Toward reliable biomarker signatures in the age of liquid biopsies - how to standardize the small RNA-Seq workflow

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 en-courage 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

The potential of circulating extracellular small RNAs (smexRNA) in veterinary diagnostics—Identifying biomarker signatures by multivariate data analysis

Worldwide growth and performance-enhancing substances are used in cattle husbandry to increase productivity. In certain countries however e.g., in the EU, these practices are forbidden to prevent the consumers from potential health risks of substance residues in food. To maximize economic profit, ‘black sheep‘ among farmers might circumvent the detection methods used in routine controls, which highlights the need for an innovative and reliable detection method. Transcriptomics is a promising new approach in the discovery of veterinary medicine biomarkers and also a missing puzzle piece, as up to date, metabolomics and proteomics are paramount. Due to increased stability and easy sampling, circulating extracellular small RNAs (smexRNAs) in bovine plasma were small RNA-sequenced and their potential to serve as biomarker candidates was evaluated using multivariate data analysis tools. After running the data evaluation pipeline, the proportion of miRNAs (microRNAs) and piRNAs (PIWI-interacting small non-coding RNAs) on the total sequenced reads was calculated. Additionally, top 10 signatures were compared which revealed that the readcount data sets were highly affected by the most abundant miRNA and piRNA profiles. To evaluate the discriminative power of multivariate data analyses to identify animals after veterinary drug application on the basis of smexRNAs, OPLS-DA was performed. In summary, the quality of miRNA models using all mapped reads for both treatment groups (animals treated with steroid hormones or the β-agonist clenbuterol) is predominant to those generated with combined data sets or piRNAs alone. Using multivariate projection methodologies like OPLS-DA have proven the best potential to generate discriminative miRNA models, supported by small RNA-Seq data. Based on the presented comparative OPLS-DA, miRNAs are the favorable smexRNA biomarker candidates in the research field of veterinary drug abuse

Toward reliable biomarker signatures in the age of liquid biopsies - how to standardize the small RNA-Seq workflow

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 en-courage 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

Optimization of Extraction of Circulating RNAs from Plasma – Enabling Small RNA Sequencing

<div><p>There are several protocols and kits for the extraction of circulating RNAs from plasma with a following quantification of specific genes via RT-qPCR. Due to the marginal amount of cell-free RNA in plasma samples, the total RNA yield is insufficient to perform Next-Generation Sequencing (NGS), the state-of-the-art technology in massive parallel sequencing that enables a comprehensive characterization of the whole transcriptome. Screening the transcriptome for biomarker signatures accelerates progress in biomarker profiling for molecular diagnostics, early disease detection or food safety. Therefore, the aim was to optimize a method that enables the extraction of sufficient amounts of total RNA from bovine plasma to generate good-quality small RNA Sequencing (small RNA-Seq) data. An increased volume of plasma (9 ml) was processed using the Qiagen miRNeasy Serum/Plasma Kit in combination with the QIAvac24 Plus system, a vacuum manifold that enables handling of high volumes during RNA isolation. 35 ng of total RNA were passed on to cDNA library preparation followed by small RNA high-throughput sequencing analysis on the Illumina HiSeq2000 platform. Raw sequencing reads were processed by a data analysis pipeline using different free software solutions. Seq-data was trimmed, quality checked, gradually selected for miRNAs/piRNAs and aligned to small RNA reference annotation indexes. Mapping to human reference indexes resulted in 4.8±2.8% of mature miRNAs and 1.4±0.8% of piRNAs and of 5.0±2.9% of mature miRNAs for <i>bos taurus</i>.</p></div

Percentage of mapped and annotated miRNA/piRNA reads compared to the total number of sequences.

<p>Percentage of mapped and annotated miRNA/piRNA reads compared to the total number of sequences.</p

High Sensitivity DNA Bioanalyzer assay as checkpoint for correct size selection during library preparation.

<p>All nine samples showed adaptor/RNA/adaptor-constructs in appropriate sizes. One electropherogram is shown as representative example. The lengths of adaptor-ligated constructs from all nine samples were reported as indicated in the column <i>peak size [bp]</i>. The initial peak at 35 bp and the final peak at 10.380 bp are marker peaks that are system inherent included in all runs.</p

Optimization process of total RNA extraction.

<p>The table compiles the tested plasma input volumes, the used isolation systems and the resulting yields of extracted total RNA [ng] measured with the Qubit 2.0 Fluorometer.</p><p>Optimization process of total RNA extraction.</p

The proportions of trimmed, annotated and non-annotated reads.

<p>The total number of sequenced reads (100%) is divided into reads that failed trimming and reads that passed trimming and were mapped to Rfam database. Reads that were not mapped to Rfam database, were mapped to miRBase. Reads separated into annotated reads in miRBase and in reads that failed miRNA annotation. Image [A] displays miRNA results from mapping to human reference indexes. Image [B] presents miRNA results from mapping to bovine references. Regarding piRNAs (Image [C]), reads that could not be mapped to miRBase were aligned to piRNA database. They separate into annotated piRNAs and unmapped piRNAs.</p

piRNA data analysis shows the composition of evaluated reads from nine animals generated by computer data analysis pipeline using free software tools.

<p>piRNA data analysis shows the composition of evaluated reads from nine animals generated by computer data analysis pipeline using free software tools.</p