LNA-based technology: A promising tool for identifying microRNA as biomarkers for disease and toxicology studies in biofluids
Michael Hansen, Ph.D.
Senior Scientist, Exiqon, Inc
microRNAs represent the best characterized class of small RNAs (21-23nt) and are becoming recognized as a promising new class of biomarkers for toxicology studies. microRNA are highly stable in common sample types (e.g., plasma, serum, urine etc.) and their levels are dramatically altered in response to drug-induced toxicity. microRNAs often exhibit tissue- and cell-type specific expression patterns, so represent good candidates for novel site-specific toxicity biomarkers. A high degree of sequence conservation facilitates rapid assay transfer between species used in preclinical studies.
We have developed an LNA™-based microRNA qPCR platform for detection of microRNAs with unparalleled sensitivity and robustness, enabling microRNA profiling in biofluids where levels are extremely low. The platform uses a single RT reaction to conduct full miRNome profiling and allows high-throughput profiling of microRNAs without the need for pre-amplification.
Thousands of biofluid samples including serum/plasma and urine have been profiled to determine normal reference ranges for circulating microRNAs. This has allowed development of qPCR arrays for toxicology studies - containing microRNAs present in various biofluids together with tissue-specific microRNA markers.
An extensive quality control and data analysis pipeline has been implemented in order to secure high quality data from biofluids. We will present our approaches to isolating the limited amount of RNA present in biofluid samples like urine or plasma, and monitoring RNA isolation efficiency and presence of undesired inhibitory components. We demonstrate how pre-analytical variables such as hemolysis can affect the microRNA profile, and how we monitor these variables. We also discuss how we address biofluid specific data analysis challenges such as normalization, thresholding and background determination. Furthermore, we will demonstrate that when close attention is focused on these important parameters the system can be quickly and robustly applied in biomarker discovery and validation projects.
