Abstract
AIM: Circulating maternal MicroRNA (miRNA) is a promising source of biomarkers for antenatal diagnostics. NanoString nCounter is a popular global screening tool due to its simplicity and ease of use, but there is a lack of standardisation in analysis methods. We examined the effect of user-defined variables upon reported changes in maternal blood miRNA during pregnancy. METHODS: Total RNA was prepared from the maternal blood of pregnant and control rats. miRNA expression was profiled using Nanostring nCounter. Raw count data were processed using nSolver using different combinations of normalisation and background correction methods as well as various background thresholds. A panel of 14 candidates in which changes were supported by multiple analysis workflows was selected for validation by RT-qPCR. We then reverse-engineered the nSolver analysis to gain further insight. RESULTS: Thirty-one putative differentially expressed miRNAs were identified by nSolver. However, each analysis workflow produced a different set of reported biomarkers and none of them was common to all analysis methods. Four miRNAs with known roles in pregnancy (miR-183, miR-196c, miR-431, miR-450a) were validated. No single nSolver analysis workflow could successfully identify all four validated changes. Reverse engineering revealed errors in nSolver data processing which compound the inherent problems associated with background correction and normalisation. CONCLUSION: Our results suggest that user-defined variables greatly influence the output of the assay. This highlights the need for standardised nSolver data analysis methods and detailed reporting of these methods. We suggest that investigators in the future should not rely on a single analysis method to identify changes and should always validate screening results.