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Updated: Oct 9, 2026

High Throughput MicroRNA Profiling: Optimized Multiplex qRT-PCR at Nanoliter Scale on the Fluidigm Dynamic ArrayTM IFCs
Published on: August 3, 2011
A target amplification-free CRISPR biosensor with digital microfluidic processing for microRNA profiling in
Di Huang1, Shanshan Shi1, Xitong Wang1
1Department of Cardiac Intensive Care Unit, Children's Hospital, Zhejiang University School of Medicine, National Clinical Research Center for Children and Adolescents' Health and Diseases, Hangzhou, 310052, China.
Abstract:
MicroRNA signatures provide an informative readout of host-response dysregulation in pediatric sepsis, but their clinical translation remains limited by low target abundance and multiplexing requirements. Here, a target amplification-free CRISPR biosensing platform was established. The sensing mechanism couples Cas13a target recognition to promoter release, template reconstruction, and transcription-driven Cas13a reactivation, enabling fluorescence amplification through internal molecular cycling without amplifying the target miRNAs. Then, an electrowetting-based digital microfluidic chip was designed to compartmentalize parallel reactions and automate droplet generation, reaction assembly, isothermal incubation, magnetic separation, and fluorescence readout. Under optimized conditions, four sepsis-associated biomarkers (miR-15a, miR-21, miR-146a, and miR-155) and the reference control (snRNA U44) were detected at attomolar levels (expected mean input of 1.67 molecules per reaction) with good specificity and minimal inter-droplet interference. The resulting miRNA profiles were then used as input features for machine learning-enhanced sepsis classification and risk assessment. In 252 clinical samples, the resulting model achieved a test-set AUC of 0.973 and an overall accuracy of 92.1%. The model-derived risk score was associated with the Phoenix Sepsis Score and, in a representative longitudinal case, showed preliminary potential to reflect disease evolution. Overall, this platform enables automated analysis of disease-associated miRNA signatures and demonstrates its potential for in-vitro diagnosis of pediatric sepsis.

