Automated Subcellular Protein Expression Quantification
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Solution Overview
Problem
Current techniques for estimating protein expression in immunohistochemically stained samples fail to differentiate between abundant low expression levels and scarce high expression levels, as they provide a single score that does not account for intensity and abundance, leading to indistinguishable scoring of strongly and weakly stained images.
Innovation Solution
Automated methods for quantifying the percent and strength of subcellular protein expression in immunofluorescently stained tissue microarrays, involving image segmentation to localize compartments and deconvolve target molecule distributions from mixed distributions, allowing separate calculation of percentage and intensity of expression.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Ease of operation
If intensity or ratio-based techniques are used to estimate protein expression, then a single score can be provided after image evaluation, but the technique fails to differentiate between abundant low expression levels and scarce high expression levels
Solution Approach 1:
The patent segments the expression quantification into two independent components: percentage of cells expressing the marker and intensity of expression. This segmentation allows the system to provide both a simple positive/negative classification (based on percentage) and detailed expression level information (based on intensity), resolving the contradiction between ease of operation and measurement precision
Solution Approach 2:
The patent transitions from a single-dimensional scoring system to a two-dimensional system by adding the intensity dimension to the percentage dimension. This dimensional expansion enables the system to distinguish between abundant low expression and scarce high expression while maintaining the simplicity of the original single-score approach for classification purposes
2Quantity of substance
If total immunofluorescence is used to determine expression score, then both strength of expression and abundance of marker are captured, but the single score fails to determine whether the marker exhibits abundant low expression levels or scarce high expression levels
Solution Approach 1:
The patent segments the total immunofluorescence measurement into two independent parameters: the percentage of cells showing expression and the intensity of expression within those cells. This segmentation preserves the information about both abundance and strength while enabling differentiation between abundant low expression and scarce high expression patterns
Solution Approach 2:
The patent introduces an intermediary analysis step that separates the contribution of cell abundance from the contribution of expression intensity. By using image segmentation and intensity profiling as intermediaries, the system can deconvolute the mixed signal of total immunofluorescence into its constituent components, thereby preventing information loss
Data Source
AI summary
The present techniques provide fully automated methods for quantifying the location, strength and percent of expressed target molecules or other biological markers in immunohistochemically stained biological samples. The samples may be automatically segmented, for example into subcellular compartments, from images of compartmental markers. Then, the distribution of a target molecule on each of these compartments is calculated that includes the percentage and strength of expression. This is different than existing intensity or ratio based methods where abundant low expression levels are indistinguishable from scarce high expression levels.