Image-based analysis of barcoded picolitre droplets in a microfluidic system
Due to the widespread use of antibiotics in medicine and agriculture more and more multiresistant bacteria arise and the need for the discovery and development of new antibiotics increases. Actinobacteria, especially their secondary metabolites, have been a valuable source for effective drugs since the middle of the 20th century. Since only a
fraction of their species are known and culturable, they seem to provide the solution for the present problem. However finding new species by screening bacterial broths from natural sources has become more difficult lately as often already known species are rediscovered.
Therefore, it is necessary to develop new methods to detect actinobacteria species and investigate their secondary metabolites under different growth conditions. The DropCode project aims to develop an ultra-high-throughput method to detect and investigate new species and their features using a droplet based microfluidic system.
Single bacteria spores are enclosed into picolitre droplets inside narrow channels, form individual colonies and can be studied by adding all kinds of substances during a dosing process. The droplets are investigated through images taken at several points of the process. As the flow rate of the droplets through the microfluidic system is high, thousands
of images are generated and automated image analysis provides the basis for studying the droplet content in a fast and effective way. As a part of this project, the thesis focusses on an online automated image analysis of the droplets and investigation of the droplet properties, such as shape, volume and bacterial growth inside the droplets. Furthermore droplets will be barcoded to identify them. Automated image anaylsis and classification strategies will be developed to decode the droplets. The algorithms will help to analyse and optimize the experimental workflow and will be integrated into the microfluidic system to build a fully automated screening device.
(2018) Aspf2 From Aspergillus fumigatus Recruits Human Immune Regulators for Immune Evasion and Cell Damage. Front Immunol 9, 1635.
(2018) Coding of Experimental Conditions in Microfluidic Droplet Assays Using Colored Beads and Machine Learning Supported Image Analysis. Small , e1802384.
(2017) Migration and interaction tracking for quantitative analysis of phagocyte-pathogen confrontation assays. Med Image Anal 36, 172.
(2015) Human neutrophils dump Candida glabrata after intracellular killing. Fungal Genet Biol 84, 37-40.
Start of PhD
June 16, 2015