Label-free droplet image analysis with Cellprofiler

Abstract

Droplet microfluidic methods used for microbiological experiments are fast, cost-effective, and provide high-throughput data. However, analysis of such image data can be difficult, and detection of molecular labels is limited by microscope parameters. Currently, there is lack of user-friendly methods to analyse a large volume of label-free droplet images without the need for trained personnel, or expensive, proprietary software. Such methods would make droplet microfluidic technology more widely accessible for a larger range of biological applications. In this paper we demonstrate an image analysis pipeline designed using Cellprofiler, a free, open-source software. This pipeline identifies water-in-oil microfluidic droplets, microplastic particles, and bacterial growth without using fluorescent or other labels.