No abstract

Small RNA-Seq has emerged as a powerful tool in transcriptomics, gene expression profiling and biomarker discovery. Sequencing cell-free nucleic acids, particularly microRNA (miRNA), from liquid biopsies additionally provides exciting possibilities for molecular diagnostics, and might help establish disease-specific biomarker signatures. The complexity of the small RNA-Seq workflow, however, bears challenges and biases that researchers need to be aware of in order to generate high-quality data. Rigorous standardization and extensive validation are required to guarantee reliability, reproducibility and comparability of research findings. Hypotheses based on flawed experimental conditions can be inconsistent and even misleading. Comparable to the well-established MIQE guidelines for qPCR experiments, this work aims at establishing guidelines for experimental design and pre-analytical sample processing, standardization of library preparation and sequencing reactions, as well as facilitating data analysis. We highlight bottlenecks in small RNA-Seq experiments, point out the importance of stringent quality control and validation, and provide a primer for differential expression analysis and biomarker discovery. Following our recommendations will encourage better sequencing practice, increase experimental transparency and lead to more reproducible small RNA-Seq results. This will ultimately enhance the validity of biomarker signatures, and allow reliable and robust clinical predictions.

Background: Quantitative PCR (qPCR) is a valuable technique for accurately and reliably profiling and quantifying gene expression. Typically, samples obtained from the organism of study have to be processed via several preparative steps before qPCR. Method: We estimated the errors of sample withdrawal and extraction, reverse transcription (RT), and qPCR that are introduced into measurements of mRNA concentrations. We performed hierarchically arranged experiments with 3 animals, 3 samples, 3 RT reactions, and 3 qPCRs and quantified the expression of several genes in solid tissue, blood, cell culture, and single cells. Results: A nested ANOVA design was used to model the experiments, and relative and absolute errors were calculated with this model for each processing level in the hierarchical design. We found that intersubject differences became easily confounded by sample heterogeneity for single cells and solid tissue. In cell cultures and blood, the noise from the RT and qPCR steps contributed substantially to the overall error because the sampling noise was less pronounced. Conclusions: We recommend the use of sample replicates preferentially to any other replicates when working with solid tissue, cell cultures, and single cells, and we recommend the use of RT replicates when working with blood. We show how an optimal sampling plan can be calculated for a limited budget. .