Abstract

It is urgent to establish a fast, convenient, accurate, and low-cost miRNA quantitative detection platform, which is important in disease development and the early diagnosis of cancer. Here, we propose a miRNA-specific detection microfluidic platform in which a self-assembled Poly-L-Lysine (PLL) substrate is integrated with microfluidic chips and conduct multiple detection of miRNAs from multiple samples at the same time based on three-segment hybridization. PLL is first self-assembled onto a clean glass slide and then integrated with a high-throughput micro-printing microfluidic chip to locally mobilize DNA probes. A sample-loading microfluidic chip is designed to realize multiple detection of multiple samples at the same time. A three-segment hybridization system is used to detect miRNAs in which the capture probe is complementary to one end of the target miRNA and the detection probe with fluorescence is complementary to the other end of the target miRNA. First, capture probes are mobilized on the chip and detection probes with fluorescence are hybridized with the target miRNA. Second, a miRNA-detection probe hybridizer is reacted with the capture probes immobilized on the chip. Finally, excessive detection probes are cleaned and the fluorescence intensity of the capture probe–miRNA–detection probe hybridizer on the chip is detected by using a laser scanner. Four significant breast cancer biomarker miRNAs are selected for simultaneous detection, and the detection limit is 1 pM with a detection time of 30 min. This microfluidic platform shows sensitive multiple detection of miRNAs in multiple samples and is promising for the early diagnosis of breast cancer.

Highlights

  • MiRNAs are a class of small RNAs (∼22bp) that are highly conserved and ubiquitous in cell development, differentiation, and death.1 recognizing the expression of these miRNAs is extremely important for cancer diagnosis.2,3 A number of methods have been developed for a quantitative detection of miRNAs

  • The platform is capable of sensing multiple miRNAs with a detection limit of 1 pM in 30 min, which is promising for the early diagnosis of breast cancer

  • A capture probe was designed with one end complementary to the other end of the target miRNA, and a detection probe was designed with the partial segment complementary to the other end of the target miRNA

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Summary

INTRODUCTION

MiRNAs are a class of small RNAs (∼22bp) that are highly conserved and ubiquitous in cell development, differentiation, and death. recognizing the expression of these miRNAs is extremely important for cancer diagnosis. A number of methods have been developed for a quantitative detection of miRNAs. Numerous methods have been used to detect miRNA, such as surface-enhanced Raman scattering (SERS), surface plasmon resonance (SPR), upconversion nanoparticles (NPs), NPs based colorimetry and fluorescence resonance energy transfer (FRET), electrochemical detection, rolling cycle amplification (RCA), and enzyme-assisted target recovery (EATR).. There are amplificationbased detection methods such as rolling cycle amplification (RCA) and enzyme-assisted target recovery (EATR) It is still challenging for most methods to perform high throughput detection of miRNAs with a small amount of sample volume feasibly and economically. We designed a miRNA quantitative detection platform that integrates the PLL self-assembled slides and high-throughput microfluidic chips based on three-segment hybridization and fluorescence imaging. The integration of PLL assembling and microfluidic chips enables the accurate quantification of sample loading volume and multiple detection and economic material cost. The platform is capable of sensing multiple miRNAs with a detection limit of 1 pM in 30 min, which is promising for the early diagnosis of breast cancer

Materials
Fabrication of PLL substrate slides
Fabrication of the microfluidic chips
Capture probe immobilization
Hybridization and detection
Microfluidic chip design strategy
Specific detection of miRNAs
Sensitivity detection of miRNAs
Simulate real samples detection of miRNAs
CONCLUSION
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