Droplet image analysis with user-friendly freeware CellProfiler

Bartkova, Simona; Vendelin, Marko; Sanka, Immanuel; Pata, Pille; Scheler, Ott

doi:10.1039/d0ay00031k

Cited by 12 publications

(30 citation statements)

References 36 publications

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“…Ilastik is a machine learning based image analysis software 30 and QuPath has been used as whole slide image analysis tool 32 . We continued with these four popular software tools and used them to detect droplets on images previously described by Bartkova et al 31 (Figure 1b). Then, we took a deeper look into the working logic of the software and assessed their performance with different key parameters (Figure 1c).…”

Section: Resultsmentioning

confidence: 99%

“…Schematic of droplet generation and image analysis. (A) We generated water-in-oil droplets using flow-focusing microfluidic chip as described previously by Bartkova et al 31 (left). We used fluorescent microscope to obtain “Raw images” of droplets that contained fluorescence producing bacteria (middle).…”

Section: Resultsmentioning

confidence: 99%

“…Here, we did not add any plugins to detect droplets and we used similar settings to see the tool’s ability to detect and count the droplets. The extension software for CellProfiler, CellProfiler Analyst (CPA) 46 , could be an option to enhance droplet detection and have been described briefly our previous research 31 . Based on our classification, CPA belongs to instinctive software because users need to supervise or train the data at the beginning.…”

Section: Resultsmentioning

confidence: 99%

“…Based on our classification, CPA belongs to instinctive software because users need to supervise or train the data at the beginning. However, this software is not a standalone software and requires feature extraction or properties file which contain the observed data from CP 31,47 .…”

Section: Resultsmentioning

confidence: 99%

“…It is possible to expand the pipeline for further analysis e.g. bacteria detection 31 , enzyme reaction measurement 48 , chemical purification analysis 49 , and metal extraction 50 . This step is usually performed to extract the different aspects of droplet (size, texture, volume and etc.)…”

Section: Resultsmentioning

confidence: 99%

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Investigation of Different Free Image Analysis Software for High-Throughput Droplet Detection

Sanka

Bartkova

Pata

et al. 2021

Preprint

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Droplet microfluidics has revealed innovative strategies in biology and chemistry. This advancement has delivered novel quantification methods, such as digital droplet polymerase chain reaction (ddPCR) and antibiotic heteroresistance analysis tool. For droplet analysis, researchers often use imaging 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 detect droplets. We select four most popular software and classify them into instinctive and objective types based on their operation logic. We test and evaluate the software's i) ability to detect droplets, ii) accuracy and precision, and iii) overall user-friendliness. In our experimental setting we find the objective type of software is better suited for droplet detection. The objective type of software also has simpler workflow or pipeline, especially aimed for non-experienced user. In our case, CellProfilerTM (CP) offers the most user-friendly experience for both single image and batch processing analysis.

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Section: Resultsmentioning

confidence: 99%

Section: Resultsmentioning

confidence: 99%

Section: Resultsmentioning

confidence: 99%

Section: Resultsmentioning

confidence: 99%

Section: Resultsmentioning

confidence: 99%

See 3 more Smart Citations

Investigation of Different Free Image Analysis Software for High-Throughput Droplet Detection

Sanka

Bartkova

Pata

et al. 2021

Preprint

Self Cite

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Deep‐dLAMP: Deep Learning‐Enabled Polydisperse Emulsion‐Based Digital Loop‐Mediated Isothermal Amplification

et al. 2022

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Digital nucleic acid amplification tests enable absolute quantification of nucleic acids, but the generation of uniform compartments and reading of the fluorescence requires specialized instruments that are costly, limiting their widespread applications. Here, the authors report deep learning‐enabled polydisperse emulsion‐based digital loop‐mediated isothermal amplification (deep‐dLAMP) for label‐free, low‐cost nucleic acid quantification. deep‐dLAMP performs LAMP reaction in polydisperse emulsions and uses a deep learning algorithm to segment and determine the occupancy status of each emulsion in images based on precipitated byproducts. The volume and occupancy data of the emulsions are then used to infer the nucleic acid concentration based on the Poisson distribution. deep‐dLAMP can accurately predict the sizes and occupancy status of each emulsion and provide accurate measurements of nucleic acid concentrations with a limit of detection of 5.6 copies µl‐1 and a dynamic range of 37.2 to 11000 copies µl‐1. In addition, deep‐dLAMP shows robust performance under various parameters, such as the vortexing time and image qualities. Leveraging the state‐of‐the‐art deep learning models, deep‐dLAMP represents a significant advancement in digital nucleic acid tests by significantly reducing the instrument cost. We envision deep‐dLAMP would be readily adopted by biomedical laboratories and be developed into a point‐of‐care digital nucleic acid test system.

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Deep‐qGFP: A Generalist Deep Learning Assisted Pipeline for Accurate Quantification of Green Fluorescent Protein Labeled Biological Samples in Microreactors

Wei,

Abbasi,

Mehmood

et al. 2023

Small Methods

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Absolute quantification of biological samples provides precise numerical expression levels, enhancing accuracy, and performance for rare templates. Current methodologies, however, face challenges‐flow cytometers are costly and complex, whereas fluorescence imaging, relying on software or manual counting, is time‐consuming and error‐prone. It is presented that Deep‐qGFP, a deep learning‐aided pipeline for the automated detection and classification of green fluorescent protein (GFP) labeled microreactors, enables real‐time absolute quantification. This approach achieves an accuracy of 96.23% and accurately measures the sizes and occupancy status of microreactors using standard laboratory fluorescence microscopes, providing precise template concentrations. Deep‐qGFP demonstrates remarkable speed, quantifying over 2000 microreactors across ten images in just 2.5 seconds, with a dynamic range of 56.52–1569.43 copies µL−1. The method demonstrates impressive generalization capabilities, successfully applied to various GFP‐labeling scenarios, including droplet‐based, microwell‐based, and agarose‐based applications. Notably, Deep‐qGFP is the first all‐in‐one image analysis algorithm successfully implemented in droplet digital polymerase chain reaction (PCR), microwell digital PCR, droplet single‐cell sequencing, agarose digital PCR, and bacterial quantification, without requiring transfer learning, modifications, or retraining. This makes Deep‐qGFP readily applicable in biomedical laboratories and holds potential for broader clinical applications.

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Droplet image analysis with user-friendly freeware CellProfiler

Abstract: We show how to use free open-source CellProfiler for droplet microfluidic image analysis.

Cited by 12 publications

References 36 publications

Investigation of Different Free Image Analysis Software for High-Throughput Droplet Detection

Investigation of Different Free Image Analysis Software for High-Throughput Droplet Detection

Deep‐dLAMP: Deep Learning‐Enabled Polydisperse Emulsion‐Based Digital Loop‐Mediated Isothermal Amplification

Deep‐qGFP: A Generalist Deep Learning Assisted Pipeline for Accurate Quantification of Green Fluorescent Protein Labeled Biological Samples in Microreactors

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