The ability for clinicians to provide effective treatments to bacterial infections with targeted antibiotics hinges on molecular diagnostics capable of identifying the pathogen and determining its susceptibility to antibiotics in a timely manner. Urinary tract infection (UTI) is a particularly representative infection because it one of the most common bacterial infections that affect all ages but is currently only diagnosed in centralized laboratories via bacterial culture, which typically takes 2-3 days for definitive diagnosis. The significant time delay between sample collection and result reporting contributes to widespread empiric use of broad-spectrum antibiotics, which has contributed towards emergence of multidrug-resistant pathogen. Toward addressing this important unmet need, our overall goal is to develop and validate an integrated diagnostic platform for bacterial pathogen identification (ID) and antibiotic susceptibility testing (AST) in a sample-to-answer manner in under 3 hours. Such rapid molecular diagnostics will transform the clinicians' ability to provide evidence-based diagnosis of bacterial infections, expedite treatments based on objective data, promote effective utilization of antibiotics. Specifically, we propose to develop an innovative droplet microfluidic network capable of combinatorially generating millions of picoliter (pL)-sized droplets of different compositions, i.e. mixtures of samples and probes or antibiotics at varying concentration levels, as the backbone technology. The microfluidic chip enables a streamlined approach for sample-reagent mixing, compartmentalization of mixtures into a massive number of droplets, and serial dilutions to simultaneously carry out pathogen ID and AST. In the pathogen ID module, single bacterial cells are encapsulated in droplets, achieving an effective concentration equivalent to 108-109 cfu/ml and thereby enabling rapid identification via the hybridization of molecular beacon probes in an amplification-free approach. In the AST module, individual bacterial cells are encapsulated and cultured in droplets that enhance local culture condition for bacterial growth and enable direct measurements of single bacterial doublings, thereby facilitating direct phenotypic AST from urine samples. We have assembled an academic-industry partnership consisted of Johns Hopkins University (droplet microfluidics and diagnostics), Stanford University (UTI, molecular probes, validation studies), University of Arizona (microfluidic AST), and GE Global Research (manufacturing and system integration). We propose the following aims: 1) to achieve single cell, amplification-free pathogen ID in a droplet-based microfluidic chip using a panel of peptide nucleic acid molecular beacons that target bacterial 16S rRNA; 2) to develop a droplet-based single cell AST capable of determining the minimum inhibitory concentration (MIC) for commonly used antibiotics for UTI; 3) to perform system integration and instrument development through partnership with our industry partner; and 4) to perform analytical and clinical validation with the integrated device. To facilitate technology translation, a Product Development Plan for future clinical deployment is proposed.

Public Health Relevance

The main goal of this research project is to develop and validate a droplet-based microfluidic platform capable of performing integrated bacterial pathogen identification (ID) and antibiotic susceptibility testing (AST) for urinary tract infectio (UTI) directly using urine samples.

Agency
National Institute of Health (NIH)
Institute
National Institute of Allergy and Infectious Diseases (NIAID)
Type
Research Project (R01)
Project #
1R01AI117032-01
Application #
8875827
Study Section
Special Emphasis Panel (ZAI1-MFH-M (J1))
Program Officer
Ritchie, Alec
Project Start
2015-04-01
Project End
2020-03-31
Budget Start
2015-04-01
Budget End
2016-03-31
Support Year
1
Fiscal Year
2015
Total Cost
$1,256,392
Indirect Cost
$238,603
Name
Johns Hopkins University
Department
Engineering (All Types)
Type
Schools of Engineering
DUNS #
001910777
City
Baltimore
State
MD
Country
United States
Zip Code
21205
Chen, Liben; Shin, Dong Jin; Zheng, Shuyu et al. (2018) Direct-qPCR Assay for Coupled Identification and Antimicrobial Susceptibility Testing of Neisseria gonorrhoeae. ACS Infect Dis 4:1377-1384
Kaushik, Aniruddha M; Hsieh, Kuangwen; Wang, Tza-Huei (2018) Droplet microfluidics for high-sensitivity and high-throughput detection and screening of disease biomarkers. Wiley Interdiscip Rev Nanomed Nanobiotechnol 10:e1522
Shin, Dong Jin; Lewis, Mitra; Hsieh, Yu-Hsiang et al. (2018) Healthcare worker feedback on a prototype smartphone-based point-of-care test platform for use in episodic care. Point Care 17:63-65
Hsieh, Kuangwen; Zec, Helena C; Chen, Liben et al. (2018) Simple and Precise Counting of Viable Bacteria by Resazurin-Amplified Picoarray Detection. Anal Chem 90:9449-9456
Kaushik, Aniruddha M; Hsieh, Kuangwen; Chen, Liben et al. (2017) Accelerating bacterial growth detection and antimicrobial susceptibility assessment in integrated picoliter droplet platform. Biosens Bioelectron 97:260-266
Shin, D J; Athamanolap, P; Chen, L et al. (2017) Mobile nucleic acid amplification testing (mobiNAAT) for Chlamydia trachomatis screening in hospital emergency department settings. Sci Rep 7:4495
Davenport, Michael; Mach, Kathleen E; Shortliffe, Linda M Dairiki et al. (2017) New and developing diagnostic technologies for urinary tract infections. Nat Rev Urol 14:296-310
Andini, Nadya; Wang, Bo; Athamanolap, Pornpat et al. (2017) Microbial Typing by Machine Learned DNA Melt Signatures. Sci Rep 7:42097
Friedrich, Sarah M; Burke, Jeffrey M; Liu, Kelvin J et al. (2017) Molecular rheotaxis directs DNA migration and concentration against a pressure-driven flow. Nat Commun 8:1213
Guan, Weihua; Chen, Liben; Rane, Tushar D et al. (2015) Droplet Digital Enzyme-Linked Oligonucleotide Hybridization Assay for Absolute RNA Quantification. Sci Rep 5:13795

Showing the most recent 10 out of 11 publications