Label-Free Cell Sorting Through Optical Image Parameters
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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 classifying and sorting label-free particles.
Innovation Solution
A method for label-free particle sorting using image parameters calculated from measured light, comparing with ground-truth classification parameters to determine sorting gates, and employing machine-learning algorithms for improved classification and sorting.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If fluorescent labeling is used for particle detection and sorting, then detection capability is improved, but cell quality is reduced and cells are damaged
Solution Approach 1:
The patent extracts and removes the fluorescent labeling step from the particle detection process. Instead of labeling particles with fluorescent dyes, the system uses label-free optical imaging to capture images of particles based on their intrinsic optical properties, thereby eliminating the harmful effects of fluorescent labels on cell quality while maintaining detection capability
Solution Approach 2:
The patent introduces an intermediary approach by using optical imaging as a mediator between the particle and detection system. Rather than directly detecting fluorescent signals from labeled particles, the system uses optical imaging to capture structural and morphological information, serving as an intermediate method that avoids direct chemical interaction with the particles
2Productivity
If traditional flow cytometry is used for particle sorting, then sorting capability is achieved, but precision in classifying label-free particles is reduced
Solution Approach 1:
The patent transitions from traditional flow cytometry's limited detection dimensions to multi-dimensional optical imaging analysis. By capturing images with multiple parameters (intensity, texture, shape, size) and applying machine learning algorithms, the system creates additional classification dimensions that significantly improve precision in sorting label-free particles while maintaining productivity
3Measurement precision
If fluorescent labels are used to facilitate detection, then detection sensitivity is improved, but cell quality for downstream applications is reduced
Solution Approach 1:
The patent converts the limitation of label-free detection (lower traditional detection sensitivity) into a benefit by utilizing machine learning algorithms that can extract subtle features from optical images. The system transforms the challenge of detecting unlabeled particles into an opportunity to use advanced image analysis, thereby maintaining detection sensitivity while preserving cell quality for downstream 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
Enhances the sensitivity and precision of particle classification and sorting, maintaining cell quality for downstream applications like adoptive cellular therapy and drug discovery, while reducing misclassification by up to 99%.
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
Droplets are passed through an electrostatic field and are deflected based on polarity and magnitude of charge on the droplet into one or more collection containers
Data Source
AI summary
Aspects of the present disclosure include methods for label-free particle sorting. Methods according to certain embodiments include 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. In some embodiments, sorting gates are determined based on image parameters calculated from the particle and ground-truth image classification parameters. Systems and integrated circuit devices (e.g., a field programmable gate array) for practicing the subject methods are also described. Non-transitory computer readable storage medium are also provided.