Surface Acoustic Wave Microfluidic Chip for Label-Free Cell Sorting
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Solution Overview
Problem
Current cell sorting methods rely on supervised learning and require fluorescent markers for classification, leading to limitations in sorting efficiency, cell viability, and system cost, while also lacking spatial information, resulting in weak specificity.
Innovation Solution
A biological sample sorting method using a surface acoustic wave microfluidic chip that aggregates samples with sound waves and employs a deep learning model for unsupervised image analysis and classification, generating signals to control sample movement for sorting without the need for fluorescent markers or labeled data.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Productivity
If fluorescence-activated cell sorting (FACS) is used to sort cells, then sorting speed and accuracy are improved, but equipment cost increases and cell information resolution remains limited to low-dimensional data
Solution Approach 1:
The patent replaces the complex optical-mechanical sorting system of FACS with an acoustic field-based sorting system. Surface acoustic waves generate radiation forces that manipulate cell positions and enable sorting based on acoustic impedance differences, eliminating the need for expensive lasers, photodetectors, and mechanical sorting components while maintaining high sorting speed.
Solution Approach 2:
The patent changes the physical parameter used for cell characterization from optical properties (light scattering and fluorescence) to acoustic properties (acoustic impedance). This parameter change enables the system to differentiate cells based on their mechanical and structural characteristics, providing higher-resolution cell information without requiring fluorescent labeling.
2Measurement precision
If imaging flow cytometry is used to obtain high-dimensional data, then cell morphology information is improved, but data processing speed decreases and real-time sorting cannot be achieved
Solution Approach 1:
The patent replaces image-based morphological analysis with acoustic field-based cell manipulation. Surface acoustic waves interact with cells based on their acoustic impedance, which is determined by cell density and compressibility - intrinsic properties that directly reflect cell morphology and state. This substitution enables real-time cell characterization without requiring complex image processing.
Solution Approach 2:
The patent enables cells to self-sort based on their inherent acoustic impedance differences when exposed to surface acoustic waves. The acoustic radiation forces automatically separate cells according to their physical properties without requiring external labeling or complex processing, achieving both high measurement precision and real-time processing speed.
3Measurement precision
If fluorescent labeling reagents are used in intelligent fluorescence-activated cell sorting, then cell identification accuracy is improved, but cell behavior may be altered and system cost increases
Solution Approach 1:
The patent replaces fluorescent labeling with acoustic field interaction. Surface acoustic waves interact directly with the physical structure of cells through acoustic impedance, which is determined by cell density and compressibility. This mechanical interaction method provides accurate cell identification without introducing chemical reagents that could alter cell behavior or require complex labeling protocols.
4Measurement precision
If supervised learning algorithms are used for cell classification, then classification accuracy is improved, but large amounts of labeled training data are required increasing system complexity and cost
Solution Approach 1:
The patent enables unsupervised cell classification by exploiting the natural acoustic impedance differences between cell types. When cells are exposed to surface acoustic waves, they automatically separate into distinct bands based on their physical properties. This self-organizing behavior provides inherent classification without requiring external labeling or supervised learning algorithms, eliminating the need for large labeled training datasets.
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
This approach enables efficient, accurate, and cost-effective sorting of biological samples with high throughput and real-time automation, maintaining cell viability and reducing equipment costs, while providing high flexibility and scalability.
Implementation Method 1
applying a first sinusoidal signal to the surface acoustic wave microfluidic chip thus forming a standing wave sound field in the surface acoustic wave microfluidic chip, making the biological samples gather at a node position in the standing wave sound field under the action of an acoustic radiation force
Data Source
AI summary
A biological sample sorting method, a surface acoustic wave microfluidic chip, a system, a terminal, and a storage medium are disclosed. The method includes: injecting a mixed solution containing biological samples into a surface acoustic wave microfluidic chip, and applying a first sinusoidal signal thereto forming a standing wave sound field therein to aggregate the biological samples; collecting images of the aggregated biological samples; performing moving target identification and tracking on the images of the biological samples using a deep learning model, performing cluster analysis and classification on a tracked moving target to obtain a target sample, and generating a delay enabling signal of the target sample according to a moving speed of the target sample; and applying a second sinusoidal signal to the surface acoustic wave microfluidic chip according to the delay enable signal to move the target sample thus sorting out the target sample.