In Situ Biological Heterogeneity Characterization via Digital Tissue Segmentation
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Solution Overview
Problem
Current methods for analyzing biological samples often lose original location information and can only assess a limited number of targets, limiting the ability to determine the spatial distribution of biomarkers and other features, which is clinically informative.
Innovation Solution
A method and system for determining cell heterogeneity in biological samples by receiving image data, segmenting cells, quantitating cell features, generating cell types based on biomarker expression and morphological features, and calculating molecular and spatial heterogeneity values to produce a heterogeneity metric.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If current techniques process the sample to detect biomarkers, then biomarker presence or concentration can be detected, but original location information is lost
Solution Approach 1:
The patent creates a digital copy of the tissue sample through high-resolution imaging that preserves spatial information. The image data serves as a replica that maintains all location information while allowing repeated analysis without further processing the physical sample. This enables both accurate biomarker detection and preservation of spatial distribution data.
Solution Approach 2:
The patent replaces physical sample processing with computational image analysis. Instead of mechanically or chemically processing the tissue sample to detect biomarkers, the system uses digital image processing algorithms to extract biomarker information while preserving the original spatial coordinates in the image data.
2Productivity
If limited number of targets are assessed from a given sample, then analysis depth is maintained, but spatial distribution of multiple biomarkers cannot be determined
Solution Approach 1:
The patent implements a universal image analysis platform that can simultaneously assess multiple biomarkers and cell features from a single image dataset. The system generates comprehensive profiles including spatial distribution, cell morphology, and biomarker expression for multiple targets concurrently, eliminating the need for separate analyses for each target.
Solution Approach 2:
The patent adds spatial dimension to traditional biomarker assessment by analyzing the spatial coordinates of all detected features in the image. This transforms the analysis from one-dimensional (presence/absence) to two-dimensional (location + presence), enabling determination of spatial distribution patterns for multiple biomarkers simultaneously.
3Adaptability or versatility
If repeated biopsy is performed for further analysis, then additional target analysis is enabled, but sample availability is limited and spatial relationships are lost
Solution Approach 1:
The patent creates a permanent digital record of the tissue sample that can be analyzed indefinitely without further biopsies. The image data serves as a reusable copy that preserves all spatial information, allowing multiple research questions to be addressed from the same original sample through computational analysis alone.
4Measurement precision
If comprehensive cell profiling is performed, then heterogeneity assessment is improved, but computational complexity increases
Solution Approach 1:
The patent segments the complex task of heterogeneity assessment into distinct analytical components: cell detection, feature extraction, clustering analysis, and spatial pattern recognition. Each component processes specific aspects of the data independently, making the overall complex analysis manageable and interpretable while maintaining comprehensive profiling capability.
Data Source
AI summary
The present disclosure relates to characterization of biological samples. By way of example, a biological sample may be contacted with a plurality of probes specific for targets in the sample, such as probes for immune markers and segmenting probes. Acquired image data of the sample may be used to segment the images into epithelial and stromal regions to characterize individual cells in the sample based on the binding of the probes. Further, the biological sample may be characterized by a heterogeneity of the characterized cells.