Interdisciplinary study about using droplet microfluidics
Single drop microfluidics technologies offer many advantages in the study and analysis of microorganisms, such as fast processing times, high-throughput and a reduction of spatial requirements compared with traditional culturing systems. However, one major weakness of the microfluidics platforms has been the inability to distinguish individual droplets from each other. Scientists from the Leibniz Institute for Natural Product Research & Infection Biology and the Friedrich Schiller University Jena have developed a method to address this shortcoming with the help of small plastic beads as droplet-identifiers coupled to analysis by artificial intelligence. The interdisciplinary study of the teams led by Dr. Martin Roth and Prof. Dr. Marc Thilo Figge (Principal Investigator of FungiNet project B4) was published in the journal Small.
A television report on microfluidics at MDR Thueringen Journal can be watched here.
Svensson CM, Shvydkiv O, Dietrich S, Mahler L, Weber T, Choudhary M, Tovar M, Figge MT, Roth M (2019) Coding of experimental conditions in microfluidic droplet assays using colored beads and machine learning supported image analysis. Small 15: e1802384.
Link to PubMed