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Single-Cell Analysis of Mycobacteria Using Microfluidics and Time-Lapse Microscopy

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Book cover Mycobacteria Protocols

Part of the book series: Methods in Molecular Biology ((MIMB,volume 2314))

Abstract

Studies on cell-to-cell phenotypic variation in microbial populations, with individuals sharing the same genetic background, provide insights not only on bacterial behavior but also on the adaptive spectrum of the population. Phenotypic variation is an innate property of microbial populations, and this can be further amplified under stressful conditions, providing a fitness advantage. Furthermore, phenotypic variation may also precede a latter step of genetic-based diversification, resulting in the transmission of the most beneficial phenotype to the progeny. While population-wide studies provide a measure of the collective average behavior, single-cell studies, which have expanded over the last decade, delve into the behavior of smaller subpopulations that would otherwise remain concealed. In this chapter, we describe approaches to carry out spatiotemporal analysis of individual mycobacterial cells using time-lapse microscopy. Our method encompasses the fabrication of a microfluidic device; the assembly of a microfluidic system suitable for long-term imaging of mycobacteria; and the quantitative analysis of single-cell behavior under varying growth conditions. Phenotypic variation is conceivably associated to the resilience and endurance of mycobacterial cells. Therefore, shedding light on the dynamics of this phenomenon, on the transience or stability of the given phenotype, on its molecular bases and its functional consequences, offers new scope for intervention.

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References

  1. Avery SV (2006) Microbial cell individuality and the underlying sources of heterogeneity. Nat Rev Microbiol 4:577–587

    Article  CAS  PubMed  Google Scholar 

  2. Locke JCW, Elowitz MB (2009) Using movies to analyse gene circuit dynamics in single cells. Nat Rev Micro 7:383–392

    Article  CAS  Google Scholar 

  3. Locke JC, Young JW, Fontes M et al (2011) Stochastic pulse regulation in bacterial stress response. Science 334:366–369

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  4. Norman TM, Lord ND, Paulsson J et al (2013) Memory and modularity in cell-fate decision making. Nature 503:481–486

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  5. Smits WK, Kuipers OP, Veening J-V (2006) Phenotypic variation in bacteria: the role of feedback regulation. Nat Rev Microbiol 4:259–271

    Article  CAS  PubMed  Google Scholar 

  6. Eldar A, Elowitz MB (2010) Functional roles for noise in genetic circuits. Nature 467:167–173

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  7. Garcia-Bernardo J, Dunlop MJ (2015) Noise and low-level dynamics can coordinate multicomponent bet hedging mechanisms. Biophys J 108:184–193

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  8. Dhar N, McKinney JD, Manina G (2016) Phenotypic heterogeneity in Mycobacterium tuberculosis. Microbiol Spectrum 4(6):TBTB2-0021-2016

    Article  Google Scholar 

  9. Desai SK, Kenney LJ (2019) Switching lifestyles is an in vivo adaptive strategy of bacterial pathogens. Front Cell Infect Microbiol 9:421. https://doi.org/10.3389/fcimb.2019.00421

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  10. Schröter L, Dersch P (2019) Phenotypic diversification of microbial pathogens–cooperating and preparing for the future. J Mol Biol 431:4645–4655

    Article  PubMed  CAS  Google Scholar 

  11. Defraine V, Fauvart M, Michiels J (2018) Fighting bacterial persistence: current and emerging anti-persister strategies and therapeutics. Drug Resist Update 38:12–26

    Article  Google Scholar 

  12. Meylan S, Andrews IW, Collins JJ (2018) Targeting antibiotic tolerance pathogen by pathogen. Cell 172:1228–1238

    Article  CAS  PubMed  Google Scholar 

  13. Richardson K, Bennion OT, Tan S et al (2016) Temporal and intrinsic factors of rifampicin tolerance in mycobacteria. Proc Natl Acad Sci U S A 113:8302–8307

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  14. Brehm-Stecher BF, Johnson EA (2004) Single-cell microbiology: tools, technologies, and applications. Microbiol Mol Biol Rev 68:538–559

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  15. Sliusarenko O, Heinritz J, Emonet T et al (2011) High-throughput, subpixel precision analysis of bacterial morphogenesis and intracellular spatio-temporal dynamics. Mol Microbiol 80:612–627

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  16. Young JW, Locke JC, Altinok A et al (2012) Measuring single-cell gene expression dynamics in bacteria using fluorescence time-lapse microscopy. Nat Protoc 7:80–88

    Article  CAS  Google Scholar 

  17. Konry T, Sarkar S, Sabhachandani P et al (2016) Innovative tools and technology for analysis of single cells and cell-cell interaction. Annu Rev Biomed Eng 18:259–284

    Article  CAS  PubMed  Google Scholar 

  18. Binder D, Drepper T, Jaeger K-E et al (2017) Homogenizing bacterial cell factories: analysis and engineering of phenotypic heterogeneity. Metab Eng 42:145–156

    Article  CAS  PubMed  Google Scholar 

  19. Potvin-Trottier L, Luro S, Paulsson J (2018) Microfluidics and single-cell microscopy to study stochastic processes in bacteria. Curr Opin Microbiol 43:186–192

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  20. Joyce G, Robertson BD, Williams KJ (2011) A modified agar pad method for mycobacterial live-cell imaging. BMC Res Notes 4:73

    Article  PubMed  PubMed Central  Google Scholar 

  21. Golchin SA, Stratford J, Curry RJ et al (2012) A microfluidic system for long-term time-lapse microscopy studies of mycobacteria. Tuberculosis (Edinb) 92:489–496

    Article  Google Scholar 

  22. Wakamoto Y, Dhar N, Chait R et al (2013) Dynamic persistence of antibiotic-stressed mycobacteria. Science 339:91–95

    Article  CAS  PubMed  Google Scholar 

  23. Martínez-Hoyos M, Perez-Herran E, Gulten G et al (2016) Antitubercular drugs for an old target: GSK693 as a promising InhA direct inhibitor. EBioMedicine 8:291–301

    Article  PubMed  PubMed Central  Google Scholar 

  24. Sakatos A, Babunovic GH, Chase MR et al (2018) Posttranslational modification of a histone-like protein regulates phenotypic resistance to isoniazid in mycobacteria. Sci Adv 4:eaao1478

    Article  PubMed  PubMed Central  CAS  Google Scholar 

  25. Manina G, Griego A, Singh LK et al (2019) Preexisting variation in DNA damage response predicts the fate of single mycobacteria under stress. EMBO J 38:e101876

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  26. Manina G, Dhar N, McKinney JD (2015) Stress and host immunity amplify Mycobacterium tuberculosis phenotypic heterogeneity and induce nongrowing metabolically active forms. Cell Host Microbe 17:32–46

    Article  CAS  PubMed  Google Scholar 

  27. Barisch C, López-Jiménez AT, Soldati T (2015) Live imaging of Mycobacterium marinum infection in Dictyostelium discoideum. Methods Mol Biol 1285:369–385

    Article  CAS  PubMed  Google Scholar 

  28. Delincé MJ, Bureau JB, López-Jiménez AT et al (2016) A microfluidic cell-trapping device for single-cell tracking of host-microbe interactions. Lab Chip 16:3276–3285

    Article  PubMed  CAS  Google Scholar 

  29. Lerner TR, Borel S, Greenwood DJ et al (2017) Mycobacterium tuberculosis replicates within necrotic human macrophages. J Cell Biol 216:583–594

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  30. Santi I, McKinney JD (2015) Chromosome organization and replisome dynamics in Mycobacterium smegmatis. MBio 6:e01999–e01914

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  31. Trojanowski D, Hołówka J, Ginda K et al (2017) Multifork chromosome replication in slow-growing bacteria. Sci Rep 7:43836

    Article  PubMed  PubMed Central  Google Scholar 

  32. Logsdon MM, Ho PY, Papavinasasundaram K et al (2017) A parallel adder coordinates mycobacterial cell-cycle progression and cell-size homeostasis in the context of asymmetric growth and organization. Curr Biol 27:3367–3374

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  33. Mann KM, Huang DL, Hooppaw AJ et al (2017) Rv0004 is a new essential member of the mycobacterial DNA replication machinery. PLoS Genet 13:e1007115

    Article  PubMed  PubMed Central  CAS  Google Scholar 

  34. Peña-Zalbidea S, Huang AY, Kavunja HW et al (2018) Chemoenzymatic radiosynthesis of 2-deoxy-2-[18F]fluoro-d-trehalose ([18F]-2-FDTre): a PET radioprobe for in vivo tracing of trehalose metabolism. Carbohydr Res 472:16–22

    Article  PubMed  PubMed Central  CAS  Google Scholar 

  35. Cheng Y, Xie J, Lee KH et al (2018) Rapid and specific labeling of single live Mycobacterium tuberculosis with a dual-targeting fluorogenic probe. Sci Transl Med 10:eaar4470

    Article  PubMed  PubMed Central  CAS  Google Scholar 

  36. Hodges HL, Brown RA, Crooks JA et al (2018) Imaging mycobacterial growth and division with a fluorogenic probe. Proc Natl Acad Sci U S A 115:5271–5276

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  37. Eskandarian HA, Odermatt PD, Ven JXY et al (2017) Division site selection linked to inherited cell surface wave troughs in mycobacteria. Nat Microbiol 2:17094

    Article  CAS  PubMed  Google Scholar 

  38. Hannebelle MTM, Ven JXY, Toniolo C et al (2020) A biphasic growth model for cell pole elongation in mycobacteria. Nat Commun 11:452

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  39. Ueno H, Kato Y, Tabata KV et al (2019) Revealing the metabolic activity of persisters in mycobacteria by single-cell D2O Raman imaging spectroscopy. Analyt Chem 91:15171–15178

    Article  CAS  Google Scholar 

  40. Whitesides G, Ostuni E, Takayama S et al (2001) Soft lithography in biology and biochemistry. Annu Rev Biomed Eng 3:335–373

    Article  CAS  PubMed  Google Scholar 

  41. Weibel DB, Diluzio WR, Whitesides GM (2007) Microfabrication meets microbiology. Nat Rev Micro 5:209–218

    Article  CAS  Google Scholar 

  42. Friend J, Yeo L (2010) Fabrication of microfluidic devices using polydimethylsiloxane. Biomicrofluidics 4:026502

    Article  PubMed  PubMed Central  CAS  Google Scholar 

  43. Dhar N, Manina G (2015) Single-cell analysis of mycobacteria using microfluidics and time-lapse microscopy. In: Parish T, Roberts DM (eds) Mycobacteria Protocols, 3rd edn. Humana Press Springer, New York

    Google Scholar 

  44. Skylaki S, Hilsenbeck O, Schroeder T (2016) Challenges in long-term imaging and quantification of single-cell dynamics. Nat Biotechnol 34:1137–1144

    Article  CAS  PubMed  Google Scholar 

  45. Wang Q, Niemi J, Tan C-M et al (2010) Image segmentation and dynamic lineage analysis in single-cell fluorescence microscopy. Cytom Part A 77:101–110

    Google Scholar 

  46. Carpenter AE, Jones TR, Lamprecht MR et al (2006) CellProfiler: image analysis software for identifying and quantifying cell phenotypes. Genome Biol 7:R100

    Article  PubMed  PubMed Central  CAS  Google Scholar 

  47. Schindelin J, Arganda-Carreras I, Frise E et al (2012) Fiji: an open-source platform for biological-image analysis. Nat Methods 9:676–682

    Article  CAS  PubMed  Google Scholar 

  48. de Chaumont F, Dallongeville S, Chenouard N et al (2012) Icy: an open bioimage informatics platform for extended reproducible research. Nat Methods 9:690–696

    Article  PubMed  CAS  Google Scholar 

  49. Ducret A, Quardokus E, Brun YV (2016) MicrobeJ, a tool for high throughput bacterial cell detection and quantitative analysis. Nat Microbiol 1:16077

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  50. Ouyang W, Mueller F, Hjelmare M et al (2019) ImJoy: an open-source computational platform for the deep learning era. Nat Methods 16:1199–1200

    Article  CAS  PubMed  Google Scholar 

  51. van Raaphorst R, Kjos M, Veening JW (2020) BactMAP: an R package for integrating, analyzing and visualizing bacterial microscopy data. Mol Microbiol 113:297–308

    Article  PubMed  CAS  Google Scholar 

  52. Patino S, Alamo L, Cimino M et al (2008) Autofluorescence of mycobacteria as a tool for detection of Mycobacterium tuberculosis. J Clin Microbiol 46:3296–3302

    Article  CAS  PubMed  PubMed Central  Google Scholar 

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Acknowledgments

This work was supported by the Institut Pasteur and by ANR grants (ANR-10-LABX-62-IBEID and ANR-17-CE11-0007-01) to GM. ND acknowledges support from the Swiss South African Joint Research Program of the Swiss National Science Foundation (Project IZLSZ3_170912). GM & ND were supported by the Innovative Medicines Initiative 2 Joint Undertaking (JU) under grant agreement No 853989. The JU receives support from the European Union’s Horizon 2020 research and innovation programme and EFPIA and Global Alliance for TB Drug Development non profit organization, Bill & Melinda Gates Foundation, University of Dundee.

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Correspondence to Giulia Manina .

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Manina, G., Dhar, N. (2021). Single-Cell Analysis of Mycobacteria Using Microfluidics and Time-Lapse Microscopy. In: Parish, T., Kumar, A. (eds) Mycobacteria Protocols. Methods in Molecular Biology, vol 2314. Humana, New York, NY. https://doi.org/10.1007/978-1-0716-1460-0_8

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  • DOI: https://doi.org/10.1007/978-1-0716-1460-0_8

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  • Publisher Name: Humana, New York, NY

  • Print ISBN: 978-1-0716-1459-4

  • Online ISBN: 978-1-0716-1460-0

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