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Sanka I, Bartkova S, Pata P, Ernits M, Meinberg MM, Agu N, Aruoja V, Smolander OP, Scheler O. User-friendly analysis of droplet array images. Anal Chim Acta 2023; 1272:341397. [PMID: 37355339 DOI: 10.1016/j.aca.2023.341397] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/16/2023] [Revised: 05/08/2023] [Accepted: 05/18/2023] [Indexed: 06/26/2023]
Abstract
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).
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Affiliation(s)
- Immanuel Sanka
- Department of Chemistry and Biotechnology, School of Science, Tallinn University of Technology, Akadeemia tee 15, 12618, Tallinn, Estonia
| | - Simona Bartkova
- Department of Chemistry and Biotechnology, School of Science, Tallinn University of Technology, Akadeemia tee 15, 12618, Tallinn, Estonia
| | - Pille Pata
- Department of Chemistry and Biotechnology, School of Science, Tallinn University of Technology, Akadeemia tee 15, 12618, Tallinn, Estonia
| | - Mart Ernits
- MATTER, Institute of Technology, University of Tartu, Nooruse 1, 50411, Tartu, Estonia
| | | | - Natali Agu
- Rapla Gymnasium, Kooli 8, 79513, Rapla, Estonia
| | - Villem Aruoja
- Laboratory of Environmental Toxicology, National Institute of Chemical Physics and Biophysics, Akadeemia tee 23, 12618, Tallinn, Estonia
| | - Olli-Pekka Smolander
- Department of Chemistry and Biotechnology, School of Science, Tallinn University of Technology, Akadeemia tee 15, 12618, Tallinn, Estonia.
| | - Ott Scheler
- Department of Chemistry and Biotechnology, School of Science, Tallinn University of Technology, Akadeemia tee 15, 12618, Tallinn, Estonia.
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Tamminen J, Lahdenperä E, Koiranen T. Image analysis based mass transfer measurement in droplet breakage phenomenon. Chem Eng Sci 2021. [DOI: 10.1016/j.ces.2021.116964] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 10/20/2022]
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Sanka I, Bartkova S, Pata P, Smolander OP, Scheler O. Investigation of Different Free Image Analysis Software for High-Throughput Droplet Detection. ACS OMEGA 2021; 6:22625-22634. [PMID: 34514234 PMCID: PMC8427638 DOI: 10.1021/acsomega.1c02664] [Citation(s) in RCA: 5] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 05/21/2021] [Accepted: 08/13/2021] [Indexed: 06/13/2023]
Abstract
Droplet microfluidics has revealed innovative strategies in biology and chemistry. This advancement has delivered novel quantification methods, such as droplet digital polymerase chain reaction (ddPCR) and an antibiotic heteroresistance analysis tool. For droplet analysis, researchers often use image-based detection techniques. Unfortunately, the analysis of images may require specific tools or programming skills to produce the expected results. In order to address the issue, we explore the potential use of standalone freely available software to perform image-based droplet detection. We select the four most popular software and classify them into rule-based and machine learning-based types after assessing the software's modules. We test and evaluate the software's (i) ability to detect droplets, (ii) accuracy and precision, and (iii) overall components and supporting material. In our experimental setting, we find that the rule-based type of software is better suited for image-based droplet detection. The rule-based type of software also has a simpler workflow or pipeline, especially aimed for non-experienced users. In our case, CellProfiler (CP) offers the most user-friendly experience for both single image and batch processing analyses.
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Hosseinzadeh M, Shirvani M, Ghaemi A. A study on mean drop size and drop size distribution in an eductor liquid–liquid extractor. Sep Purif Technol 2018. [DOI: 10.1016/j.seppur.2018.03.020] [Citation(s) in RCA: 6] [Impact Index Per Article: 0.9] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/01/2022]
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