Biology subjects

Jett, S.

Publications and source records attributed to Jett, S..

2 recordsLinked to original sources

bioRxiv2018

Joint single cell DNA-Seq and RNA-Seq of gastric cancer reveals subclonal signatures of genomic instability and gene expression

Sequencing the genomes of individual cancer cells provides the highest resolution of intratumoral heterogeneity. To enable high throughput single cell DNA-Seq across thousands of individual cells per sample, we developed a droplet-based, automated partitioning technology for whole genome sequencing. We applied this approach on a set of gastric cancer cell lines and a primary gastric tumor. In parallel, we conducted a separate single cell RNA-Seq analysis on these same cancers and used copy number to compare results. This joint study, covering thousands of single cell genomes and transcriptomes, revealed extensive cellular diversity based on distinct copy number changes, numerous subclonal populations and in the case of the primary tumor, subclonal gene expression signatures. We found genomic evidence of positive selection - where the percentage of replicating cells per clone is higher than expected - indicating ongoing tumor evolution. Our study demonstrates that joining single cell genomic DNA and transcriptomic features provides novel insights into cancer heterogeneity and biology. SIGNIFICANCEWe conducted a massively parallel DNA sequencing analysis on a set of gastric cancer cell lines and a primary gastric tumor in combination with a joint single cell RNA-Seq analysis. This joint study, covering thousands of single cell genomes and transcriptomes, revealed extensive cellular diversity based on distinct copy number changes, numerous subclonal populations and in the case of the primary tumor, subclonal gene expression signatures. We found genomic evidence of positive selection where the percentage of replicating cells per clone is higher than expected indicating ongoing tumor evolution. Our study demonstrates that combining single cell genomic DNA and transcriptomic features provides novel insights into cancer heterogeneity and biology.

genomics↗

bioRxiv2017

Resolving the Full Spectrum of Human Genome Variation using Linked-Reads

Large-scale population based analyses coupled with advances in technology have demonstrated that the human genome is more diverse than originally thought. To date, this diversity has largely been uncovered using short read whole genome sequencing. However, standard short-read approaches, used primarily due to accuracy, throughput and costs, fail to give a complete picture of a genome. They struggle to identify large, balanced structural events, cannot access repetitive regions of the genome and fail to resolve the human genome into its two haplotypes. Here we describe an approach that retains long range information while harnessing the advantages of short reads. Starting from only [~]1ng of DNA, we produce barcoded short read libraries. The use of novel informatic approaches allows for the barcoded short reads to be associated with the long molecules of origin producing a novel datatype known as Linked-Reads. This approach allows for simultaneous detection of small and large variants from a single Linked-Read library. We have previously demonstrated the utility of whole genome Linked-Reads (lrWGS) for performing diploid, de novo assembly of individual genomes (Weisenfeld et al. 2017). In this manuscript, we show the advantages of Linked-Reads over standard short read approaches for reference based analysis. We demonstrate the ability of Linked-Reads to reconstruct megabase scale haplotypes and to recover parts of the genome that are typically inaccessible to short reads, including phenotypically important genes such as STRC, SMN1 and SMN2. We demonstrate the ability of both lrWGS and Linked-Read Whole Exome Sequencing (lrWES) to identify complex structural variations, including balanced events, single exon deletions, and single exon duplications. The data presented here show that Linked-Reads provide a scalable approach for comprehensive genome analysis that is not possible using short reads alone.

genomics↗