Abstract
BackgroundFluorescence image analysis in biochemical science often involves the complex tasks of identifying samples for analysis and calculating the desired information from the intensity traces. Analyzing giant unilamellar vesicles (GUVs) is one of these tasks. Researchers need to identify many vesicles to statistically analyze the degree of molecular interaction or state of molecular organization on the membranes. This analysis is complicated, requiring a careful manual examination by researchers, so automating the analysis can significantly aid in improving its efficiency and reliability.ResultsWe developed a convolutional neural network (CNN) assisted intelligent analysis routine based on the whole 3D z-stack images. The programs identify the vesicles with desired morphology and analyzes the data automatically. The programs can perform protein binding analysis on the membranes or state decision analysis of domain phase separation. We also show that the method can easily be applied to similar problems, such as intensity analysis of phase-separated protein droplets. CNN-based classification approach enables the identification of vesicles even from relatively complex samples. We demonstrate that the proposed artificial intelligence-assisted classification can further enhance the accuracy of the analysis close to the performance of manual examination in vesicle selection and vesicle state determination analysis.ConclusionsWe developed a MATLAB based software capable of efficiently analyzing confocal fluorescence image data of giant unilamellar vesicles. The program can automatically identify GUVs with desired morphology and perform intensity-based calculation and state decision for each vesicle. We expect our method of CNN implementation can be expanded and applied to many similar problems in image data analysis.
Highlights
Fluorescence image analysis in biochemical science often involves the complex tasks of identifying samples for analysis and calculating the desired infor‐ mation from the intensity traces
Example studies include the study of lipid membrane phase separation [2,3,4], phase modulation by membrane anchored proteins [5, 6], reconstitution of membrane remodeling processes [7, 8], the viral assembly process [9], measurement of mechanical tension [10, 11], effect of osmotic pressure [12], membrane protein reconstitution [13], and monitoring protein binding to the membrane [14] to list a few
The most common mode of analysis in Giant unilamellar vesicle (GUV) fluorescence imaging is manually studying each individual vesicle to examine enough vesicles for statistical analysis from a population of GUVs [5]. This may be suitable for experiments with a relatively small number of GUVs, for larger-scale sample numbers, manual analysis is very time consuming and often difficult to implement, when researchers are not very experienced with the nature of the GUV experiments or when the lab does not have enough resources of labor to spend on the analysis
Summary
Fluorescence image analysis in biochemical science often involves the complex tasks of identifying samples for analysis and calculating the desired infor‐ mation from the intensity traces. Researchers need to identify many vesicles to statistically analyze the degree of molecular interaction or state of molecular organization on the membranes. This analysis is complicated, requiring a careful manual examination by researchers, so automating the analysis can significantly aid in improving its efficiency and reliability. The most common mode of analysis in GUV fluorescence imaging is manually studying each individual vesicle to examine enough vesicles for statistical analysis from a population of GUVs [5] This may be suitable for experiments with a relatively small number of GUVs, for larger-scale sample numbers, manual analysis is very time consuming and often difficult to implement, when researchers are not very experienced with the nature of the GUV experiments or when the lab does not have enough resources of labor to spend on the analysis. Automated analysis can provide an effective solution to these problems by standardizing the analysis procedure
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