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

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

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.

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

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.

Supplementary Information

The online version contains supplementary material available at 10.1186/s12859-022-04577-2.