Multi-Dimensional Flow Cytometry for Abnormal Cell Detection
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Solution Overview
Problem
Current techniques for detecting abnormal cells using flow cytometry require time-consuming data analysis by professionals and rely on patient-specific panels, which are cumbersome and limited in detecting residual disease.
Innovation Solution
A method involving multi-dimensional analysis using flow cytometry to characterize normal and test sets of cells, where a centroid and radius are defined for normal cell clusters, allowing for the detection of low levels of tumor cells based on phenotypic differences.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If conventional flow cytometry techniques are used to detect abnormal cells, then detection capability is achieved, but data analysis is time-consuming and requires professional expertise
Solution Approach 1:
The patent creates a reference template representing normal cell populations in n-dimensional space, which serves as a copy or model of normalcy. This template can be automatically compared against test samples, eliminating the need for professionals to manually analyze each case while maintaining detection accuracy.
Solution Approach 2:
The patent replaces the mechanical process of manual professional analysis with an automated computational system. The system uses algorithms to define n-dimensional spaces, create reference templates, and automatically compare test cells against the template, substituting human expertise with machine-based automated analysis.
2Measurement precision
If patient-specific panels are used for detecting residual disease, then detection specificity is improved, but device complexity and ease of operation deteriorate
Solution Approach 1:
The patent creates a universal reference template that can be applied across multiple patients and disease types. Instead of requiring separate patient-specific panels, the system uses a generalizable n-dimensional approach with reference templates that can detect residual disease across different contexts, making the system multi-functional and reducing complexity.
Solution Approach 2:
The patent transitions from traditional two-dimensional flow cytometry analysis to n-dimensional space analysis. By adding multiple dimensions (different cell characteristics and markers), the system achieves high detection specificity without requiring complex patient-specific panel designs, as the additional dimensions provide more discriminative power.
3Productivity
If traditional flow cytometry analysis is used, then cell characterization is achieved, but accuracy of detection for low levels of tumor cells is insufficient
Solution Approach 1:
The patent extends analysis from traditional 2D to n-dimensional space by incorporating multiple cell characteristics simultaneously. This dimensional expansion allows the system to detect subtle phenotypic differences in low-level tumor cells that would be invisible in lower-dimensional analysis, thereby improving detection accuracy without sacrificing efficiency.
Solution Approach 2:
The patent creates a reference template that captures the complete n-dimensional profile of normal cell populations. By comparing test cells against this comprehensive reference copy, the system can accurately identify even low-level tumor cells that deviate from normal patterns, improving detection accuracy while maintaining efficient automated processing.
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
Facilitates the accurate detection of low levels of tumor cells and simplifies data analysis, enabling the identification of abnormal cell populations with increased sensitivity and specificity.
Implementation Method 1
measuring a corresponding plurality of fluorescence intensities of each cell in the normal set of biological cells using a second protocol
Data Source
AI summary
A system, method, and article for diagnosing a test set of biological cells. For example, in one embodiment a normal set of cells is characterized using flow cytometry. A centroid and radius are defined for a set of clusters in an n-dimensional space corresponding to a normal maturation for a cell lineage in the normal set of cells. A test set of cells is characterized using flow cytometry and the characterization is compared to the set of clusters.