Spatial Tissue Expression Mapping with ROI-Coded Biomarker Visualization
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Solution Overview
Problem
Current methods for identifying biomarkers in tumor microenvironments require tissue destruction, losing spatial information and leading to errors in image registration and misinterpretation due to limitations in fluorescence and bright-field imaging.
Innovation Solution
A biological expression mapping system that spatially maps biological expressions in tissue samples using processors to display demarcated regions-of-interest (ROIs) with color-coding and visualization tools, enabling high-plex, high-throughput, non-destructive characterization.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Loss of information
If fluorescence and bright-field imaging are used to provide visual maps of biomarkers, then spatial information is preserved, but the number of fluorophores that can be captured is limited, requiring multiple rounds of immunostaining
Solution Approach 1:
The patent divides the tissue sample into multiple regions of interest (ROIs) that can be independently processed and analyzed. This segmentation allows the system to handle complex tissue structures and perform multiple analyses on different regions without requiring multiple rounds of immunostaining on the entire sample.
Solution Approach 2:
The patent transitions from traditional 2D imaging to 3D spatial mapping by creating volumetric ROIs that extend through the tissue depth. This dimensional expansion enables the system to capture and analyze biomarker distribution in three dimensions, increasing the number of detectable fluorophores and biological insights without additional staining rounds.
2Quantity of substance
If multiple rounds of immunostaining and imaging are performed on the same sample, then more biomarkers can be detected, but the sample degrades over time leading to errors in image registration
Solution Approach 1:
The patent performs all necessary immunostaining and imaging operations on multiple ROIs simultaneously in a single round, rather than sequentially over multiple rounds. This preliminary completion of all detections in one session eliminates time-dependent sample degradation and maintains consistent registration accuracy across all biomarker visualizations.
Solution Approach 2:
The patent combines multiple immunostaining detections and imaging operations into a single integrated workflow that processes multiple biomarkers simultaneously across different ROIs. This merging of operations in one session preserves sample integrity and ensures consistent spatial registration without the cumulative errors that would arise from multiple separate rounds.
3Measurement precision
If tissue destruction is used to identify biomarkers in the tumor microenvironment, then biomarker identification is achieved, but spatial information about the biomarkers is sacrificed
Solution Approach 1:
The patent creates digital copies and visual representations of biomarker distributions within preserved tissue sections. By generating computational models and virtual maps of biomarker spatial distribution, the system enables precise biomarker identification while maintaining the intact tissue structure and spatial context for further analysis.
Solution Approach 2:
The patent changes the detection parameters from destructive methods to non-destructive imaging techniques, allowing biomarkers to be identified through optical detection while the tissue structure remains intact. This parameter change enables simultaneous acquisition of biomarker information and spatial contextual data without sacrificing either.
4Adaptability or versatility
If the system provides comprehensive spatial mapping of biological expressions, then tissue heterogeneity can be characterized, but the computational processing requirements increase
Solution Approach 1:
The patent segments the tissue into discrete ROIs with defined spatial boundaries, allowing the computational system to process and analyze each region independently. This segmentation reduces the complexity of analyzing entire tissue sections by breaking them into manageable units that can be processed in parallel, maintaining comprehensive heterogeneity characterization while reducing computational burden.
Solution Approach 2:
The patent applies different computational analysis methods to different ROIs based on their specific characteristics and biological questions. This localized approach allows the system to optimize processing for each region's specific requirements, improving heterogeneity characterization efficiency by avoiding uniform high-complexity processing across the entire tissue sample.
Data Source
AI summary
Systems, apparatuses and methods for spatially mapping at least one biological expression of a target biological component contained in a tissue sample to an image of the tissue sample are provided. In some embodiments, the system includes a processor and instructions that, when executed by the processor, cause the system to display, in a first display, a scans pane including at least the image of the tissue sample, the image including at least one demarcation corresponding to a region-of-interest (ROI(s)), where the ROI(s) correspond to a portion of the tissue within the tissue image. The instructions are further configured to cause the system to display, in a second display, a visualization pane including a visualization of the biological expression contained in the ROI(s); and to augment the first display by coding the ROI(s) in the tissue image to show the spatial mapping of the biological expression within the ROI(s).