Integrating scRNA-seq SNVs and gene expression modalities, a perspective on long read sequencing

Abstract

Mutations play a critical role in cancer, as some of these will confer growth advantages over neighboring cells and drive tumor progression. This leads to selection and proliferation of specific clones, a process named clonal expansion. Using single cell RNA-sequencing (scRNA-seq) one can analyze the transcriptional heterogeneity of clones. However, the majority of scRNA-seq is processed using short-read sequencing (SRS) technologies such as Illumina. This method relies on reads of around 100bp into the 3’ or 5’ end of transcripts, providing an adequate coverage for transcriptomic analysis but limiting mutation detection. Therefore, linkage between clonality and transcriptomic heterogeneity remains an outstanding problem in the field. Long-read sequencing (LRS) has recently been made compatible with scRNA-seq, supporting whole-transcript reads (with the highest current read being of 4Mbp) and substantially improving coverage. In this study, we are evaluating Oxford Nanopore Technologies (ONT), a type of LRS, comparing its performance to Illumina. We assessed transcriptomic analysis and SNV detection using datasets from Acute Lymphoblastic Leukemia (ALL) patients sequenced with both technologies. Additionally, we explored the integration of single-cell SNVs information with transcriptomic information, which could potentially shed light on the relation between the genetic and phenotypic state of the cell. With this study we hope to bridge different sources of heterogeneity in cancer and gain further understanding of tumor progression.