Single-Cell Immune Profiling to Resolve Bulk Heterogeneity
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Solution Overview
Problem
Current methods for analyzing immune cells rely on 'top-down' approaches that mask individual cell heterogeneities, limiting the understanding of complex immune system behaviors and interactions, particularly in diseases like HIV infection, where rare cell types and intercellular communication play crucial roles.
Innovation Solution
The use of single-cell genomic approaches, such as scRNA-Seq, to profile individual immune cells and identify specific gene and protein signatures associated with immune phenotypes and behaviors, allowing for the identification of rare cell populations and their interactions, and the application of computational methods to rebalance immune subset composition using immunomodulators like TLR3 ligands.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Quantity of substance
If bulk analysis methods are used to measure immune system responses, then the overall system behavior can be measured, but individual cell heterogeneities are masked and lost
Solution Approach 1:
The patent segments the bulk immune cell population into individual single cells for analysis. By using single-cell RNA sequencing and mass cytometry, the method divides the homogeneous bulk sample into discrete cellular units, allowing each cell's unique molecular profile to be captured separately while still enabling population-level statistical analysis.
Solution Approach 2:
The patent adds a new dimension of analysis by measuring multiple molecular parameters (hundreds to thousands of genes or proteins) simultaneously at the single-cell level. This multi-dimensional molecular profiling approach transforms the traditional single-parameter bulk measurement into a high-dimensional single-cell dataset, revealing heterogeneity that was previously invisible.
2Ease of manufacture
If top-down approaches based on pre-selected markers are used to divide immune cells into subpopulations, then major cell types can be cataloged, but experimental design is biased and novel heterogeneities are missed
Solution Approach 1:
The patent inverts the traditional top-down classification approach by first performing unbiased molecular profiling of all cells using single-cell RNA sequencing or mass cytometry, then using computational clustering to discover cell subpopulations de novo. This bottom-up discovery approach identifies cell states based on actual molecular differences rather than pre-selected markers, revealing novel heterogeneities while still enabling systematic classification.
3Measurement precision
If single-cell genomic approaches are used to profile individual immune cells, then cell heterogeneity and rare populations can be identified, but technical complexity and cost increase
Solution Approach 1:
The patent merges multiple single-cell technologies (RNA sequencing, mass cytometry, and computational analysis) into an integrated workflow. By combining these approaches, the method achieves comprehensive molecular profiling that captures both transcriptomic and proteomic heterogeneity, while the unified analytical framework manages the technical complexity through standardized data processing pipelines.
Data Source
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AI summary
The present invention provides tools and methods for the systematic analysis of genetic interactions in immune cells. The present invention provides tools and methods for modulating immune 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.