High-throughput single-nucleus hybrid sequencing reveals genome-transcriptome correlations in cancer

Abstract

AbstractSingle-cell genomic analyses can provide information on cellular mutation and tumor heterogeneity, whereas single-cell transcriptomic analyses can distinguish cell types and states. However, the disconnect between genomic and transcriptomic spaces limits our understanding of cancer development. To address this, we developed a novel high-throughput method that simultaneously captures both DNA and RNA from single nuclei and new algorithms for the quantitative clustering and filtering of single-cell data. We applied this hybrid protocol to 65,499 single nuclei extracted from frozen biopsies of five different endometrial cancer patients and separately clustered the genome and expression data. We also analyzed 34,651 and 21,432 nuclei using RNA-only and DNA-only protocols, respectively, from the same samples to verify the clustering. Multiple tumor genome and/or expression clusters were often present within an individual patient, and different tumor clones could project into distinct or shared expression states. Almost all possible genome-transcriptome correlations were observed in the cohort. Stromal clusters were largely shared between patients, but some patients possessed unique stromal components, or mutant stroma with a significant loss of the X chromosome. This study reveals the complex landscape involving genome and transcriptome interactions at single-cell level, and provides new insights into mutant stroma as a potential clinical biomarker.