Liquid Biopsy Cell Clustering Using Fluorescence and Morphology
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Solution Overview
Problem
Existing methods for detecting and characterizing rare and common events in liquid biopsy samples are inadequate due to the complexity and variability of cancer cells, which do not neatly fit into specific categories, and there is a need for improved techniques to identify and characterize these cells based on their fluorescence characteristics and morphology.
Innovation Solution
A system and method for identifying biological structures in liquid biopsy samples using an optical imaging system with fluorescence and brightfield microscopy, combined with a processing system to analyze fluorescence intensity and morphology, enabling the differentiation of common and rare biological structures through the use of fluorophores and antibodies, and forming identification buckets based on these characteristics.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If traditional enrichment methods are used to detect rare cells based on known phenotypes, then detection of specific cell types is improved, but the ability to detect complex and varied rare cells that do not fit neat categories deteriorates
Solution Approach 1:
The system performs self-service by automatically discovering and categorizing cell types without requiring pre-programmed knowledge of specific cell phenotypes. The unsupervised machine learning algorithm autonomously analyzes imaging data, identifies patterns, and creates classification categories, enabling the system to adapt to any cell type present in the sample rather than being limited to pre-defined cell types.
Solution Approach 2:
The system changes parameters by transitioning from fixed phenotype-based detection to dynamic, data-driven parameter identification. Instead of using predetermined cell markers and phenotypes, the system extracts multiple imaging parameters (morphological features, texture characteristics, intensity distributions) and uses machine learning to determine which parameters are most discriminative for distinguishing different cell types in the actual sample.
2Measurement precision
If multiple imaging parameters and unsupervised machine learning are employed to accurately categorize diverse cells, then cell classification accuracy is improved, but system complexity increases
Solution Approach 1:
The system segments the complex task of cell classification into distinct functional modules: image acquisition, feature extraction, dimensionality reduction, clustering analysis, and classification. This segmentation allows each component to be optimized independently and facilitates implementation using standard computational tools and machine learning libraries, reducing overall system complexity.
Solution Approach 2:
The system achieves universality by implementing a phenotype-agnostic classification framework that can handle any cell type without requiring customization. The unsupervised machine learning approach and multi-parameter imaging analysis create a universal platform that adapts to different sample types and cell populations, eliminating the need for separate systems or extensive reconfiguration for different applications.
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 identification and characterization of various biological structures, including rare cancer cells, by forming disease maps and atlases, facilitating diagnosis and treatment of diseases based on the identified structures.
Implementation Method 1
The biological structure(s) are labeled with a fluorophore; illuminate the liquid biopsy sample with electromagnetic radiation having a wavelength that can be absorbed by the fluorophore; detect and determine an intensity and a wavelength of fluorescence emitted by the fluorophore
Data Source
AI summary
A system for identification of a biological structure present in a liquid biopsy sample is provided. The system identifies common biological structures and rare biological structures based on their fluorescence characteristics and morphology. The identified biological structures may be used in diagnosis and treatment of a human afflicted with a disease. Examples described in this disclosure also relate to methods and assays that may be used together with the systems of this disclosure for diagnosis and treatment of a human afflicted with a disease.