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Genetic Heterogeneity Profiling by Single Cell RNA Sequencing

Detection of genetically distinct subclones and profiling the transcriptomic differences between subclones is important for studying the evolutionary dynamics of tumors, as well as for accurate prognosis and effective treatment of cancer in the clinic. Single cell RNA-sequencing (scRNA-seq) is now ubiquitously adopted in ongoing and planned cancer studies for the profiling of intra-tumor transcriptional heterogeneity. Inference of clonal membership from scRNA-seq is currently unreliable. We propose DENDRO, an analysis method for scRNA-seq data that detects genetically distinct subclones, assigns each single cell to a subclone, and reconstructs the evolutionary tree. DENDRO utilizes information from single nucleotide mutations in transcribed regions and accounts for single-cell level expression stochasticity and technical noise. The accuracy of DENDRO is benchmarked on spike-in datasets and on scRNA-seq data with known subpopulation structure. We applied DENDRO to two scRNA-seq data sets, where the new approach allowed us to elucidate the interplay between genetic and transcriptomic intratumor variation.