Profiling and perturbing the inheritance of drug-induced metabolic states in cancer with Inheritance-Seq

Non-genetic acquired resistance has been implicated in the therapy failure of multiple cancer types. Therefore, there have been significant efforts to uncover the mechanisms regulating such drug-resistant cellular states and how these states may be perturbed to promote cancer cell death and curb disease progression. While there has been much progress in understanding and characterizing drug-induced cellular states, little is known about what controls the inheritance of these states by daughter cells. Understanding the heritability of drug-induced states is not only useful to determine dosing frequency but also is critical in designing effective combination treatment regimens. Existing approaches to characterize cell state inheritance fall short of this goal because the features needed to define cell states are inaccessible, the temporal windows needed to observe state transitions are exceedingly long, and the cellular throughput needed to robustly read out effects of systematic perturbation is prohibitively high. Here, we propose inheritance-Seq, a new method that enables quantification of cell state heritability to meet the need for an approach to understand the heritability of drug-induced states. Inheritance-Seq combines CRISPR-based genetic lineage tracing and phenotyping with microscopy and in situ sequencing (ISS) to enable scalable measurement of phenotype inheritance with systematic pooled genetic perturbations to explore the mechanisms of phenotypic trait inheritance in cells. In the proposed work, we will design and implement a general framework to quantify the heritability of cell states and validate the performance of inheritance-Seq by identifying known factors regulating the inheritance of drug-induced metabolic state changes in OVCAR8 cells following carboplatin treatment.