Fluorescence Signal Separation Using Cell Morphology and Reference Spectra
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Solution Overview
Problem
Existing fluorescence imaging techniques struggle with accurate fluorescence separation, particularly in multi-marker immunostaining, due to variations in autofluorescence spectra between pixels, leading to inconsistent results even in morphologically similar cells.
Innovation Solution
An information processing apparatus and system that utilize an inference model incorporating morphological information and machine learning to separate fluorescence signals from autofluorescence, using image and spectrum data to enhance accuracy and reduce noise.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If conventional fluorescence separation methods are used, then the process is simple, but the separation accuracy deteriorates due to autofluorescence variations between pixels
Solution Approach 1:
The patent extracts autofluorescence reference spectra from unstained sections of the same tissue block before performing fluorescence separation on stained sections. This preliminary extraction of reference data enables more accurate separation by accounting for pixel-specific autofluorescence characteristics, directly improving measurement precision without requiring complex real-time adjustments during the main measurement process.
Solution Approach 2:
The patent introduces an inference model that uses extracted autofluorescence spectra and morphological information as intermediary data to predict and compensate for autofluorescence in stained regions. This intermediary processing step acts as a mediator between the raw fluorescence signal and the final separated result, improving accuracy by removing the harmful effect of autofluorescence variations.
2Loss of information
If multiple fluorescent dyes are used in multicoloring, then the information content increases, but the difficulty of fluorescence separation increases due to overlapping spectra and autofluorescence
Solution Approach 1:
The patent performs preliminary extraction of autofluorescence reference spectra from unstained sections before multicolor fluorescence imaging. This advance preparation of reference data for each pixel enables the subsequent separation algorithm to effectively distinguish multiple fluorescent dye signals from autofluorescence, maintaining information integrity while reducing separation difficulty in multicolor applications.
Solution Approach 2:
The patent transforms the fluorescence separation problem by changing parameters - using morphological information and extracted autofluorescence spectra as additional parameters to constrain and guide the separation process. This parameter transformation enables accurate separation of multiple overlapping fluorescent signals by incorporating spatial and spectral constraints that reduce the complexity of multicolor analysis.
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
The system achieves more precise fluorescence separation by accounting for variations in autofluorescence and noise, improving image contrast and consistency across cell regions.
Implementation Method 1
a separation unit that separates a fluorescence signal derived from a fluorescent reagent from a fluorescence image on the basis of the fluorescence image of a biological sample containing a cell, a reference spectrum derived from the biological sample or the fluorescent reagent
Data Source
AI summary
An information processing apparatus that includes a separation unit that separates a fluorescence signal derived from a fluorescent reagent from a fluorescence image on the basis of the fluorescence image of a biological sample containing a cell, a reference spectrum derived from the biological sample or the fluorescent reagent, and morphological information of the cell.