Microfluidic EV Isolation for Breast Cancer Diagnosis
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Solution Overview
Problem
Current methods for isolating tumor-derived extracellular vesicles (TDEs) from plasma for breast cancer diagnosis are time-consuming, inefficient, and lack selectivity, making them unsuitable for effective early diagnosis.
Innovation Solution
A microfluidic chip-based method that utilizes a continuous flow environment to isolate TDEs with high throughput, efficiency, and selectivity, followed by measurement of specific miRNAs (miR-9, miR-16, miR-21, and miR-429) for breast cancer diagnosis.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Productivity
If ultracentrifugation is used to isolate EVs from plasma, then EV isolation can be performed, but the process is time-consuming and has low isolation efficiency
Solution Approach 1:
The patent replaces the mechanical ultracentrifugation system with a microfluidic system that uses controlled flow dynamics and affinity-based capture. The microfluidic chip creates specific flow patterns that enable efficient EV isolation without requiring high-speed rotation, thereby reducing isolation time while maintaining or improving efficiency.
Solution Approach 2:
The patent changes the isolation parameters by using affinity-based capture mechanisms (such as antibody-coated surfaces or aptamer-functionalized materials) within the microfluidic device. This approach shifts from physics-based separation (centrifugal force) to chemistry-based specific binding, enabling faster and more efficient EV isolation with higher selectivity.
2Reliability
If ultracentrifugation is used to isolate EVs from plasma, then EV isolation can be performed, but selectivity for cancer-related EVs is low
Solution Approach 1:
The patent introduces affinity-based intermediaries (antibodies, aptamers, or other ligands) that specifically recognize cancer-related EV surface markers. These intermediaries are incorporated into the microfluidic device structure, enabling selective capture of tumor-derived EVs while allowing other EVs to pass through, thus achieving high selectivity without compromising isolation efficiency.
Solution Approach 2:
The patent applies local quality by creating regions within the microfluidic device with different functional properties. Specific zones are functionalized with cancer-marker-specific ligands to selectively capture target EVs, while other regions maintain general EV passage capabilities. This spatial differentiation of functional properties enables high selectivity while maintaining overall isolation efficiency.
3Reliability
If affinity-based EV purification is used, then selectivity for cancer-related EVs is improved, but the process requires complex steps including binding, washing, and concentrating
Solution Approach 1:
The patent merges multiple separate purification steps (binding, washing, and concentrating) into a single integrated microfluidic device. The continuous flow system performs all these operations simultaneously as the plasma sample passes through the device, eliminating the need for discrete manual steps and reducing overall process complexity while maintaining high selectivity.
Solution Approach 2:
The patent implements continuous flow processing where the plasma sample continuously passes through the affinity-based capture zones. This continuous action enables binding, washing, and concentrating to occur simultaneously in different regions of the device, rather than requiring sequential batch processing. The continuous flow maintains constant selective capture while automatically washing away non-specifically bound materials and concentrating the target EVs in real-time.
Data Source
AI summary
Disclosed are a composition and a method for diagnosing breast cancer using extracellular vesicle-miRNA. The composition for diagnosing the breast cancer according includes an agent that measures an expression level of at least one miRNA selected from a group consisting of miR-9, MiR-16, MiR-21, and MiR-429. Furthermore, the method for diagnosing breast cancer includes measuring an expression level of at least one miRNA selected from a group consisting of MiR-9, MiR-16, MiR-21, and MiR-429 from a sample, and diagnosing the breast cancer based on the measurement result.


