Antimicrobial Susceptibility Assay and Kit
20210395794 · 2021-12-23
Inventors
Cpc classification
C12Q1/18
CHEMISTRY; METALLURGY
G01N1/30
PHYSICS
C12N1/04
CHEMISTRY; METALLURGY
International classification
C12Q1/18
CHEMISTRY; METALLURGY
C12N1/04
CHEMISTRY; METALLURGY
G01N1/30
PHYSICS
Abstract
The invention relates to a method for rapidly determining the susceptibility of a microorganism to an antimicrobial agent comprising the steps: a) contacting a first sample containing the microorganism with a first growth medium so as to form a first mixture, wherein the first growth medium is selected to enable the microorganism to proliferate and/or encourage the microorganism cell cycle to commence proliferation; b) contacting a second sample containing the microorganism with a second growth medium so as to form a second mixture, wherein the second growth medium is substantially the same as the first growth medium but further comprises a first antimicrobial agent which may inhibit or slow the proliferation of the microorganism; c) incubating the first and second mixtures, for 30 minutes or less, under conditions suitable to enable or encourage proliferation of the microorganism; d) passing the first and second mixture, or portion thereof, through a flow cytometer in order to assess one or more biochemical and/or biophysical parameters of the microorganisms in both mixtures; and e) comparing the parameters of the microorganisms in the first mixture with that of the second mixture, after incubation, in order to detect whether the first antimicrobial agent inhibits or slows the proliferation of the microorganism so as to determine the susceptibility of a microorganism to said agent. The method is particularly suited for identifying the which antimicrobial agents would be suitable for the treatment of microbial infections, such as Urinary Tract Infections (UTIs)
Claims
1. A method for rapidly determining the susceptibility of a microorganism to an antimicrobial agent comprising the steps: a) contacting a first sample containing the microorganism with a first growth medium so as to form a first mixture, wherein the first growth medium is selected to enable the microorganism to proliferate and/or encourage the microorganism cell cycle to commence proliferation; b) contacting a second sample containing the microorganism with a second growth medium so as to form a second mixture, wherein the second growth medium is substantially the same as the first growth medium but further comprises a first antimicrobial agent which may inhibit or slow the proliferation of the microorganism; c) incubating the first and second mixtures, for 30 minutes or less, under conditions suitable to enable or encourage proliferation of the microorganism; d) passing the first and second mixture, or portion thereof, through a flow cytometer in order to assess one or more biochemical and/or biophysical parameters of the microorganisms in both mixtures; and e) comparing the parameters of the microorganisms in the first mixture with that of the second mixture, after incubation, in order to detect whether the first antimicrobial agent inhibits or slows the proliferation of the microorganism so as to determine the susceptibility of a microorganism to said agent.
2. The method as claimed in claim 1, wherein step b) further comprises contacting one or more further samples containing the microorganism with a one or more further growth media so as to form one or more further mixtures, wherein the one or more further growth media is the same as the first growth medium but further comprises one or more further antimicrobial agents which may inhibit or slow the proliferation of the microorganism, wherein said one or more further antimicrobial agents are different from one another and different from the first antimicrobial agent.
3. The method as claimed in any proceeding claim, wherein the one or more biochemical and/or biophysical parameters of the microorganisms is selected from one or more of the following: cell size, cell number, cell membrane energisation and/or nucleic acid content and/or distribution.
4. The method as claimed in any preceding claim, wherein the one or more biochemical and/or biophysical parameters of the microorganisms is determined by assessing the uptake of one of more fluorescent or other stains.
5. The method as claimed in any one preceding claim, wherein the parameter is cell size, cell number and/or cell membrane energisation, and wherein the medium further comprises a carbocyanine dye or prior to step d), carbocyanine dye is added to the mixture or part of the mixture.
6. The method as claimed in claim 5, wherein the carbocyanine dye comprises 3,3′-dipropylthiadicarbocyanine iodide (di-S-C3(5)).
7. The method as claimed in either claim 5 or 6, wherein the carbocyanine dye is present in the mixtures at a concentrate in the range of about 1 μM to about 5 μM.
8. The method as claimed in claim 7, wherein the carbocyanine dye is present in the mixtures at a concentrate in the range of about 3 μM.
9. The method as claimed in any one of claims 5 to 8, wherein the flow cytometer relies upon excitation at 640 nm and the parameters are assessed at 675±15 nm.
10. The method as claimed in any preceding claim, wherein the parameter is nucleic acid and said nucleic acid comprises DNA.
11. The method as claimed in claim 9, wherein prior to step d), mithramycin and/or a nucleic acid stain are added to the mixture or part of the mixture, and optionally, DNA distribution is assessed on the flow cytometer at around 572 nm.
12. The method as claimed in claim 11, wherein the nucleic acid strain comprises SYBR Green or ethidium bromide.
13. The method as claimed in any preceding claim, wherein the growth medium comprises Terrific Broth.
14. The method as claimed in any preceding claim, wherein step c) takes place at a temperature in the range of about 35° C. and 40° C.
15. The method as claimed in 14, wherein step c) takes place at a temperature of about 37° C.
16. The method as claim in any preceding claim, wherein a portion of the first and second mixture, or portion of the one or more further mixtures when dependent upon claim 2, is assessed at multiple time points.
17. The method as claimed in claim 16, wherein the multiple time points comprise one or more of the following time points, 0 minutes, 5 minutes, 10 minutes, 15 minutes, 20 minutes, 25 minutes and/or 30 minutes.
18. The method as claimed in any one of claims 1 to 15, wherein step c) is about 20 minutes or less.
19. The method as claimed in any one of claims 1 to 15, wherein step c) is in the range of about 15 minutes to about 20 minutes.
20. The method as claimed in any preceding claim, wherein step d) is conducted prior to, and after step c).
21. The method as claimed in any preceding claim, wherein the microorganism is obtained from a biological sample derived from an individual believed to be suffering from a microorganism infection.
22. The method of claim 21, wherein the biological sample is urine.
23. The method as claimed in claim 21, wherein the microorganism infection is a Urinary Tract Infection (UTI).
24. The method as claimed in any preceding claim, for determining the antimicrobial agent for use in the treatment of a microorganism infection in an individual, wherein the method comprises taking a biological sample from the individual, assessing the susceptibility of the microorganism, in the biological sample, to two or more antimicrobial agents and identifying which antimicrobial agent to administer to the individual based which antimicrobial agent inhibits or slows the proliferation of the microorganism.
25. A kit for rapidly determining the susceptibility of a microorganism to an antimicrobial agent comprising: a) an enriched growth medium; b) one or more antimicrobial agents; and c) a carbocyanine dye.
26. The kit as claimed in claim 25, wherein the kit further comprises: d) a flow cytometer.
27. The kit as claimed in claim 25 or 26, wherein the enriched growth medium comprises Terrific Broth.
28. The kit as claimed in claim 26, wherein the flow cytometer comprises at least one red laser.
29. The kit as claimed in any of claim 25 or 28, wherein the kit is for use in the method as claimed in any one of claims 1 to 24.
Description
DETAILED DESCRIPTION OF THE INVENTION
[0041] Embodiments of the invention are described below, by way of example only, with reference to the accompanying figures in which:
[0042]
[0043]
[0044]
[0045]
[0046]
[0047]
[0048]
[0049]
[0050] MATERIALS AND METHODS
[0051] Microbial Strains.
[0052] E. coli MG1655 and a series of sensitive and resistant strains were taken from a laboratory collection.
[0053] Culture
[0054] E. coli strains were grown from inocula of appropriate concentrations in conical flasks using Lysogeny Broth to an optical density (600 nm) of 1.5-2, representing stationary phase in this medium. They were held in stationary phase for 2-4 h before being inoculated at concentration of 10.sup.5 cells.Math.mL.sup.−1 (or as noted) into Terrific Broth (Tartof and Hobbs 1987). We did not here study cells held in a long stationary phase (Finkel 2006; Navarro Llorens et al. 2010) (exceeding 3d).
[0055] Assessment of Growth by Bulk OD Measurements
[0056] Bulk OD measurements were performed in 96-well plates and read at 600 nm as per the manufacturer's instructions in an Omega plate reader spectrophotometer (BMG Labtech, UK) instrument. The ‘background’ due to scattering from the plates, etc., was not subtracted.
[0057] Flow Cytometry
[0058] Initial studies used a Sony SH-800 instrument, but all studies reported here used an Intellicyt® iQue screener PLUS. This instrument is based in significant measure on developments by Sklar and colleagues (e.g. (Edwards et al. 2009; Sklar et al. 2007; Tegos et al. 2014)), and uses segmented flow (Skeggs 1957) to sample from 96- or 384-well U- or V-bottom plates prior to their analysis. The iQue Plus contains three excitation sources (405 nm, 488 nm, 640 nm) and 7 fixed filter detectors (with a midpoint/range in nm of 445/45, 530/30, 572/28, 585/40, 615/24, 675/30, 780/60, giving 13 fluorescence channels) whose outputs are stored as both ‘height’ and area, using the FCS3.0 data file standard (Seamer et al. 1997). Forward and side scatter are obtained from the 488 nm excitation source. Detection channels are referred to by the laser used (405 nm violet VL, 488 nm blue BL, and 640 nm red RL) and the detector number in order of possible detectors with a longer wavelength. Thus RL1, as used for detecting di-S-C3(5), implies the red laser and the 675/30 detector. Data are collected from all channels, using a dynamic range of 7 logs. Many parameters may be used to vary the precise performance of the instrument. Those we found material to provide the best reproducibility and to minimise carryover, and their selected values, are as follows: Automatic prime—60 secs (in Qsol buffer); Pre-plate shake—15 s and 1500 rpm; Sip time—2 s (actual sample uptake); Additional sip time—0.5 s (the gap between sips); Pump speed—29 rpm (1.5 μL.Math.s.sup.−1 sample uptake); Plate model—U-bottom well plate (for 96 well plates); Mid plate cleanup—After every well (4 washes; 0.5 s each in Qsol buffer); Inter-well shake—1500 rpm; after 6 wells, 4 sec in Qsol buffer; Flush and Clean—30 sec with Decon and Clean buffers followed by 60 sec with deionised water. The Forecyt™ software supplied with the instrument may be used to gate and display all the analyses post hoc. It, and the FlowJo software, were used in the preparation of the cytograms shown. Where used, di-S-C3(5) was present at a final concentration of 3 μM; its analysis used excitation at 640 nm and detection at 675±15 nm, the fluorescence channel being referred to as RL1. For DNA analyses, cells were fixed by injection into ice-cold ethanol (final concentration 70%), washed twice by centrifugation in 0.1 M-Tris/HCl buffer, pH 7.4, before resuspension in the same buffer containing mithramycin (50 μg mL.sup.−1) and ethidium bromide (25 μg mL.sup.−1), MgCl.sub.2, (25 mM) and NaCl (100 mM) (Boye et al. 1983). Under these circumstances, the excitation energy absorbed by mithramycin (excitation 405 or 488 nm) is transferred to the ethidium bromide, providing a large Stokes shift (emission at 572, 585 or 615 nm; we chose 572 nm as it provided the best signal) and high selectivity for DNA (as mithramycin does not bind to RNA). All the solutions and media used were filtered through 0.2 μm filter.
[0059] UTI Samples
[0060] Following ethics approval from the University of Manchester and the obtaining of signed consent forms, patients attending the Firsway clinic with suspected UTI were offered to opportunity to have their urine samples analysed by our method as well as the reference method used in a centralised pathology laboratory. Samples were taken at various times during the day, kept at 4° C., delivered to the Manchester laboratory by taxi, plated out (LB agar containing as appropriate the stated antibiotics at 3 times normal MIC) to assess microbial numbers and antibiotic sensitivity, the remaining sample kept again at 4° C., and analysed flow cytometrically within 18 h. For flow cytometric assessment, cells were diluted into 37° C. Terrific Broth containing 3 μM diS-C3(5) plus any appropriate antibiotic, and assayed as above. For other experiments (not shown) cells were filtered (0.45 μm) and diluted as appropriate into warmed terrific broth. No significant differences were discernible in the two methods.
[0061] Reagents
[0062] All reagents were of analytical grade where available. Flow cytometric dyes were obtained from AAT Bioquest.
[0063] Results
[0064] Initial Assessment of Regrowth by Bulk Light Scattering Measurements in 96-Well Plates
[0065]
[0066] Flow Cytometric Assessment of Cells and Cell Proliferation
[0067]
Z′=1−3(SD Sample+SD control)/(mean of sample−mean of control) Eq. 1.
[0068] It is normally considered (Zhang et al. 1999) that a Z′ factor exceeding 0.5 provides for a satisfactory assay.
[0069]
[0070]
[0071] Flow Cytometric Assessment of Antibiotic Sensitivity
[0072]
[0073] Since we had seen rapid changes in side scatter within 5 min (
[0074] Of course different antibiotics have different modes of action (Brochado et al. 2018; Zampieri et al. 2018), and the optimal readout needs to reflect this. Thus, nitrofurantoin is widely prescribed for UTIs and its effects on our standard laboratory system are shown in
[0075] Flow Cytometric Assessment of DNA Distributions
[0076] Another important strategy for detecting bacteria uses their DNA (e.g. (Hammes and Egli 2010; Jernaes and Steen 1994; Müller and Nebe-von-Caron 2010)). Thus, another high-level guide to the physiology of E. coli cells and cultures is the flow cytometrically observable distribution of DNA therein, as this can vary widely as a function of growth substrate, temperature, and during the cell cycle (Boye and Løbner-Olesen 1991; Skarstad et al. 1986; Skarstad et al. 1985; Steen and Boye 1980; Stokke et al. 2012). Specifically, the solution to the problem that DNA replication rates are fixed while growth rates can both vary and exceed them is to allow multiple replication forks in a given cell (Cooper and Helmstetter 1968). To this end, we compared the DNA distributions of our cultures under various conditions.
[0077] Flow Cytometric Analysis of UTI Samples
[0078] Finally, we wished to determine whether this method, as developed in laboratory cultures, could be applied to candidate UTI specimens ‘as received’ in a doctor's surgery. To this end, we analysed 23 samples, of which six were in fact positive as judged by a reference method performed in a central microbiology laboratory. Each of these was also found to be positive using our methods, and with the antibiotic sensitivities given in Table 1 below. These were again consistent with the reference method.
TABLE-US-00001 TABLE 1 Antibiotic sensitivity profile for the six positive samples (taken to be ≥10.sup.5 .Math. mL.sup.−1) obtained from the Firsway clinic. Antibiotic sensitivity (R—resistant; S—sensitive) Sample Date Ampicillin Trimethoprim Ciprofloxacin Nitrofurantoin May 25, 2018 R R S S May 25, 2018 S S S S Jun. 6, 2018 R S S S Jul. 16, 2018 R S S S Jul. 18, 2018 R S R R Jul. 20, 2018 S R S S
[0079] Typical cytograms for sensitive and resistant strains are given in
DISCUSSION AND CONCLUSIONS
[0080] It is often considered that the lag′ phase of bacterial growth is one in which very little is happening, and that what is happening is happening quite slowly. This notion probably stems from the fact that changes in OD observable by the naked eye in laboratory cultures (Kaprelyants and Kell 1993) are indeed quite sluggish. However, the very few papers that have studied this in any detail (Baltekin et al. 2017; Madar et al. 2013; Novotna et al. 2003; Pin et al. 2009; Rolfe et al. 2012; Roostalu et al. 2008; Schoepp et al. 2017; Yu et al. 2018) have found that changes in expression profiles (albeit mainly measured at a bulk level) actually occur on a very rapid timescale indeed, possibly in 4 minutes or less following reinoculation into a rich growth medium. For antibiotics to have an observable, and in terms of sensitivity to them a differentially observable, effect on cells, the cells need to be in a replicative state. This might be thought to preclude any such observations in the lag phase, but what is clear from the present observations is that cells can re-initiate or continue their cell cycles very rapidly, such that observable proliferation can occur in as little as 15-20 min after reinoculation of starved, stationary phase cells into rich medium. Consequently it is not necessary to wait for a full period of ‘lag-plus-first-division time’ (Baltekin et al. 2017), which can be well over one hour (Pin and Baranyi 2006, 2008). The rapid proliferation that we describe could be observed by light scattering, by cell counting, by carbocyanine fluorescence (membrane energisation), and by changes in the magnitude and distribution of DNA in the population. This has allowed us to determine, using any or all of these phenotypic assays, antibiotic susceptibility at a phenotypic level in what would appear to be a record time. Pin and Baranyi (Pin and Baranyi 2006, 2008) observed a more stochastic and somewhat slower process than that which we observed here, but in their case they were measuring CFU only, and the inoculation was into the less rich LB, while we used Terrific Broth. Indeed, the exit from lag phase can be very heterogeneous when organisms are measured individually (Aguirre et al. 2013; Aguirre and Koutsoumanis 2016; Baltekin et al. 2017; Stylianidou et al. 2016).
[0081] While we did not study this at the level of the transcriptome here, the dynamics of the physiological changes observed during the early lag and regrowth phases as observed by the uptake of the carbocyanine dye are of interest. Classically, its uptake has been considered to reflect a transmembrane potential difference (negative inside) (e.g. (Bashford 1981; Ghazi et al. 1981; Johnson et al. 1981; Shapiro 2000; Waggoner 1976; Waggoner 1979), but cf. (Felle et al. 1978)) based on bilayer-mediated equilibration according to the Nernst equation (Rottenberg 1979). However, we recognise that such cyanine dyes, much as ethidium bromide (Jernaes and Steen 1994) and other xenobiotics (Kell et al. 2013; Kell and Oliver 2014), are likely to be both influx and efflux substrates for various transporters (Wu et al. 2015), so such an interpretation should be treated with some caution.
[0082] A similar strategy may usefully be applied to other cells (including pathogens in more difficult matrices such as urine), other antibiotics and other stains. However, the present work provides a very useful springboard for these by showing that one may indeed expect to be able to determine antibiotic susceptibility in a phenotypic assay in 20 minutes or less. This could be a very useful attribute in the fight against anti-microbial resistance.
[0083] The forgoing embodiments are not intended to limit the scope of the protection afforded by the claims, but rather to describe examples of how the invention may be put into practice.
REFERENCES
[0084] Aguirre J S, Gonzalez A, Ozcelik N, Rodriguez M R, Garcia de Fernando G D: Modeling the Listeria innocua micropopulation lag phase and its variability. Int J Food Microbiol 2013; 164:60-69. [0085] Aguirre J S, Koutsoumanis K P: 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. [0086] Åkerlund T, Nordstrom 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. [0087] Álvarez-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. [0088] Andersson D I, Hughes D: Antibiotic resistance and its cost: is it possible to reverse resistance? Nat Rev Microbiol 2010; 8:260-271. [0089] Baker Ö, Thomson N, Weill F X, Holt K E: Genomic insights into the emergence and spread of antimicrobial-resistant bacterial pathogens. Science 2018; 360:733-738. [0090] Baltekin Ö, Boucharin A, Tano E, Andersson D I, Elf J: Antibiotic susceptibility testing in less than 30 min using direct single-cell imaging. Proc Natl Acad Sci USA 2017; 114:9170-9175. [0091] Baranyi J, Pin C: Estimating bacterial growth parameters by means of detection times. Appl Environ Microbiol 1999; 65:732-736. [0092] Baranyi J, Roberts T A: A dynamic approach to predicting bacterial growth in food. Int J Food Microbiol 1994; 23:277-294. [0093] Bashford C L: The measurement of membrane potential using optical indicators. Biosci Rep 1981; 1:183-196. [0094] Baty F, Delignette-Muller M L: Estimating the bacterial lag time: which model, which precision? Int J Food Microbiol 2004; 91:261-277. [0095] Baty F, Flandrois J P, Delignette-Muller M L: Modeling the lag time of Listeria monocytogenes from viable count enumeration and optical density data. Appl Environ Microbiol 2002; 68:5816-5825. [0096] Bertrand R L: 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. [0097] Boi P, Manti A, Pianetti A, Sabatini L, Sisti D, Rocchi M B, Bruscolini F, Galluzzi L, Papa S: 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. [0098] Boye E, Løbner-Olesen A: Bacterial Growth Control Studied by Flow Cytometry. Res Microbiol 1991; 142:131-135. [0099] Boye E, Steen H B, Skarstad K: Flow Cytometry of Bacteria: A Promising Tool in Experimental and Clinical Microbiology. J Gen Microbiol 1983; 129:973-980. [0100] Brochado A R, Telzerow A, Bobonis J, Banzhaf M, Mateus A, Selkrig J, Huth E, Bassler S, Zamarreno Beas J, Zietek M, Ng N, Foerster S, Ezraty B, Py B, Barras F, Savitski M M, Bork P, Göttig S, Typas A: Species-specific activity of antibacterial drug combinations. Nature 2018; 559:259-263. [0101] Bryce A, Hay A D, Lane I F, Thornton H V, Wootton M, Costelloe C: 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. [0102] Buchan B W, Ledeboer N A: Emerging technologies for the clinical microbiology laboratory. Clin Microbiol Rev 2014; 27:783-822. [0103] Cek M, Tandogdu Z, Wagenlehner F, Tenke P, Naber K, Bjerklund-Johansen T E: 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. Chandra A, Singh N: Bacterial growth sensing in microgels using pH-dependent fluorescence emission. Chem Commun (Camb) 2018; 54:1643-1646. [0104] Chien T I, Kao J T, Liu H L, Lin P C, Hong J S, Hsieh H P, Chien M J: Urine sediment examination: a comparison of automated urinalysis systems and manual microscopy. Clin Chim Acta 2007; 384:28-34. [0105] Choi J, Yoo J, Lee M, Kim E G, Lee J S, Lee S, Joo S, Song S H, Kim E C, Lee J C, Kim H C, Jung Y G, Kwon S: A rapid antimicrobial susceptibility test based on single-cell morphological analysis. Sci Transl Med 2014; 6:267ra174. [0106] Coates A R, Halls G, Hu Y: Novel classes of antibiotics or more of the same? Br J Pharmacol 2011; 163:184-194. [0107] Cooper S, Helmstetter C E: Chromosome Replication and the Division Cycle of Escherichia coli B/r. J Mol Biol 1968; 31:519-540. [0108] Dalgaard P, Ross T, Kamperman L, Neumeyer K, McMeekin T A: Estimation of bacterial growth rates from turbidimetric and viable count data. Int J Food Microbiol 1994; 23:391-404. [0109] Davey H M: Life, death, and in-between: meanings and methods in microbiology. Appl Environ Microbiol 2011; 77:5571-5576. [0110] Davey H M, Kell D B: Flow cytometry and cell sorting of heterogeneous microbial populations: the importance of single-cell analysis. Microbiol Rev 1996; 60:641-696. [0111] Detweiler K, Mayers D, Fletcher S G: Bacteruria and urinary tract infections in the elderly. Urol Clin North Am 2015; 42:561-568. [0112] Didelot X, Bowden R, Wilson D J, Peto T E A, Crook D W: Transforming clinical microbiology with bacterial genome sequencing. Nat Rev Genet 2012; 13:601-612. [0113] Du D, Wang-Kan X, Neuberger A, van Veen H W, Pos K M, Piddock L J V, Luisi B F: Multidrug efflux pumps: structure, function and regulation. Nat Rev Microbiol 2018. [0114] Dunne W M, Jr., Jaillard M, Rochas O, Van Belkum A: Microbial genomics and antimicrobial susceptibility testing. Expert Rev Mol Diagn 2017; 17:257-269. [0115] Edwards B S, Young S M, Ivnitsky-Steele I, Ye R D, Prossnitz E R, Sklar L A: High-content screening: flow cytometry analysis. Methods Mol Biol 2009; 486:151-165. [0116] Ejrnæs K: Bacterial characteristics of importance for recurrent urinary tract infections caused by Escherichia coli. Dan Med Bull 2011; 58:64187. [0117] Farha M A, Brown E D: Chemical probes of Escherichia coli uncovered through chemical-chemical interaction profiling with compounds of known biological activity. Chem Biol 2010; 17:852-862. [0118] Felle H, Stetson D L, Long W S, Slayman C L: Direct measurement of membrane potential and resistance in giant cells of Escherichia colil. Front Biol Energet 1978; 2:1399-1407. Finkel S E: Long-term survival during stationary phase: evolution and the GASP phenotype. Nat Rev Microbiol 2006; 4:113-120. [0119] Foxman B: The epidemiology of urinary tract infection. Nat Rev Urol 2010; 7:653-660. Gant V A, Warnes G, Phillips I, Savidge G F: The application of flow cytometry to the study of bacterial responses to antibiotics. J Med Microbiol 1993; 39:147-154. [0120] Gelband H, Laxminarayan R: Tackling antimicrobial resistance at global and local scales. Trends Microbiol 2015; 23:524-526. [0121] Ghazi A, Schechter E, Letellier L, Labedan B: Probes of membrane potential in Escherichia coli cells. FEBS Lett 1981; 125:197-200. [0122] Hammes F, Egli T: Cytometric methods for measuring bacteria in water: advantages, pitfalls and applications. Anal Bioanal Chem 2010; 397:1083-1095. [0123] Hewitt C J, 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. [0124] Himeoka Y, Kaneko K: Theory for Transitions Between Exponential and Stationary Phases: Universal Laws for Lag Time. Phys Rev X 2017; 7. [0125] Hong W, Karanja C W, Abutaleb N S, Younis W, Zhang X, Seleem M N, Cheng J X: Antibiotic susceptibility determination within one cell cycle at single-bacterium level by stimulated Raman metabolic imaging. Anal Chem 2018; 90:3737-3743. [0126] Iyer R, Ferrari A, Rijnbrand R, Erwin A L: A fluorescent microplate assay quantifies bacterial efflux and demonstrates two distinct compound binding sites in AcrB. Antimicrob Agents Chemother 2015; 59:2388-2397. [0127] Jernaes M W, Steen H B: Staining of Escherichia coli for flow cytometry: influx and efflux of ethidium bromide. Cytometry 1994; 17:302-309. [0128] Jõers A, Tenson T: Growth resumption from stationary phase reveals memory in Escherichia coli cultures. Sci Rep 2016; 6:24055. [0129] Johnson L V, Walsh M L, Bockus B J, Chen L B: Monitoring of relative mitochondrial membrane potential in living cells by fluorescence microscopy. J Cell Biol 1981; 88:526-535. [0130] Kaprelyants A S, Kell D B: Dormancy in stationary-phase cultures of Micrococcus luteus: flow cytometric analysis of starvation and resuscitation. Appl Env Microbiol 1993; 59:3187-3196. [0131] Kaprelyants A S, Kell D B: Rapid assessment of bacterial viability and vitality using rhodamine 123 and flow cytometry. J Appl Bacteriol 1992; 72:410-422. [0132] Kell D B, Dobson P D, Bilsland E, Oliver S G: 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 Disc Today 2013; 18:218-239. [0133] Kell D B, Kaprelyants A S, Weichart D H, Harwood C L, Barer M R: Viability and activity in readily culturable bacteria: a review and discussion of the practical issues. Antonie van Leeuwenhoek 1998; 73:169-187. [0134] Kell D B, Oliver S G: 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. [0135] Kell D B, 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. [0136] Kell D B, Ryder H M, Kaprelyants A S, Westerhoff H V: Quantifying heterogeneity: Flow cytometry of bacterial cultures. Antonie van Leeuwenhoek 1991; 60:145-158. [0137] Kelley S O: New technologies for rapid bacterial identification and antibiotic resistance profiling. SLAS Technol 2017; 22:113-121. [0138] Kerremans J J, Verboom P, Stijnen T, Hakkaart-van Roijen L, Goessens W, Verbrugh H A, Vos M C: Rapid identification and antimicrobial susceptibility testing reduce antibiotic use and accelerate pathogen-directed antibiotic use. J Antimicrob Chemother 2008; 61:428-435. [0139] Kessel D, Beck W T, Kukuruga D, Schulz V: Characterization of multidrug resistance by fluorescent dyes. Cancer Res 1991; 51:4665-4670. [0140] Kirchhoff J, Glaser U, Bohnert J A, Pletz M W, Popp J, Neugebauer U: Simple Ciprofloxacin Resistance Test and Determination of Minimal Inhibitory Concentration within 2 h Using Raman Spectroscopy. Anal Chem 2018; 90:1811-1818. [0141] Kirkup B C, Mahlen S, Kallstrom G: Future-generation sequencing and clinical microbiology. Clinics in laboratory medicine 2013; 33:685-704. [0142] Kline K A, Lewis A L: Gram-Positive Uropathogens, Polymicrobial Urinary Tract Infection, and the Emerging Microbiota of the Urinary Tract. Microbiology spectrum 2016; 4. [0143] Kohanski M A, Dwyer D J, Hayete B, Lawrence C A, Collins J J: A common mechanism of cellular death induced by bactericidal antibiotics. Cell 2007; 130:797-810. [0144] Koken T, Aktepe O C, Serteser M, Samli M, Kahraman A, Dogan N: 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. [0145] Koser C U, Ellington M J, Cartwright E J, Gillespie S H, Brown N M, Farrington M, Holden M T, Dougan G, Bentley S D, Parkhill J, Peacock S J: Routine use of microbial whole genome sequencing in diagnostic and public health microbiology. PLoS pathogens 2012; 8:e1002824. [0146] Köves B, Cai T, Veeratterapillay R, Pickard R, Seisen T, Lam T B, Yuan C Y, Bruyere F, Wagenlehner F, Bartoletti R, Geerlings S E, Pilatz A, Pradere B, Hofmann F, Bonkat G, Wullt B: 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. [0147] Kwong J C, McCallum N, Sintchenko V, Howden B P: Whole genome sequencing in clinical and public health microbiology. Pathology 2015; 47:199-210. [0148] Laxminarayan R, Sridhar D, Blaser M, Wang M, Woolhouse M: Achieving global targets for antimicrobial resistance. Science 2016; 353:874-875. [0149] 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. [0150] 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. [0151] Macedo R S, Onita J H, Wille M P, Furtado G H: Pharmacokinetics and pharmacodynamics of antimicrobial drugs in intensive care unit patients. Shock 2013; 39 Suppl 1:24-28. [0152] Madar D, Dekel E, Bren A, Zimmer A, Porat Z, Alon U: Promoter activity dynamics in the lag phase of Escherichia coli. BMC Syst Biol 2013; 7:136. [0153] Mason D J, Allman R, Stark J M, Lloyd D: Rapid Estimation of Bacterial Antibiotic Susceptibility With Flow-Cytometry. Journal of Microscopy-Oxford 1994; 176:8-16. [0154] Mason D J, Power E G M, Talsania H, Phillips I, Gant V A: Antibacterial action of ciprofloxacin. Antimicrob Agents Ch 1995; 39:2752-2758. [0155] Mehnert-Kay S A: Diagnosis and management of uncomplicated urinary tract infections. American Family Physician 2005; 72:451-456. [0156] Mendelson M, Balasegaram M, Jinks T, Pulcini C, Sharland M: Antibiotic resistance has a language problem. Nature 2017; 545:23-25. [0157] Mody L, Juthani-Mehta M: Urinary tract infections in older women: a clinical review. JAMA 2014; 311:844-854. [0158] Müller S, Losche A, Bley T: Staining procedures for flow cytometric monitoring of bacterial populations. Acta Biotechnol 1993; 13:289-297. [0159] 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. [0160] Murray C, Adeyiga O, Owsley K, Di Carlo D: Research highlights: microfluidic analysis of antimicrobial susceptibility. Lab Chip 2015; 15:1226-1229. [0161] Navarro Llorens J M, Tormo A, Martinez-Garcia E: Stationary phase in gram-negative bacteria. FEMS Microbiol Rev 2010; 34:476-495. [0162] Nebe-von Caron G, Badley R A: Viability assessment of bacteria in mixed populations using flow cytometry. J Microsc 1995; 179:55-66. [0163] Novotna J, Vohradsky J, Berndt P, Gramajo H, Langen H, Li X M, Minas W, Orsaria L, Roeder D, Thompson C J: 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. [0164] Pieretti B, Brunati P, Pini B, Colzani C, Congedo P, Rocchi M, Terramocci R: Diagnosis of bacteriuria and leukocyturia by automated flow cytometry compared with urine culture. Journal of clinical microbiology 2010; 48:3990-3996. [0165] Pin C, Baranyi J: Kinetics of single cells: observation and modeling of a stochastic process. Appl Environ Microbiol 2006; 72:2163-2169. [0166] Pin C, Baranyi J: Single-cell and population lag times as a function of cell age. Appl Environ Microbiol 2008; 74:2534-2536. [0167] Pin C, Rolfe M D, Muñoz-Cuevas M, Hinton J C D, Peck M W, Walton N J, Baranyi J: Network analysis of the transcriptional pattern of young and old cells of Escherichia coli during lag phase. BMC Syst Biol 2009; 3:108. [0168] Pirt S J: Principles of microbe and cell cultivation. London: Wiley, 1975. [0169] 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. [0170] 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. [0171] Roach D J, Burton J N, Lee C, Stackhouse B, Butler-Wu S M, Cookson B T, Shendure J, Salipante S J: 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. [0172] Roca I, Akova M, Baquero F, Carlet J, Cavaleri M, Coenen S, Cohen J, Findlay D, Gyssens I, Heure O E, Kahlmeter G, Kruse H, Laxminarayan R, Liébana E, López-Cerero L, MacGowan A, Martins M, Rodriguez-Bano J, Rolain J M, Segovia C, Sigauque B, Taconelli E, Wellington E, Vila J: The global threat of antimicrobial resistance: science for intervention. New Microbes New Infect 2015; 6:22-29. [0173] Rolfe M D, Rice C J, Lucchini S, Pin C, Thompson A, Cameron A D S, Alston M, Stringer M F, Betts R P, Baranyi J, Peck M W, Hinton J C D: Lag phase is a distinct growth phase that prepares bacteria for exponential growth and involves transient metal accumulation. J Bacteriol 2012; 194:686-701. [0174] 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. [0175] Rottenberg H: The measurement of membrane potential and deltapH in cells, organelles, and vesicles. Methods Enzymol 1979; 55:547-569. [0176] Sauls J T, Li D, Jun S: Adder and a coarse-grained approach to cell size homeostasis in bacteria. Curr Opin Cell Biol 2016; 38:38-44. [0177] Schmidt K, Mwaigwisya S, Crossman L C, Doumith M, Munroe D, Pires C, Khan A M, Woodford N, Saunders N J, Wain J, O'Grady J, Livermore D M: Identification of bacterial pathogens and antimicrobial resistance directly from clinical urines by nanopore-based metagenomic sequencing. J Antimicr Chemother 2016. [0178] Schmiemann G, Kniehl E, Gebhardt K, Matejczyk M M, Hummers-Pradier E: The diagnosis of urinary tract infection: a systematic review. Dtsch Ärztebl Int 2010; 107:361-367. [0179] Schoepp N G, Schlappi T S, Curtis M S, Butkovich S S, Miller S, Humphries R M, Ismagilov R F: Rapid pathogen-specific phenotypic antibiotic susceptibility testing using digital LAMP quantification in clinical samples. Sci Transl Med 2017; 9. [0180] Schultz D, Kishony R: Optimization and control in bacterial lag phase. BMC Biol 2013; 11:120. [0181] Seamer L C, Bagwell C B, Barden L, Redelman D, Salzman G C, Wood J C S, Murphy R F: Proposed new data file standard for flow cytometry, version FCS 3.0. Cytometry 1997; 28:118-122. [0182] Senyurek I, Paulmann M, Sinnberg T, Kalbacher H, Deeg M, Gutsmann T, Hermes M, Kohler T, Gotz F, Wolz C, Peschel A, Schittek B: Dermcidin-derived peptides show a different mode of action than the cathelicidin LL-37 against Staphylococcus aureus. Antimicrob Agents Chemother 2009; 53:2499-2509. [0183] Shang Y J, Wang Q Q, Zhang J R, Xu Y L, Zhang W W, Chen Y, Gu M L, Hu Z D, Deng A M: Systematic review and meta-analysis of flow cytometry in urinary tract infection screening. Clin Chim Acta 2013; 424:90-95. [0184] Shapiro H M: Flow cytometry of bacterial membrane potential and permeability. Methods Mol Med 2008; 142:175-186. [0185] Shapiro H M: Membrane potential estimation by flow cytometry. Methods 2000; 21:271-279. [0186] Shapiro H M: Multiparameter flow cytometry of bacteria: implications for diagnostics and therapeutics. Cytometry 2001; 43:223-226. [0187] Shapiro H M: Practical Flow Cytometry, 4th edition. New York: John Wiley, 2003. [0188] 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. Clin Chem Lab Med 2007; 45:1251-1256. [0189] Si F, Li D, Cox S E, Sauls J T, Azizi O, Sou C, Schwartz A B, Erickstad M J, Jun Y, Li X, Jun S: Invariance of Initiation Mass and Predictability of Cell Size in Escherichia coli. Curr Biol 2017; 27:1278-1287. [0190] Skarstad K, Boye E, Steen H B: Timing of Initiation of Chromosome Replication in Individual Escherichia coli cells. EMBO Journal 1986; 5:1711-1717. [0191] Skarstad K, Steen H B, Boye E: Escherichia coli DNA Distributions Measured by Flow Cytometry and Compared with Theoretical Computer Simulations. J Bacteriol 1985; 163:661-668. [0192] Skeggs L T, Jr.: An automatic method for colorimetric analysis. Am J Clin Pathol 1957; 28:311-322. [0193] Sklar L A, Carter M B, Edwards B S: Flow cytometry for drug discovery, receptor pharmacology and high-throughput screening. Curr Opin Pharmacol 2007; 7:527-534. Steen H B: Flow cytometric studies of microorganisms. In Melamed M R, Lindmo T, Mendelsohn M L (eds.): Flow Cytometry and Sorting (2nd Edition). New York: Wiley-Liss Inc., 1990:605-622. [0194] Steen H B, Boye E: Bacterial growth studied by flow cytometry. Cytometry 1980; 1:32-36. 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. [0195] Stylianidou S, Brennan C, Nissen S B, Kuwada N J, Wiggins P A: SuperSegger: robust image segmentation, analysis and lineage tracking of bacterial cells. Mol Microbiol 2016; 102:690-700. [0196] Swinnen I A M, Bernaerts K, Dens E J J, Geeraerd A H, Van Impe J F: Predictive modelling of the microbial lag phase: a review. Int J Food Microbiol 2004; 94:137-159. [0197] Taheri-Araghi S, Brown S D, Sauls J T, McIntosh D B, Jun S: Single-Cell Physiology. Annu Rev Biophys 2015; 44:123-142. [0198] Tandogdu Z, Wagenlehner F M: Global epidemiology of urinary tract infections. Curr Opin Infect Dis 2016; 29:73-79. [0199] Tartof K D, Hobbs C A: Improved Media for Growing Plasmid and Cosmid Clones. Bethseda Research Laboratories Focus 1987; 9:12. [0200] Tegos G P, Evangelisti A M, Strouse J J, Ursu O, Bologa C, Sklar L A: A high throughput flow cytometric assay platform targeting transporter inhibition. Drug Disc Today Technol 2014; 12:e95-e103. [0201] Tsai E A, Shakbatyan R, Evans J, Rossetti P, Graham C, Sharma H, Lin C F, Lebo M S: Bioinformatics Workflow for Clinical Whole Genome Sequencing at Partners HealthCare Personalized Medicine. J Pers Med 2016; 6. [0202] 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. [0203] van Belkum A, Dunne W M, Jr.: Next-generation antimicrobial susceptibility testing. Journal of clinical microbiology 2013; 51:2018-2024. [0204] Waggoner A: Optical probes of membrane potential. J Membr Biol 1976; 27:317-334. Waggoner A S: Dye indicators of membrane potential. Annu Rev Biophys Bioeng 1979; 8:47-68. [0205] Walberg M, Gaustad P, Steen H B: Rapid assessment of ceftazidime, ciprofloxacin, and gentamicin susceptibility in exponentially-growing E-coli cells by means of flow cytometry. Cytometry 1997; 27:169-178. [0206] Wallden M, Fange D, Lundius E G, Baltekin Ö, Elf J: The synchronization of replication and division cycles in individual E. coli cells. Cell 2016; 166:729-739. [0207] 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. [0208] Willis L, Huang K C: Sizing up the bacterial cell cycle. Nat Rev Microbiol 2017; 15:606-620. Wilson M L, Gaido L: Laboratory diagnosis of urinary tract infections in adult patients. Clin Infect Dis 2004; 38:1150-1158. [0209] Wu J B Y, Shi C H, Chu G C Y, Xu Q J, Zhang Y, Li Q L, Yu J S, Zhau H Y E, Chung L W K: Near-infrared fluorescence heptamethine carbocyanine dyes mediate imaging and targeted drug delivery for human brain tumor. Biomaterials 2015; 67:1-10. [0210] Yu H, Jing W, Iriya R, Yang Y, Syal K, Mo M, Grys T E, Haydel S E, Wang S, Tao N: Phenotypic antimicrobial susceptibility testing with deep learning video microscopy. Anal Chem 2018; 90:6314-6322. [0211] Zampieri M, Szappanos B, Buchieri M V, Trauner A, Piazza I, Picotti P, Gagneux S, Borrell S, Gicquel B, Lelievre J, Papp B, Sauer U: High-throughput metabolomic analysis predicts mode of action of uncharacterized antimicrobial compounds. Sci Transl Med 2018; 10. [0212] Zhang J H, Chung T D Y, Oldenburg K R: A simple statistical parameter for use in evaluation and validation of high throughput screening assays. J Biomol Screening 1999; 4:67-73. [0213] Zheng H, Ho P Y, Jiang M, Tang B, Liu W, Li D, Yu X, Kleckner N E, Amir A, Liu C: Interrogating the Escherichia coli cell cycle by cell dimension perturbations. Proc Natl Acad Sci USA 2016; 113:15000-15005.