) Size laws and division ring dynamics in filamentous Escherichia coli cells. Curr. Biol., 28, 972–979.e5CrossRefGoogle Scholar
  60. 60.
    Hashimoto M., Nozoe T., Nakaoka H., Okura R., Akiyoshi S., Kaneko K., Kussell E. and Wakamoto Y. (2016) Noise-driven growth rate gain in clonal cellular populations. Proc. Natl. Acad. Sci. USA, 113, 3251–3256CrossRefGoogle Scholar
  61. 61.
    Taheri-Araghi S., Bradde S., Sauls J. T., Hill N. S., Levin P. A., Paulsson J., Vergassola M. and Jun S. (2015) Cell-size control and homeostasis in bacteria. Curr. Biol., 25, 385–391CrossRefGoogle Scholar
  62. 62.
    Sauls J. T., Li D. and Jun S. (2016) Adder and a coarse-grained approach to cell size homeostasis in bacteria. Curr. Opin. Cell Biol., 38, 38–44CrossRefGoogle Scholar
  63. 63.
    Murata A., Isoda K., Ikeuchi T., Matsui T., Shiraishi F. and Oba M. (2016) Classification method of severe accident condition for the development of severe accident instrumentation and monitoring system in nuclear power plant. J. Nucl. Sci. Technol., 53, 870–877CrossRefGoogle Scholar
  64. 64.
    Van Houten B. and Kad N. M. (2018) Single-cell mutagenic responses and cell death revealed in real time. Proc. Natl. Acad. Sci. USA, 115, 7168–7170CrossRefGoogle Scholar
  65. 65.
    Osella M., Nugent E. and Cosentino Lagomarsino M. (2014) Concerted control of Escherichia coli cell division. Proc. Natl. Acad. Sci. USA, 111, 3431–3435CrossRefGoogle Scholar
  66. 66.
    Yang D., Jennings A. D., Borrego E., Retterer S. T. and Männik J. (2018) Analysis of factors limiting bacterial growth in PDMS mother machine devices. Front. Microbiol., 9, 871CrossRefGoogle Scholar
  67. 67.
    Taheri-Araghi S. and Jun S. (2015) Single-cell cultivation in microfluidic devices. Can. Vet. J., 11, 5–16Google Scholar
  68. 68.
    Martins B. M. C. and Locke J. C. W. (2015) Microbial individuality: how single-cell heterogeneity enables population level strategies. Curr. Opin. Microbiol., 24, 104–112CrossRefGoogle Scholar
  69. 69.
    Fu X., Kato S., Long J., Mattingly H. H., He C., Vural D. C., Zucker S. W. and Emonet T. (2018) Spatial self-organization resolves conflicts between individuality and collective migration. Nat. Commun., 9, 2177CrossRefGoogle Scholar
  70. 70.
    Lopatkin A. J., Huang S., Smith R. P., Srimani J. K., Sysoeva T. A., Bewick S., Karig D. K. and You L. (2016) Antibiotics as a selective driver for conjugation dynamics. Nat. Microbiol., 1, 16044CrossRefGoogle Scholar
  71. 71.
    Yoney A. and Salman H. (2015) Precision and variability in bacterial temperature sensing. Biophys. J., 108, 2427–2436CrossRefGoogle Scholar
  72. 72.
    Murugesan N., Panda T. and Das S. K. (2016) Effect of gold nanoparticles on thermal gradient generation and thermotaxis of E. coli cells in microfluidic device. Biomed. Microdevices, 18, 53CrossRefGoogle Scholar
  73. 73.
    Murugesan N., Dhar P., Panda T. and Das S. K. (2017) Interplay of chemical and thermal gradient on bacterial migration in a diffusive microfluidic device. Biomicrofluidics, 11, 024108CrossRefGoogle Scholar
  74. 74.
    Berne C., Ellison C. K., Ducret A. and Brun Y. V. (2018) Bacterial adhesion at the single-cell level. Nat. Rev. Microbiol., 16, 616–627CrossRefGoogle Scholar
  75. 75.
    Kim H. J., Boedicker J. Q., Choi J. W. and Ismagilov R. F. (2008) Defined spatial structure stabilizes a synthetic multispecies bacterial community. Proc. Natl. Acad. Sci. USA, 105, 18188–18193CrossRefGoogle Scholar
  76. 76.
    Kohanski M. A., Dwyer D. J. and Collins J. J. (2010) How antibiotics kill bacteria: from targets to networks. Nat. Rev. Microbiol., 8, 423–435CrossRefGoogle Scholar
  77. 77.
    Meredith H. R., Srimani J. K., Lee A. J., Lopatkin A. J. and You L. (2015) Collective antibiotic tolerance: mechanisms, dynamics and intervention. Nat. Chem. Biol., 11, 182–188CrossRefGoogle Scholar
  78. 78.
    Srimani J. K., Huang S., Lopatkin A. J. and You L. (2017) Drug detoxification dynamics explain the postantibiotic effect. Mol. Syst. Biol., 13, 948CrossRefGoogle Scholar
  79. 79.
    Zwietering M. H., Jongenburger I., Rombouts F. M. and van 't Riet K. (1990) Modeling of the bacterial growth curve. Appl. Environ. Microbiol., 56, 1875–1881Google Scholar
  80. 80.
    Kargi F. (2009) Re-interpretation of the logistic equation for batch microbial growth in relation to Monod kinetics. Lett. Appl. Microbiol., 48, 398–401CrossRefGoogle Scholar
  81. 81.
    Scott M., Gunderson C. W., Mateescu E. M., Zhang Z. and Hwa T. (2010) Interdependence of cell growth and gene expression: origins and consequences. Science, 330, 1099–1102CrossRefGoogle Scholar
  82. 82.
    Fulwyler M. J. (1965) Electronic separation of biological cells by volume. Science, 150, 910–911CrossRefGoogle Scholar
  83. 83.
    Moffitt J. R., Lee J. B. and Cluzel P. (2012) The single-cell chemostat: an agarose-based, microfluidic device for highthroughput, single-cell studies of bacteria and bacterial communities. Lab Chip, 12, 1487–1494CrossRefGoogle Scholar
  84. 84.
    Balleza E., Kim J. M. and Cluzel P. (2018) Systematic characterization of maturation time of fluorescent proteins in living cells. Nat. Methods, 15, 47–51CrossRefGoogle Scholar
  85. 85.
    Knott G. J. and Doudna J. A. (2018) CRISPR-Cas guides the future of genetic engineering. Science, 361, 866–869CrossRefGoogle Scholar
  86. 86.
    Schermelleh L., Ferrand A., Huser T., Eggeling C., Sauer M., Biehlmaier O. and Drummen G. P. C. (2019) Super-resolution microscopy demystified. Nat. Cell Biol., 21, 72–84CrossRefGoogle Scholar
  87. 87.
    Gurjav U., Jelfs P., Hill-Cawthorne G. A., Marais B. J. and Sintchenko V. (2016) Genotype heterogeneity of Mycobacterium tuberculosis within geospatial hotspots suggests foci of imported infection in Sydney, Australia. Infect. Genet. Evol., 40, 346–351CrossRefGoogle Scholar
  88. 88.
    Ackermann M. (2015) A functional perspective on phenotypic heterogeneity in microorganisms. Nat. Rev. Microbiol., 13, 497–508CrossRefGoogle Scholar

Copyright information

© Higher Education Press and Springer-Verlag GmbH Germany, part of Springer Nature 2019

Authors and Affiliations

  • Zhixin Ma
    • 1
    • 2
  • Pan M. Chu
    • 1
    • 2
  • Yingtong Su
    • 1
    • 2
  • Yue Yu
    • 1
  • Hui Wen
    • 1
  • Xiongfei Fu
    • 1
  • Shuqiang Huang
    • 1
    Email author
  1. 1.Shenzhen Institute of Synthetic Biology, Shenzhen Institutes of Advance TechnologyChinese Academy of SciencesShenzhenChina
  2. 2.University of Chinese Academy of SciencesBeijingChina

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