RNA sequencing – the teenage years

Over the past decade, RNA sequencing (RNA-seq) has become an indispensable tool for transcriptome-wide analysis of differential gene expression and differential splicing of mRNAs. However, as next-generation sequencing technologies have developed, so too has RNA-seq. Now, RNA-seq methods are available for studying many different aspects of RNA biology, including single-cell gene expression, translation (the translatome) and RNA structure (the structurome). Exciting new applications are being explored, such as spatial transcriptomics (spatialomics). Together with new long-read and direct RNA-seq technologies and better computational tools for data analysis, innovations in RNA-seq are contributing to a fuller understanding of RNA biology, from questions such as when and where transcription occurs to the folding and intermolecular interactions that govern RNA function…

The key concepts of single-cell and spatial RNA-seq

a | An overview of the single-cell RNA sequencing (RNA-seq) workflow. Single-cell sequencing begins with the isolation of single cells from a sample, such as dissociated skin tissue, by any one of a number of methods, including micropipetting into individual microfuge tubes or flow sorting into 96 or 384 well plates containing a lysis buffer, capture in a microfluidic chip, distribution in nanowells, microfluidic isolation in reagent-filled droplets or marking cells with in situ barcodes. Cells are reverse transcribed in order to produce cDNA (usually tagged with unique molecular identifiers (UMIs)) for RNA-seq library preparation and sequencing. Quality control (QC), differential gene expression (DGE) and 2D visualization (t-distributed stochastic neighbour embedding (tSNE)), along with unsupervised clustering and network analysis, of the single-cell RNA-seq data are used to determine discrete cell populations. The number of cells usually profiled is indicated alongside each technology, as is the RNA-seq strategy — for example, 3΄ or 5΄ mRNA or full-length cDNA. b | An overview of the spatialomics workflow. Spatial encoding requires a frozen tissue section to be applied to oligo-arrayed microarray slides or to ‘pucks’ of densely packed oligo-coated beads. The mRNA diffuses to the slide surface and hybridizes to oligo-dT cDNA synthesis primers that encode UMIs and spatial barcodes. It is then reverse transcribed to produce cDNA, which is pooled for library preparation and sequencing. Computational analysis of the spatialomics data maps sequence reads back to their spatial coordinates after DGE analysis and allows differential spatial expression to be visualized. Single-cell and spatialomics RNA-seq data are usually generated on short-read sequencers.