Multiomic single cell and spatial interrogation of mechanisms in cellular adaptation to stress - PROJECT SUMMARY Cellular response and adaptation to external stress is a fundamental aspect of normal tissue homeostasis. In cancer, these mechanisms of cellular adaptation become detrimental as they allow cancer cells to survive through therapy, thus contributing to therapy resistance. The emergence of therapy resistance in cancer is an example of a phenomenon shaped by both selection and adaptation: While most cells die upon treatment (selection), a rare subpopulation survives, and a smaller subset even adapts to proliferate in drug medium. Thus, when studying resistance in cancer, a sample of cells at any time during treatment contains a mixture of cells with different fates: growth, senescence and death. Our preliminary data has revealed a complex and fascinating interplay between cellular mechanisms for survival and those for adaptation, with some mechanisms shared between stressors and others unique to specific stressors. Due to the pervasive role of cellular stress-response mechanisms in tissue homeostasis and disease, an understanding of how (and which) cells survive and adapt under different stresses is fundamental both for the understanding of basic tissue biology as well as the development of disease treatments. Currently, there is a lack of methods for single-cell fate tracking in these complex systems where cell fate is driven by survival and adaptation. One promising experimental approach is lineage barcoding, which uses unique DNA sequences to label and track individual cells over time. However, there is yet a lack of both experimental and computational frameworks that integrate lineage tracing with the latest multimodal single cell and spatial sequencing technologies. Further, the pervasive adoption of single-cell sequencing methods in the study of human disease underscores the importance of computational approaches for trajectory reconstruction and cell fate prediction that is applicable to a clinical setting, without the need for genetic barcoding. In this project, we develop new genetic experimental systems for integration of lineage barcoding with spatial barcoding and with single-cell multiome RNA and ATAC sequencing; in parallel, we develop a general computational framework for the prediction of cell fates in the absence of genetic lineage tracing. The new experimental systems serve as new protocols for the scientific community as well as a powerful validation framework for the computational methods development. The new computational methods for lineage reconstruction and fate prediction using multiomic RNA and ATAC sequencing data will be released as open-source software, addressing critical limitations in current methods. These methods will be applied to the study of cancer cells treated with a panel of different drugs and stresses. This human biological system is rich with lineage dynamics and multiple cellular fates as each cancer cell has the possibility of growth, senescence, or cell death. Thus, our work will reveal fundamental biology about the complex relationship between cancer cell heterogeneity and the cellular fate of drug resistance.
