Development of a rapid and simple Xeno Nucleic Acid (XNA) sensor-based microRNA detection platform for Parkinson's disease diagnostics.

Current biomarkers for Parkinson's disease (PD) diagnosis, including imaging tracers, α-synuclein seeding assays, proteomic markers, and metabolomic signatures, are often limited by invasiveness, cost, scalability, or insufficient reproducibility. Circulating microRNAs (miRNAs) are attractive minimally invasive biomarkers because they are mechanistically linked to disease-associated pathways and remain stable in biofluids. However, the clinical translation of miRNA biomarkers has been hindered by multistep workflows and analytical variability arising from adapter ligation, reverse transcription, amplification drift, and cross-platform inconsistency in qRT-PCR, sequencing, and hybridization-based assays. To address these limitations, we developed XENO-Q, a rapid three-step platform that enables bias-minimized miRNA quantification through target-selective synthesis, in which only sensor-confirmed miRNAs are converted into amplifiable chimera products. XENO-Q enables linear quantification across several orders of magnitude with femtomolar sensitivity and high reproducibility while maintaining compatibility with qRT-PCR, ddPCR, and nanopore sequencing workflows. As a demonstration of clinical applicability, XENO-Q identified a two-marker circulating miRNA signature that accurately distinguished PD from other neurodegenerative conditions. These findings establish XENO-Q as a scalable and clinically translatable framework for next-generation miRNA diagnostics beyond Parkinson's disease.