Digital Cytometry of Mixed Cell Populations Without Cell Sorting
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Solution Overview
Problem
Comprehensive assessment of cellular heterogeneity in tissues is challenging due to the complexity of cell types and the lack of effective methods for studying tissue composition without physical dissociation or prior knowledge.
Innovation Solution
A computational framework, CIBERSORTx, is used to infer cell type abundance and gene expression from RNA profiles of intact tissues using single-cell experiments or sorted cell subsets, enabling digital purification of cell types from bulk RNA admixtures through techniques like cross-platform data normalization and in silico cell purification.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If physical dissociation or cell sorting methods are used to study tissue composition, then cell type identification accuracy is improved, but tissue integrity and sample complexity are worsened
Solution Approach 1:
The patent replaces physical/mechanical cell separation methods (dissociation, sorting) with a computational/mathematical approach. The deconvolution algorithm mathematically separates bulk tissue gene expression profiles into constituent cell type proportions and signatures, eliminating the need for physical manipulation of cells while maintaining identification accuracy.
Solution Approach 2:
The patent introduces a computational intermediary (the deconvolution algorithm and reference signatures) that mediates between the bulk tissue RNA input and the cell type composition output. This computational layer enables indirect separation and identification of cell types without direct physical intervention.
2Stability of the object's composition
If bulk RNA sequencing is performed on intact tissues, then sample integrity is maintained, but cell type-specific gene expression resolution is reduced
Solution Approach 1:
The patent applies computational segmentation by decomposing the bulk tissue gene expression profile into distinct cell type-specific components. The deconvolution algorithm segments the mixed signal into individual cell type contributions, enabling resolution of cell type-specific expression while the physical tissue remains intact.
Solution Approach 2:
The patent changes the analytical parameter from direct physical measurement of individual cells to mathematical deconvolution of bulk signals. By transforming the problem from spatial/physical separation to computational parameter optimization, the method achieves cell type resolution without compromising tissue integrity.
3Measurement precision
If traditional cell separation methods are used, then cell type purity is improved, but time consumption and procedural complexity increase
Solution Approach 1:
The patent replaces time-consuming mechanical cell separation procedures with rapid computational analysis. The in silico deconvolution process achieves cell type purification in silico without the hours or days required for physical sorting, isolation, and validation methods.
Solution Approach 2:
The patent uses pre-established reference cell type signatures as a foundation for the deconvolution analysis. These reference profiles are prepared in advance, enabling the bulk tissue analysis to proceed rapidly by comparing against known signatures rather than requiring de novo cell separation and characterization.
Data Source
AI summary
The present disclosure provides systems and methods for analyzing a mixed population of cells. In particular, the present disclosure provides systems and methods for digital cytometry of a biological sample, digital analysis of a biological sample, digital purification of a biological sample, evaluation of a disease in an individual, and prediction of a clinical outcome of a disease therapy.