Nature CommunicationsGenomicsPeer-reviewed journal article
High-throughput targeted long-read single cell sequencing reveals the clonal and transcriptional landscape of lymphocytes
Mandeep SinghiD, Ghamdan Al‐EryaniiD, Shaun CarswelliD, James M. FergusoniD, James BlackburniD, Kirston BartoniD, Daniel Lee RodeniD, Fabio LucianiiD, Tri Giang PhaniD, Simon R. JunankariD, Katherine JacksoniD, Christopher Carl GoodnowiD, Martin A. SmithiD, Alexander SwarbrickiD
Abstract
High-throughput single-cell RNA-Sequencing is a powerful technique for gene expression profiling of complex and heterogeneous cellular populations such as the immune system. However, these methods only provide short-read sequence from one end of a cDNA template, making them poorly suited to the investigation of gene-regulatory events such as mRNA splicing, adaptive immune responses or somatic genome evolution. To address this challenge, we have developed a method that combines targeted long-read sequencing with short-read based transcriptome profiling of barcoded single cell libraries generated by droplet-based partitioning. We use Repertoire And Gene Expression sequencing (RAGE-seq) to accurately characterize full-length T cell (TCR) and B cell (BCR) receptor sequences and transcriptional profiles of more than 7,138 lymphocytes sampled from the primary tumour and draining lymph node of a breast cancer patient. With this method we show that somatic mutation, alternate splicing and clonal evolution of T and B lymphocytes can be tracked across these tissue compartments. Our results demonstrate that RAGE-Seq is an accessible and cost-effective method for high-throughput deep single cell profiling, applicable to a wide range of biological challenges.
- organism: human
- assay: scRNA-seq
- assay: long-read sequencing
- research focus: cell type annotation
- research focus: clustering
- research focus: differential expression
- research focus: gene regulatory network inference
- approach: experimental assay
- Citations
- 308
- FWCI
- 13.74
- Field percentile
- top 1%
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