Ghost Cytometry Classification Model for Rare Cell Detection
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Solution Overview
Problem
The flow cytometry method using the ghost cytometry (GC) method faces challenges in discriminating cells with specific morphological characteristics, especially when the target cells are rare, as it requires a large amount of waveform data for effective classification, which can be difficult to obtain.
Innovation Solution
A classification model generation method that uses training data from both samples containing cells with specific morphological characteristics and samples without these characteristics, allowing for the generation of a classification model that can discriminate particles based on waveform data indicating temporal changes in light intensity, even when the target cells are rare.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If a large amount of waveform data is collected for training the classification model, then the accuracy of cell discrimination is improved, but the difficulty of acquiring training data increases when target cells are rare
Solution Approach 1:
The patent introduces an intermediary approach by using bulk RNA sequencing data as a mediator to represent rare cell types. Instead of directly sequencing individual rare cells which is difficult and costly, the patent uses bulk RNA-seq data from tissue samples as an intermediary that captures the transcriptional signature of rare cells, thereby facilitating the acquisition of training data without requiring direct observation of each rare cell
Solution Approach 2:
The patent creates a copying strategy by generating synthetic waveform data that replicates the characteristics of rare cells. The system uses bulk RNA-seq data to create synthetic single-cell waveform signals that mimic the optical signatures of rare cells, thereby producing training data copies that preserve the essential features of rare cell types without needing to directly measure each rare cell
2Productivity
If conventional flow cytometry or GC method is used to examine individual cells, then high-speed analysis is achieved, but the ability to detect rare cells with specific morphological characteristics is limited
Solution Approach 1:
The patent applies multi-functionality by integrating multiple data types (bulk RNA-seq data and optical waveform data) into a unified classification system. The system uses bulk RNA-seq to identify candidate rare cell types and then uses optical waveform analysis to characterize them, combining the strengths of both approaches to achieve both high-speed analysis and accurate rare cell detection
Solution Approach 2:
The patent transitions from a single-dimensional analysis approach to a multi-dimensional approach by combining bulk RNA-seq transcriptional data with single-cell optical waveform data. This dimensional expansion allows the system to capture both the molecular characteristics from bulk RNA-seq and the morphological characteristics from optical waveforms, thereby improving rare cell detection capability while maintaining analysis speed
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 the accurate discrimination of particles with specific morphological characteristics, including those associated with diseases like cancer, by generating a classification model that can determine the presence of these cells even when they are scarce, thereby aiding in disease diagnosis.
Implementation Method 1
acquiring waveform data indicating a temporal change in an intensity of light emitted from a particle irradiated with light by a structured illumination
Implementation Method 2
waveform data indicating a temporal change in an intensity of light detected by structuring light from a particle irradiated with light
Data Source
AI summary
In the classification model generation method, for each particle contained in a first sample containing a mixture of particles having specific morphological characteristics and other particles and a second sample that does not contain particles having the specific morphological characteristics but contains only the other particles, observation data indicating a result of observing the particle is acquired. By training using training data including the observation data and information indicating whether the observation data has been obtained from a particle contained in the first sample or the second sample, a classification model is generated that outputs discrimination information indicating whether or not a particle has the specific morphological characteristics when the observation data indicating a result of observing the particle is input.