Understanding isoform expression by pairing long-read sequencing with single-cell and spatial transcriptomics

Abstract

RNA isoform diversity, produced via alternative splicing, and alternative usage of transcription start and poly(A) sites, results in varied transcripts being derived from the same gene. Distinct isoforms can play important biological roles, including by changing the sequences or expression levels of protein products. The first single-cell approaches to RNA sequencing-and later, spatial approaches-which are now widely used for the identification of differentially expressed genes, rely on short reads and offer the ability to transcriptomically compare different cell types but are limited in their ability to measure differential isoform expression. More recently, long-read sequencing methods have been combined with single-cell and spatial technologies in order to characterize isoform expression. In this review, we provide an overview of the emergence of single-cell and spatial long-read sequencing and discuss the challenges associated with the implementation of these technologies and interpretation of these data. We discuss the opportunities they offer for understanding the relationships between the distinct variable elements of transcript molecules and highlight some of the ways in which they have been used to characterize isoforms' roles in development and pathology. Single-nucleus long-read sequencing, a special case of the single-cell approach, is also discussed. We attempt to cover both the limitations of these technologies and their significant potential for expanding our still-limited understanding of the biological roles of RNA isoforms.

Figure 1.

Multiple RNA variables contribute to the production of distinct isoforms from the same gene. In alternative exon inclusion, an exon can be either included or spliced out of a transcript. In alternative TSS usage, transcription can start at two different positions. In alternative poly(A) site usage, the 3′ untranslated region can vary in length. In alternative donor or acceptor site usage, the boundary between an exon and an intron can shift. In intron retention, an intron can be included in the mature mRNA.

Figure 2.

Several key features differentiate the commonly used single-cell and spatial sequencing methods. (A) In the standard single-cell RNA-seq approach, a single-cell suspension is created and passed through a microfluidic system that captures each individual cell in an oil droplet with a bead containing a unique barcode. After short-read sequencing, cells can be clustered based on gene expression into groups representing distinct cell types. Each read covers only a small section of a transcript, and there is a bias toward the ends of a transcript (most often the 3′ end). Relatively few reads cover an exon junction. (B) With long-read sequencing, a read covers all or most of a transcript, including multiple exon junctions. This enables comparisons between cell types in terms of the relative abundance of particular isoforms or RNA variables such as alternative exons. Many cells can be sequenced, but relatively few transcripts are sequenced per cell. (C) Smart-seq approaches allow for better coverage over the full length of a transcript, including over exon junctions, than with standard single-cell RNA-seq, while still relying on short reads. More molecules are sequenced per cell than in single-cell long-read sequencing. (D) When the entire cell is used as the source of RNA, most sequenced molecules are cytoplasmic and thus fully spliced. However, in single-nucleus sequencing, where only nuclear RNA is available, many molecules will be unspliced or partially spliced. Also, because some of the introns present in sequenced transcripts will have “decoy” poly(A) stretches, some reads will start there rather than at the 3′ end of the transcript. (E) In spatial methods, a tissue slice is tagged with a grid of barcodes that encode each transcript's position within the slice rather than pointing to an individual cell of origin.

Figure 3.

The full-transcript view offered by long-read sequencing allows for the observation of coordination between RNA variables. (A) When two alternative exons are coordinated, only molecules with either both exons included or excluded are observed in long-read sequencing data. (B) In TSS–exon coordination, an alternative exon may only be present in molecules using one of two alternative TSS. (C) In exon–poly(A) site coordination, an alternative exon may only be present in molecules using one of two alternative poly(A) sites.