Development of rapid and simple Xeno Nucleic Acid (XNA) sensor-based microRNA detection method for Parkinson’s disease diagnostics
Development of rapid and simple Xeno Nucleic Acid (XNA) sensor-based microRNA detection method for Parkinson’s disease diagnostics
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Publication status: preprint
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Original abstract
Abstract Reliable quantification of circulating microRNAs (miRNAs) has been limited by amplification bias, nonlinear signal distortion, and poor cross-platform reproducibility in qRT-PCR, sequencing, and hybridization-based assays. This persistent analytical barrier represents a major unmet demand in miRNA diagnostics, preventing biologically validated miRNA biomarkers from achieving clinical translation. To address this gap, we developed XENO-Q, a rapid three-step platform that enables bias-free miRNA quantification through target-selective amplification, in which only sensor-confirmed miRNAs undergo controlled amplification. XENO-Q achieves linear quantification across given orders of magnitude with femtomolar sensitivity and high reproducibility. As an example of clinical applicability, we applied XENO-Q to monitor circulating miRNAs and identified a two-marker signature that accurately distinguished Parkinson’s disease from other neurodegenerative conditions. These results establish XENO-Q as a generalizable and scalable framework for next-generation miRNA diagnostics beyond any single disease application.