Polydisperse emulsion digital assay to enhance time to detection and extend dynamic range in bacterial cultures enabled by a statistical framework. Issue 12 (21st May 2018)

• Record Type: Journal Article
• Authors: Byrnes, Samantha A.; Phillips, Elizabeth A.; Huynh, Toan; Weigl, Bernhard H.; Nichols, Kevin P.
• Is Part Of: Analyst. Volume 143:Issue 12(2018)
• Page Start: 2828
• Page End: 2836
• Publication Date: 2018-05-21
• Subjects: Chemistry, Analytic -- Periodicals; 543
• DOI: 10.1039/c8an00029h
• Languages: English
• ISSNs: 0003-2654

Abstract

Abstract : We have developed a simple method for preparing small-volume droplets for performing digital culture, allowing for the detection of bacteria. Abstract : Microbiological culture remains the most sensitive method for detecting viable and infectious bacteria, but these methods often require at least 24 hours to visibly identify bacterial growth. Lab-on-a-chip applications have utilized methods to isolate bacteria in picoliter-sized reaction vessels, resulting in digitized signals that offer improved time-to-detection and improved quantification. Although a great improvement, these approaches typically require expensive and specialized equipment, trained laboratory personnel, and maximum addressable volumes that can be orders of magnitude less than needed for clinically relevant limits of detection. To address these limitations, we have developed a simple method for preparing and semi-quantitatively analyzing small-volume droplets for performing digital culture, allowing for the detection of bacteria. This work includes a description of the method, characterization of resulting droplet sizes, comparison to traditional culture, and a statistical framework to quantify results. Though polydisperse, the droplet size distribution was consistent over different experiments, and there was a correlation between the observed number of positive droplets and the bulk concentration that can serve as a calibration curve for samples with unknown droplet size distributions. This statistical framework enables the simplification of droplet preparation and allows for accurate quantification even with polydisperse droplet sizes. The application of this method can also be extended to a variety of settings for the detection or quantification of bacteria in complex samples.