Multi-Dimensional Flow Cytometry for Automated Abnormal Cell Detection
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Solution Overview
Problem
Current flow cytometry techniques for detecting abnormal cells require time-consuming data analysis by trained professionals and are limited by the need for patient-specific panels, which can be impractical in clinical settings and fail to detect phenotypic changes in neoplastic cells.
Innovation Solution
A method using multi-dimensional analysis to define centroids and radii for normal cell clusters, allowing automated detection of abnormal cells by comparing test sets to these defined clusters, facilitating the identification of low-level tumor cells based on phenotypic differences.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If conventional flow cytometry techniques are used with patient-specific panels, then detection accuracy for residual disease can be achieved, but the process becomes time-consuming and requires expert analysis
Solution Approach 1:
The patent creates a reference database of normal cell populations and uses automated algorithms to compare test samples against this pre-established reference. Instead of requiring experts to manually analyze each sample, the system copies the characteristics of normal cells and automatically detects deviations, thereby maintaining high detection accuracy while significantly reducing analysis time.
Solution Approach 2:
The patent replaces the mechanical process of manual expert analysis with an automated computational system. The flow cytometry data is processed through computer algorithms that automatically identify abnormal cells by comparing them to reference normal populations, substituting human expertise with an automated mechanical/electronic system that performs the same function more efficiently.
2Reliability
If manual analysis by trained professionals is performed, then accurate distinction between normal and abnormal cell populations can be made, but the learning process is long and expertise is difficult to replicate
Solution Approach 1:
The system copies the analytical capability of trained professionals by encoding their expertise into automated algorithms. The reference database captures the characteristics of normal cell populations as defined by experts, and the automated system replicates their detection logic through computer processing, making the expertise reproducible and independent of individual human analysts.
Solution Approach 2:
The system enables self-service analysis where the automated algorithm independently performs the detection function that previously required trained professionals. The reference database serves as a self-contained knowledge base that the system uses to automatically distinguish normal from abnormal cells without requiring continuous human intervention or expertise.
3Measurement precision
If patient-specific panels are used for monitoring therapy response, then detection of residual disease is possible, but the process becomes impractical in clinical settings
Solution Approach 1:
The patent creates a universal reference database that can be applied across different patients and clinical scenarios. Instead of requiring custom patient-specific panels for each case, the system uses a single comprehensive reference of normal cell populations that serves multiple functions: detecting residual disease, monitoring therapy response, and identifying abnormal cell populations across various hematological disorders.
Solution Approach 2:
The system performs preliminary action by pre-establishing the reference database of normal cell populations before actual clinical testing. This reference is built in advance and stored for use in subsequent analyses, eliminating the need to create custom reference panels for each patient and allowing immediate application to new samples.
4Measurement precision
If conventional flow cytometry is used, then detection of abnormal cells can be performed, but the ability to detect phenotypic changes in neoplastic cells is limited
Solution Approach 1:
The patent transitions from traditional two-dimensional flow cytometry plots to multi-dimensional analysis by incorporating additional parameters such as cell size, granularity, and multiple fluorescence markers. This dimensional expansion enables the system to detect subtle phenotypic changes in neoplastic cells that would be invisible in conventional two-dimensional analyses, significantly improving detection sensitivity.
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
Enables accurate and automated detection of abnormal cells, reducing the need for expert analysis and enabling standardized, rapid detection of residual disease across various hematological disorders, including leukemias and myelodysplasia, with improved sensitivity and consistency.
Implementation Method 1
measuring a corresponding plurality of fluorescence intensities of each cell in the normal set of biological cells
Data Source
AI summary
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 defined set of clusters. Support Vector Machine (SVM) subroutines are employed to identify reference populations of interest by generating multidimensional boundary definitions. These boundary definitions may be used to identify reference populations to use in defining or refining a centroid line or a radius or radii defining a set of normal clusters, and to characterize and compare a test set of cells to the defined set of normal clusters.