Clonealign: statistical integration of independent single-cell RNA and DNA sequencing data from human cancers

Kieran R Campbell,Emma Laks,Beixi Wang,Andrew Mcpherson,Hans Zahn,Adi Steif,Sohrab P Shah,Daniel Lai,Imaxt Consortium,Hossein Farahani,Pascale Walters,Alexandre Bouchard-Côté,Justina Biele,Samuel Aparicio,Farhia Kabeer,Jazmine Brimhall,Ciara O’Flanagan

doi:10.1186/s13059-019-1645-z

Abstract

Measuring gene expression of tumor clones at single-cell resolution links functional consequences to somatic alterations. Without scalable methods to simultaneously assay DNA and RNA from the same single cell, parallel single-cell DNA and RNA measurements from independent cell populations must be mapped for genome-transcriptome association. We present clonealign, which assigns gene expression states to cancer clones using single-cell RNA and DNA sequencing independently sampled from a heterogeneous population. We apply clonealign to triple-negative breast cancer patient-derived xenografts and high-grade serous ovarian cancer cell lines and discover clone-specific dysregulated biological pathways not visible using either sequencing method alone.

Highlights

Recent advances in genomic measurement technologies have allowed for unprecedented scalable interrogation of the genomes and transcriptomes of single cells [1, 2]
We assume clones are defined through grouped cell subsets which share to a first approximation similar genomic copy number structure
In order to relate the independent measurements, we assume that an increase in the copy number of a gene will result in a corresponding increase in that gene’s expression and vice versa (Fig. 1b), a relationship previously observed in joint RNA-DNA assays in bulk tissues [12] and at the single-cell level [9, 10, 13]

Summary

Introduction

Recent advances in genomic measurement technologies have allowed for unprecedented scalable interrogation of the genomes and transcriptomes of single cells [1, 2]. Such technologies are of particular interest in cancer, enabling measurement of cell-autonomous properties which constitute tumors as a whole. Combined assays sequencing both RNA and DNA from the same single cell will provide a measurement of genomic alterations impacting transcriptional. Assuming a population structure with a fixed number of clones, this can be expressed as a mapping problem, whereby cells measured with transcriptome assays must be aligned to those measured with a genome assay

Methods

Results

Conclusion