Introduction to Targeted RNA-Sequencing

Targeted RNA-sequencing (RNA-Seq) is a precise method for identifying and sequencing specific transcripts. It provides quantitative as well as qualitative data. Enrichment or amplicon-based approaches can be used to achieve targeted RNA-Seq, which allows gene expression analysis in a specific set of genes. In many types of samples, including formalin-fixed paraffin-embedded (FFPE) tissue, enrichment assays can identify both known and novel gene fusion partners.

Figure 1. Targeted RNA-Seq by target capture. (Hrdlickova, 2017)

Targeted RNA-Sequencing Protocol

When matched to whole transcriptome and mRNA sequencing, which uses a fragmentation workflow, targeted RNA sequencing is a simple and cost-effective alternative. Targeted RNA sequencing can be used to focus on specific transcripts of interest without the need for rRNA depletion, thanks to targeted cDNA amplification. With limited RNA samples, whether low input amounts or highly degraded samples like RNA material from formalin-fixed paraffin-embedded (FFPE) tissue, a targeted approach can be used. The actual data analysis workflow, which is now streamlined to analyze particular transcripts, is one downstream benefit.

Isolation of RNA

The isolation of RNA from a biological sample is the first step in transcriptome sequencing. The RNA should be of adequate quality to create a library for sequencing in order to achieve a successful RNA-Seq experiment. RNA quality is typically assessed using an Agilent Bioanalyzer, which generates an RNA Integrity Number (RIN) ranging from 1 to 10, with 10 representing the highest quality samples with the least degradation.

Library Preparation Method

Following RNA isolation, the creation of an RNA-Seq library is the next step in transcriptome sequencing, which varies depending on the RNA species used and the NGS platform used. Isolating desired RNA molecules, reverse-transcribing the RNA to cDNA, fragmenting or amplifying randomly primed cDNA molecules, and ligating sequencing adaptors are the main steps in the construction of sequencing libraries. Several choices in library construction and experimental design must be carefully made within these basic steps, depending on the specific needs of the researcher. Furthermore, the nature of the library construction has a significant impact on the accuracy of detection for specific types of RNAs. Although there are only a few basic steps to creating RNA-Seq libraries, each one can be tweaked to improve the detection of certain transcripts while limiting the detection of others.

Selection of Tissue or Cell Population

One of the first things to think about when starting an RNA-Seq experiment is what kind of biological material to use for library construction and sequencing. There are hundreds of cell types in over 200 different tissues that make up more than 50 different organs in humans alone, so this decision is not easy. Gene expression has temporal specificity in addition to spatial specificity, so different developmental stages will have different expression signatures. In the end, the biological material chosen will be determined by the experimental goals as well as the feasibility of the experiment.

RNA-Sequencing Data Analysis Workflow

Generating FASTQ-format files containing reads sequenced from an NGS platform, aligning these reads to an annotated reference genome, and quantifying gene expression are all part of the standard RNA-Seq pipeline. Although basic sequencing analysis tools are more accessible than ever before, RNA-Seq analysis poses unique computational challenges not seen in other sequencing-based analyses and necessitates special attention to the biases inherent in expression data.

Applications of Targeted RNA-Sequencing

Due to the prominence of high-throughput sequencing technology, genomic data such as RNA-seq has become commonly accessible. RNA sequencing has made significant contributions to a variety of fields, particularly cancer research, including studies on differential gene expression analysis and cancer biomarkers, cancer heterogeneity and evolution, cancer drug resistance, cancer microenvironment and immunotherapy, and neoantigens.

References:

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