Biomarker Intensity Normalization for Multiplexed Tissue Analysis
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Solution Overview
Problem
Current methods for analyzing biomarker expression in tissue specimens are limited by the need for iterative staining and bleaching processes, which restrict the number of biomarkers that can be examined simultaneously, and lack efficient methods for correlating biomarker expression data with clinical assessments across multiple fields of view or patients.
Innovation Solution
A computer-implemented method that normalizes biomarker expression intensity data across multiple fields of view or patients, identifies optimal threshold values, and correlates these with meta-information to determine positive cell percentages, thereby identifying the most relevant cell features and expression cutoffs that align with clinical assessments.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Adaptability or versatility
If iterative staining and bleaching processes are used to examine multiple biomarkers, then the number of biomarkers that can be examined increases, but the time required for analysis and the complexity of the process increase
Solution Approach 1:
The patent applies preliminary action by performing spectral unmixing and normalization calculations on the fluorescent signals before final analysis. The system pre-processes the multiplexed imaging data to separate overlapping spectra and normalize intensity variations, enabling simultaneous examination of multiple biomarkers without requiring iterative staining and bleaching steps, thus reducing total analysis time while maintaining the capability to examine numerous biomarkers
2Adaptability or versatility
If iterative staining and bleaching processes are used to examine multiple biomarkers, then the number of biomarkers that can be examined increases, but the complexity of the process increases
Solution Approach 1:
The patent extracts and separates the individual biomarker signals from the multiplexed fluorescent image data through spectral unmixing algorithms. By mathematically decomposing the overlapping fluorescent spectra into component signals, the system eliminates the need for complex iterative staining and bleaching procedures, allowing examination of multiple biomarkers through a single staining step followed by computational separation of signals
Solution Approach 2:
The patent replaces the mechanical and chemical iterative staining-bleaching process with a computational approach using spectral unmixing and normalization algorithms. Instead of physically removing and reapplying stains multiple times, the system uses mathematical transformations on the captured fluorescent images to separate and quantify multiple biomarkers simultaneously, significantly reducing procedural complexity
3Measurement precision
If normalization and thresholding methods are applied to biomarker expression data, then the correlation with clinical assessments improves, but the computational processing required increases
Solution Approach 1:
The patent applies parameter changes by implementing normalization transformations on the biomarker expression intensity values to account for variations in staining efficiency, imaging conditions, and tissue properties. The system transforms raw intensity data into normalized expression levels that correlate more accurately with clinical assessments, using computational algorithms that adjust intensity parameters based on reference standards and tissue-specific characteristics
Data Source
AI summary
A computer implemented method of analyzing tissue features based on multiplexed biometric images includes storing a data set including cell profile data and an assessment associated with a field of view or a patient. The cell profile data includes biomarker expression intensity data for at least one cell feature. The method includes normalizing the biomarker expression intensity data for each field of view. The method includes determining a plurality of positive cell percentages for a first cell feature for each field of view or for each patient based on a plurality of normalized expression cutoffs for all fields of view. The method further includes correlating positive cell percentages with assessments for each field of view or each patient. The method also includes identifying a combination of a cell feature for expression of a biomarker and a normalized expression cutoff that most closely correlate the positive cell percentage with the assessment.