Spatial Transcriptomics for Immune Cell Infiltration Analysis
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Solution Overview
Problem
Current methods fail to accurately analyze immune cell infiltration in cancerous regions of biological samples, particularly in tumors, due to limitations in spatially resolving analyte data and identifying specific immune cell distributions, which hampers personalized treatment approaches and prognosis assessment.
Innovation Solution
A method involving generating datasets from biological samples that include analyte data and image data linked by registration, using trained machine learning modules to identify cancerous, stromal, and immune cell regions, and determining immune cell infiltration by sequencing and hybridization with capture probes and analyte binding moieties, enabling precise localization and abundance analysis.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If standardized visual approaches are used to quantify TILs, then pathologists can perform analysis, but precision and accuracy of immune cell identification remain insufficient
Solution Approach 1:
The patent replaces the mechanical/visual approach used by pathologists with a molecular biology-based approach using spatial transcriptomics. By capturing and sequencing mRNA molecules at specific spatial locations within tissue sections, the system objectively identifies immune cells through their gene expression profiles rather than relying on visual morphology, thereby significantly improving identification precision and accuracy.
Solution Approach 2:
The patent changes the measurement parameters from visual morphology assessment to molecular expression profiling. By quantifying the abundance and spatial distribution of specific mRNA transcripts (such as CD3D, CD8A for T cells, CD79A for B cells) at single-cell resolution, the system transforms subjective visual estimation into objective molecular measurement, enabling precise characterization of immune cell types and their spatial relationships with tumor cells.
2Quantity of substance
If spatial heterogeneity is studied with limited analyte data, then tissue context is preserved, but comprehensive immune cell characterization is not achieved
Solution Approach 1:
The patent applies segmentation by dividing the tissue section into discrete spatial locations or regions of interest. Each location is independently analyzed for mRNA content, allowing the system to simultaneously characterize multiple analytes (genes) at each spatial position. This segmentation approach enables comprehensive immune cell profiling while preserving spatial context, as each segmented location maintains its positional information within the tissue architecture.
Solution Approach 2:
The patent adds a spatial dimension to molecular analysis by mapping gene expression data to specific physical locations within the tissue. Instead of analyzing analytes in bulk without spatial information, the system integrates molecular data with spatial coordinates, creating a three-dimensional understanding that combines cellular composition, gene expression, and tissue architecture. This dimensional integration enables simultaneous comprehensive analyte analysis and spatial context preservation.
3Measurement precision
If single-cell analyte data is provided without spatial information, then detailed molecular profiling is achieved, but cell position and tissue context are lost
Solution Approach 1:
The patent merges molecular profiling data with spatial location information by capturing mRNA molecules at their original positions within the tissue and sequencing them while preserving spatial coordinates. This merging approach allows the system to simultaneously achieve detailed molecular profiling of individual cells and maintain precise spatial position information, enabling analysis of both gene expression and the physical arrangement of cells within the tissue microenvironment.
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
This approach allows for precise identification and characterization of immune cell infiltration patterns, enhancing the ability to develop personalized treatments and improve cancer prognosis by providing detailed spatial and quantitative data on immune cell distributions within tumor microenvironments.
Implementation Method 1
hybridizing the analyte associated with the cancerous region, the analyte associated with the stromal region, and/or the analyte associated with an immune cell to the capture probe
Data Source
AI summary
Provided herein are methods for analyzing immune cell infiltration in a cancer stromal region of a biological sample obtained from a subject using machine learning modules. For example, the methods may include (a) identifying a cancerous region or an analyte associated with the cancerous region in the biological sample; (b) identifying a stromal region or an analyte associated with the stromal region in the biological sample; (c) identifying one or more immune cells or an analyte associated with an immune cell in one or more locations in the biological sample; and (d) using (i) the identified cancerous and stromal regions or associated analytes thereof in the biological sample and (ii) the identified one or more immune cells or associated analytes thereof to analyze immune cell infiltration in the cancer stromal region of the biological sample obtained from the subject.