The analysis of genomic and transcriptomic heterogeneity at the single cell level has provided new insights into many research areas, including cancer research, cell development and function, and immunology. However, the use of traditional short-read sequencing technology can introduce limitations in single-cell assays; for example, in transcriptome studies, it is not possible to identify transcript abundance at the isoform level. Long nanopore sequencing reads resolve these challenges, enabling end-to-end sequencing of full-length transcripts and large genomic regions in single reads, and spanning repetitive regions and structural variants.
Single-cell, isoform-level characterisation of RNA transcripts
Short-read based single-cell RNA sequencing (scRNA-Seq) methodologies only yield information from a small region close to one end of the transcript, precluding the facility to analyse splicing, chimeric transcripts, and sequence diversity across the molecule. In contrast, nanopore technology, which has no requirement for fragmentation, nor read length limitations, sequences the entire RNA (cDNA) molecule. As a result, full-length transcripts can be sequenced in single reads, allowing accurate, isoform-level characterisation and quantification (Figure 1).
Full-length single-cell cDNA data reveals gene expression heterogeneity
Single-cell transcriptomics reveals intercellular gene expression heterogeneity at a level that cannot be achieved from bulk-cell analyses alone. Isoform-level expression data obtained from nanopore sequencing of single-cell libraries can reveal cell-type-specific differences in transcript splicing that are undetectable in short-read single-cell transcriptomic data. This is particularly useful for identifying phenomena such as isoform switching during cell development. For example, Lebrigand et al. demonstrated that, in the mouse brain, the Clathrin light chain A gene (‘Clta’) undergoes isoform switching during neuronal maturation, which they suggested may fine-tune the role this protein plays in different developmental stages (Figure 2). In this study, the team identified a total of 76 genes with cell-type specific transcript usage.
View our single-cell sequencing protocol based on the work of Lebrigand et al.
Comprehensive characterization of single-cell full-length isoforms in human and mouse with long-read sequencing
‘…because we have full-length isoform information, we are not just restricted to only looking for mutations in the final exon’
In the single-cell breakout session at the Nanopore Community Meeting 2020, Matthew Ritchie (The Walter and Eliza Hall Institute of Medical Research, Melbourne) presented a single-cell sample preparation method to generate full-length transcripts for nanopore sequencing ('FLT-seq') and an analysis tool for exploring isoform expression heterogeneity at the single-cell level ('FLAMES'). With these two methods his team explored differential transcript usage in single cells from various sample types, and identified variants with distinctive distributions between cell clusters.
Within this session, presentations were also given by Rebecca Berrens (University of Cambridge, UK), who described her work investigating transposable element expression in single cells using nanopore technology, and Christoph Dieterich (University Hospital Heidelberg, Germany), who presented the ScNapBar software for processing and analysing single-cell data.
Discover how the subtleties of cellular diversity can be revealed with a combination of single-cell and spatial transcriptomics approaches.
How do I prepare single-cell cDNA for sequencing using nanopore technology?
The most common single-cell sample preparation methods use emulsions to isolate single cells and generate full-length cDNA; however, the cDNA is often subsequently fragmented prior to sequencing. Methods have been developed within the nanopore user community which skip the cDNA fragmentation step and allow preparation of full-length cDNA libraries for nanopore sequencing, using the Ligation Sequencing Kit for library preparation. With this approach, Lebrigand et al. have demonstrated that sequencing a full-length cDNA single-cell library on a single PromethION Flow Cell typically generates ~50 million cell and UMI-assigned reads. For more information, including analysis options, we recommend referring to the publication by Lebrigand et al., and others listed in the ‘Related resources’ section below.