Microsphere Immunoassay Imaging for Clustered Bead Localization
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Solution Overview
Problem
Existing immunoassay technologies using microspheres face challenges in accurately analyzing randomly distributed and potentially clustered beads due to difficulties in determining bead centers, leading to incorrect readouts and contamination issues with flow-cytometers.
Innovation Solution
An image-based approach utilizing a mathematical model for deconvolution and a source point localization algorithm to identify bead centers in a multi-channel fluorescence microscopy image, employing a realistic physical observation model and iterative optimization methods to accurately determine bead positions and intensities.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Extent of automation
If flow-cytometers are used to analyze microspheres, then automated detection and quantification can be achieved, but the instrument becomes contaminated with pathogens and requires yearly maintenance
Solution Approach 1:
The patent creates an optical copy of the microsphere data by capturing fluorescent images and processing them through deconvolution algorithms to extract positional information, replacing the need for physical flow-cytometer analysis and eliminating pathogen contamination while maintaining automated detection capabilities
Solution Approach 2:
The patent replaces the mechanical flow-cytometer system with an optical imaging and computational processing system, substituting physical fluid flow and laser alignment mechanics with digital image capture and algorithmic analysis, thereby eliminating contamination risks
2Adaptability or versatility
If microspheres are randomly distributed in the assay well, then multiplexing capability is achieved, but accurate localization of bead centers becomes difficult
Solution Approach 1:
The patent applies deconvolution algorithms and point spread function modeling before final bead center localization to pre-process the fluorescent images, accounting for optical distortions and cluster effects in advance, which enables accurate center determination even for randomly distributed microspheres
Solution Approach 2:
The patent transforms the image data through mathematical deconvolution operations that change the parameter representation from raw fluorescent intensity to processed positional probability maps, enabling precise bead center identification despite random distribution and clustering
3Quantity of substance
If cluster of beads is analyzed, then higher concentration detection is achieved, but incorrect readouts occur due to difficulty in determining individual bead centers
Solution Approach 1:
The patent segments the cluster analysis problem by applying deconvolution to separate overlapping bead signals into individual bead center positions, allowing accurate identification and quantification of each bead within a cluster, thereby maintaining reliability even at higher concentrations
Applied Scientific Principles
This section explains which scientific principles are used to turn an abstract innovation direction into a practical engineering solution.
Function Achieved in This Case
Enables accurate and efficient analysis of microspheres without contact, overcoming the limitations of flow-cytometers by precisely locating bead centers within clusters and improving the reliability of immunoassay results.
Implementation Method 1
The intensity of the signal is proportional to the concentration of the analyte in the sample and many times involves a two-step detection process with a biotinylated detection antibody in combination with a streptavidin fluorophore conjugate
Data Source
AI summary
A method for determining positions of microspheres in an image of an immunoassay utilizing microspheres, wherein the image includes a plurality of depictions of microspheres, wherein the method includes determining the positions of microspheres by determining a distribution (P) based on a deconvolution of an observed luminescence (Od) and a mathematical representation (b) of a microsphere (bead) as observed by a microscope, wherein the distribution (P) provides the likelihood that there is a microsphere at a given position in the image.