Visualization of HIV-1 Gag Binding to Giant Unilamellar Vesicle (GUV) Membranes

Olety, Balaji; Veatch, Sarah L.; Ono, Akira

doi:10.3791/54293

Cited by 3 publications

(3 citation statements)

References 33 publications

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“…GUV was prepared with the electroformation [ 44 , 45 ] and gentle hydration [ 5 ]. Briefly, for electroformation, a lipid mixture of a desired composition was deposited on an indium tin oxide (ITO)-coated glass (Delta Technologies) at 55 °C.…”

Section: Methodsmentioning

confidence: 99%

Intelligent fluorescence image analysis of giant unilamellar vesicles using convolutional neural network

et al. 2022

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Background Fluorescence 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. Results We 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. Conclusions We 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.

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Section: Methodsmentioning

confidence: 99%

Intelligent fluorescence image analysis of giant unilamellar vesicles using convolutional neural network

et al. 2022

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“…This assembly is required for the viral particle formation, as any defect in the membrane binding of Gag will lead to the impairment of the viral particle production. Molecular mechanisms underlying the Gag-membrane binding were characterized by SPR NMR, liposome flotation centrifugation, and fluorescence lipid bead binding [36].…”

Section: Infectious Diseasesmentioning

confidence: 99%

Surface plasmon resonance based‐optical biosensor: Emerging diagnostic tool for early detection of diseases

Singh

Anwar

Pradhan

et al. 2023

Journal of Biophotonics

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The development of diagnostic tools remains at the center of the health care system. In recent times optical biosensors have been widely applied in the scientific community, especially for monitoring protein–protein or nucleic acid hybridization interactions. Optical biosensors‐derived surface plasmon resonance (SPR) technology has appeared as a revolutionary technology at the current times. This review focuses on the research work in molecular biomarker evaluation using the technique based on SPR for translational clinical diagnosis. The review has covered both communicable and noncommunicable diseases by using different bio‐fluids of the patient's sample for diagnosis of the diseases. An increasing number of SPR approaches have been developed in healthcare research and fundamental biological studies. The utility of SPR in the area of biosensing basically lies in its noninvasive diagnostic and prognostic feature due to its label‐free high sensitivity and specificity properties. This makes SPR an invaluable tool with precise application in the recognition of different stages of the disease.

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“…GUV was prepared with the electroformation method. 42,43 Brie y, a lipid mixture of a desired composition was deposited on an indium tin oxide (ITO)-coated glass (Delta Technologies) at 55°C. The lipid was dried by nitrogen gas and further dried in a vacuum chamber.…”

Section: Sample Preparationmentioning

confidence: 99%

Intelligent Fluorescence Image Analysis of Giant Unilamellar Vesicles and Protein Liquid Phase Droplets by Whole Z-stack Analysis

Lee¹,

Passaro²,

Ozturk³

et al. 2021

Preprint

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Fluorescence 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 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. We developed an intelligent analysis routine based on the 3D information of whole z-stack images. The program identifies the valid vesicles to analyze and calculates the desired data automatically. The program can examine the amount of protein binding on the membranes and determine the state of domain phase separation by calculating the fluorescence intensity trace along the membranes. We also show that the method can easily be applied to similar analyses, such as intensity analysis of phase-separated protein droplets. A deep learning-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.

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Visualization of HIV-1 Gag Binding to Giant Unilamellar Vesicle (GUV) Membranes

Cited by 3 publications

References 33 publications

Intelligent fluorescence image analysis of giant unilamellar vesicles using convolutional neural network

Intelligent fluorescence image analysis of giant unilamellar vesicles using convolutional neural network

Surface plasmon resonance based‐optical biosensor: Emerging diagnostic tool for early detection of diseases

Intelligent Fluorescence Image Analysis of Giant Unilamellar Vesicles and Protein Liquid Phase Droplets by Whole Z-stack Analysis

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