Label-Free Cell Sorting Through Multi-Parameter Image Analysis
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Solution Overview
Problem
Existing flow-type particle sorting systems rely on fluorescent labeling, which can damage cells and reduce their quality, and lack precision in label-free sorting.
Innovation Solution
A method for label-free particle sorting using image parameters calculated from measured light, generating sorting gates based on ground-truth classification parameters, and employing machine learning algorithms to enhance classification and sorting precision.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If fluorescent labeling is used for particle sorting, then detection sensitivity is improved, but cell quality is degraded
Solution Approach 1:
The patent removes the fluorescent labeling step from the particle sorting process. Instead of using fluorescent dyes to label particles for detection, the system directly images label-free particles using transmitted light and calculates image parameters (area, circularity, aspect ratio, etc.) to achieve both detection sensitivity and maintain cell quality.
Solution Approach 2:
The patent introduces image parameter calculations as an intermediary between light transmission measurement and particle classification. By computing multiple parameters (area, circularity, aspect ratio, solidity, etc.) from transmitted light images, the system achieves sensitive detection without requiring fluorescent labels that would harm cell quality.
2Object-affected harmful factors
If label-free sorting is used, then cell quality is maintained, but classification precision is reduced
Solution Approach 1:
The patent transitions from single-parameter fluorescent intensity measurement to multi-dimensional image parameter analysis. By calculating five or more image parameters (area, circularity, aspect ratio, solidity, maximum intensity) from transmitted light images, the system creates a multi-dimensional classification space that achieves high precision for label-free particles.
Solution Approach 2:
The patent segments the particle characterization into multiple independent image parameters rather than relying on a single fluorescent signal. Each parameter (area, circularity, aspect ratio, solidity, maximum intensity) provides independent discriminatory information, enabling precise classification of label-free particles through combined parameter analysis.
3Measurement precision
If multiple image parameters are calculated, then classification precision is improved, but computational complexity increases
Solution Approach 1:
The patent performs preliminary calculation of image parameters (area, circularity, aspect ratio, solidity, maximum intensity) during the particle imaging phase, before classification decisions are made. This pre-computation approach allows the system to use multiple parameters for high-precision classification without adding significant computational burden during the critical sorting decision window.
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
Improves the sensitivity and precision of particle classification by up to 99% without labeling, maintaining cell quality for downstream applications like adoptive cellular therapy and drug discovery.
Implementation Method 1
measuring light from a sample having label-free particles in a flow stream
Implementation Method 2
measuring light from a sample having label-free particles in a flow stream
Implementation Method 3
Particles or components thereof can be labeled with fluorescent dyes to facilitate detection
Data Source
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AI summary
Aspects of the present disclosure include methods for label-free particle sorting. The method the present disclosureincludes measuring light from a sample having label-free particles in a flow stream, generating an image of one or more of the particles from the measured light, calculating image parameters from the generated image of the one or more particles and generating a particle sort decision based on the calculated image parameters. One or more sorting gates are determined based on image parameters calculated from the particle and ground-truth image classification parameters. The system and integrated circuit device (e.g., a field programmable gate array) for practicing the method are also described. Non-transitory computer readable storage medium are also provided.