Accelerating bacterial growth detection and antimicrobial susceptibility assessment in integrated picoliter droplet platform.

There remains an urgent need for rapid diagnostic methods that can evaluate antibiotic resistance for pathogenic bacteria in order to deliver targeted antibiotic treatments. Toward this end, we present a rapid and integrated single-cell biosensing platform, termed dropFAST, for bacterial growth detection and antimicrobial susceptibility assessment. DropFAST utilizes a rapid resazurin-based fluorescent growth assay coupled with stochastic confinement of bacteria in 20 pL droplets to detect signal from growing bacteria after 1h incubation, equivalent to 2-3 bacterial replications. Full integration of droplet generation, incubation, and detection into a single, uninterrupted stream also renders this platform uniquely suitable for in-line bacterial phenotypic growth assessment. To illustrate the concept of rapid digital antimicrobial susceptibility assessment, we employ the dropFAST platform to evaluate the antibacterial effect of gentamicin on E. coli growth.

[1]  S. Bertolotti,et al.  The Excited-State Interaction of Resazurin and Resorufin with Aminesin Aqueous Solutions. Photophysics and Photochemical Reaction¶ , 2002, Photochemistry and photobiology.

[2]  A. deMello,et al.  The Poisson distribution and beyond: methods for microfluidic droplet production and single cell encapsulation. , 2015, Lab on a chip.

[3]  Arnab Mukherjee,et al.  A multiplexed microfluidic platform for rapid antibiotic susceptibility testing. , 2013, Biosensors & bioelectronics.

[4]  C. A. Smith,et al.  High-throughput screening of antibiotic-resistant bacteria in picodroplets. , 2016, Lab on a chip.

[5]  K. Skarstad,et al.  Organization of sister origins and replisomes during multifork DNA replication in Escherichia coli , 2007, The EMBO journal.

[6]  F. Tenover.  Potential impact of rapid diagnostic tests on improving antimicrobial use , 2010, Annals of the New York Academy of Sciences.

[7]  N. Krogan,et al.  Phenotypic Landscape of a Bacterial Cell , 2011, Cell.

[8]  Frank Delvigne,et al.  Metabolic variability in bioprocessing: implications of microbial phenotypic heterogeneity. , 2014, Trends in biotechnology.

[9]  A. Lee,et al.  Droplet microfluidics for amplification-free genetic detection of single cells. , 2012, Lab on a chip.

[10]  G. Whitesides,et al.  Soft Lithography. , 1998, Angewandte Chemie.

[11]  A. Lee,et al.  Droplet microfluidics. , 2008, Lab on a chip.

[12]  S. Bertolotti,et al.  The Excited‐State Interaction of Resazurin and Resorufin with Aminesin Aqueous Solutions. Photophysics and Photochemical Reaction ¶ , 2002 .

[13]  R. Ismagilov,et al.  Isolation, incubation, and parallel functional testing and identification by FISH of rare microbial single-copy cells from multi-species mixtures using the combination of chemistrode and stochastic confinement. , 2009, Lab on a chip.

[14]  K J Ryan,et al.  Antimicrobial Susceptibility Testing , 2016, Advances in Experimental Medicine and Biology.

[15]  Sunghoon Kwon,et al.  Rapid antibiotic susceptibility testing by tracking single cell growth in a microfluidic agarose channel system. , 2013, Lab on a chip.

[16]  Piotr Garstecki,et al.  Droplet microfluidics for microbiology: techniques, applications and challenges. , 2016, Lab on a chip.

[17]  Edward Topp,et al.  The scourge of antibiotic resistance: the important role of the environment. , 2013, Clinical infectious diseases : an official publication of the Infectious Diseases Society of America.

[18]  E. Powell,et al.  Growth rate and generation time of bacteria, with special reference to continuous culture. , 1956, Journal of general microbiology.

[19]  Tza-Huei Wang,et al.  Droplet Digital Enzyme-Linked Oligonucleotide Hybridization Assay for Absolute RNA Quantification , 2015, Scientific Reports.

[20]  Clinical,et al.  Performance Standards for Antimicrobial Susceptibility Testing; Eighteenth Informational Supplement , 2008 .

[21]  J. Cooper,et al.  Gradient Microfluidics Enables Rapid Bacterial Growth Inhibition Testing , 2014, Analytical chemistry.

[22]  Vincent Gau,et al.  Antimicrobial susceptibility testing using high surface-to-volume ratio microchannels. , 2010, Analytical chemistry.

[23]  Brad Spellberg,et al.  Combating antimicrobial resistance: policy recommendations to save lives. , 2011, Clinical infectious diseases : an official publication of the Infectious Diseases Society of America.

[24]  Tza-Huei Wang,et al.  A Serial Sample Loading System , 2012, Journal of laboratory automation.

[25]  Stanislas Leibler,et al.  Dynamic Persistence of Antibiotic-Stressed Mycobacteria , 2013, Science.

[26]  Antibiotic resistance—the need for global solutions , 2014, BDJ.

[27]  N. Thakor,et al.  Continuous-flow C. elegans fluorescence expression analysis with real-time image processing through microfluidics. , 2016, Biosensors & bioelectronics.

[28]  Qin Tu,et al.  High-throughput microfluidic system for long-term bacterial colony monitoring and antibiotic testing in zero-flow environments. , 2011, Biosensors & bioelectronics.

[29]  J. Bartlett,et al.  Bad bugs, no drugs: no ESKAPE! An update from the Infectious Diseases Society of America. , 2009, Clinical infectious diseases : an official publication of the Infectious Diseases Society of America.

[30]  Christoph A. Merten,et al.  Drop-based microfluidic devices for encapsulation of single cells. , 2008, Lab on a chip.

[31]  Miles A. Miller,et al.  Single cell multiplexed assay for proteolytic activity using droplet microfluidics. , 2016, Biosensors & bioelectronics.

[32]  H. Goossens,et al.  Society's failure to protect a precious resource: antibiotics , 2011, The Lancet.

[33]  S. Leibler,et al.  Bacterial Persistence as a Phenotypic Switch , 2004, Science.

[34]  Shoji Takeuchi,et al.  Encapsulating bacteria in agarose microparticles using microfluidics for high-throughput cell analysis and isolation. , 2011, ACS chemical biology.

[35]  Slawomir Jakiela,et al.  Automated generation of libraries of nL droplets. , 2012, Lab on a chip.

[36]  Tza-Huei Wang,et al.  Microfluidic platform for on-demand generation of spatially indexed combinatorial droplets. , 2012, Lab on a chip.

[37]  R. Ismagilov,et al.  Detecting bacteria and determining their susceptibility to antibiotics by stochastic confinement in nanoliter droplets using plug-based microfluidics. , 2008, Lab on a chip.

[38]  Anson Hatch,et al.  Rapid detection of trace bacteria in biofluids using porous monoliths in microchannels. , 2014, Biosensors & bioelectronics.

[39]  Vincent Gau,et al.  Single cell antimicrobial susceptibility testing by confined microchannels and electrokinetic loading. , 2013, Analytical chemistry.

[40]  Cliburn Chan,et al.  Discriminative variable subsets in Bayesian classification with mixture models, with application in flow cytometry studies , 2015, Biostatistics.

[41]  Joseph C Liao,et al.  Biosensor diagnosis of urinary tract infections: a path to better treatment? , 2011, Trends in pharmacological sciences.

[42]  Cliburn Chan,et al.  Statistical mixture modeling for cell subtype identification in flow cytometry , 2008, Cytometry. Part A : the journal of the International Society for Analytical Cytology.

[43]  F. Baquero,et al.  The global threat of antimicrobial resistance: science for intervention , 2015, New microbes and new infections.

[44]  Tza-Huei Wang,et al.  New and developing diagnostic technologies for urinary tract infections , 2017, Nature Reviews Urology.

[45]  T. Tupasi,et al.  Use of antibiotics. A brief exposition of the problem and some tentative solutions. , 1973, Annals of internal medicine.