MicroRNAs (miRNAs) are short non-coding RNAs that mediate protein translation and are known to be dysregulated in diseases including diabetes, Alzheimer's, and cancer. [1][2][3] With greater stability and predictive value than mRNA, this relatively small class of biomolecules has become increasingly important in determining disease diagnosis and prognosis. However, the sequence homology, wide range of abundance, and common secondary structures of miRNAs have complicated efforts to develop accurate, unbiased quantification techniques. [4,5] Applications in the discovery and clinical fields require high-throughput processing, large coding libraries for multiplexed analysis, and the flexibility to develop custom assays. Microarray approaches provide high sensitivity and multiplexing capacity, but their low-throughput, complexity, and fixed design make them less than ideal for use in a clinical setting. [6,7] PCR-based strategies suffer from similar throughput issues, yet offer highly sensitive and specific detection for genome-wide miRNA expression profiling. [8] Alternative bead-based systems provide a high sample throughput, but with reduced sensitivity, [9] dynamic range, and multiplexing capacities (luminexcorp.com). miRNA profiling by deep sequencing is emerging as a powerful tool for small RNA analysis; however, the high cost of implementation and need for large amounts of input RNA currently limit its utility. [10] The ideal system for miRNA quantification would offer the detection performance of array and PCR-based methods, the throughput of bead-based systems, and improved reproducibility with a user-friendly workflow.