User-friendly analysis of droplet array images

Water-in-oil droplets allow performing massive experimental parallelization and high-throughput studies, such as single-cell experiments. However, analyzing such vast arrays of droplets usually requires advanced expertise and sophisticated workflow tools, which limits accessibility for a wider user...

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Bibliographic Details
Published in:Analytica chimica acta 2023-09, Vol.1272, p.341397-341397, Article 341397
Main Authors: Sanka, Immanuel, Bartkova, Simona, Pata, Pille, Ernits, Mart, Meinberg, Monika Merje, Agu, Natali, Aruoja, Villem, Smolander, Olli-Pekka, Scheler, Ott
Format: Article
Language:English
Subjects:
Droplet analysis
EasyFlow
High-throughput analysis
User-friendly
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Summary:Water-in-oil droplets allow performing massive experimental parallelization and high-throughput studies, such as single-cell experiments. However, analyzing such vast arrays of droplets usually requires advanced expertise and sophisticated workflow tools, which limits accessibility for a wider user base in the fields of chemistry and biology. Thus, there is a need for more user-friendly tools for droplet analysis. In this article, we deliver a set of analytical pipelines for user-friendly analysis of typical scenarios in droplet experiments. We built pipelines that combine various open-source image-analysis software with a custom-developed data processing tool called “EasyFlow”. Our pipelines are applicable to the typical experimental scenarios that users encounter when working with droplets: i) mono- and polydisperse droplets, ii) brightfield and fluorescent images, iii) droplet and object detection, iv) signal profile of droplets and objects (e.g., fluorescence). [Display omitted] •Image-analysis software are combined with data visualization tool EasyFlow for user-friendly analysis of droplet arrays.•EasyFlow simplifies building analysis and visualization pipelines for droplet arrays.•EasyFlow analyses your droplet data based on their size, signal and labels.
ISSN:0003-2670
1873-4324
DOI:10.1016/j.aca.2023.341397