miRNA Assays for Clinical Diagnostics: Detection Technologies, Workflows, and Automation
MicroRNAs are promising circulating biomarkers for cancer, cardiovascular disease, and treatment monitoring, yet standardized clinical workflows remain absent. This review systematically compares detection technologies including RT-qPCR, microarrays, NGS-based small RNA sequencing, and digital PCR for miRNA quantification in blood and derived fractions. The authors evaluate each platform's suitability for clinical laboratory implementation, addressing pre-analytical variables, normalization strategies, and data analysis requirements for translating raw measurements into actionable diagnostic results.
The original study
miRNA assays in the clinical laboratory: workflow, detection technologies and automation aspects.
- Authors
- Kappel A, Keller A
- Journal
- Clinical chemistry and laboratory medicine
- Type
- Journal Article, Review
- PMID
- 27987355
Original abstract
microRNAs (miRNAs) are short non-coding RNA molecules that regulate gene expression in eukaryotes. Their differential abundance is indicative or even causative for a variety of pathological processes including cancer or cardiovascular disorders. Due to their important biological function, miRNAs represent a promising class of novel biomarkers that may be used to diagnose life-threatening diseases, and to monitor disease progression. Further, they may guide treatment selection or dosage of drugs. miRNAs from blood or derived fractions are particularly interesting candidates for routine laboratory applications, as they can be measured in most clinical laboratories already today. This assures a good accessibility of respective tests. Albeit their great potential, miRNA-based diagnostic tests have not made their way yet into the clinical routine, and hence no standardized workflows have been established to measure miRNAs for patients' benefit. In this review we summarize the detection technologies and workflow options that exist to measure miRNAs, and we describe the advantages and disadvantages of each of these options. Moreover, we also provide a perspective on data analysis aspects that are vital for translation of raw data into actionable diagnostic test results.