Single-cell RNA sequencing (scRNA-seq) has transformed our understanding of phenotypic heterogeneity. Although the predominant focus of scRNA-seq analyses has been assessing gene expression changes, several approaches have been proposed in recent years to identify changes at the DNA level from scRNA-seq data. In this study, we evaluated the relative performance of six strategies for calling single-nucleotide variants from scRNA-seq data using 381 single-cell transcriptomes from five cancer patients. Specifically, we focused on the quality of the inferred genotypes and the resulting single-cell phylogenies. We found that scAllele, Monopogen, and Monovar consistently returned phylogenetically informative genotype calls, providing more precise signals of discrimination between tumor and normal cells within heterogeneous samples and among distinct subclonal lineages in longitudinal samples. In addition, we evaluated the evolution of gene expression along the cell phylogenies. While most transcriptomic variation was very plastic and did not correlate with the cell phylogeny, a group of genes associated with cell cycle processes showed a strong phylogenetic signal in one of the patients, underscoring a potential link between gene expression patterns and lineage-specific traits in the context of cancer progression. In summary, our study highlights the potential of scRNA-seq data for inferring cell phylogenies to decipher the evolutionary dynamics of cell populations.
Alves et al. (Thu,) studied this question.