Spatially-Resolved Single-Cell RNA Sequencing for Tumor Immunity
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Solution Overview
Problem
Current methods for understanding immune cell behaviors and intra-tumoral heterogeneity are limited by the inability to accurately measure gene expression changes in single cells and the impact of immune evasion mechanisms across tumor regions, leading to a lack of knowledge on how immune evasion impacts tumor-infiltrating lymphocytes and the variability of immune evasion genes within tumors.
Innovation Solution
A spatially-resolved single-cell RNA sequencing approach that spatially tags cells in tumor sections with unique oligonucleotides to link neighboring tumor and immune cell behaviors, identify immune evasion genes, and validate their effects on tumor immunity using over-expression and knockout methods.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If single-cell RNA sequencing is performed on disaggregated tissues, then gene expression changes in single cells can be measured, but spatial information linking neighboring cell states is lost
Solution Approach 1:
The patent applies preliminary action by spatially barcoding cells with oligonucleotide tags before tissue disaggregation and sequencing. This pre-labeling ensures that spatial information is captured and preserved throughout subsequent processing steps, allowing reconstruction of spatial relationships from single-cell RNA sequencing data without losing positional context during tissue dissociation.
Solution Approach 2:
The patent uses oligonucleotide barcodes as informational copies of spatial positions. Each cell receives a unique spatial barcode that replicates its location information, which is then maintained through disaggregation and sequencing processes. This copying mechanism allows the original spatial arrangement to be reconstructed from the sequenced data.
2Productivity
If average gene expression across cell populations is measured, then overall tissue characteristics can be determined, but heterogeneity in immune evasion mechanisms is obscured
Solution Approach 1:
The patent applies segmentation by analyzing gene expression at the single-cell level rather than averaging across populations. This division of the tissue into individual cell units allows detection of heterogeneity in immune evasion mechanisms, enabling identification of distinct cell states and their spatial relationships that would be masked in bulk analysis.
Solution Approach 2:
The patent implements local quality by examining gene expression patterns in specific spatial contexts and individual cells rather than uniform population averages. This approach reveals localized variations in immune evasion gene expression and identifies region-specific cellular phenotypes that contribute to tumor heterogeneity.
3Reliability
If known subpopulations are sorted and profiled, then specific cell types can be studied, but discovery of novel cell states and immune evasion mechanisms is limited
Solution Approach 1:
The patent applies inversion by reversing the traditional approach: instead of sorting cells based on known markers and then profiling them, the method profiles all cells first using spatially-resolved single-cell RNA sequencing, then identifies cell types and states from the data. This unbiased approach enables discovery of novel cell states and immune evasion mechanisms without being constrained by pre-existing knowledge.
Data Source
AI summary
The present invention provides tools and methods for the systematic analysis of genetic interactions between cells. The present invention provides tools and methods for modulating cell phenotypes and compositions, combinatorial probing of cellular circuits, for dissecting cellular circuitry, for delineating molecular pathways, and/or for identifying relevant targets for therapeutics development.