(17,18) Image-based droplet analysis (IDA) often requires specific skills in programming that are not widely available in non-specialist laboratories. (13) This approach has been used for a wide range of experiments, such as bacterial surveillance of foodborne contamination, (14) screening of specific substrates, (15) single-cell analysis, (16) and detecting viable bacteria or viruses (e.g., SARS-CoV-2). (12) The analysis has been implemented in different types of image data, from single static image up to real-time data, either by bright-field or fluorescence microscopy.
Image-based analysis has often been used in droplet microfluidic experiments. In our case, CellProfiler (CP) offers the most user-friendly experience for both single image and batch processing analyses.
#CELLPROFILER PROPERTIES FILE SOFTWARE#
The rule-based type of software also has a simpler workflow or pipeline, especially aimed for non-experienced users. In our experimental setting, we find that the rule-based type of software is better suited for image-based droplet detection. We test and evaluate the software’s (i) ability to detect droplets, (ii) accuracy and precision, and (iii) overall components and supporting material. We select the four most popular software and classify them into rule-based and machine learning-based types after assessing the software’s modules. In order to address the issue, we explore the potential use of standalone freely available software to perform image-based droplet detection. Unfortunately, the analysis of images may require specific tools or programming skills to produce the expected results. For droplet analysis, researchers often use image-based detection techniques. This advancement has delivered novel quantification methods, such as droplet digital polymerase chain reaction (ddPCR) and an antibiotic heteroresistance analysis tool. Droplet microfluidics has revealed innovative strategies in biology and chemistry.
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