Droplet Microfluidics: Coding of Experimental Conditions in Microfluidic Droplet Assays Using Colored Beads and Machine Learning Supported Image Analysis (Small 4/2019)
Source abstract
Abstract
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.
Source: openalex_inverted_index · Quality: baseline
Research context
OpenAlex topics: Innovative Microfluidic and Catalytic Techniques Innovation · Cell Image Analysis Techniques · Microfluidic and Bio-sensing Technologies
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Affiliations
Applied Systems Biology Leibniz Institute for Natural Product Research and Infection Biology Hans Knöll Institute 07745 Jena Germany
Bio Pilot Plant Leibniz Institute for Natural Product Research and Infection Biology Hans Knöll Institute 07745 Jena Germany
Faculty of Biological Sciences Friedrich Schiller University 07743 Jena Germany
References
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