Direct detection and drug-resistance profiling of bacteremias using inertial microfluidics
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Direct detection.pdf
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Author(s)
Hou, Han Wei
Bhattacharyya, Roby P.
Hung, Deborah T.
Han, Jongyoon
Date Issued
April 2015
Journal
Lab on a Chip
Publisher
Royal Society of Chemistry, The
Citation
Hou, Han Wei, et al. “Direct Detection and Drug-Resistance Profiling of Bacteremias Using Inertial Microfluidics.” Lab on a Chip 15, 10 (May 2015): 2297–2307 © 2015 The Royal Society of Chemistry
Version
Author's final manuscript
Abstract
Detection of bacteria in bloodstream infections and their antibiotic susceptibility patterns is critical to guide therapeutic decision-making for optimal patient care. Current culture-based assays are too slow (>48 h), leading to excessive up-front use of broad-spectrum antibiotics and/or incorrect antibiotic choices due to resistant bacteria, each with deleterious consequences for patient care and public health. To approach this problem, we describe a method to rapidly isolate bacteria from whole blood using inertial microfluidics and directly determine pathogen identity and antibiotic susceptibility with hybridization-based RNA detection. Using the principle of Dean flow fractionation, bacteria are separated from host blood cells in a label-free separation method with efficient recovery of even low abundance bacteria. Ribosomal RNA detection can then be applied for direct identification of low abundance pathogens (~100 per mL) from blood without culturing or enzymatic amplification. Messenger RNA detection of antibiotic-responsive transcripts after brief drug exposure permits rapid susceptibility determination from bacteria with minimal culturing (~105 per mL). This unique coupling of microfluidic cell separation with RNA-based molecular detection techniques represents significant progress towards faster diagnostics (~8 hours) to guide antibiotic therapy.
MIT Department
Massachusetts Institute of Technology. Department of Electrical Engineering and Computer Science
Massachusetts Institute of Technology. Research Laboratory of Electronics
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Creative Commons Attribution-Noncommercial-Share Alike
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DOI of Published Version
https://doi.org/10.1039/c5lc00311c
