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Droplet Microfluidics: Coding of Experimental Conditions in Microfluidic Droplet Assays Using Colored Beads and Machine Learning Supported Image Analysis (Small 4/2019)
Author(s) -
Svensson CarlMagnus,
Shvydkiv Oksana,
Dietrich Stefanie,
Mahler Lisa,
Weber Thomas,
Choudhary Mahipal,
Tovar Miguel,
Figge Marc Thilo,
Roth Martin
Publication year - 2019
Publication title -
small
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 3.785
H-Index - 236
eISSN - 1613-6829
pISSN - 1613-6810
DOI - 10.1002/smll.201970021
Subject(s) - microfluidics , colored , materials science , population , coding (social sciences) , nanotechnology , polystyrene , biological system , biology , mathematics , demography , sociology , composite material , polymer , statistics
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.