1932

Abstract

In the face of looming threats from multi-drug resistant microorganisms, there is a growing need for technologies that will enable rapid identification and drug susceptibility profiling of these pathogens in health care settings. In particular, recent progress in microfluidics and nucleic acid amplification is pushing the boundaries of timescale for diagnosing bacterial infections. With a diverse range of techniques and parallel developments in the field of analytical chemistry, an integrative perspective is needed to understand the significance of these developments. This review examines the scope of new developments in assay technologies grouped by key enabling domains of research. First, we examine recent development in nucleic acid amplification assays for rapid identification and drug susceptibility testing in bacterial infections. Next, we examine advances in microfluidics that facilitate acceleration of diagnostic assays via integration and scale. Lastly, recentdevelopments in biosensor technologies are reviewed. We conclude this review with perspectives on the use of emerging concepts to develop paradigm-changing assays.

Loading

Article metrics loading...

/content/journals/10.1146/annurev-anchem-061318-115529
2019-06-12
2024-04-14
Loading full text...

Full text loading...

/deliver/fulltext/ac/12/1/annurev-anchem-061318-115529.html?itemId=/content/journals/10.1146/annurev-anchem-061318-115529&mimeType=html&fmt=ahah

Literature Cited

  1. 1.
    Kumar A, Roberts D, Wood KE, Light B, Parrillo JE et al. 2006. Duration of hypotension before initiation of effective antimicrobial therapy is the critical determinant of survival in human septic shock. Crit. Care Med. 34:1589–96
    [Google Scholar]
  2. 2.
    Lagier JC, Edouard S, Pagnier I, Mediannikov O, Drancourt M, Raoult D 2015. Current and past strategies for bacterial culture in clinical microbiology. Clin. Microbiol. Rev. 28:208–36
    [Google Scholar]
  3. 3.
    Jorgensen JH, Ferraro MJ. 2009. Antimicrobial susceptibility testing: a review of general principles and contemporary practices. Clin. Infect. Dis. 49:1749–55
    [Google Scholar]
  4. 4.
    Patel R. 2015. MALDI-TOF MS for the diagnosis of infectious diseases. Clin. Chem. 61:100–11
    [Google Scholar]
  5. 5.
    Murray PR. 2012. What is new in clinical microbiology-microbial identification by MALDI-TOF mass spectrometry: a paper from the 2011 William Beaumont Hospital Symposium on molecular pathology. J. Mol. Diagn. 14:419–23
    [Google Scholar]
  6. 6.
    Martinez RM, Bauerle ER, Fang FC, Butler-Wu SM 2014. Evaluation of three rapid diagnostic methods for direct identification of microorganisms in positive blood cultures. J. Clin. Microbiol. 52:2521–29
    [Google Scholar]
  7. 7.
    Leber AL, Everhart K, Daly JA, Hopper A, Harrington A et al. 2018. Multicenter evaluation of BioFire FilmArray Respiratory Panel 2 for detection of viruses and bacteria in nasopharyngeal swab samples. J. Clin. Microbiol. 56: https://doi.org/10.1128/JCM.01945-17
    [Crossref] [Google Scholar]
  8. 8.
    Zacharioudakis IM, Zervou FN, Mylonakis E 2018. T2 magnetic resonance assay: overview of available data and clinical implications. J. Fungi 4:45
    [Google Scholar]
  9. 9.
    Ecker DJ, Sampath R, Li H, Massire C, Matthews HE et al. 2010. New technology for rapid molecular diagnosis of bloodstream infections. Expert Rev. Mol. Diagn. 10:399–415
    [Google Scholar]
  10. 10.
    Metzgar D, Frinder M, Lovari R, Toleno D, Massire C et al. 2013. Broad-spectrum biosensor capable of detecting and identifying diverse bacterial and Candida species in blood. J. Clin. Microbiol. 51:2670–78
    [Google Scholar]
  11. 11.
    Metzgar D, Frinder MW, Rothman RE, Peterson S, Carroll KC et al. 2016. The IRIDICA BAC BSI assay: rapid, sensitive and culture-independent identification of bacteria and Candida in blood. PLOS ONE 11:e0158186
    [Google Scholar]
  12. 12.
    Farrar JS, Wittwer CT. 2015. Extreme PCR: efficient and specific DNA amplification in 15–60 seconds. Clin. Chem. 61:145–53
    [Google Scholar]
  13. 13.
    Wittwer CT, Garling DJ. 1991. Rapid cycle DNA amplification: time and temperature optimization. Biotechniques 10:76–83
    [Google Scholar]
  14. 14.
    Misawa Y, Yoshida A, Saito R, Yoshida H, Okuzumi K et al. 2007. Application of loop-mediated isothermal amplification technique to rapid and direct detection of methicillin-resistant Staphylococcus aureus (MRSA) in blood cultures. J. Infect. Chemother. 13:134–40
    [Google Scholar]
  15. 15.
    Zhao X, Li Y, Wang L, You L, Xu Z et al. 2010. Development and application of a loop-mediated isothermal amplification method on rapid detection Escherichia coli O157 strains from food samples. Mol. Biol. Rep. 37:2183–88
    [Google Scholar]
  16. 16.
    Wang L, Li Y, Chu J, Xu Z, Zhong Q 2012. Development and application of a simple loop-mediated isothermal amplification method on rapid detection of Listeria monocytogenes strains. Mol. Biol. Rep. 39:445–49
    [Google Scholar]
  17. 17.
    Zhang X, Lowe SB, Gooding JJ 2014. Brief review of monitoring methods for loop-mediated isothermal amplification (LAMP). Biosens. Bioelectron. 61:491–99
    [Google Scholar]
  18. 18.
    Safavieh M, Kanakasabapathy MK, Tarlan F, Ahmed MU, Zourob M et al. 2016. Emerging loop-mediated isothermal amplification-based microchip and microdevice technologies for nucleic acid detection. ACS Biomater. Sci. Eng. 2:278–94
    [Google Scholar]
  19. 19.
    Rane TD, Chen LB, Zec HC, Wang TH 2015. Microfluidic continuous flow digital loop-mediated isothermal amplification (LAMP). Lab Chip 15:776–82
    [Google Scholar]
  20. 20.
    Montgomery JL, Sanford LN, Wittwer CT 2010. High-resolution DNA melting analysis in clinical research and diagnostics. Expert Rev. Mol. Diagn. 10:219–40
    [Google Scholar]
  21. 21.
    Wittwer CT. 2009. High-resolution DNA melting analysis: advancements and limitations. Hum. Mutat. 30:857–59
    [Google Scholar]
  22. 22.
    Wittwer CT, Reed GH, Kent JO 2007. High-resolution DNA melting analysis for simple and efficient molecular diagnostics. Pharmacogenomics 8:597–608
    [Google Scholar]
  23. 23.
    Yang S, Ramachandran P, Rothman R, Hsieh YH, Hardick A et al. 2009. Rapid identification of biothreat and other clinically relevant bacterial species by use of universal PCR coupled with high-resolution melting analysis. J. Clin. Microbiol. 47:2252–55
    [Google Scholar]
  24. 24.
    Rothman R, Ramachandran P, Yang S, Hardick A, Won H et al. 2010. Use of quantitative broad-based polymerase chain reaction for detection and identification of common bacterial pathogens in cerebrospinal fluid. Acad. Emerg. Med. 17:741–47
    [Google Scholar]
  25. 25.
    Won H, Rothman R, Ramachandran P, Hsieh YH, Kecojevic A et al. 2010. Rapid identification of bacterial pathogens in positive blood culture bottles by use of a broad-based PCR assay coupled with high-resolution melt analysis. J. Clin. Microbiol. 48:3410–13
    [Google Scholar]
  26. 26.
    Hardick J, Won H, Jeng K, Hsieh YH, Gaydos CA et al. 2012. Identification of bacterial pathogens in ascitic fluids from patients with suspected spontaneous bacterial peritonitis by use of broad-range PCR (16S PCR) coupled with high-resolution melt analysis. J. Clin. Microbiol. 50:2428–32
    [Google Scholar]
  27. 27.
    Zhang Y, Park S, Yang S, Wang TH 2010. An all-in-one microfluidic device for parallel DNA extraction and gene analysis. Biomed. Microdevices 12:1043–49
    [Google Scholar]
  28. 28.
    Jeng K, Yang S, Won H, Gaydos CA, Hsieh YH et al. 2012. Application of a 16S rRNA PCR-high-resolution melt analysis assay for rapid detection of Salmonella bacteremia. J. Clin. Microbiol. 50:1122–24
    [Google Scholar]
  29. 29.
    Jeng K, Gaydos CA, Blyn LB, Yang S, Won H et al. 2012. Comparative analysis of two broad-range PCR assays for pathogen detection in positive-blood-culture bottles: PCR-high-resolution melting analysis versus PCR-mass spectrometry. J. Clin. Microbiol. 50:3287–92
    [Google Scholar]
  30. 30.
    Masek BJ, Hardick J, Won H, Yang S, Hsieh YH et al. 2014. Sensitive detection and serovar differentiation of typhoidal and nontyphoidal Salmonella enterica species using 16S rRNA gene PCR coupled with high-resolution melt analysis. J. Mol. Diagn. 16:261–66
    [Google Scholar]
  31. 31.
    Fraley SI, Hardick J, Masek BJ, Athamanolap P, Rothman RE et al. 2013. Universal digital high-resolution melt: a novel approach to broad-based profiling of heterogeneous biological samples. Nucleic Acids Res 41:e175
    [Google Scholar]
  32. 32.
    Athamanolap P, Parekh V, Fraley SI, Agarwal V, Shin DJ et al. 2014. Trainable high resolution melt curve machine learning classifier for large-scale reliable genotyping of sequence variants. PLOS ONE 9:e109094
    [Google Scholar]
  33. 33.
    Fraley SI, Athamanolap P, Masek BJ, Hardick J, Carroll KC et al. 2016. Nested machine learning facilitates increased sequence content for large-scale automated high resolution melt genotyping. Sci. Rep. 6:19218
    [Google Scholar]
  34. 34.
    Andini N, Wang B, Athamanolap P, Hardick J, Masek BJ et al. 2017. Microbial typing by machine learned DNA melt signatures. Sci. Rep. 7:42097
    [Google Scholar]
  35. 35.
    O'Keefe CM, Pisanic TR, Zec HC, Overman MJ, Herman JG, Wang TH 2018. Facile profiling of molecular heterogeneity by microfluidic digital melt. Sci. Adv. 4:eaat6459
    [Google Scholar]
  36. 36.
    Nikkari S, McLaughlin IJ, Bi W, Dodge DE, Relman DA 2001. Does blood of healthy subjects contain bacterial ribosomal DNA. ? J. Clin. Microbiol. 39:1956–59
    [Google Scholar]
  37. 37.
    Cremonesi P, Cortimiglia C, Picozzi C, Minozzi G, Malvisi M et al. 2016. Development of a droplet digital polymerase chain reaction for rapid and simultaneous identification of common foodborne pathogens in soft cheese. Front. Microbiol. 7:1725
    [Google Scholar]
  38. 38.
    Jang M, Koh I, Lee SJ, Cheong JH, Kim P 2017. Droplet-based microtumor model to assess cell-ECM interactions and drug resistance of gastric cancer cells. Sci. Rep. 7:41541
    [Google Scholar]
  39. 39.
    Deurenberg RH, Bathoorn E, Chlebowicz MA, Couto N, Ferdous M et al. 2017. Application of next generation sequencing in clinical microbiology and infection prevention. J. Biotechnol. 243:16–24
    [Google Scholar]
  40. 40.
    Barczak AK, Gomez JE, Kaufmann BB, Hinson ER, Cosimi L et al. 2012. RNA signatures allow rapid identification of pathogens and antibiotic susceptibilities. PNAS 109:6217–22
    [Google Scholar]
  41. 41.
    Cao MD, Ganesamoorthy D, Elliott AG, Zhang H, Cooper MA, Coin LJ 2016. Streaming algorithms for identification of pathogens and antibiotic resistance potential from real-time MinION™ sequencing. Gigascience 5:32
    [Google Scholar]
  42. 42.
    Abril MK, Barnett AS, Wegermann K, Fountain E, Strand A et al. 2016. Diagnosis of Capnocytophaga canimorsus sepsis by whole-genome next-generation sequencing. Open Forum Infect. Dis. 3:ofw144
    [Google Scholar]
  43. 43.
    Hong DK, Blauwkamp TA, Kertesz M, Bercovici S, Truong C, Banaei N 2018. Liquid biopsy for infectious diseases: sequencing of cell-free plasma to detect pathogen DNA in patients with invasive fungal disease. Diagn. Microbiol. Infect. Dis. 92:210–13
    [Google Scholar]
  44. 44.
    De Vlaminck I, Martin L, Kertesz M, Patel K, Kowarsky M et al. 2015. Noninvasive monitoring of infection and rejection after lung transplantation. PNAS 112:13336–41
    [Google Scholar]
  45. 45.
    Horiba K, Kawada JI, Okuno Y, Tetsuka N, Suzuki T et al. 2018. Comprehensive detection of pathogens in immunocompromised children with bloodstream infections by next-generation sequencing. Sci. Rep. 8:3784
    [Google Scholar]
  46. 46.
    Nölling J, Rapireddy S, Amburg JI, Crawford EM, Prakash RA et al. 2016. Duplex DNA-invading γ-modified peptide nucleic acids enable rapid identification of bloodstream infections in whole blood. MBio 7:e00345–16
    [Google Scholar]
  47. 47.
    Aghazadeh A, Lin AY, Sheikh MA, Chen AL, Atkins LM et al. 2016. Universal microbial diagnostics using random DNA probes. Sci. Adv. 2:e1600025
    [Google Scholar]
  48. 48.
    Gootenberg JS, Abudayyeh OO, Lee JW, Essletzbichler P, Dy AJ et al. 2017. Nucleic acid detection with CRISPR-Cas13a/C2c2. Science 356:438–42
    [Google Scholar]
  49. 49.
    Chen JS, Ma E, Harrington LB, Da Costa M, Tian X et al. 2018. CRISPR-Cas12a target binding unleashes indiscriminate single-stranded DNase activity. Science 360:436–39
    [Google Scholar]
  50. 50.
    East-Seletsky A, O'Connell MR, Knight SC, Burstein D, Cate JH et al. 2016. Two distinct RNase activities of CRISPR-C2c2 enable guide-RNA processing and RNA detection. Nature 538:270–73
    [Google Scholar]
  51. 51.
    Gootenberg JS, Abudayyeh OO, Kellner MJ, Joung J, Collins JJ, Zhang F 2018. Multiplexed and portable nucleic acid detection platform with Cas13, Cas12a, and Csm6. Science 360:439–44
    [Google Scholar]
  52. 52.
    Unemo M, Shafer WM. 2014. Antimicrobial resistance in Neisseria gonorrhoeae in the 21st century: past, evolution, and future. Clin. Microbiol. Rev. 27:587–613
    [Google Scholar]
  53. 53.
    Bard JD, Lee F. 2018. Why can't we just use PCR? The role of genotypic versus phenotypic testing for antimicrobial resistance testing. Clin. Microbiol. Newsl. 40:87–95
    [Google Scholar]
  54. 54.
    Rolain JM, Mallet MN, Fournier PE, Raoult D 2004. Real-time PCR for universal antibiotic susceptibility testing. J. Antimicrob. Chemother. 54:538–41
    [Google Scholar]
  55. 55.
    Hunfeld KP, Bittner T, Rodel R, Brade V, Cinatl J 2004. New real-time PCR-based method for in vitro susceptibility testing of Anaplasma phagocytophilum against antimicrobial agents. Int. J. Antimicrob. Agents 23:563–71
    [Google Scholar]
  56. 56.
    Waldeisen JR, Wang T, Mitra D, Lee LP 2011. A real-time PCR antibiogram for drug-resistant sepsis. PLOS ONE 6:e28528
    [Google Scholar]
  57. 57.
    Beuving J, Verbon A, Gronthoud FA, Stobberingh EE, Wolffs PF 2011. Antibiotic susceptibility testing of grown blood cultures by combining culture and real-time polymerase chain reaction is rapid and effective. PLOS ONE 6:e27689
    [Google Scholar]
  58. 58.
    Chen L, Shin DJ, Zheng S, Melendez JH, Gaydos CA, Wang TH 2018. Direct-qPCR assay for coupled identification and antimicrobial susceptibility testing of Neisseria gonorrhoeae. ACS Infect. Dis. 4:1377–84
    [Google Scholar]
  59. 59.
    Athamanolap P, Hsieh K, Chen L, Yang S, Wang TH 2017. Integrated bacterial identification and antimicrobial susceptibility testing using PCR and high-resolution melt. Anal. Chem. 89:11529–36
    [Google Scholar]
  60. 60.
    Andini N, Hu A, Zhou L, Cogill S, Wang TH et al. 2018. A “culture” shift: broad bacterial detection, identification, and antimicrobial susceptibility testing directly from whole blood. Clin. Chem. https://doi.org/10.1373/clinchem.2018.290189
    [Crossref] [Google Scholar]
  61. 61.
    Schoepp NG, Khorosheva EM, Schlappi TS, Curtis MS, Humphries RM et al. 2016. Digital quantification of DNA replication and chromosome segregation enables determination of antimicrobial susceptibility after only 15 minutes of antibiotic exposure. Angew. Chem. Int. Ed. 55:9557–61
    [Google Scholar]
  62. 62.
    Schoepp NG, Schlappi TS, Curtis MS, Butkovich SS, Miller S et al. 2017. Rapid pathogen-specific phenotypic antibiotic susceptibility testing using digital LAMP quantification in clinical samples. Sci. Transl. Med. 9:eaal3693
    [Google Scholar]
  63. 63.
    Jensen PA, Zhu Z, van Opijnen T 2017. Antibiotics disrupt coordination between transcriptional and phenotypic stress responses in pathogenic bacteria. Cell Rep 20:1705–16
    [Google Scholar]
  64. 64.
    Farrar JS, Wittwer CT. 2015. Extreme PCR: efficient and specific DNA amplification in 15–60 seconds. Clin. Chem. 61:145–53
    [Google Scholar]
  65. 65.
    Woodman ME. 2008. Direct PCR of intact bacteria (colony PCR). Curr. Protoc. Microbiol. 9:A.3D.1–.6
    [Google Scholar]
  66. 66.
    Holcomb ZE, Tsalik EL, Woods CW, McClain MT 2017. Host-based peripheral blood gene expression analysis for diagnosis of infectious diseases. J. Clin. Microbiol. 55:360–68
    [Google Scholar]
  67. 67.
    Gliddon HD, Herberg JA, Levin M, Kaforou M 2018. Genome-wide host RNA signatures of infectious diseases: discovery and clinical translation. Immunology 153:171–78
    [Google Scholar]
  68. 68.
    Correia CN, Nalpas NC, McLoughlin KE, Browne JA, Gordon SV et al. 2017. Circulating microRNAs as potential biomarkers of infectious disease. Front. Immunol. 8:118
    [Google Scholar]
  69. 69.
    Mitchell PS, Parkin RK, Kroh EM, Fritz BR, Wyman SK et al. 2008. Circulating microRNAs as stable blood-based markers for cancer detection. PNAS 105:10513–18
    [Google Scholar]
  70. 70.
    Wang K, Zhang S, Weber J, Baxter D, Galas DJ 2010. Export of microRNAs and microRNA-protective protein by mammalian cells. Nucleic Acids Res 38:7248–59
    [Google Scholar]
  71. 71.
    Sutherland A, Thomas M, Brandon RA, Brandon RB, Lipman J et al. 2011. Development and validation of a novel molecular biomarker diagnostic test for the early detection of sepsis. Crit. Care 15:R149
    [Google Scholar]
  72. 72.
    Sweeney TE, Shidham A, Wong HR, Khatri P 2015. A comprehensive time-course-based multicohort analysis of sepsis and sterile inflammation reveals a robust diagnostic gene set. Sci. Transl. Med. 7:287ra71
    [Google Scholar]
  73. 73.
    Hu X, Yu J, Crosby SD, Storch GA 2013. Gene expression profiles in febrile children with defined viral and bacterial infection. PNAS 110:12792–97
    [Google Scholar]
  74. 74.
    Ramilo O, Allman W, Chung W, Mejias A, Ardura M et al. 2007. Gene expression patterns in blood leukocytes discriminate patients with acute infections. Blood 109:2066–77
    [Google Scholar]
  75. 75.
    Suarez NM, Bunsow E, Falsey AR, Walsh EE, Mejias A, Ramilo O 2015. Superiority of transcriptional profiling over procalcitonin for distinguishing bacterial from viral lower respiratory tract infections in hospitalized adults. J. Infect. Dis. 212:213–22
    [Google Scholar]
  76. 76.
    Sweeney TE, Wong HR, Khatri P 2016. Robust classification of bacterial and viral infections via integrated host gene expression diagnostics. Sci. Transl. Med. 8:346ra91
    [Google Scholar]
  77. 77.
    Li Y, Yang X, Zhao W 2017. Emerging microtechnologies and automated systems for rapid bacterial identification and antibiotic susceptibility testing. SLAS Technol 22:585–608
    [Google Scholar]
  78. 78.
    Kovarik ML, Gach PC, Ornoff DM, Wang Y, Balowski J et al. 2012. Micro total analysis systems for cell biology and biochemical assays. Anal. Chem. 84:516–40
    [Google Scholar]
  79. 79.
    Maurer FP, Christner M, Hentschke M, Rohde H 2017. Advances in rapid identification and susceptibility testing of bacteria in the clinical microbiology laboratory: implications for patient care and antimicrobial stewardship programs. Infect. Dis. Rep. 9:6839
    [Google Scholar]
  80. 80.
    Babady NE, England MR, Jurcic Smith KL, He T, Wijetunge DS et al. 2018. Multicenter evaluation of the ePlex Respiratory Pathogen panel for the detection of viral and bacterial respiratory tract pathogens in nasopharyngeal swabs. J. Clin. Microbiol. 56: https://doi.org/10.1128/JCM.01658-17
    [Crossref] [Google Scholar]
  81. 81.
    Chakravorty S, Simmons AM, Rowneki M, Parmar H, Cao Y et al. 2017. The new Xpert MTB/RIF Ultra: improving detection of Mycobacterium tuberculosis and resistance to rifampin in an assay suitable for point-of-care testing. MBio 8: https://doi.org/10.1128/MBIO.00812-17
    [Crossref] [Google Scholar]
  82. 82.
    Shin DJ, Athamanolap P, Chen L, Hardick J, Lewis M et al. 2017. Mobile nucleic acid amplification testing (mobiNAAT) for Chlamydia trachomatis screening in hospital emergency department settings. Sci. Rep. 7:4495
    [Google Scholar]
  83. 83.
    Shin DJ, Wang TH. 2014. Magnetic droplet manipulation platforms for nucleic acid detection at the point of care. Ann. Biomed. Eng. 42:2289–302
    [Google Scholar]
  84. 84.
    Zhang Y, Park S, Liu K, Tsuan J, Yang S, Wang TH 2011. A surface topography assisted droplet manipulation platform for biomarker detection and pathogen identification. Lab Chip 11:398–406
    [Google Scholar]
  85. 85.
    Long Z, Shetty AM, Solomon MJ, Larson RG 2009. Fundamentals of magnet-actuated droplet manipulation on an open hydrophobic surface. Lab Chip 9:1567–75
    [Google Scholar]
  86. 86.
    Zhang Y, Wang TH. 2013. Full-range magnetic manipulation of droplets via surface energy traps enables complex bioassays. Adv. Mater. 25:2903–8
    [Google Scholar]
  87. 87.
    Shin DJ, Trick AY, Hsieh YH, Thomas DL, Wang TH 2018. Sample-to-answer droplet magnetofluidic platform for point-of-care hepatitis C viral load quantitation. Sci. Rep. 8:9793
    [Google Scholar]
  88. 88.
    Chiou CH, Shin DJ, Zhang Y, Wang TH 2013. Topography-assisted electromagnetic platform for blood-to-PCR in a droplet. Biosens. Bioelectron. 50:91–99
    [Google Scholar]
  89. 89.
    Shin DJ, Zhang Y, Wang TH 2014. A droplet microfluidic approach to single-stream nucleic acid isolation and mutation detection. Microfluid. Nanofluid. 17:425–30
    [Google Scholar]
  90. 90.
    Stark A, Shin DJ, Pisanic T 2nd, Hsieh K, Wang TH 2016. A parallelized microfluidic DNA bisulfite conversion module for streamlined methylation analysis. Biomed. Microdevices 18:5
    [Google Scholar]
  91. 91.
    Stark A, Shin DJ, Wang TH 2018. A sample-to-answer droplet magnetofluidic assay platform for quantitative methylation-specific PCR. Biomed. Microdevices 20:31
    [Google Scholar]
  92. 92.
    Connelly JT, Rolland JP, Whitesides GM 2015. “Paper machine” for molecular diagnostics. Anal. Chem. 87:7595–601
    [Google Scholar]
  93. 93.
    Yang Y, Noviana E, Nguyen MP, Geiss BJ, Dandy DS, Henry CS 2017. Paper-based microfluidic devices: emerging themes and applications. Anal. Chem. 89:71–91
    [Google Scholar]
  94. 94.
    Magro L, Jacquelin B, Escadafal C, Garneret P, Kwasiborski A et al. 2017. Paper-based RNA detection and multiplexed analysis for Ebola virus diagnostics. Sci. Rep. 7:1347
    [Google Scholar]
  95. 95.
    Rodriguez NM, Wong WS, Liu L, Dewar R, Klapperich CM 2016. A fully integrated paperfluidic molecular diagnostic chip for the extraction, amplification, and detection of nucleic acids from clinical samples. Lab Chip 16:753–63
    [Google Scholar]
  96. 96.
    Kaur N, Toley BJ. 2018. Paper-based nucleic acid amplification tests for point-of-care diagnostics. Analyst 143:2213–34
    [Google Scholar]
  97. 97.
    Yang Z, Xu G, Reboud J, Ali SA, Kaur G et al. 2018. Rapid veterinary diagnosis of bovine reproductive infectious diseases from semen using paper-origami DNA microfluidics. ACS Sens 3:403–9
    [Google Scholar]
  98. 98.
    Shetty P, Ghosh D, Singh M, Tripathi A, Paul D 2016. Rapid amplification of Mycobacterium tuberculosis DNA on a paper substrate. RSC Adv 6:56205–12
    [Google Scholar]
  99. 99.
    Ali MM, Aguirre SD, Xu Y, Filipe CD, Pelton R, Li Y 2009. Detection of DNA using bioactive paper strips. Chem. Commun. 43:6640–42
    [Google Scholar]
  100. 100.
    Yeh EC, Fu CC, Hu L, Thakur R, Feng J, Lee LP 2017. Self-powered integrated microfluidic point-of-care low-cost enabling (SIMPLE) chip. Sci. Adv. 3:e1501645
    [Google Scholar]
  101. 101.
    Dimov IK, Basabe-Desmonts L, Garcia-Cordero JL, Ross BM, Park Y et al. 2011. Stand-alone self-powered integrated microfluidic blood analysis system (SIMBAS). Lab Chip 11:845–50
    [Google Scholar]
  102. 102.
    Boedicker JQ, Li L, Kline TR, Ismagilov RF 2008. Detecting bacteria and determining their susceptibility to antibiotics by stochastic confinement in nanoliter droplets using plug-based microfluidics. Lab Chip 8:1265–72
    [Google Scholar]
  103. 103.
    Rane TD, Zec HC, Puleo C, Lee AP, Wang TH 2012. Droplet microfluidics for amplification-free genetic detection of single cells. Lab Chip 12:3341–47
    [Google Scholar]
  104. 104.
    Kang DK, Ali MM, Zhang KX, Huang SS, Peterson E et al. 2014. Rapid detection of single bacteria in unprocessed blood using Integrated Comprehensive Droplet Digital Detection. Nat. Commun. 5:5427
    [Google Scholar]
  105. 105.
    Kaushik AM, Hsieh K, Wang TH 2018. Droplet microfluidics for high-sensitivity and high-throughput detection and screening of disease biomarkers. Wiley Interdiscip Rev. Nanomed. Nanobiotechnol. 10:e1522
    [Google Scholar]
  106. 106.
    Álvarez-Barrientos A, Arroyo J, Cantón R, Nombela C, Sánchez-Pérez M 2000. Applications of flow cytometry to clinical microbiology. Clin. Microbiol. Rev. 13:167–95
    [Google Scholar]
  107. 107.
    van Belkum A, Dunne WM 2013. Next-generation antimicrobial susceptibility testing. J. Clin. Microbiol. 51:2018–24
    [Google Scholar]
  108. 108.
    Jepras RI, Paul FE, Pearson SC, Wilkinson MJ 1997. Rapid assessment of antibiotic effects on Escherichia coli by bis-(1,3-dibutylbarbituric acid) trimethine oxonol and flow cytometry. Antimicrob. Agents Ch. 41:2001–5
    [Google Scholar]
  109. 109.
    Kaushik AM, Hsieh K, Chen L, Shin DJ, Liao JC, Wang TH 2017. Accelerating bacterial growth detection and antimicrobial susceptibility assessment in integrated picoliter droplet platform. Biosens. Bioelectron. 97:260–66
    [Google Scholar]
  110. 110.
    Weibull E, Antypas H, Kjall P, Brauner A, Andersson-Svahn H, Richter-Dahlfors A 2014. Bacterial nanoscale cultures for phenotypic multiplexed antibiotic susceptibility testing. J. Clin. Microbiol. 52:3310–17
    [Google Scholar]
  111. 111.
    Hsieh K, Zec HC, Chen L, Kaushik A, Wang TH 2016. Rapid, accurate, and general single-cell antibiotic susceptibility test in digital bacteria picoarray. The 20th International Conference on Miniaturized Systems for Chemistry and Life Sciences, MicroTAS 2016174–75 Dublin, Ireland: October 9–13
    [Google Scholar]
  112. 112.
    Hsieh K, Zec HC, Chen L, Kaushik AM, Mach KE et al. 2018. Simple and precise counting of viable bacteria by resazurin-amplified picoarray detection. Anal. Chem. 90:9449–56
    [Google Scholar]
  113. 113.
    Avesar J, Rosenfeld D, Truman-Rosentsvit M, Ben-Arye T, Geffen Y et al. 2017. Rapid phenotypic antimicrobial susceptibility testing using nanoliter arrays. PNAS 114:E5787–95
    [Google Scholar]
  114. 114.
    Fredborg M, Rosenvinge FS, Spillum E, Kroghsbo S, Wang M, Sondergaard TE 2015. Rapid antimicrobial susceptibility testing of clinical isolates by digital time-lapse microscopy. Eur J. Clin. Microbiol. Infect. Dis. 34:2385–94
    [Google Scholar]
  115. 115.
    Choi J, Yoo J, Lee M, Kim EG, Lee JS et al. 2014. A rapid antimicrobial susceptibility test based on single-cell morphological analysis. Sci. Transl. Med. 6:267ra174
    [Google Scholar]
  116. 116.
    Malmberg C, Yuen P, Spaak J, Cars O, Tangden T, Lagerback P 2016. A novel microfluidic assay for rapid phenotypic antibiotic susceptibility testing of bacteria detected in clinical blood cultures. PLOS ONE 11:e0167356
    [Google Scholar]
  117. 117.
    Baltekin O, Boucharin A, Tano E, Andersson DI, Elf J 2017. Antibiotic susceptibility testing in less than 30 min using direct single-cell imaging. PNAS 114:9170–75
    [Google Scholar]
  118. 118.
    Greninger AL, Naccache SN, Federman S, Yu G, Mbala P et al. 2015. Rapid metagenomic identification of viral pathogens in clinical samples by real-time nanopore sequencing analysis. Genome Med 7:99
    [Google Scholar]
  119. 119.
    Vogel R, Willmott G, Kozak D, Roberts GS, Anderson W et al. 2011. Quantitative sizing of nano/microparticles with a tunable elastomeric pore sensor. Anal. Chem. 83:3499–506
    [Google Scholar]
  120. 120.
    Ali S, Hassan A, Hassan G, Eun CH, Bae J et al. 2018. Disposable all-printed electronic biosensor for instantaneous detection and classification of pathogens. Sci. Rep. 8:5920
    [Google Scholar]
  121. 121.
    Shi X, Kadiyala U, VanEpps JS, Yau ST 2018. Culture-free bacterial detection and identification from blood with rapid, phenotypic, antibiotic susceptibility testing. Sci. Rep. 8:3416
    [Google Scholar]
  122. 122.
    Mader A, Gruber K, Castelli R, Hermann BA, Seeberger PH et al. 2012. Discrimination of Escherichia coli strains using glycan cantilever array sensors. Nano Lett 12:420–23
    [Google Scholar]
  123. 123.
    Wang JH, Morton MJ, Elliott CT, Karoonuthaisiri N, Segatori L, Biswal SL 2014. Rapid detection of pathogenic bacteria and screening of phage-derived peptides using microcantilevers. Anal. Chem. 86:1671–78
    [Google Scholar]
  124. 124.
    Longo G, Kasas S. 2014. Effects of antibacterial agents and drugs monitored by atomic force microscopy. Wires Nanomed. Nanobiotechnol. 6:230–44
    [Google Scholar]
  125. 125.
    Longo G, Alonso-Sarduy L, Rio LM, Bizzini A, Trampuz A et al. 2013. Rapid detection of bacterial resistance to antibiotics using AFM cantilevers as nanomechanical sensors. Nat. Nanotechnol. 8:522–26
    [Google Scholar]
  126. 126.
    Etayash H, Khan MF, Kaur K, Thundat T 2016. Microfluidic cantilever detects bacteria and measures their susceptibility to antibiotics in small confined volumes. Nat. Commun. 7:12947
    [Google Scholar]
  127. 127.
    Burg TP, Godin M, Knudsen SM, Shen W, Carlson G et al. 2007. Weighing of biomolecules, single cells and single nanoparticles in fluid. Nature 446:1066–69
    [Google Scholar]
  128. 128.
    Schneider BK, Harris P et al. 2016. Rapid antimicrobial susceptibility tests by mass measurement on a 96-well plate Paper presented at the ASM Microbe Conference Boston: June 16–20
  129. 129.
    Meylan S, Andrews IW, Collins JJ 2018. Targeting antibiotic tolerance, pathogen by pathogen. Cell 172:1228–38
    [Google Scholar]
  130. 130.
    Edmiston CE, Garcia R, Barnden M, DeBaun B, Johnson HB 2018. Rapid diagnostics for bloodstream infections: a primer for infection preventionists. Am. J. Infect. Control 46:1060–68
    [Google Scholar]
/content/journals/10.1146/annurev-anchem-061318-115529
Loading
/content/journals/10.1146/annurev-anchem-061318-115529
Loading

Data & Media loading...

  • Article Type: Review Article
This is a required field
Please enter a valid email address
Approval was a Success
Invalid data
An Error Occurred
Approval was partially successful, following selected items could not be processed due to error