Expanding single-cell and spatial technologies to link clonal genotypeswith phenotypes during cancer evolution

ABSTRACT Clonal expansions are observed in a wide range of normal human tissues, as well as in cancer evolution. These outgrowths lead to clonal heterogeneity which can drive the development of treatment-resistant disease. Clones contain somatic mutations in known cancer driver genes and show evidence of positive selection. However, how these driver mutations potentiate changes to the cellular states of cells to allow clones to outcompete neighboring wild-type counterparts remains poorly characterized. So far, the identification of clonal outgrowths in normal or malignant human tissues has been mostly restricted to genotyping analysis only, as these clones often constitute a minority of cells in a sample and do not have distinguishable cell-surface markers. To address the challenge of charting clonal outgrowth during somatic and cancer evolution, we developed a range of advanced single-cell technologies that can capture multiple layers of information from a single cell. These technologies can gather information on genotypes, transcriptomes, methylomes, and protein expression. To overcome the challenge of genotyping using single-cell RNA sequencing, we developed the Genotyping of Transcriptomes (GoT) technique. With GoT, we can compare mutant and wild-type cells within the same individual to characterize the transcriptional consequences of somatic mutations. To further study the molecular mechanisms of how somatic mutations give clonal growth advantage, we have extended our multi- omics single-cell toolkit to include other modalities. As epigenetic mutations are highly frequent in cancer, we developed and applied targeted single-cell genotyping and single-cell ATAC-seq (GoT-ChA), which provides genotyping information in the context of chromatin accessibility. We aim to further expand our platform by developing single-cell methods to capture additional chromatin features, including post-translational modifications of histones (GoT-NTT), transcription factor binding (D&D-seq/GoT-D&D), and high-throughput automated joint measurement of RNA expression and whole genome sequencing (SMART-PTA). Additionally, to define clonal driver genotypes in their spatial context, we will adapt spatial transcriptomics by adding the critical feature of genotyping (GoT-Stradivari), allowing us to characterize how clone growth is affected by its interaction with the microenvironment. Our ultimate goal is to identify the underpinnings of fitness advantage in clonal outgrowth by generating multi-omic comparisons at the single-cell level between wild-type and mutant cells. The proposed comprehensive GoT toolkit will enable us to link single-cell genotypes with transcriptional, protein, epigenetic, and spatial phenotypes at high throughput. We believe that these advances will transform the study of clonal mosaicism as a harbinger of cancer and resistance to cancer therapies.