Single-cell aneuploidy and chromosomal arm imbalances define subclones with divergent transcriptomic phenotypes.

Cancers are characterized by genomic instability events such as aneuploidy, chromosomal arm imbalances, and segmental copy number changes. These genomic features frequently define different subclones within a tumor. Single-cell DNA sequencing (scDNA-seq) identifies these large-scale genomic alterations that define subclonal features. However, scDNA-seq does not provide biological phenotypic information on individual subclones. Single-cell RNA sequencing (scRNA-seq) offers biological information but is less accurate in discovering genomic instability events. We developed a computational framework, scAlign, for integrating scRNA-seq and scDNA-seq from the same specimen and define subclonal cellular phenotypes at the resolution of individual cells. Subclones were defined by aneuploidy and chromosomal arm imbalance among primary and metastatic cancers. Using the cells in the G0/G1 phase, the extensive cellular sampling from both assays characterized the subclonal architecture of these cancers. The scDNA-seq provided a ground truth for copy number-based subclones. From the scRNA-seq data, the epithelial cells in G0/G1 were identified and assigned to specific subclones by the scAlign based on gene dosage. Afterward, we determined the differential gene expression and biological pathway activities of specific clones. Overall, integrative multi-omics analysis of single-cell datasets is more informative than any individual genomic modality, provides deep insights into intratumoral heterogeneity and reveals subclonal biology. scAlign is available at https://github.com/XQBai/scAlign.