1887

Abstract

Rapid changes in the number and flow cytometric behaviour of cells of E. coli taken from a stationary phase and inoculated into rich medium.

Cells of E. coli were grown in LB medium, taken from a stationary phase of 2–4 h, and re-inoculated into fresh media at a concentration (10 ml or lower) characteristic of bacteriuria. Flow cytometry was used to assess how quickly we could detect changes in cell size, number, membrane energization (using a carbocyanine dye) and DNA distribution. It transpired that while the lag phase observable macroscopically via bulk OD measurements could be as long as 4 h, the true lag phase could be less than 15–20 min, and was accompanied by many observable biochemical changes. Antibiotics to which the cells were sensitive affected these changes within 20 min of re-inoculation, providing the possibility of a very rapid antibiotic susceptibility test on a timescale compatible with a visit to a GP clinic. The strategy was applied successfully to genuine potential urinary tract infection (UTI) samples taken from a doctor’s surgery. The methods developed could prove of considerable value in ensuring the correct prescription and thereby lowering the spread of antimicrobial resistance.

  • This is an open-access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
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2019-02-11
2024-12-13
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References

  1. Baker S, Thomson N, Weill FX, Holt KE. Genomic insights into the emergence and spread of antimicrobial-resistant bacterial pathogens. Science 2018; 360:733–738 [View Article][PubMed]
    [Google Scholar]
  2. Andersson DI, Hughes D. Antibiotic resistance and its cost: is it possible to reverse resistance?. Nat Rev Microbiol 2010; 8:260–271 [View Article][PubMed]
    [Google Scholar]
  3. Gelband H, Laxminarayan R. Tackling antimicrobial resistance at global and local scales. Trends Microbiol 2015; 23:524–526 [View Article][PubMed]
    [Google Scholar]
  4. Laxminarayan R, Sridhar D, Blaser M, Wang M, Woolhouse M. Achieving global targets for antimicrobial resistance. Science 2016; 353:874–875 [View Article][PubMed]
    [Google Scholar]
  5. Roca I, Akova M, Baquero F, Carlet J, Cavaleri M et al. The global threat of antimicrobial resistance: science for intervention. New Microbes New Infect 2015; 6:22–29 [View Article]
    [Google Scholar]
  6. Mendelson M, Balasegaram M, Jinks T, Pulcini C, Sharland M. Antibiotic resistance has a language problem. Nature 2017; 545:23–25 [View Article][PubMed]
    [Google Scholar]
  7. MacEdo RS, Onita JH, Wille MP, Furtado GH. Pharmacokinetics and pharmacodynamics of antimicrobial drugs in intensive care unit patients. Shock 2013; 39:24–28 [View Article][PubMed]
    [Google Scholar]
  8. Li B, Qiu Y, Shi H, Yin H. The importance of lag time extension in determining bacterial resistance to antibiotics. Analyst 2016; 141:3059–3067 [View Article][PubMed]
    [Google Scholar]
  9. Schmidt K, Mwaigwisya S, Crossman LC, Doumith M, Munroe D et al. Identification of bacterial pathogens and antimicrobial resistance directly from clinical urines by nanopore-based metagenomic sequencing. J Antimicr Chemother 2016
    [Google Scholar]
  10. Tuite N, Reddington K, Barry T, Zumla A, Enne V. Rapid nucleic acid diagnostics for the detection of antimicrobial resistance in Gram-negative bacteria: is it time for a paradigm shift?. J Antimicrob Chemother 2014; 69:1729–1733 [View Article][PubMed]
    [Google Scholar]
  11. Köser CU, Ellington MJ, Cartwright EJ, Gillespie SH, Brown NM et al. Routine use of microbial whole genome sequencing in diagnostic and public health microbiology. PLoS Pathog Research Support, Non-U.S. Gov't 2012; 8:e1002824 [View Article][PubMed]
    [Google Scholar]
  12. Kwong JC, McCallum N, Sintchenko V, Howden BP. Whole genome sequencing in clinical and public health microbiology. Pathology 2015; 47:199–210 [View Article][PubMed]
    [Google Scholar]
  13. Roach DJ, Burton JN, Lee C, Stackhouse B, Butler-Wu SM et al. A Year of infection in the intensive care unit: prospective whole genome sequencing of bacterial clinical isolates reveals cryptic transmissions and novel microbiota. PLoS Genet 2015; 11:e1005413 [View Article][PubMed]
    [Google Scholar]
  14. Tsai EA, Shakbatyan R, Evans J, Rossetti P, Graham C et al. Bioinformatics Workflow for Clinical Whole Genome Sequencing at Partners HealthCare Personalized Medicine. J Pers Med 2016; 6:12 [View Article][PubMed]
    [Google Scholar]
  15. Buchan BW, Ledeboer NA. Emerging technologies for the clinical microbiology laboratory. Clin Microbiol Rev 2014; 27:783–822 [View Article][PubMed]
    [Google Scholar]
  16. Didelot X, Bowden R, Wilson DJ, Peto TEA, Crook DW. Transforming clinical microbiology with bacterial genome sequencing. Nat Rev Genet 2012; 13:601–612 [View Article][PubMed]
    [Google Scholar]
  17. Kirkup BC, Mahlen S, Kallstrom G. Future-generation sequencing and clinical microbiology. Clin Lab Med 2013; 33:685–704 [View Article][PubMed]
    [Google Scholar]
  18. Dunne WM, Jaillard M, Rochas O, van Belkum A. Microbial genomics and antimicrobial susceptibility testing. Expert Rev Mol Diagn 2017; 17:257–269 [View Article][PubMed]
    [Google Scholar]
  19. van Belkum A, Dunne WM. Next-generation antimicrobial susceptibility testing. J Clin Microbiol 2013; 51:2018–2024 [View Article][PubMed]
    [Google Scholar]
  20. Farha MA, Brown ED. Chemical probes of Escherichia coli uncovered through chemical-chemical interaction profiling with compounds of known biological activity. Chem Biol 2010; 17:852–862 [View Article][PubMed]
    [Google Scholar]
  21. Murray C, Adeyiga O, Owsley K, di Carlo D. Research highlights: microfluidic analysis of antimicrobial susceptibility. Lab Chip 2015; 15:1226–1229 [View Article][PubMed]
    [Google Scholar]
  22. Schmiemann G, Kniehl E, Gebhardt K, Matejczyk MM, Hummers-Pradier E. The diagnosis of urinary tract infection: a systematic review. Dtsch Ärztebl Int 2010; 107:361–367
    [Google Scholar]
  23. Kelley SO. New technologies for rapid bacterial identification and antibiotic resistance profiling. SLAS Technol 2017; 22:113–121 [View Article][PubMed]
    [Google Scholar]
  24. Kohanski MA, Dwyer DJ, Hayete B, Lawrence CA, Collins JJ. A common mechanism of cellular death induced by bactericidal antibiotics. Cell 2007; 130:797–810 [View Article][PubMed]
    [Google Scholar]
  25. Coates AR, Halls G, Hu Y. Novel classes of antibiotics or more of the same?. Br J Pharmacol 2011; 163:184–194 [View Article]
    [Google Scholar]
  26. Cek M, Tandoğdu Z, Wagenlehner F, Tenke P, Naber K et al. Healthcare-associated urinary tract infections in hospitalized urological patients-a global perspective: results from the GPIU studies 2003-2010. World J Urol 2014; 32:1587–1594 [View Article][PubMed]
    [Google Scholar]
  27. Foxman B. The epidemiology of urinary tract infection. Nat Rev Urol 2010; 7:653–660 [View Article][PubMed]
    [Google Scholar]
  28. Wilson ML, Gaido L. Laboratory diagnosis of urinary tract infections in adult patients. Clin Infect Dis 2004; 38:1150–1158 [View Article][PubMed]
    [Google Scholar]
  29. Mehnert-Kay SA. Diagnosis and management of uncomplicated urinary tract infections. Am Fam Physician 2005; 72:451–456[PubMed]
    [Google Scholar]
  30. Ejrnæs K. Bacterial characteristics of importance for recurrent urinary tract infections caused by Escherichia coli. Dan Med Bull 2011; 58:B4187[PubMed]
    [Google Scholar]
  31. Kline KA, Lewis AL. Gram-positive uropathogens, polymicrobial urinary tract infection, and the emerging microbiota of the urinary tract. Microbiol Spectr 2016; 4: [View Article][PubMed]
    [Google Scholar]
  32. Tandogdu Z, Wagenlehner FM. Global epidemiology of urinary tract infections. Curr Opin Infect Dis 2016; 29:73–79 [View Article][PubMed]
    [Google Scholar]
  33. Bryce A, Hay AD, Lane IF, Thornton HV, Wootton M et al. Global prevalence of antibiotic resistance in paediatric urinary tract infections caused by Escherichia coli and association with routine use of antibiotics in primary care: systematic review and meta-analysis. BMJ 2016; 352:i939 [View Article][PubMed]
    [Google Scholar]
  34. Kerremans JJ, Verboom P, Stijnen T, Hakkaart-van Roijen L, Goessens W et al. Rapid identification and antimicrobial susceptibility testing reduce antibiotic use and accelerate pathogen-directed antibiotic use. J Antimicrob Chemother 2008; 61:428–435 [View Article][PubMed]
    [Google Scholar]
  35. Kirchhoff J, Glaser U, Bohnert JA, Pletz MW, Popp J et al. Simple ciprofloxacin resistance test and determination of minimal inhibitory concentration within 2 h using raman spectroscopy. Anal Chem 2018; 90:1811–1818 [View Article][PubMed]
    [Google Scholar]
  36. Köves B, Cai T, Veeratterapillay R, Pickard R, Seisen T et al. Benefits and harms of treatment of asymptomatic bacteriuria: a systematic review and meta-analysis by the european association of urology urological infection guidelines panel. Eur Urol 2017; 72:865–868 [View Article][PubMed]
    [Google Scholar]
  37. Kell D, Potgieter M, Pretorius E. Individuality, phenotypic differentiation, dormancy and 'persistence' in culturable bacterial systems: commonalities shared by environmental, laboratory, and clinical microbiology. F1000Res 2015; 4:179 [View Article][PubMed]
    [Google Scholar]
  38. Steen HB, Boye E. Bacterial growth studied by flow cytometry. Cytometry 1980; 1:32–36 [View Article][PubMed]
    [Google Scholar]
  39. Skarstad K, Steen HB, Boye E. Escherichia coli DNA distributions measured by flow cytometry and compared with theoretical computer simulations. J Bacteriol 1985; 163:661–668[PubMed]
    [Google Scholar]
  40. Skarstad K, Boye E, Steen HB. Timing of initiation of chromosome replication in individual Escherichia coli cells. EMBO J 1986; 5:1711–1717 [View Article][PubMed]
    [Google Scholar]
  41. Boye E, Løbner-Olesen A. Bacterial growth control studied by flow cytometry. Res Microbiol 1991; 142:131–135 [View Article][PubMed]
    [Google Scholar]
  42. Stokke C, Flåtten I, Skarstad K. An easy-to-use simulation program demonstrates variations in bacterial cell cycle parameters depending on medium and temperature. PLoS One 2012; 7:e30981 [View Article]
    [Google Scholar]
  43. Akerlund T, Nordström K, Bernander R. Analysis of cell size and DNA content in exponentially growing and stationary-phase batch cultures of Escherichia coli. J Bacteriol 1995; 177:6791–6797 [View Article][PubMed]
    [Google Scholar]
  44. Kell DB, Ryder HM, Kaprelyants AS, Westerhoff HV. Quantifying heterogeneity: flow cytometry of bacterial cultures. Antonie van Leeuwenhoek 1991; 60:145–158 [View Article]
    [Google Scholar]
  45. Taheri-Araghi S, Brown SD, Sauls JT, McIntosh DB, Jun S. Single-cell physiology. Annu Rev Biophys 2015; 44:123–142 [View Article][PubMed]
    [Google Scholar]
  46. Boye E, Steen HB, Skarstad K. Flow cytometry of bacteria: a promising tool in experimental and clinical microbiology. J Gen Microbiol 1983; 129:973–980 [View Article][PubMed]
    [Google Scholar]
  47. Steen HB. Flow cytometric studies of microorganisms. In Melamed MR, Lindmo T, Mendelsohn ML. (editors) Flow Cytometry and Sorting, 2nd ed. New York: Wiley-Liss Inc; 1990 pp. 605–622
    [Google Scholar]
  48. Müller S, Lösche A, Bley T. Staining procedures for flow cytometric monitoring of bacterial populations. Acta Biotechnologica 1993; 13:289–297 [View Article]
    [Google Scholar]
  49. Caron GN-VON, Badley RA. Viability assessment of bacteria in mixed populations using flow cytometry*. J Microsc 1995; 179:55–66 [View Article]
    [Google Scholar]
  50. Davey HM, Kell DB. Flow cytometry and cell sorting of heterogeneous microbial populations: the importance of single-cell analyses. Microbiol Rev 1996; 60:641–696[PubMed]
    [Google Scholar]
  51. Shapiro HM. Multiparameter flow cytometry of bacteria: implications for diagnostics and therapeutics. Cytometry 2001; 43:223–226 [View Article][PubMed]
    [Google Scholar]
  52. Shapiro HM. Practical Flow Cytometry, 4th ed. New York: John Wiley; 2003
    [Google Scholar]
  53. Shapiro HM. Flow cytometry of bacterial membrane potential and permeability. Methods Mol Med 2008; 142:175–186 [View Article][PubMed]
    [Google Scholar]
  54. Hewitt CJ, Nebe-von-Caron G. The application of multi-parameter flow cytometry to monitor individual microbial cell physiological state. Adv Biochem Eng Biotechnol 2004; 89:197–223
    [Google Scholar]
  55. Davey HM. Life, death, and in-between: meanings and methods in microbiology. Appl Environ Microbiol 2011; 77:5571–5576 [View Article][PubMed]
    [Google Scholar]
  56. Koken T, Aktepe OC, Serteser M, Samli M, Kahraman A et al. Determination of cut-off values for leucocytes and bacteria for urine flow cytometer (UF-100) in urinary tract infections. Int Urol Nephrol 2002; 34:175–178 [View Article][PubMed]
    [Google Scholar]
  57. Chien TI, Kao JT, Liu HL, Lin PC, Hong JS et al. Urine sediment examination: a comparison of automated urinalysis systems and manual microscopy. Clin Chim Acta 2007; 384:28–34 [View Article][PubMed]
    [Google Scholar]
  58. Shayanfar N, Tobler U, von Eckardstein A, Bestmann L. Automated urinalysis: first experiences and a comparison between the Iris iQ200 urine microscopy system, the Sysmex UF-100 flow cytometer and manual microscopic particle counting. Clinical Chemical Laboratory Medicine 2007; 45:1251–1256 [View Article]
    [Google Scholar]
  59. Wang J, Zhang Y, Xu D, Shao W, Lu Y. Evaluation of the Sysmex UF-1000i for the diagnosis of urinary tract infection. Am J Clin Pathol 2010; 133:577–582 [View Article][PubMed]
    [Google Scholar]
  60. Shang YJ, Wang QQ, Zhang JR, Xu YL, Zhang WW et al. Systematic review and meta-analysis of flow cytometry in urinary tract infection screening. Clin Chim Acta 2013; 424:90–95 [View Article][PubMed]
    [Google Scholar]
  61. Choi J, Yoo J, Lee M, Kim EG, Lee JS et al. A rapid antimicrobial susceptibility test based on single-cell morphological analysis. Sci Transl Med 2014; 6:267ra174 [View Article][PubMed]
    [Google Scholar]
  62. Baltekin Ö, Boucharin A, Tano E, Andersson DI, Elf J. Antibiotic susceptibility testing in less than 30 min using direct single-cell imaging. Proc Natl Acad Sci USA 2017; 114:9170–9175 [View Article][PubMed]
    [Google Scholar]
  63. Gant VA, Warnes G, Phillips I, Savidge GF. The application of flow cytometry to the study of bacterial responses to antibiotics. J Med Microbiol 1993; 39:147–154 [View Article][PubMed]
    [Google Scholar]
  64. Mason DJ, Allman R, Stark JM, Lloyd D. Rapid estimation of bacterial antibiotic susceptibility with flow cytometry. J Microsc 1994; 176:8–16 [View Article][PubMed]
    [Google Scholar]
  65. Mason DJ, Power EG, Talsania H, Phillips I, Gant VA. Antibacterial action of ciprofloxacin. Antimicrob Agents Chemother 1995; 39:2752–2758 [View Article][PubMed]
    [Google Scholar]
  66. Iyer R, Ferrari A, Rijnbrand R, Erwin AL. A fluorescent microplate assay quantifies bacterial efflux and demonstrates two distinct compound binding sites in AcrB. Antimicrob Agents Chemother 2015; 59:2388–2397 [View Article][PubMed]
    [Google Scholar]
  67. Kessel D, Beck WT, Kukuruga D, Schulz V. Characterization of multidrug resistance by fluorescent dyes. Cancer Res 1991; 51:4665–4670[PubMed]
    [Google Scholar]
  68. Du D, Wang-Kan X, Neuberger A, van Veen HW, Pos KM et al. Multidrug efflux pumps: structure, function and regulation. Nat Rev Microbiol 2018
    [Google Scholar]
  69. Senyürek I, Paulmann M, Sinnberg T, Kalbacher H, Deeg M et al. Dermcidin-derived peptides show a different mode of action than the cathelicidin LL-37 against Staphylococcus aureus. Antimicrob Agents Chemother 2009; 53:2499–2509 [View Article][PubMed]
    [Google Scholar]
  70. Boi P, Manti A, Pianetti A, Sabatini L, Sisti D et al. Evaluation of Escherichia coli viability by flow cytometry: a method for determining bacterial responses to antibiotic exposure. Cytometry B Clin Cytom 2015; 88:149–153 [View Article][PubMed]
    [Google Scholar]
  71. Alvarez-Barrientos A, Arroyo J, Cantón R, Nombela C, Sánchez-Pérez M. Applications of flow cytometry to clinical microbiology. Clin Microbiol Rev 2000; 13:167–195 [View Article][PubMed]
    [Google Scholar]
  72. Walberg M, Gaustad P, Steen HB. Rapid assessment of ceftazidime, ciprofloxacin, and gentamicin susceptibility in exponentially-growing E. coli cells by means of flow cytometry. Cytometry 1997; 27:169–178 [View Article][PubMed]
    [Google Scholar]
  73. Pieretti B, Brunati P, Pini B, Colzani C, Congedo P et al. Diagnosis of bacteriuria and leukocyturia by automated flow cytometry compared with urine culture. J Clin Microbiol 2010; 48:3990–3996 [View Article][PubMed]
    [Google Scholar]
  74. Kell DB, Kaprelyants AS, Weichart DH, Harwood CR, Barer MR. Viability and activity in readily culturable bacteria: a review and discussion of the practical issues. Antonie van Leeuwenhoek 1998; 73:169–187 [View Article][PubMed]
    [Google Scholar]
  75. Kaprelyants AS, Kell DB. Rapid assessment of bacterial viability and vitality by rhodamine 123 and flow cytometry. J Appl Bacteriol 1992; 72:410–422 [View Article]
    [Google Scholar]
  76. Kaprelyants AS, Kell DB. Dormancy in stationary-phase cultures of Micrococcus luteus: flow cytometric analysis of starvation and resuscitation. Appl Env Microbiol 1993; 59:3187–3196
    [Google Scholar]
  77. Waggoner A. Optical probes of membrane potential. J Membr Biol 1976; 27:317–334 [View Article][PubMed]
    [Google Scholar]
  78. Waggoner AS. Dye indicators of membrane potential. Annu Rev Biophys Bioeng 1979; 8:47–68 [View Article][PubMed]
    [Google Scholar]
  79. Navarro Llorens JM, Tormo A, Martínez-García E. Stationary phase in gram-negative bacteria. FEMS Microbiol Rev 2010; 34:476–495 [View Article][PubMed]
    [Google Scholar]
  80. Rolfe MD, Rice CJ, Lucchini S, Pin C, Thompson A et al. Lag phase is a distinct growth phase that prepares bacteria for exponential growth and involves transient metal accumulation. J Bacteriol 2012; 194:686–701 [View Article][PubMed]
    [Google Scholar]
  81. Swinnen IA, Bernaerts K, Dens EJ, Geeraerd AH, van Impe JF. Predictive modelling of the microbial lag phase: a review. Int J Food Microbiol 2004; 94:137–159 [View Article][PubMed]
    [Google Scholar]
  82. Finkel SE. Long-term survival during stationary phase: evolution and the GASP phenotype. Nat Rev Microbiol 2006; 4:113–120 [View Article][PubMed]
    [Google Scholar]
  83. Roostalu J, Jõers A, Luidalepp H, Kaldalu N, Tenson T. Cell division in Escherichia coli cultures monitored at single cell resolution. BMC Microbiol 2008; 8:68 [View Article][PubMed]
    [Google Scholar]
  84. Pin C, Rolfe MD, Muñoz-Cuevas M, Hinton JC, Peck MW et al. Network analysis of the transcriptional pattern of young and old cells of Escherichia coli during lag phase. BMC Syst Biol 2009; 3:108 [View Article][PubMed]
    [Google Scholar]
  85. Himeoka Y, Kaneko K. Theory for transitions between exponential and stationary phases: universal laws for lag time. Phys Rev X 2017; 7: [View Article]
    [Google Scholar]
  86. Jõers A, Tenson T. Growth resumption from stationary phase reveals memory in Escherichia coli cultures. Sci Rep 2016; 6:24055 [View Article]
    [Google Scholar]
  87. Bertrand RL. Lag phase-associated iron accumulation is likely a microbial counter-strategy to host iron sequestration: role of the ferric uptake regulator (fur). J Theor Biol 2014; 359:72–79 [View Article][PubMed]
    [Google Scholar]
  88. Pirt SJ. Principles of Microbe and Cell Cultivation London: Wiley; 1975 p. 14
    [Google Scholar]
  89. Baranyi J, Pin C. Estimating bacterial growth parameters by means of detection times. Appl Environ Microbiol 1999; 65:732–736[PubMed]
    [Google Scholar]
  90. Baty F, Flandrois JP, Delignette-Muller ML. Modeling the lag time of Listeria monocytogenes from viable count enumeration and optical density data. Appl Environ Microbiol 2002; 68:5816–5825 [View Article][PubMed]
    [Google Scholar]
  91. Baty F, Delignette-Muller ML. Estimating the bacterial lag time: which model, which precision?. Int J Food Microbiol 2004; 91:261–277 [View Article][PubMed]
    [Google Scholar]
  92. Prats C, López D, Giró A, Ferrer J, Valls J. Individual-based modelling of bacterial cultures to study the microscopic causes of the lag phase. J Theor Biol 2006; 241:939–953 [View Article]
    [Google Scholar]
  93. Prats C, Giró A, Ferrer J, López D, Vives-Rego J. Analysis and IbM simulation of the stages in bacterial lag phase: basis for an updated definition. J Theor Biol 2008; 252:56–68 [View Article][PubMed]
    [Google Scholar]
  94. Madar D, Dekel E, Bren A, Zimmer A, Porat Z et al. Promoter activity dynamics in the lag phase of Escherichia coli. BMC Syst Biol 2013; 7:136 [View Article][PubMed]
    [Google Scholar]
  95. Dalgaard P, Ross T, Kamperman L, Neumeyer K, McMeekin TA. Estimation of bacterial growth rates from turbidimetric and viable count data. Int J Food Microbiol 1994; 23:391–404 [View Article][PubMed]
    [Google Scholar]
  96. Novotna J, Vohradsky J, Berndt P, Gramajo H, Langen H et al. Proteomic studies of diauxic lag in the differentiating prokaryote Streptomyces coelicolor reveal a regulatory network of stress-induced proteins and central metabolic enzymes. Mol Microbiol 2003; 48:1289–1303 [View Article][PubMed]
    [Google Scholar]
  97. Link H, Fuhrer T, Gerosa L, Zamboni N, Sauer U. Real-time metabolome profiling of the metabolic switch between starvation and growth. Nat Methods 2015; 12:1091–1097 [View Article]
    [Google Scholar]
  98. Baranyi J, Roberts TA. A dynamic approach to predicting bacterial growth in food. Int J Food Microbiol 1994; 23:277–294 [View Article]
    [Google Scholar]
  99. Hong W, Karanja CW, Abutaleb NS, Younis W, Zhang X et al. Antibiotic susceptibility determination within one cell cycle at single-bacterium level by stimulated raman metabolic imaging. Anal Chem 2018; 90:3737–3743 [View Article][PubMed]
    [Google Scholar]
  100. Yu H, Jing W, Iriya R, Yang Y, Syal K et al. Phenotypic antimicrobial susceptibility testing with deep learning video microscopy. Anal Chem 2018; 90:6314–6322 [View Article][PubMed]
    [Google Scholar]
  101. Schoepp NG, Schlappi TS, Curtis MS, Butkovich SS, Miller S et al. Rapid pathogen-specific phenotypic antibiotic susceptibility testing using digital LAMP quantification in clinical samples. Sci Transl Med 2017; 9:eaal3693 [View Article][PubMed]
    [Google Scholar]
  102. Mody L, Juthani-Mehta M. Urinary tract infections in older women: a clinical review. JAMA 2014; 311:844–854 [View Article][PubMed]
    [Google Scholar]
  103. Detweiler K, Mayers D, Fletcher SG. Bacteruria and urinary tract infections in the elderly. Urol Clin North Am 2015; 42:561–568 [View Article][PubMed]
    [Google Scholar]
  104. Alrabiah H, Xu Y, Rattray NJ, Vaughan AA, Gibreel T et al. Multiple metabolomics of uropathogenic E. coli reveal different information content in terms of metabolic potential compared to virulence factors. Analyst 2014; 139:4193–4199 [View Article][PubMed]
    [Google Scholar]
  105. Dawson SE, Gibreel T, Nicolaou N, Alrabiah H, Xu Y et al. Implementation of Fourier transform infrared spectroscopy for the rapid typing of uropathogenic Escherichia coli. Eur J Clin Microbiol Infect Dis 2014; 33:983–988 [View Article][PubMed]
    [Google Scholar]
  106. Tartof KD, Hobbs CA. Improved media for growing plasmid and cosmid clones. Bethesda Res Labs Focus 1987; 9:12
    [Google Scholar]
  107. Edwards BS, Young SM, Ivnitsky-Steele I, Ye RD, Prossnitz ER et al. High-content screening: flow cytometry analysis. Methods Mol Biol 2009; 486:151–165 [View Article][PubMed]
    [Google Scholar]
  108. Sklar LA, Carter MB, Edwards BS. Flow cytometry for drug discovery, receptor pharmacology and high-throughput screening. Curr Opin Pharmacol 2007; 7:527–534 [View Article][PubMed]
    [Google Scholar]
  109. Tegos GP, Evangelisti AM, Strouse JJ, Ursu O, Bologa C et al. A high throughput flow cytometric assay platform targeting transporter inhibition. Drug Discov Today Technol 2014; 12:e95e103 [View Article][PubMed]
    [Google Scholar]
  110. Skeggs LT. An automatic method for colorimetric analysis. Am J Clin Pathol 1957; 28:311–322 [View Article][PubMed]
    [Google Scholar]
  111. Seamer LC, Bagwell CB, Barden L, Redelman D, Salzman GC et al. Proposed new data file standard for flow cytometry, version FCS 3.0. Cytometry 1997; 28:118–122 [View Article]
    [Google Scholar]
  112. Chandra A, Singh N. Bacterial growth sensing in microgels using pH-dependent fluorescence emission. Chem Commun 2018; 54:1643–1646 [View Article][PubMed]
    [Google Scholar]
  113. Pin C, Baranyi J. Kinetics of single cells: observation and modeling of a stochastic process. Appl Environ Microbiol 2006; 72:2163–2169 [View Article][PubMed]
    [Google Scholar]
  114. Pin C, Baranyi J. Single-cell and population lag times as a function of cell age. Appl Environ Microbiol 2008; 74:2534–2536 [View Article][PubMed]
    [Google Scholar]
  115. Zhang JH, Chung TD, Oldenburg KR. A simple statistical parameter for use in evaluation and validation of high throughput screening assays. J Biomol Screen 1999; 4:67–73 [View Article][PubMed]
    [Google Scholar]
  116. Brochado AR, Telzerow A, Bobonis J, Banzhaf M, Mateus A et al. Species-specific activity of antibacterial drug combinations. Nature 2018; 559:259–263 [View Article][PubMed]
    [Google Scholar]
  117. Zampieri M, Szappanos B, Buchieri MV, Trauner A, Piazza I et al. High-throughput metabolomic analysis predicts mode of action of uncharacterized antimicrobial compounds. Sci Transl Med 2018; 10:eaal3973 [View Article][PubMed]
    [Google Scholar]
  118. Jernaes MW, Steen HB. Staining of Escherichia coli for flow cytometry: influx and efflux of ethidium bromide. Cytometry 1994; 17:302–309 [View Article][PubMed]
    [Google Scholar]
  119. Hammes F, Egli T. Cytometric methods for measuring bacteria in water: advantages, pitfalls and applications. Anal Bioanal Chem 2010; 397:1083–1095 [View Article][PubMed]
    [Google Scholar]
  120. Müller S, Nebe-von-Caron G. Functional single-cell analyses: flow cytometry and cell sorting of microbial populations and communities. FEMS Microbiol Rev 2010; 34:554–587 [View Article][PubMed]
    [Google Scholar]
  121. Cooper S, Helmstetter CE. Chromosome replication and the division cycle of Escherichia coli B/r. J Mol Biol 1968; 31:519–540 [View Article][PubMed]
    [Google Scholar]
  122. Sauls JT, Li D, Jun S. Adder and a coarse-grained approach to cell size homeostasis in bacteria. Curr Opin Cell Biol 2016; 38:38–44 [View Article][PubMed]
    [Google Scholar]
  123. Si F, Li D, Cox SE, Sauls JT, Azizi O et al. Invariance of initiation mass and predictability of cell size in Escherichia coli. Curr Biol 2017; 27:1278–1287 [View Article][PubMed]
    [Google Scholar]
  124. Willis L, Huang KC. Sizing up the bacterial cell cycle. Nat Rev Microbiol 2017; 15:606–620 [View Article][PubMed]
    [Google Scholar]
  125. Zheng H, Ho PY, Jiang M, Tang B, Liu W et al. Interrogating the Escherichia coli cell cycle by cell dimension perturbations. Proc Natl Acad Sci USA 2016; 113:15000–15005 [View Article][PubMed]
    [Google Scholar]
  126. Stylianidou S, Brennan C, Nissen SB, Kuwada NJ, Wiggins PA. SuperSegger: robust image segmentation, analysis and lineage tracking of bacterial cells. Mol Microbiol 2016; 102:690–700 [View Article][PubMed]
    [Google Scholar]
  127. Aguirre JS, González A, Ozçelik N, Rodríguez MR, García de Fernando GD. Modeling the Listeria innocua micropopulation lag phase and its variability. Int J Food Microbiol 2013; 164:60–69 [View Article][PubMed]
    [Google Scholar]
  128. Aguirre JS, Koutsoumanis KP. Towards lag phase of microbial populations at growth-limiting conditions: The role of the variability in the growth limits of individual cells. Int J Food Microbiol 2016; 224:1–6 [View Article][PubMed]
    [Google Scholar]
  129. Johnson LV, Walsh ML, Bockus BJ, Chen LB. Monitoring of relative mitochondrial membrane potential in living cells by fluorescence microscopy. J Cell Biol 1981; 88:526–535 [View Article][PubMed]
    [Google Scholar]
  130. Bashford CL. The measurement of membrane potential using optical indicators. Biosci Rep 1981; 1:183–196 [View Article][PubMed]
    [Google Scholar]
  131. Ghazi A, Schechter E, Letellier L, Labedan B. Probes of membrane potential in Escherichia coli cells. FEBS Lett 1981; 125:197–200 [View Article][PubMed]
    [Google Scholar]
  132. Shapiro HM. Membrane potential estimation by flow cytometry. Methods 2000; 21:271–279 [View Article][PubMed]
    [Google Scholar]
  133. Felle H, Stetson DL, Long WS, Slayman CL. Direct measurement of membrane potential and resistance in giant cells of Escherichia colil. Front Biol Energet 1978; 2:1399–1407
    [Google Scholar]
  134. Rottenberg H. The measurement of membrane potential and deltapH in cells, organelles, and vesicles. Methods Enzymol 1979; 55:547–569[PubMed]
    [Google Scholar]
  135. Kell DB, Dobson PD, Bilsland E, Oliver SG. The promiscuous binding of pharmaceutical drugs and their transporter-mediated uptake into cells: what we (need to) know and how we can do so. Drug Discov Today 2013; 18:218–239 [View Article][PubMed]
    [Google Scholar]
  136. Kell DB, Oliver SG. How drugs get into cells: tested and testable predictions to help discriminate between transporter-mediated uptake and lipoidal bilayer diffusion. Front Pharmacol 2014; 5:231 [View Article][PubMed]
    [Google Scholar]
  137. Wu JB, Shi C, Chu GC, Xu Q, Zhang Y et al. Near-infrared fluorescence heptamethine carbocyanine dyes mediate imaging and targeted drug delivery for human brain tumor. Biomaterials 2015; 67:1–10 [View Article][PubMed]
    [Google Scholar]
  138. Wallden M, Fange D, Lundius EG, Baltekin Ö, Elf J. The synchronization of replication and division cycles in individual E. coli Cells. Cell 2016; 166:729–739 [View Article][PubMed]
    [Google Scholar]
  139. Schultz D, Kishony R. Optimization and control in bacterial lag phase. BMC Biol 2013; 11:120 [View Article][PubMed]
    [Google Scholar]
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