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Single-cell sequencing

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-read nanopore sequencing resolves 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.

Sequence full-length RNA transcripts for isoform-level single cell transcriptomics

Sequence full-length RNA transcripts for isoform-level single cell transcriptomics

Characterise splicing, chimeric transcripts, and sequence diversity across entire molecules

Characterise splicing, chimeric transcripts, and sequence diversity across entire molecules

Scale to your requirements with a range of nanopore sequencing platforms

Scale to your requirements with a range of nanopore sequencing platforms



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).

How barcoding UMI's work

Figure 1: The power of single-cell sequencing comes from the facility to pool, sequence, and subsequently identify transcripts from hundreds of individual cells. All transcripts derived from the same cell can be identified using a unique cell barcode (cellBC). A unique molecular identifier (UMI), added to the original transcripts prior to amplification, further enables the identification of PCR duplicates that could impact transcript quantification, and can be used to generate more accurate consensus sequences. Short-read sequencing technologies only read approximately 50 bp of transcript sequence, precluding the identification of transcript isoforms. In contrast, long nanopore sequencing reads can span complete transcripts enabling in-depth, isoform-level gene expression analysis from single cells.

Introduction 2
Rolling Circle Amplification to Concatemeric Consensus method

Figure 2: The Rolling Circle Amplification to Concatemeric Consensus (R2C2) method developed by Volden et al. provides high yields of highly accurate full-length transcripts. Figure courtesy of Dr. Christopher Vollmers, University of California, Santa Cruz.

Accurate, highly multiplexed, full-length single-cell cDNA sequencing

Researchers at University California, Santa Cruz, have demonstrated that the high sequencing yields delivered by MinION and PromethION Flow Cells combined with the utilisation of unique molecular identifiers (UMIs) and their novel R2C2 methodology, enables the generation of highly accurate transcript consensus sequences — completely negating any requirement for short reads and simplifying the experimental workflow (Figure 2). Using this method, Volden et al. sequenced 9 million full-length cDNA molecules from approximately 3,000 peripheral blood mononuclear cells. The data enabled clustering of cell type (i.e. B cells, T cells, monocytes) based on transcript profile. Furthermore, isoform-level transcriptomes were generated for each cell type.

According to the researchers: ‘This work represents a new, simple, and powerful approach that – using a single sequencing method – can extract an unprecedented amount of information from thousands of single cells’.

Case study


High throughput, error-corrected nanopore single-cell transcriptome sequencing

‘…combining the high throughput of nanopore sequencing with UMI guided error correction allows both high confidence definition of transcript isoforms and identification of sequence heterogeneity in single cells.’

Lebrigand et al.

Lebrigand et al. demonstrated how the addition of long nanopore sequencing reads to traditional short-read single-cell sequencing approaches enabled the accurate analysis of full-length transcripts. Following cell isolation using 10x Genomics technology, 1,141 cells from an E19 mouse brain library, across two batches, were subject to both long-read nanopore sequencing on the PromethION device and short-read sequencing. The short-read data was used to identify the cell barcode (cellBC) for each transcript and the associated unique molecular identifiers (UMIs). These data were then used to support the assignment of cellBC and UMIs to the genome-aligned nanopore sequencing reads.

In total, the team identified 18,439 previously-annotated transcripts and 15,317 novel transcripts, demonstrating the enhanced power of incorporating long sequencing reads to single-cell transcriptome studies. The data enabled the segregation of different cell types and the identification of differential isoform expression between cell types. Furthermore, the full-length reads provided more detailed insight into the association of individual SNVs in the glutamate receptor Gria2, and the identification of sequence heterogeneity within and between cell types.

Sequencing workflow
PromethION with kits and flow cells
Interested in analysing cell-free DNA?

Use UMIs to support the detection of low-frequency variants in cell-free DNA (cfDNA).

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Sequencing workflow

How do I perform single-cell RNA sequencing using nanopore technology?

The R2C2 protocol described by Volden et al. generated up to 4.9 million and 22 million reads per MinION or PromethION Flow Cell respectively, with a median read length of approximately 3 kb. Library preparation was performed using the Ligation Sequencing Kit. A number of tools are available for the analysis of nanopore RNA sequencing data both from Oxford Nanopore and the wider Nanopore Community. For more detailed insight into the analysis of single-cell transcriptomics data, we recommend the papers by Volden et al. and Lebrigand et al.

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Single-cell transcriptomics of human and animal cells

To obtain high yields of human or animal single-cell transcriptome data, we recommend the following:

portable MinION
Looking to QC your library first?

Find out about the versatile Flongle platform.

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