Vibrational Spectroscopy for Quantifying Antigen Retrieval Progress
Find Innovative SolutionsGenerate Solutions
Solution Overview
Problem
Current methods for antigen and target retrieval in formalin-fixed tissues lack a universal and quantitative method to assess the efficiency of unmasking processes, which is crucial for accurate immunohistochemical and in-situ hybridization procedures, as they are dependent on enzyme activity and fixation time, and can lead to tissue degradation.
Innovation Solution
A system and method using vibrational spectroscopy to predict the unmasking status of biological specimens by deriving features from spectral data, employing machine learning algorithms to quantify the unmasking process conditions such as temperature and duration, applicable to both antigen and target retrieval.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If proteolytic enzyme treatment is used for antigen retrieval, then antigen accessibility is improved, but tissue degradation and epitope destruction occur
Solution Approach 1:
The patent replaces proteolytic enzyme treatment (chemical/biological system) with vibrational spectroscopy-based quantitative assessment (physical/optical system). This substitution allows monitoring of antigen retrieval progress without introducing harmful enzymatic degradation, using spectroscopic signatures to track unmasking efficiency in real-time.
Solution Approach 2:
The patent implements a feedback mechanism where vibrational spectroscopy continuously monitors the antigen retrieval process. The spectroscopic data provides real-time information about antigen accessibility and tissue integrity, allowing dynamic adjustment of retrieval conditions to optimize antigen exposure while preventing tissue degradation and epitope destruction.
2Reliability
If prolonged proteolytic treatment is applied, then antigen unmasking is enhanced, but three-dimensional protein structure is lost
Solution Approach 1:
Vibrational spectroscopy provides real-time feedback on protein structural integrity during antigen retrieval. By monitoring characteristic protein vibrational modes, the system can detect early signs of three-dimensional structure loss and signal for process termination or parameter adjustment, preserving protein conformation while achieving adequate antigen unmasking.
Solution Approach 2:
The patent replaces prolonged proteolytic treatment with a non-invasive spectroscopic monitoring approach. Instead of using enzymes that progressively degrade proteins over extended periods, the system uses light-matter interactions to assess retrieval progress, eliminating the mechanism that causes structural damage while maintaining unmasking effectiveness.
3Productivity
If enzyme incubation time is extended, then antigen retrieval efficiency is improved, but measurement precision of optimal time is reduced
Solution Approach 1:
Vibrational spectroscopy provides continuous, real-time feedback during enzyme incubation, transforming the discrete endpoint assessment into a continuous monitoring process. This allows precise identification of the optimal incubation time point where antigen retrieval is maximized before tissue degradation begins, enabling accurate temporal measurement rather than relying on fixed predetermined intervals.
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 quick prediction of unmasking status, improving the detection of biomarkers by ensuring optimal unmasking conditions, thereby enhancing the quality of immunohistochemistry and in-situ hybridization assays.
Implementation Method 1
obtain test vibrational spectral data from the test biological specimen
Data Source
Figure 1
Figure 2
Figure 3
AI summary
The present disclosure relates to automated systems and methods for quantitatively determining an unmasking status of a biological specimen subjected to an unmasking process (e.g. an antigen retrieval process and/or a target retrieval process) using a trained unmasking status estimation engine. In some embodiments, the trained unmasking status estimation engine comprises a machine learning algorithm based on a projection onto latent structure regression model. In some embodiments, the trained unmasking status estimation engine includes a neural network.