ABSTRACT
The determination of individual cell trajectories through a high-dimensional cell-state space is an outstanding challenge for understanding biological changes ranging from cellular differentiation to epigenetic responses of diseased cells upon drugging. We integrate experiments and theory to determine the trajectories that single BRAFV600E mutant melanoma cancer cells take between drug-naive and drug-tolerant states. Although single-cell omics tools can yield snapshots of the cell-state landscape, the determination of individual cell trajectories through that space can be confounded by stochastic cell-state switching. We assayed for a panel of signaling, phenotypic, and metabolic regulators at points across 5 days of drug treatment to uncover a cell-state landscape with two paths connecting drug-naive and drug-tolerant states. The trajectory a given cell takes depends upon the drug-naive level of a lineage-restricted transcription factor. Each trajectory exhibits unique druggable susceptibilities, thus updating the paradigm of adaptive resistance development in an isogenic cell population.
Subject(s)
Drug Tolerance , Genomics , Melanoma/drug therapy , Single-Cell Analysis , Cell Line, Tumor , Genes, Reporter , Green Fluorescent Proteins/metabolism , Humans , Metabolomics , Microphthalmia-Associated Transcription Factor , Models, Molecular , Proteomics , Proto-Oncogene Proteins B-raf/genetics , Reproducibility of ResultsABSTRACT
Biological function arises from the interplay of proteins, transcripts, and metabolites. An ongoing revolution in miniaturization technologies has created tools to analyze any one of these species in single cells, thus resolving the heterogeneity of tissues previously invisible to bulk measurements. An emerging frontier is single cell multi-omics, which is the measurement of multiple classes of analytes from single cells. Here, we combine bead-based transcriptomics with microchip-based proteomics to measure intracellular proteins and transcripts from single cells and defined small numbers of cells. The transcripts and proteins are independently measured by sequencing and fluorescent immunoassays respectively, to preserve their optimal measurement modes, and linked by encoding the physical address locations of the cells into digital sequencing space using spatially patterned DNA barcodes. We resolve cell-type-specific protein and transcript signatures and present a path forward to scaling the platform to high-throughput.
