Applications of single-cell technology on bacterial analysis

Zhixin Ma, Pan M. Chu, Yingtong Su, Yue Yu, Hui Wen, Xiongfei Fu, Shuqiang Huang

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Quant. Biol. ›› 2019, Vol. 7 ›› Issue (3) : 171-181. DOI: 10.1007/s40484-019-0177-6
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Applications of single-cell technology on bacterial analysis

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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.

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Keywords

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

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Zhixin Ma, Pan M. Chu, Yingtong Su, Yue Yu, Hui Wen, Xiongfei Fu, Shuqiang Huang. Applications of single-cell technology on bacterial analysis. Quant. Biol., 2019, 7(3): 171‒181 https://doi.org/10.1007/s40484-019-0177-6

References

[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, 701–712
CrossRef Pubmed Google scholar
[2]
Crick F. (1970) Central dogma of molecular biology. Nature, 227, 561–563
CrossRef Pubmed Google scholar
[3]
Monod J. (1949) The growth of bacterial cultures. Annu. Rev. Microbiol., 3, 371–394
CrossRef Google scholar
[4]
Elowitz M. B., Levine A. J., Siggia E. D. and Swain P. S. (2002) Stochastic gene expression in a single cell. Science, 297, 1183–1186
CrossRef Pubmed Google scholar
[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, 69–73
CrossRef Pubmed Google scholar
[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, 1962–1965
CrossRef Pubmed Google scholar
[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, 1099–1103
CrossRef Pubmed Google scholar
[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, 1283–1286
CrossRef Pubmed Google scholar
[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, 1420–1424
CrossRef Pubmed Google scholar
[10]
Jones D. L., Brewster R. C. and Phillips R. (2014) Promoter architecture dictates cell-to-cell variability in gene expression. Science, 346, 1533–1536
CrossRef Pubmed Google scholar
[11]
Golding I., Paulsson J., Zawilski S. M. and Cox E. C. (2005) Real-time kinetics of gene activity in individual bacteria. Cell, 123, 1025–1036
CrossRef Pubmed Google scholar
[12]
Huh D. and Paulsson J. (2011) Non-genetic heterogeneity from stochastic partitioning at cell division. Nat. Genet., 43, 95–100
CrossRef Pubmed Google scholar
[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, 986–989
CrossRef Google scholar
[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, 1519–1523
CrossRef Pubmed Google scholar
[15]
Elowitz M. B. and Leibler S. (2000) A synthetic oscillatory network of transcriptional regulators. Nature, 403, 335–338
CrossRef Pubmed Google scholar
[16]
Wallden M., Fange D., Lundius E. G., Baltekin Ö. and Elf J. (2016) The synchronization of replication and division cycles in individual E. coli cells. Cell, 166, 729–739
CrossRef Pubmed Google scholar
[17]
Amir A. and Balaban N. Q. (2018) Learning from noise: how observing stochasticity may aid microbiology. Trends Microbiol., 26, 376–385
CrossRef Pubmed Google scholar
[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, 345–361
CrossRef Google scholar
[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, 339–348
CrossRef Pubmed Google scholar
[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, 974–984
CrossRef Pubmed Google scholar
[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, 1360–1363
CrossRef Pubmed Google scholar
[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, 2293–2298
Pubmed
[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, 159–168
CrossRef Pubmed Google scholar
[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, 89–96
CrossRef Pubmed Google scholar
[25]
Chong S., Chen C., Ge H. and Xie X. S. (2014) Mechanism of transcriptional bursting in bacteria. Cell, 158, 314–326
CrossRef Pubmed Google scholar
[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, 136–143
CrossRef Pubmed Google scholar
[27]
Paige J. S., Wu K. Y. and Jaffrey S. R. (2011) RNA mimics of green fluorescent protein. Science, 333, 642–646
CrossRef Pubmed Google scholar
[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, 1219–1224
CrossRef Pubmed Google scholar
[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, high-affinity complex for RNA labeling and tracking. ACS Chem. Biol., 9, 2412–2420
CrossRef Pubmed Google scholar
[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, 16299–16308
CrossRef Pubmed Google scholar
[31]
Arora A., Sunbul M. and Jäschke A. (2015) Dual-colour imaging of RNAs using quencher- and fluorophore-binding aptamers. Nucleic Acids Res., 43, e144
CrossRef Pubmed Google scholar
[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, 223–239
Pubmed
[33]
Stearns T. (1995) Green fluorescent protein. The green revolution. Curr. Biol., 5, 262–264
CrossRef Pubmed Google scholar
[34]
Norman T. M., Lord N. D., Paulsson J. and Losick R. (2013) Memory and modularity in cell-fate decision making. Nature, 503, 481–486
CrossRef Pubmed Google scholar
[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, e238
CrossRef Pubmed Google scholar
[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, 737–740
CrossRef Pubmed Google scholar
[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, 1–18
CrossRef Pubmed Google scholar
[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, 533–538
CrossRef Pubmed Google scholar
[39]
Tan C., Marguet P. and You L. (2009) Emergent bistability by a growth-modulating positive feedback circuit. Nat. Chem. Biol., 5, 842–848
CrossRef Pubmed Google scholar
[40]
Cluzel P., Surette M. and Leibler S. (2000) An ultrasensitive bacterial motor revealed by monitoring signaling proteins in single cells. Science, 287, 1652–1655
CrossRef Pubmed Google scholar
[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, 1094–1097
CrossRef Pubmed Google scholar
[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, 528–531
CrossRef Pubmed Google scholar
[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, 9160–9164
CrossRef Pubmed Google scholar
[44]
Gawad C., Koh W. and Quake S. R. (2016) Single-cell genome sequencing: current state of the science. Nat. Rev. Genet., 17, 175–188
CrossRef Pubmed Google scholar
[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, 3205–3214
CrossRef Pubmed Google scholar
[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., (2013) Insights into the phylogeny and coding potential of microbial dark matter. Nature, 499, 431–437
CrossRef Pubmed Google scholar
[47]
Zhang Y., Gao J., Huang Y. and Wang J. (2018) Recent developments in single-Cell RNA-seq of microorganisms. Biophys. J., 115, 173–180
CrossRef Pubmed Google scholar
[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, 925–935
CrossRef Pubmed Google scholar
[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 RNA-sequencing. Genome Biol., 18, 200
CrossRef Pubmed Google scholar
[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 RNA-seq ties macrophage polarization to growth rate of intracellular Salmonella. Nat. Microbiol., 2, 16206
CrossRef Pubmed Google scholar
[51]
Gale E. F. (2009) Bacterial Physiology. Vol. 2, 1st edition. Elsevier
[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, 607–616
CrossRef Pubmed Google scholar
[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, 592–606
CrossRef Pubmed Google scholar
[54]
Brehm-Stecher B. F. and Johnson E. A. (2004) Single-cell microbiology: tools, technologies, and applications. Microbiol. Mol. Biol. Rev., 68, 538–559
CrossRef Pubmed Google scholar
[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, 80–88
CrossRef Pubmed Google scholar
[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, 208101
CrossRef Pubmed Google scholar
[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., 368
CrossRef Google scholar
[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, 1433–1446
CrossRef Pubmed Google scholar
[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.e5
CrossRef Pubmed Google scholar
[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, 3251–3256
CrossRef Pubmed Google scholar
[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, 385–391
CrossRef Pubmed Google scholar
[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, 38–44
CrossRef Pubmed Google scholar
[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, 870–877
CrossRef Google scholar
[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, 7168–7170
CrossRef Pubmed Google scholar
[65]
Osella M., Nugent E. and Cosentino Lagomarsino M. (2014) Concerted control of Escherichia coli cell division. Proc. Natl. Acad. Sci. USA, 111, 3431–3435
CrossRef Pubmed Google scholar
[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, 871
CrossRef Pubmed Google scholar
[67]
Taheri-Araghi S. and Jun S. (2015) Single-cell cultivation in microfluidic devices. Can. Vet. J., 11, 5–16
[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, 104–112
CrossRef Pubmed Google scholar
[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, 2177
CrossRef Pubmed Google scholar
[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, 16044
CrossRef Pubmed Google scholar
[71]
Yoney A. and Salman H. (2015) Precision and variability in bacterial temperature sensing. Biophys. J., 108, 2427–2436
CrossRef Pubmed Google scholar
[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, 53
CrossRef Pubmed Google scholar
[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, 024108
CrossRef Pubmed Google scholar
[74]
Berne C., Ellison C. K., Ducret A. and Brun Y. V. (2018) Bacterial adhesion at the single-cell level. Nat. Rev. Microbiol., 16, 616–627
CrossRef Pubmed Google scholar
[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, 18188–18193
CrossRef Pubmed Google scholar
[76]
Kohanski M. A., Dwyer D. J. and Collins J. J. (2010) How antibiotics kill bacteria: from targets to networks. Nat. Rev. Microbiol., 8, 423–435
CrossRef Pubmed Google scholar
[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, 182–188
CrossRef Pubmed Google scholar
[78]
Srimani J. K., Huang S., Lopatkin A. J. and You L. (2017) Drug detoxification dynamics explain the postantibiotic effect. Mol. Syst. Biol., 13, 948
CrossRef Pubmed Google scholar
[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, 1875–1881
Pubmed
[80]
Kargi F. (2009) Re-interpretation of the logistic equation for batch microbial growth in relation to Monod kinetics. Lett. Appl. Microbiol., 48, 398–401
CrossRef Pubmed Google scholar
[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, 1099–1102
CrossRef Pubmed Google scholar
[82]
Fulwyler M. J. (1965) Electronic separation of biological cells by volume. Science, 150, 910–911
CrossRef Pubmed Google scholar
[83]
Moffitt J. R., Lee J. B. and Cluzel P. (2012) The single-cell chemostat: an agarose-based, microfluidic device for high-throughput, single-cell studies of bacteria and bacterial communities. Lab Chip, 12, 1487–1494
CrossRef Pubmed Google scholar
[84]
Balleza E., Kim J. M. and Cluzel P. (2018) Systematic characterization of maturation time of fluorescent proteins in living cells. Nat. Methods, 15, 47–51
CrossRef Pubmed Google scholar
[85]
Knott G. J. and Doudna J. A. (2018) CRISPR-Cas guides the future of genetic engineering. Science, 361, 866–869
CrossRef Pubmed Google scholar
[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, 72–84
CrossRef Pubmed Google scholar
[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, 346–351
CrossRef Pubmed Google scholar
[88]
Ackermann M. (2015) A functional perspective on phenotypic heterogeneity in microorganisms. Nat. Rev. Microbiol., 13, 497–508
CrossRef Pubmed Google scholar

ACKNOWLEDGEMENTS

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 JCYJ20180302145817753); 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.

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