Use of RNA sequencing to evaluate rheumatic disease patients

Abstract

Studying the factors that control gene expression is of substantial importance for rheumatic diseases with poorly understood etiopathogenesis. In the past, gene expression microarrays have been used to measure transcript abundance on a genome-wide scale in a particular cell, tissue or organ. Microarray analysis has led to gene signatures that differentiate rheumatic diseases, and stages of a disease, as well as response to treatments. Nowadays, however, with the advent of next-generation sequencing methods, massive parallel sequencing of RNA tends to be the technology of choice for gene expression profiling, due to several advantages over microarrays, as well as for the detection of non-coding transcripts and alternative splicing events. In this review, we describe how RNA sequencing enables unbiased interrogation of the abundance and complexity of the transcriptome, and present a typical experimental workflow and bioinformatics tools that are often used for RNA sequencing analysis. We also discuss different uses of this next-generation sequencing technology to evaluate rheumatic disease patients and investigate the pathogenesis of rheumatic diseases such as rheumatoid arthritis, systemic lupus erythematosus, juvenile idiopathic arthritis and Sjögren's syndrome.

Fig. 1

A typical RNA-seq workflow. RNA sequencing (RNA-seq) is a multi-step process that involves designing the experiment, preparing the RNA sample and the input library, using a next generation sequencing platform, and performing analysis on the short sequenced reads. NGS, next-generation sequencing; PE, paired-end; SR, single-read