Fluorescence-microscopy-based image analysis for analyte-dependent particle doublet detection in a single-step...

Area of Science:

• Biomedical Engineering
• Analytical Chemistry
• Biophysics

Background:

• Immunoagglutination assays are crucial for biomolecule quantification.
• Accurate particle counting is essential for reliable assay results.
• Existing methods may lack precision or require large sample volumes.

Purpose of the Study:

• To develop and validate a novel fluorescence-microscopy-based image analysis method for particle classification.
• To apply this method to a particle-based immunoagglutination assay for biomolecule quantification.
• To theoretically and experimentally investigate the performance of general immunoagglutination assays.

Main Methods:

• Developed a pattern recognition algorithm using Gaussian-base-function fitting for particle classification (singlets, doublets, agglomerates).
• Applied the method to a biotin-streptavidin interaction model for immunoagglutination.
• Created a theoretical model for agglutination kinetics incorporating particle diffusion and steric factors.
• Verified the image analysis method using flow cytometric agglutination analysis.

Main Results:

• The image analysis method robustly identified and counted fluorescent microparticles.
• Experimental results showed good agreement with the developed theoretical model.
• The method demonstrated effectiveness in quantifying biomolecules in microliter-volume samples.

Conclusions:

• The novel image analysis method provides accurate particle classification for immunoagglutination assays.
• The theoretical model aids in optimizing assay sensitivity, dynamic range, and understanding binding parameters.
• This approach enhances the reliability and efficiency of biomolecule quantification assays.