Droplet Microfluidics: Coding of Experimental Conditions in Microfluidic Droplet Assays Using Colored Beads and Machine Learning Supported Image Analysis (Small 4/2019)

In article number 1802384, Miguel Tovar, Marc Thilo Figge, and co‐workers encode a population of microfluidic pico‐liter droplets with colored polystyrene beads for the simultaneous study of multiple experimental conditions. Droplets are imaged using brightfield microscopy and are successfully decod...

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Bibliographic Details
Published in:Small (Weinheim an der Bergstrasse, Germany) Germany), 2019-01, Vol.15 (4), p.n/a
Main Authors: Svensson, Carl‐Magnus, Shvydkiv, Oksana, Dietrich, Stefanie, Mahler, Lisa, Weber, Thomas, Choudhary, Mahipal, Tovar, Miguel, Figge, Marc Thilo, Roth, Martin
Format: Article
Language:English
Subjects:
antibiotic susceptibility testing
Bayesian inference
droplet coding
droplet microfluidics
image analysis
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Summary:In article number 1802384, Miguel Tovar, Marc Thilo Figge, and co‐workers encode a population of microfluidic pico‐liter droplets with colored polystyrene beads for the simultaneous study of multiple experimental conditions. Droplets are imaged using brightfield microscopy and are successfully decoded and evaluated by machine learning supported image analysis. The novel encoding strategy is applied to antibiotic susceptibility testing of droplet‐encapsulated E. coli bacteria as a proof‐of‐principle.
ISSN:1613-6810
1613-6829
DOI:10.1002/smll.201970021