Single-Nucleotide Polymorphisms Detection Base on Active-Matrix Digital Microfluidics Pyrosequencing Lab-on-a-chip Platform

Abstract

Single-nucleotide polymorphisms (SNPs), as the most prevalent form of single-base variations within the genome, play a critical role in modulating protein function, regulating gene expression, and determing individual disease susceptibility, thereby serving as key biomarkers in personalised medicine. Currently, the mainstream method for detecting SNPs in known short sequences is pyrosequencing, yet this approach faces limitations such as complex operational procedures and limited automation. Digital microfluidics (DMF) provides an ideal platform for accomplishing complex biochemical processes through electrically programmable manipulation of discrete droplets, thereby simplifying operational procedures. In this study, we report the design of a pyrosequencing system based on an active-matrix digital microfluidic (AM-DMF) chip. This work represents the first demonstration of combining AM-DMF with pyrosequencing for on-chip SNP genotyping, enabling direct and automated genetic analysis within a sinlge microfluics platform. This fills a gap in the DMF platform for pharmacogenomics SNPs detection. Compared with conventional passive-matrix digital microfluidics (PM-DMF), the AM-DMF offers greater scalability and higher electrode utilisation, making it well suited for larger-scale integration and high-throughput application. This study utilized DNA samples from 12 patients to achieve rapid assessment of clopidogrel metabolism through genotyping of the CYP2C19*2 and *3 loci. Experimental results demonstrate that the system completes a single sample analysis in under 30 minutes, with results reaching 100% accuracy in total agreement with the commercial pyrosequencer (PyroMark Q24). Furthermore, reagent consumption was reduced by approximately 50%, significantly lowering per-sample cost. These results indicate that the proposed AM-DMF-based pyrosequencing platform enables rapid, accurate, and cost-effective SNPs detection, and holds substantial potential for clinical applications in precision and personalised medicine.