New and developing diagnostic technologies for urinary tract infections

Key Points

  • UTIs are increasingly caused by multidrug-resistant organisms as a result of the overuse of empirical, broad-spectrum antibiotic therapy

  • Antimicrobial susceptibility, determined by the phenotypic response to antibiotic exposure, is key for clinical decision making for treating the wide variety of uropathogens and identifying resistance markers

  • Existing technologies (such as PCR, fluorescence in situ hybridization, and mass spectrometry) and new technologies (such as droplet microfluidic and biosensor platforms) need to focus on direct urine testing to expedite objective diagnoses

  • Integrated biosensor–microfluidic platforms have the most potential for point-of-care testing, as they facilitate direct urine analysis and can encompass all assay steps in a compact device

  • New technologies are a key step towards improved antimicrobial stewardship

Abstract

Timely and accurate identification and determination of the antimicrobial susceptibility of uropathogens is central to the management of UTIs. Urine dipsticks are fast and amenable to point-of-care testing, but do not have adequate diagnostic accuracy or provide microbiological diagnosis. Urine culture with antimicrobial susceptibility testing takes 2–3 days and requires a clinical laboratory. The common use of empirical antibiotics has contributed to the rise of multidrug-resistant organisms, reducing treatment options and increasing costs. In addition to improved antimicrobial stewardship and the development of new antimicrobials, novel diagnostics are needed for timely microbial identification and determination of antimicrobial susceptibilities. New diagnostic platforms, including nucleic acid tests and mass spectrometry, have been approved for clinical use and have improved the speed and accuracy of pathogen identification from primary cultures. Optimization for direct urine testing would reduce the time to diagnosis, yet these technologies do not provide comprehensive information on antimicrobial susceptibility. Emerging technologies including biosensors, microfluidics, and other integrated platforms could improve UTI diagnosis via direct pathogen detection from urine samples, rapid antimicrobial susceptibility testing, and point-of-care testing. Successful development and implementation of these technologies has the potential to usher in an era of precision medicine to improve patient care and public health.

Access options

Rent or Buy article

Get time limited or full article access on ReadCube.

from$8.99

All prices are NET prices.

Figure 1: Overview of the clinical workflow of existing and future diagnostic technologies for UTI.
Figure 2: Biosensor-based diagnosis of UTI.
Figure 3: Single-cell analysis of antimicrobial susceptibility.

References

  1. 1

    Foxman, B. The epidemiology of urinary tract infection. Nat. Rev. Urol. 7, 653–660 (2010).

  2. 2

    Griebling, T. L. Urologic diseases in America project: trends in resource use for urinary tract infections in women. J. Urol. 173, 1281–1287 (2005).

  3. 3

    Griebling, T. L. Urologic diseases in America project: trends in resource use for urinary tract infections in men. J. Urol. 173, 1288–1294 (2005).

  4. 4

    Nicolle, L. E. Urinary tract infection. Crit. Care Clin. 29, 699–715 (2013).

  5. 5

    Wagenlehner, F. M. et al. Diagnosis and management for urosepsis. Int. J. Urol. 20, 963–970 (2013).

  6. 6

    Wilson, M. L. & Gaido, L. Laboratory diagnosis of urinary tract infections in adult patients. Clin. Infect. Dis. 38, 1150–1158 (2004).

  7. 7

    Kauffman, C. A. Diagnosis and management of fungal urinary tract infection. Infect. Dis. Clin. North Am. 28, 61–74 (2014).

  8. 8

    Sobel, J. D., Fisher, J. F., Kauffman, C. A. & Newman, C. A. Candida urinary tract infections — epidemiology. Clin. Infect. Dis. 52 (Suppl. 6), S433–S436 (2011).

  9. 9

    Wise, G. J. & Schlegel, P. N. Sterile pyuria. N. Engl. J. Med. 372, 1048–1054 (2015).

  10. 10

    President's Council of Advisors on Science and Technology. National action plan for combating antibiotic-resistant bacteria. cdc.gov https://www.cdc.gov/drugresistance/pdf/national_action_plan_for_combating_antibotic-resistant_bacteria.pdf (2015).

  11. 11

    Aminov, R. I. The role of antibiotics and antibiotic resistance in nature. Environ. Microbiol. 11, 2970–2988 (2009).

  12. 12

    Holmes, A. H. et al. Understanding the mechanisms and drivers of antimicrobial resistance. Lancet 387, 176–187 (2016).

  13. 13

    Balcazar, J. L. Bacteriophages as vehicles for antibiotic resistance genes in the environment. PLoS Pathog. 10, e1004219 (2014).

  14. 14

    Colomer-Lluch, M., Jofre, J. & Muniesa, M. Antibiotic resistance genes in the bacteriophage DNA fraction of environmental samples. PLoS ONE 6, e17549 (2011).

  15. 15

    Sharfstein, J. M. Antibiotic resistance and the use of antibiotics in animal agriculture. FDA http://www.fda.gov/NewsEvents/Testimony/ucm219015.htm (2010).

  16. 16

    Boyd, L. B. et al. Increased fluoroquinolone resistance with time in Escherichia coli from >17,000 patients at a large county hospital as a function of culture site, age, sex, and location. BMC Infect. Dis. 8, 4 (2008).

  17. 17

    Johnson, L. et al. Emergence of fluoroquinolone resistance in outpatient urinary Escherichia coli isolates. Am. J. Med. 121, 876–884 (2008).

  18. 18

    Sanchez, G. V. et al. Antibiotic resistance among urinary isolates from female outpatients in the United States in 2003 and 2012. Antimicrob. Agents Chemother. 60, 2680–2683 (2016).

  19. 19

    Bouchillon, S. K., Badal, R. E., Hoban, D. J. & Hawser, S. P. Antimicrobial susceptibility of inpatient urinary tract isolates of gram-negative bacilli in the United States: results from the study for monitoring antimicrobial resistance trends (SMART) program: 2009–2011. Clin. Ther. 35, 872–877 (2013).

  20. 20

    Cox, H. U. & Luther, D. G. Determination of antimicrobial susceptibility of Pseudomonas aeruginosa by disk diffusion and microdilution methods. Am. J. Vet. Res. 41, 906–909 (1980).

  21. 21

    Cai, T. et al. Asymptomatic bacteriuria treatment is associated with a higher prevalence of antibiotic resistant strains in women with urinary tract infections. Clin. Infect. Dis. 61, 1655–1661 (2015).

  22. 22

    Gross, P. A. & Patel, B. Reducing antibiotic overuse: a call for a national performance measure for not treating asymptomatic bacteriuria. Clin. Infect. Dis. 45, 1335–1337 (2007).

  23. 23

    Suriano, F. et al. Bacteriuria in patients with an orthotopic ileal neobladder: urinary tract infection or asymptomatic bacteriuria? BJU Int. 101, 1576–1579 (2008).

  24. 24

    Liss, M. A. et al. AUA White Paper: the prevention and treatment of the more common complications related to prostate biopsy update. AUAnet.org https://www.auanet.org/common/pdf/education/clinical-guidance/AUA-PNB-White-Paper.pdf (2016).

  25. 25

    Loeb, S., Carter, H. B., Berndt, S. I., Ricker, W. & Schaeffer, E. M. Complications after prostate biopsy: data from SEER-Medicare. J. Urol. 186, 1830–1834 (2011).

  26. 26

    Halpern, J. A. et al. Indications, utilization, and complications following prostate biopsy: a New York state analysis. J. Urol. http://dx.doi.org/10.1016/j.juro.2016.11.081 (2016).

  27. 27

    Cussans, A., Somani, B. K., Basarab, A. & Dudderidge, T. J. The role of targeted prophylactic antimicrobial therapy before transrectal ultrasonography-guided prostate biopsy in reducing infection rates: a systematic review. BJU Int. 117, 725–731 (2016).

  28. 28

    Deville, W. L. et al. The urine dipstick test useful to rule out infections. A meta-analysis of the accuracy. BMC Urol. 4, 4 (2004).

  29. 29

    Kunin, C. M. (ed.) Urinary Tract Infections: Detection, Prevention, and Management (Williams & Wilkins, 1997).

  30. 30

    D'Souza, H. A., Campbell, M. & Baron, E. J. Practical bench comparison of BBL CHROMagar Orientation and standard two-plate media for urine cultures. J. Clin. Microbiol. 42, 60–64 (2004).

  31. 31

    Arena, F., Viaggi, B., Galli, L. & Rossolini, G. M. Antibiotic susceptibility testing: present and future. Pediatr. Infect. Dis. J. 34, 1128–1130 (2015).

  32. 32

    Clinical and Laboratory Standards Institute. Performance standards for antimicrobial susceptibility testing in M100. CLSI http://clsi.org/m100/ (2015).

  33. 33

    Eigner, U., Schmid, A., Wild, U., Bertsch, D. & Fahr, A. M. Analysis of the comparative workflow and performance characteristics of the VITEK 2 and Phoenix systems. J. Clin. Microbiol. 43, 3829–3834 (2005).

  34. 34

    Thomson, K. S. et al. Comparison of Phoenix and VITEK 2 extended-spectrum-beta-lactamase detection tests for analysis of Escherichia coli and Klebsiella isolates with well-characterized beta-lactamases. J. Clin. Microbiol. 45, 2380–2384 (2007).

  35. 35

    Caliendo, A. M. et al. Better tests, better care: improved diagnostics for infectious diseases. Clin. Infect. Dis. 57 (Suppl. 3), S139–S170 (2013).

  36. 36

    Society for Healthcare Epidemiology, Infectious Diseases Society of America & Pediatric Infectious Diseases Society. Policy statement on antimicrobial stewardship by the Society for Healthcare Epidemiology of America (SHEA), the Infectious Diseases Society of America (IDSA), and the Pediatric Infectious Diseases Society (PIDS). Infect. Control Hosp. Epidemiol. 33, 322–327 (2012).

  37. 37

    Bignardi, G. E. Validation and verification of automated urine particle analysers. J. Clin. Pathol. 70, 94–101 (2016).

  38. 38

    Yusuf, E., Van Herendael, B. & van Schaeren, J. Performance of urinalysis tests and their ability in predicting results of urine cultures: a comparison between automated test strip analyser and flow cytometry in various subpopulations and types of samples. J. Clin. Pathol. http://dx.doi.org/10.1136/jclinpath-2016-204108 (2016).

  39. 39

    Lammers, R. L., Gibson, S., Kovacs, D., Sears, W. & Strachan, G. Comparison of test characteristics of urine dipstick and urinalysis at various test cutoff points. Ann. Emerg. Med. 38, 505–512 (2001).

  40. 40

    McNair, R. D., MacDonald, S. R., Dooley, S. L. & Peterson, L. R. Evaluation of the centrifuged and Gram-stained smear, urinalysis, and reagent strip testing to detect asymptomatic bacteriuria in obstetric patients. Am. J. Obstet. Gynecol. 182, 1076–1079 (2000).

  41. 41

    Stapleton, A. E. et al. Performance of a new rapid immunoassay test kit for point-of-care diagnosis of significant bacteriuria. J. Clin. Microbiol. 53, 2805–2809 (2015).

  42. 42

    Rajwa, B. et al. Discovering the unknown: detection of emerging pathogens using a label-free light-scattering system. Cytometry A 77, 1103–1112 (2010).

  43. 43

    Steen, H. B. Light scattering measurement in an arc lamp-based flow cytometer. Cytometry 11, 223–230 (1990).

  44. 44

    Fouchet, P., Jayat, C., Hechard, Y., Ratinaud, M. H. & Frelat, G. Recent advances of flow cytometry in fundamental and applied microbiology. Biol. Cell 78, 95–109 (1993).

  45. 45

    Broeren, M. A., Bahceci, S., Vader, H. L. & Arents, N. L. Screening for urinary tract infection with the Sysmex UF-1000i urine flow cytometer. J. Clin. Microbiol. 49, 1025–1029 (2011).

  46. 46

    Geerts, N. et al. Urine flow cytometry can rule out urinary tract infection, but cannot identify bacterial morphologies correctly. Clin. Chim. Acta 448, 86–90 (2015).

  47. 47

    Inigo, M. et al. Evaluation of the SediMax automated microscopy sediment analyzer and the Sysmex UF-1000i flow cytometer as screening tools to rule out negative urinary tract infections. Clin. Chim. Acta 456, 31–35 (2016).

  48. 48

    Inigo, M. et al. Direct identification of urinary tract pathogens from urine samples, combining urine screening methods and matrix-assisted laser desorption ionization-time of flight mass spectrometry. J. Clin. Microbiol. 54, 988–993 (2016).

  49. 49

    Wang, X. H. et al. Direct identification of bacteria causing urinary tract infections by combining matrix-assisted laser desorption ionization-time of flight mass spectrometry with UF-1000i urine flow cytometry. J. Microbiol. Methods 92, 231–235 (2013).

  50. 50

    Zboromyrska, Y. et al. Development of a new protocol for rapid bacterial identification and susceptibility testing directly from urine samples. Clin. Microbiol. Infect. 22, 561.e1–561.e6 (2016).

  51. 51

    Hale, D. C. et al. Rapid screening for bacteriuria by light scatter photometry (Autobac): a collaborative study. J. Clin. Microbiol. 13, 147–150 (1981).

  52. 52

    Jenkins, R. D., Hale, D. C. & Matsen, J. M. Rapid semiautomated screening and processing of urine specimens. J. Clin. Microbiol. 11, 220–225 (1980).

  53. 53

    Wada, A. et al. Rapid discrimination of Gram-positive and Gram-negative bacteria in liquid samples by using NaOH-sodium dodecyl sulfate solution and flow cytometry. PLoS ONE 7, e47093 (2012).

  54. 54

    Gessoni, G., Saccani, G., Valverde, S., Manoni, F. & Caputo, M. Does flow cytometry have a role in preliminary differentiation between urinary tract infections sustained by gram positive and gram negative bacteria? An Italian polycentric study. Clin. Chim. Acta 440, 152–156 (2015).

  55. 55

    Land, M. et al. Insights from 20 years of bacterial genome sequencing. Funct. Integr. Genomics 15, 141–161 (2015).

  56. 56

    Mizrahi-Man, O., Davenport, E. R. & Gilad, Y. Taxonomic classification of bacterial 16S rRNA genes using short sequencing reads: evaluation of effective study designs. PLoS ONE 8, e53608 (2013).

  57. 57

    Zankari, E. et al. Identification of acquired antimicrobial resistance genes. J. Antimicrob. Chemother. 67, 2640–2644 (2012).

  58. 58

    McArthur, A. G. & Wright, G. D. Bioinformatics of antimicrobial resistance in the age of molecular epidemiology. Curr. Opin. Microbiol. 27, 45–50 (2015).

  59. 59

    Fields, F. R., Lee, S. W. & McConnell, M. J. Using bacterial genomes and essential genes for the development of new antibiotics. Biochem. Pharmacol. http://dx.doi.org/10.1016/j.bcp.2016.12.002 (2016).

  60. 60

    Fairfax, M. R. & Salimnia, H. Diagnostic molecular microbiology: a 2013 snapshot. Clin. Lab. Med. 33, 787–803 (2013).

  61. 61

    Tang, Y. W., Procop, G. W. & Persing, D. H. Molecular diagnostics of infectious diseases. Clin. Chem. 43, 2021–2038 (1997).

  62. 62

    Veron, L. et al. Rapid urine preparation prior to identification of uropathogens by MALDI-TOF MS. Eur. J. Clin. Microbiol. Infect. Dis. 34, 1787–1795 (2015).

  63. 63

    Wu, Q., Li, Y., Wang, M., Pan, X. P. & Tang, Y. F. Fluorescence in situ hybridization rapidly detects three different pathogenic bacteria in urinary tract infection samples. J. Microbiol. Methods 83, 175–178 (2010).

  64. 64

    Kothari, A., Morgan, M. & Haake, D. A. Emerging technologies for rapid identification of bloodstream pathogens. Clin. Infect. Dis. 59, 272–278 (2014).

  65. 65

    Seng, P. et al. Ongoing revolution in bacteriology: routine identification of bacteria by matrix-assisted laser desorption ionization time-of-flight mass spectrometry. Clin. Infect. Dis. 49, 543–551 (2009).

  66. 66

    Ferreira, L. et al. Direct identification of urinary tract pathogens from urine samples by matrix-assisted laser desorption ionization-time of flight mass spectrometry. J. Clin. Microbiol. 48, 2110–2115 (2010).

  67. 67

    Burillo, A. et al. Gram-stain plus MALDI-TOF MS (matrix-assisted laser desorption ionization-time of flight mass spectrometry) for a rapid diagnosis of urinary tract infection. PLoS ONE 9, e86915 (2014).

  68. 68

    Kohling, H. L. et al. Direct identification of bacteria in urine samples by matrix-assisted laser desorption/ionization time-of-flight mass spectrometry and relevance of defensins as interfering factors. J. Med. Microbiol. 61, 339–344 (2012).

  69. 69

    Singhal, N., Kumar, M., Kanaujia, P. K. & Virdi, J. S. MALDI-TOF mass spectrometry: an emerging technology for microbial identification and diagnosis. Frontiers Microbiol. 6, 791 (2015).

  70. 70

    Angeletti, S. Matrix assisted laser desorption time of flight mass spectrometry (MALDI-TOF MS) in clinical microbiology. J. Microbiol. Methods http://dx.doi.org/10.1016/j.mimet.2016.09.003 (2016).

  71. 71

    Tran, A., Alby, K., Kerr, A., Jones, M. & Gilligan, P. H. Cost savings realized by implementation of routine microbiological identification by matrix-assisted laser desorption ionization-time of flight mass spectrometry. J. Clin. Microbiol. 53, 2473–2479 (2015).

  72. 72

    Armbruster, C. E. & Mobley, H. L. Merging mythology and morphology: the multifaceted lifestyle of Proteus mirabilis. Nat. Rev. Microbiol. 10, 743–754 (2012).

  73. 73

    Kline, K. A. & Lewis, A. L. Gram-positive uropathogens, polymicrobial urinary tract infection, and the emerging microbiota of the urinary tract. Microbiol. Spectr. http://dx.doi.org/10.1128/microbiolspec.UTI-0012-2012 (2016).

  74. 74

    Siegman-Igra, Y. The significance of urine culture with mixed flora. Curr. Opin. Nephrol. Hypertens. 3, 656–659 (1994).

  75. 75

    Jung, J. S. et al. Rapid detection of antibiotic resistance based on mass spectrometry and stable isotopes. Eur. J. Clin. Microbiol. Infect. Dis. 33, 949–955 (2014).

  76. 76

    Moter, A. & Gobel, U. B. Fluorescence in situ hybridization (FISH) for direct visualization of microorganisms. J. Microbiol. Methods 41, 85–112 (2000).

  77. 77

    Lane, D. J. et al. Rapid determination of 16S ribosomal RNA sequences for phylogenetic analyses. Proc. Natl Acad. Sci. USA 82, 6955–6959 (1985).

  78. 78

    Ludwig, W. & Schleifer, K. H. Bacterial phylogeny based on 16S and 23S rRNA sequence analysis. FEMS Microbiol. Rev. 15, 155–173 (1994).

  79. 79

    Zwirglmaier, K., Ludwig, W. & Schleifer, K. H. Recognition of individual genes in a single bacterial cell by fluorescence in situ hybridization — RING-FISH. Mol. Microbiol. 51, 89–96 (2004).

  80. 80

    Stender, H., Fiandaca, M., Hyldig-Nielsen, J. J. & Coull, J. PNA for rapid microbiology. J. Microbiol. Methods 48, 1–17 (2002).

  81. 81

    Deck, M. K. et al. Multicenter evaluation of the Staphylococcus QuickFISH method for simultaneous identification of Staphylococcus aureus and coagulase-negative staphylococci directly from blood culture bottles in less than 30 minutes. J. Clin. Microbiol. 50, 1994–1998 (2012).

  82. 82

    Deck, M. K. et al. Rapid detection of Enterococcus spp. direct from blood culture bottles using Enterococcus QuickFISH method: a multicenter investigation. Diagn. Microbiol. Infect. Dis. 78, 338–342 (2014).

  83. 83

    Sakarikou, C., Parisato, M., Lo Cascio, G. & Fontana, C. Beacon-based (bbFISH®) technology for rapid pathogens identification in blood cultures. BMC Microbiol. 14, 99 (2014).

  84. 84

    Oliveira, K., Procop, G. W., Wilson, D., Coull, J. & Stender, H. Rapid identification of Staphylococcus aureus directly from blood cultures by fluorescence in situ hybridization with peptide nucleic acid probes. J. Clin. Microbiol. 40, 247–251 (2002).

  85. 85

    Sogaard, M., Stender, H. & Schonheyder, H. C. Direct identification of major blood culture pathogens, including Pseudomonas aeruginosa and Escherichia coli, by a panel of fluorescence in situ hybridization assays using peptide nucleic acid probes. J. Clin. Microbiol. 43, 1947–1949 (2005).

  86. 86

    Egholm, M. et al. PNA hybridizes to complementary oligonucleotides obeying the Watson–Crick hydrogen-bonding rules. Nature 365, 566–568 (1993).

  87. 87

    Nielsen, P. E. & Egholm, M. An introduction to peptide nucleic acid. Curr. Issues Mol. Biol. 1, 89–104 (1999).

  88. 88

    Saiki, R. K. et al. Primer-directed enzymatic amplification of DNA with a thermostable DNA polymerase. Science 239, 487–491 (1988).

  89. 89

    Lehmann, L. E. et al. Rapid qualitative urinary tract infection pathogen identification by SeptiFast real-time PCR. PLoS ONE 6, e17146 (2011).

  90. 90

    Blaschke, A. J. et al. Rapid identification of pathogens from positive blood cultures by multiplex polymerase chain reaction using the FilmArray system. Diagn. Microbiol. Infect. Dis. 74, 349–355 (2012).

  91. 91

    Buss, S. N. et al. Multicenter evaluation of the BioFire FilmArray gastrointestinal panel for etiologic diagnosis of infectious gastroenteritis. J. Clin. Microbiol. 53, 915–925 (2015).

  92. 92

    Altun, O., Almuhayawi, M., Ullberg, M. & Ozenci, V. Clinical evaluation of the FilmArray blood culture identification panel in identification of bacteria and yeasts from positive blood culture bottles. J. Clin. Microbiol. 51, 4130–4136 (2013).

  93. 93

    Salimnia, H. et al. Evaluation of the FilmArray blood culture identification panel: results of a multicenter controlled trial. J. Clin. Microbiol. 54, 687–698 (2016).

  94. 94

    Gaydos, C. A. et al. Performance of the Cepheid CT/NG Xpert rapid PCR test for detection of Chlamydia trachomatis and Neisseria gonorrhoeae. J. Clin. Microbiol. 51, 1666–1672 (2013).

  95. 95

    Tabrizi, S. N. et al. Analytical evaluation of GeneXpert CT/NG, the first genetic point-of-care assay for simultaneous detection of Neisseria gonorrhoeae and Chlamydia trachomatis. J. Clin. Microbiol. 51, 1945–1947 (2013).

  96. 96

    Buchan, B. W. & Ledeboer, N. A. Emerging technologies for the clinical microbiology laboratory. Clin. Microbiol. Rev. 27, 783–822 (2014).

  97. 97

    Nadkarni, M. A., Martin, F. E., Jacques, N. A. & Hunter, N. Determination of bacterial load by real-time PCR using a broad-range (universal) probe and primers set. Microbiology 148, 257–266 (2002).

  98. 98

    Fredborg, M. et al. Real-time optical antimicrobial susceptibility testing. J. Clin. Microbiol. 51, 2047–2053 (2013).

  99. 99

    Fredborg, M. et al. Rapid antimicrobial susceptibility testing of clinical isolates by digital time-lapse microscopy. Eur. J. Clin. Microbiol. Infect. Dis. 34, 2385–2394 (2015).

  100. 100

    Price, C. S., Kon, S. E. & Metzger, S. Rapid antibiotic susceptibility phenotypic characterization of Staphylococcus aureus using automated microscopy of small numbers of cells. J. Microbiol. Methods 98, 50–58 (2014).

  101. 101

    Keller, M. S. et al. Reproducibility of the Accelerate ID/AST blood culture assay at multiple clinical sites. AcceleratedDiagnostics http://acceleratediagnostics.com/wp-content/uploads/2016/09/ASM-2016-Reproducibility-Study-Poster.pdf (2016).

  102. 102

    Douglas, I. S. et al. Rapid automated microscopy for microbiological surveillance of ventilator-associated pneumonia. Am. J. Respir. Crit. Care Med. 191, 566–573 (2015).

  103. 103

    Metzger, S., Frobel, R. A. & Dunne, W. M. Jr. Rapid simultaneous identification and quantitation of Staphylococcus aureus and Pseudomonas aeruginosa directly from bronchoalveolar lavage specimens using automated microscopy. Diagn. Microbiol. Infect. Dis. 79, 160–165 (2014).

  104. 104

    Wilson, M. R. et al. Actionable diagnosis of neuroleptospirosis by next-generation sequencing. N. Engl. J. Med. 370, 2408–2417 (2014).

  105. 105

    Sin, M. L., Mach, K. E., Wong, P. K. & Liao, J. C. Advances and challenges in biosensor-based diagnosis of infectious diseases. Expert Rev. Mol. Diagn. 14, 225–244 (2014).

  106. 106

    Syedmoradi, L. et al. Point of care testing: the impact of nanotechnology. Biosens. Bioelectron. 87, 373–387 (2017).

  107. 107

    Rapp, B. E., Gruhl, F. J. & Lange, K. Biosensors with label-free detection designed for diagnostic applications. Anal. Bioanal. Chem. 398, 2403–2412 (2010).

  108. 108

    Li, B., Yu, Q. & Duan, Y. Fluorescent labels in biosensors for pathogen detection. Crit. Rev. Biotechnol. 35, 82–93 (2015).

  109. 109

    Mach, K. E. et al. A biosensor platform for rapid antimicrobial susceptibility testing directly from clinical samples. J. Urol. 185, 148–153 (2011).

  110. 110

    Kadlec, M. W., You, D., Liao, J. C. & Wong, P. K. A. Cell phone-based microphotometric system for rapid antimicrobial susceptibility testing. J. Lab. Autom. 19, 258–266 (2014).

  111. 111

    Smith, G. T. et al. Robust dipstick urinalysis using a low-cost, micro-volume slipping manifold and mobile phone platform. Lab Chip 16, 2069–2078 (2016).

  112. 112

    Whitesides, G. M. The origins and the future of microfluidics. Nature 442, 368–373 (2006).

  113. 113

    Mach, K. E., Wong, P. K. & Liao, J. C. Biosensor diagnosis of urinary tract infections: a path to better treatment? Trends Pharmacol. Sci. 32, 330–336 (2011).

  114. 114

    Roine, A. et al. Rapid and accurate detection of urinary pathogens by mobile IMS-based electronic nose: a proof-of-principle study. PLoS ONE 9, e114279 (2014).

  115. 115

    Carey, J. R. et al. Rapid identification of bacteria with a disposable colorimetric sensing array. J. Am. Chem. Soc. 133, 7571–7576 (2011).

  116. 116

    Hong, J. I. & Chang, B. Y. Development of the smartphone-based colorimetry for multi-analyte sensing arrays. Lab Chip 14, 1725–1732 (2014).

  117. 117

    Lim, S. H. et al. Bacterial culture detection and identification in blood agar plates with an optoelectronic nose. Analyst 141, 918–925 (2016).

  118. 118

    Lim, S. H. et al. Colorimetric sensor array allows fast detection and simultaneous identification of sepsis-causing bacteria in spiked blood culture. J. Clin. Microbiol. 52, 592–598 (2014).

  119. 119

    Altobelli, E. et al. Integrated biosensor assay for rapid uropathogen identification and phenotypic antimicrobial susceptibility testing. Eur. Urol. http://dx.doi.org/10.1016/j.euf.2015.12.010 (2016).

  120. 120

    Mach, K. E. et al. Multiplex pathogen identification for polymicrobial urinary tract infections using biosensor technology: a prospective clinical study. J. Urol. 182, 2735–2741 (2009).

  121. 121

    Ouyang, M. et al. An AC electrokinetics facilitated biosensor cassette for rapid pathogen identification. Analyst 138, 3660–3666 (2013).

  122. 122

    Mohan, R. et al. Clinical validation of integrated nucleic acid and protein detection on an electrochemical biosensor array for urinary tract infection diagnosis. PLoS ONE 6, e26846 (2011).

  123. 123

    Halford, C. et al. Rapid antimicrobial susceptibility testing by sensitive detection of precursor rRNA using a novel electrochemical biosensing platform. Antimicrob. Agents Chemother. 57, 936–943 (2013).

  124. 124

    Mach, K. E. et al. Development of a biosensor-based rapid urine test for detection of urogenital schistosomiasis. PLoS Negl. Trop. Dis. 9, e0003845 (2015).

  125. 125

    Sin, M. L., Gao, J., Liao, J. C. & Wong, P. K. System integration — a major step toward lab on a chip. J. Biol. Eng. 5, 6 (2011).

  126. 126

    Pan, Y. et al. Electrochemical immunosensor detection of urinary lactoferrin in clinical samples for urinary tract infection diagnosis. Biosens. Bioelectron. 26, 649–654 (2010).

  127. 127

    Sin, M. L., Gau, V., Liao, J. C. & Wong, P. K. Electrothermal fluid manipulation of high-conductivity samples for laboratory automation applications. JALA 15, 426–432 (2010).

  128. 128

    Sin, M. L., Gau, V., Liao, J. C. & Wong, P. K. A universal electrode approach for automated electrochemical molecular analyses. J. Microelectromech. Syst. 22, 1126–1132 (2013).

  129. 129

    Garcia Leoni, M. E. & Esclarin De Ruz, A. Management of urinary tract infection in patients with spinal cord injuries. Clin. Microbiol. Infect. 9, 780–785 (2003).

  130. 130

    Jayawardena, V. & Midha, M. Significance of bacteriuria in neurogenic bladder. J. Spinal Cord Med. 27, 102–105 (2004).

  131. 131

    Tullus, K. Difficulties in diagnosing urinary tract infections in small children. Pediatr. Nephrol. 26, 1923–1926 (2011).

  132. 132

    Choi, J. et al. Rapid antibiotic susceptibility testing by tracking single cell growth in a microfluidic agarose channel system. Lab Chip 13, 280–287 (2013).

  133. 133

    Choi, J. et al. A rapid antimicrobial susceptibility test based on single-cell morphological analysis. Sci. Transl Med. 6, 267ra174 (2014).

  134. 134

    Cira, N. J., Ho, J. Y., Dueck, M. E. & Weibel, D. B. A self-loading microfluidic device for determining the minimum inhibitory concentration of antibiotics. Lab Chip 12, 1052–1059 (2012).

  135. 135

    Chen, C. H. et al. Antimicrobial susceptibility testing using high surface-to-volume ratio microchannels. Anal. Chem. 82, 1012–1019 (2010).

  136. 136

    Lu, Y. et al. Single cell antimicrobial susceptibility testing by confined microchannels and electrokinetic loading. Anal. Chem. 85, 3971–3976 (2013).

  137. 137

    Baraban, L. et al. Millifluidic droplet analyser for microbiology. Lab Chip 11, 4057–4062 (2011).

  138. 138

    Boedicker, J. Q., Li, L., Kline, T. R. & Ismagilov, R. F. Detecting bacteria and determining their susceptibility to antibiotics by stochastic confinement in nanoliter droplets using plug-based microfluidics. Lab Chip 8, 1265–1272 (2008).

  139. 139

    Churski, K. et al. Rapid screening of antibiotic toxicity in an automated microdroplet system. Lab Chip 12, 1629–1637 (2012).

  140. 140

    Eun, Y. J., Utada, A. S., Copeland, M. F., Takeuchi, S. & Weibel, D. B. Encapsulating bacteria in agarose microparticles using microfluidics for high-throughput cell analysis and isolation. ACS Chem. Biol. 6, 260–266 (2011).

  141. 141

    Sinn, I. et al. Asynchronous magnetic bead rotation (AMBR) biosensor in microfluidic droplets for rapid bacterial growth and susceptibility measurements. Lab Chip 11, 2604–2611 (2011).

  142. 142

    Zhang, Y., Shin, D. J. & Wang, T. H. Serial dilution via surface energy trap-assisted magnetic droplet manipulation. Lab Chip 13, 4827–4831 (2013).

  143. 143

    Rane, T. D., Zec, H. C. & Wang, T. H. A serial sample loading system: interfacing multiwell plates with microfluidic devices. J. Lab. Autom. 17, 370–377 (2012).

  144. 144

    Rane, T. D., Zec, H. C., Puleo, C., Lee, A. P. & Wang, T. H. Droplet microfluidics for amplification-free genetic detection of single cells. Lab Chip 12, 3341–3347 (2012).

  145. 145

    Kaushik, A., Hsieh, K., Chen, L., Shin, D. J. & Wang, T. H. Rapid-assessment of bacterial vitality and antibiotic susceptibility via high-throughput picoliter-droplet single-cell assay. 19th International Conference on Miniaturized Systems for Chemistry and Life Sciences http://engineering.jhu.edu/old-thwang/publications/ (2015).

  146. 146

    Li, B. et al. Gradient microfluidics enables rapid bacterial growth inhibition testing. Anal. Chem. 86, 3131–3137 (2014).

  147. 147

    Leung, K. et al. A programmable droplet-based microfluidic device applied to multiparameter analysis of single microbes and microbial communities. Proc. Natl Acad. Sci. USA 109, 7665–7670 (2012).

  148. 148

    Rane, T. D., Zec, H. C. & Wang, T. H. A barcode-free combinatorial screening platform for matrix metalloproteinase screening. Anal. Chem. 87, 1950–1956 (2015).

  149. 149

    Zec, H., Rane, T. D. & Wang, T. H. Microfluidic platform for on-demand generation of spatially indexed combinatorial droplets. Lab Chip 12, 3055–3062 (2012).

Download references

Acknowledgements

We thank members of the Liao and Wang Laboratories for helpful discussions. Research is supported in part by National Institutes of Health (NIH)/National Institute of Allergy and Infectious Diseases grant R01 AI117032 (J.C.L. and T.W.) and U01 AI082457 (J.C.L.).

Author information

All authors made substantial contributions to discussions of content and reviewed and edited the article before submission. M.D., K.E.M., N.B., T.-H.W. and J.C.L. researched data for the article and M.D., K.E.M., T.-H.W. and J.C.L. wrote the manuscript.

Correspondence to Joseph C. Liao.

Ethics declarations

Competing interests

The authors declare no competing financial interests.

PowerPoint slides

Glossary

Antimicrobial susceptibility

Antimicrobial susceptibility refers to phenotypic response of the bacteria in the presence of antimicrobial agents.

Multidrug-resistant pathogens

Bacterial pathogens that have developed resistance to multiple antimicrobials. Common multidrug resistant uropathogens include Enterobacteriaceae that produce AmpC β-lactamase, extended-spectrum β-lactamase and carbapenamase.

Antimicrobial resistance

Antimicrobial resistance refers to the inherent or acquired genetic mechanisms by which bacteria withstand antimicrobial agents.

Antimicrobial stewardship

Coordinated interventions to improve the appropriate use of antimicrobials by reducing the administration of unnecessary antimicrobials and promoting the selection of the optimal antimicrobial drug, dose, duration of therapy, and route of administration when needed. The major goals of antimicrobial stewardship include achieving optimal clinical outcomes at the same time minimizing toxicity and adverse events, limiting the selection pressure on bacterial populations that drives the emergence of antimicrobial-resistant strains, and reducing excessive costs related to suboptimal antimicrobial use.

Sample preparation

Multistep assay preparation that includes pipetting (such as reagent transfer and mixing), centrifugation (separation and concentration), and washing.

Lateral flow assays

A single-use, point-of-care diagnostic tool based on liquid transport driven by capillary action without the requirement of external support. The major advantages of these test strips include simplicity, portability, and cost-effectiveness. Examples include urinalysis test strips.

Mass spectrometry

A technique in which charged molecules are created by ionization and their identity determined based on the mass:charge ratio. Matrix-assisted laser desorption ionization–time of flight (MALDI–TOF) mass spectrometry can be used for the identification of large biological molecules enabling its use in pathogen identification. In the current clinical application of MALD–TOF mass spectrometry for pathogen identification, the sample (such as urine) is first cultured to isolate the bacteria and a colony from the culture plate is analysed by MALDI–TOF mass spectrometry.

System integration

Integration of the functional building blocks of microfluidic components including pumps, mixers, concentrators, and valves to create an automated system capable of 'sample-in, answer-out' for the end users. System integration is a major hurdle in translating microfluidic devices into practical applications. Key factors include throughput, cost, multiplexity, diversity of components, accuracy, and programmability.

Fluorescence in situ hybridization

(FISH). A cytogenetic technique that uses fluorescent probes that bind to complementary sequences in target cells (such as bacterial pathogens).

Matrix

Components present in biological samples can affect the detection of the analyte of interest. Urinary constituents that can cause matrix effects in diagnostics include somatic cells, electrolytes, organic molecules, proteins, and crystals. Matrix effects can affect assay sensitivity and reproducibility.

Minimum inhibitory concentration

(MIC). The lowest dose of antimicrobial to which a bacterial strain is sensitive.

Biosensors

A molecular sensing device composed of a recognition element that binds specifically to a target analyte and generates a measurable signal via a transducer. For quantitative detection, the magnitude of the signal is proportional to the analyte concentration.

Microfluidics

A multidisciplinary field based on the manipulation of small amounts of fluids at the micron scale. Microfluidics-based platforms commonly integrate reagent transfer, target isolation, and sample-mixing steps in a multilayered cartridge containing channels, valves, and reagent reservoirs. Such 'lab-on-a-chip' platforms offer the potential advantages of microfluidics including low fluid volumes (reduced reagent use and cost), short assay time, low power consumption, rapid generation of small liquid compartments, and a high degree of parallelization.

Rights and permissions

Reprints and Permissions

About this article

Verify currency and authenticity via CrossMark

Cite this article

Davenport, M., Mach, K., Shortliffe, L. et al. New and developing diagnostic technologies for urinary tract infections. Nat Rev Urol 14, 296–310 (2017). https://doi.org/10.1038/nrurol.2017.20

Download citation

Further reading