Coding of experimental conditions in microfluidic droplet assays using colored beads and machine learning supported image analysis.

Svensson C-M*, Shydkiv O*, Dietrich S, Mahler L, Weber T, Choudhary M, Tovar M, Figge MT**, Roth M**; *authors contributed equally; *corresponding authors; **authors contributed equally (2019) Coding of experimental conditions in microfluidic droplet assays using colored beads and machine learning supported image analysis. Small 15(4), e1802384.

*equal contribution

Abstract

To efficiently exploit the potential of several millions of droplets that can be considered as individual bioreactors in microfluidic experiments, methods to encode different experimental conditions in droplets are needed. The approach presented here is based on co-encapsulation of colored polystyrene beads with biological samples. The decoding of the droplets, as well as content quantification, are performed by automated analysis of triggered images of individual droplets in-flow using bright-field microscopy. The decoding strategy combines bead classification using a random forest classifier and Bayesian inference to identify different codes and thus experimental conditions. Antibiotic susceptibility testing of nine different antibiotics and the determination of the minimal inhibitory concentration of a specific antibiotic against a laboratory strain of Escherichia coli are presented as a proof-of-principle. It is demonstrated that this method allows successful encoding and decoding of 20 different experimental conditions within a large droplet population of more than 105 droplets per condition. The decoding strategy correctly assigns 99.6% of droplets to the correct condition and a method for the determination of minimal inhibitory concentration using droplet microfluidics is established. The current encoding and decoding pipeline can readily be extended to more codes by adding more bead colors or color combinations.

Leibniz-HKI-Autor*innen

Mahipal Choudhary
Stefanie Dietrich
Marc Thilo Figge
Lisa Mahler
Martin Roth
Oksana Shvydkiv
Carl-Magnus Svensson
Miguel Angel Tovar Ballen
Thomas Weber

Identifier

doi: 10.1002/smll.201802384

PMID: 30549235