Spatial Transcriptomics for Mapping Tumor Heterogeneity
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Solution Overview
Problem
Existing methods fail to provide spatially resolved data on analyte levels within biological samples, particularly in the context of intact tissues, limiting the ability to characterize cellular and genetic heterogeneity in tumors, which is crucial for personalized cancer treatment and diagnosis.
Innovation Solution
The use of spatial transcriptomics methods involving probes with spatial barcodes that bind specifically to nucleic acids associated with immune or cancer cells, allowing for the determination of their location and characterization of heterogeneity within a biological sample.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Quantity of substance
If bulk methods such as whole-genome and whole-transcriptome sequencing are used, then comprehensive analyte data can be obtained, but the ability to resolve fine grain molecular signatures and spatial heterogeneity is lost
Solution Approach 1:
The biological sample is divided into multiple spatial regions or spots, each assigned a unique spatial barcode. This segmentation allows the sample to be analyzed in discrete locations while maintaining spatial context, resolving the contradiction between comprehensive analysis and spatial resolution.
Solution Approach 2:
The patent adds a spatial dimension to traditional bulk sequencing by incorporating spatial barcodes that encode location information. This transforms the analysis from a single-dimensional bulk measurement to a multi-dimensional spatially-resolved measurement, enabling both comprehensive analyte data and precise spatial localization.
2Loss of information
If techniques providing data for a small handful of analytes in intact tissue are used, then spatial context is maintained, but comprehensive analyte data and single-cell resolution are lost
Solution Approach 1:
The spatial barcode system serves multiple functions simultaneously: it maintains spatial context, enables comprehensive analyte detection through sequencing, and provides single-cell or sub-cellular resolution. This multi-functionality resolves the contradiction by making the spatial barcoding system universally applicable to multiple analytical goals.
Solution Approach 2:
The spatial barcode acts as an intermediary element that bridges the gap between intact tissue architecture and comprehensive molecular analysis. It captures spatial information while allowing downstream sequencing to generate comprehensive analyte data, thus mediating between these two requirements.
3Quantity of substance
If single-cell analysis is performed, then comprehensive analyte data is obtained, but spatial position information is lost
Solution Approach 1:
The spatial barcode is attached to the capture probe before the analysis process begins. This preliminary tagging ensures that spatial position information is preserved from the outset, allowing subsequent single-cell or sub-cellular analysis to recover both comprehensive analyte data and spatial context.
Solution Approach 2:
The spatial barcode is nested within the sequence data generated during sequencing analysis. The barcode sequence is embedded in the read data, allowing spatial position information to be extracted alongside comprehensive analyte information from the same sequencing run, effectively nesting one type of information within another.
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 the identification and characterization of tumor regions, predicting treatment efficacy, selecting appropriate therapies, and determining prognosis by providing spatially resolved data on gene expression and immune cell distribution, thereby improving cancer management.
Implementation Method 1
a capture probe, wherein the capture probe comprises a spatial barcode and a capture domain, wherein the capture domain is capable of hybridizing to a target nucleic acid
Data Source
AI summary
Provided herein are methods of characterizing tumors or a region of interest in a biological sample.


