Steering cell-state and phenotype transitions by causal disentanglement learning

Understanding and manipulating cell-state and phenotype transitions is essential for advancing biological research and therapeutic treatment. We introduce CauFinder, an advanced framework designed to accurately identify causal regulators of these transitions and further precisely steer such transitions by integrating causal disentanglement modelling with network control based solely on observed data. By leveraging do-calculus and optimizing information flow metrics, CauFinder can distinguish causal factors from spurious ones, ensuring precise control over desired state transitions. One significant advantage of CauFinder is its ability to identify those variables causally affecting the cell-state/phenotype transitions among all observed variables, both theoretically and computationally, leading to the identification of their master regulators when combined with network control. Consequently, by employing a counterfactual algorithm, CauFinder is able effectively to facilitate desirable state transitions or steer these transitional trajectories/paths by modulating these causal drivers. Beyond its theoretical advantages, CauFinder outperforms existing approaches computationally in both simulated and real-world settings. CauFinder is able to not only reveal natural biological transition processes such as (a) cell differentiation, (b) lung adenocarcinoma (LUAD) to lung squamous cell carcinoma (LUSC) transdifferentiation and (c) drug-sensitive to drug-resistant transitions but also identify the causal regulators of their reverse transition processes, such as (A) cell dedifferentiation, (B) LUSC to LUAD transdifferentiation and (C) drug-resistant to drug-sensitive transitions. These findings highlight its superior ability to causally uncover essential regulatory mechanisms and accurately steer cell-state/phenotype transitions, thus providing novel therapeutic strategies.