Multiomic single-cell cancer analysis — from mutation detection to understanding disease mechanisms

Unlocking new biology

Cancer is a highly heterogeneous disease with a wide distribution of mutations and clonal variation. To capture this data, single-cell RNA-sequencing (scRNA-seq) has become a ubiquitous tool for characterising tumours and the tumour microenvironment (TME). Having already run scRNA-seq experiments on cancer samples using a short-read sequencing technology, many researchers have a freezer of full-length cDNA samples — and unexplored biology.

To that end, Hanlee and his team looked to develop a method to analyse the mutational landscape of existing full-length single-cell cDNA using long nanopore sequencing reads. As Hanlee explained, this is an ‘unexplored, undiscovered country that’s highly biologically relevant and very impactful in cancer’.

Developing a new single-cell approach to characterise mutations

Leveraging existing data from tumour sample sequencing, Hanlee and his team utilised unique adaptive sampling — a method unique to Oxford Nanopore — to target cancer genes for long-read sequencing. Adaptive sampling takes advantage of real-time nanopore sequencing and uses software perform on-device enrichment for cDNA sequences of interest. Non-target sequences are ejected to free up the nanopore, while target sequences are retained and fully sequenced.

As part of a proof-of-concept study, single-cell cDNA was prepared from three cancer research samples (two appendiceal carcinomas and a B-cell lymphoma), each with two tumour sites. Fragmented and full-length libraries were prepared from the same cDNA research samples and sequenced using a short-read sequencing device or using an Oxford Nanopore MinION device with adaptive sampling to generate long reads, respectively.

For data analysis, the team leveraged a previously developed tool to merge the targeted nanopore long reads with short-read whole-transcriptome data, using cell barcodes to match and overlay the reads. Using this approach, the researchers were able to create a more informative dataset, matching mutations from the nanopore reads to phenotypes derived from the short-read data for individual cells.

Revealing the mutational landscape of cancer cells

Data from the proof-of-concept study showed that the long nanopore reads were able to cover entire exons and identify mutations in transcripts from individual cells. Hanlee showed an example of substitution mutations in key driver genes (SF3B1, KRAS, SMAD2, and GNAS) for the first appendiceal carcinoma research sample, in which they were able to identify the number of cells with the wild type versus mutation. Zooming in further on a clinically relevant substitution mutation, KRAS: G12D, the researchers could identify the individual cell types — such as epithelial or TME — with the mutation. Hanlee also showed similar results for the second appendiceal carcinoma sample, demonstrating the feasibility of using targeted single-cell nanopore sequencing to identify mutations¹.

Identifying a key gene fusion in B-cell lymphoma

In the same proof-of-concept study, the team analysed 161 gene targets in a B-cell lymphoma research sample. They were able to identify that a series of BCL2 mutations were most prevalent and associated with the B-cell malignancy. Additionally, with long nanopore reads, they were able to identify a prevalent BCL2 IGH rearrangement that occurs in up to 70% of subjects¹.

‘It is feasible to identify cancer mutations and rearrangements … with single-cell resolution’

Characterising the functional effects of somatic variants

While there are many known mutations associated with the development and progression of cancer, it is practically unknown how they mechanistically contribute to the disease. To take their targeted nanopore sequencing approach even further, Hanlee and his team turned their attention to understanding the phenotypic effects of detected mutations in cancer.

In another proof-of-concept study, the researchers paired single-cell CRISPR screening with targeted nanopore sequencing to take known TP53 mutations and engineer them into individual cells to look at the phenotypic consequences. Hanlee successfully demonstrated this method by introducing substitution mutations into the TP53 gene transcripts using a CRISPR library and sequencing the full-length cDNA transcripts using the single-cell workflow with nanopore sequencing. From this workflow, they were then able to identify several hundred mutations in the gene across individual cells².

Figure 1. Overview of the single-cell cDNA sequencing workflow for CRISPR-edited cells. Image from Kim et al.² and available under Creative Commons license (creativecommons.org/licenses/by/4.0).

By combining the single-cell phenotypic data from the short reads with the single-cell mutational data from the long nanopore reads, the researchers were able to distinguish the functionally significant variants from wild-type or wild-type-like variants, giving the team ‘the ability to phenotype mutations individually by combining these technologies’. Specifically, the long nanopore reads revealed data at the transcript level by sequencing full-length cDNA, enabling Hanlee to uncover more information about specific mutations in individual cells and characterise their functional effects.