Trio RNA-Seq for viral detection


Next generation sequencing (NGS) analysis of RNA isolated from clinical samples can reveal the presence of RNA viruses, expression levels of transcriptionally active DNA and RNA viruses, and identify correlations between host gene expression and viral levels, allowing for investigation of host cell changes that occur upon infection. Other current approaches and assays for viral detection in clinical specimens, such as PCR and target capture followed by sequencing, are limited by requiring previous knowledge of target sequences and by the level of multiplexing possible. While traditional RNA-Seq provides unbiased detection of all nucleic acids present in a sample, and therefore hypothesis-free data, it is limited by rather high input requirements (typically 50-100 ng of more of total RNA). In addition, sequencing data of mixed viral/host RNA-Seq libraries is typically dominated by host reads. Therefore, without deep sequencing of each RNA-Seq library, viral reads may be missed altogether. We present here a simple, robust, hypothesis-free RNA-Seq method that overcomes the above challenges. As little as 500 pg of total RNA is converted to cDNA and amplified with Single Primer Isothermal Amplification (SPIA®). After enzymatic fragmentation and NGS library generation, specific abundant and uninformative host transcripts are targeted for depletion, resulting in a significant reduction of the number of sequencing reads required to achieve viral detection as compared to traditional RNA-Seq methods. In this study, we present data demonstrating the utility of the Trio RNA-Seq workflow to detect viruses in various clinically relevant samples. The negative selection step to remove unwanted sequences, termed AnyDeplete™, is fully customizable, allowing users to target any class of transcript for depletion within their final libraries. Additionally, the SPIA cDNA amplification method has been used extensively for sample preparation for downstream expression arrays, including in HIV virus discovery and characterization (Malboeuf et al., NAR 2013). The combination of these technologies is a powerful tool for viral analysis in clinical samples.

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