Control of cell state transitions

Abstract

Understanding cell state transitions and purposefully controlling them is a longstanding challenge in biology. Here, we present cell State Transition Assessment and Regulation (cSTAR), an approach to map cell states, model transitions between them, and predict targeted interventions to convert cell fate decisions. cSTAR uses omics data as input, classifies cell states, and develops a workflow that transforms the input data into mechanistic models that identify a core signaling network, which controls cell fate transitions by influencing whole-cell networks. By integrating signaling and phenotypic data, cSTAR models how cells maneuver in Waddington's landscape 1 and make decisions about which cell fate to adopt. Importantly, cSTAR devises interventions to control the movement of cells in Waddington's landscape. Testing cSTAR in a cellular model of differentiation and proliferation shows a high correlation between quantitative predictions and experimental data. Applying cSTAR to different types of perturbation and omics datasets including single cell data demonstrates its flexibility and scalability and provides new biological insights. The ability of cSTAR to identify targeted perturbations that interconvert cell fates will allow designer approaches for manipulating cellular development pathways and mechanistically underpinned therapeutic interventions.The concept of cell states is a useful lens to view and understand the organization of tissues and organisms, their development, and responses to exogenous and endogenous changes. While initially based on phenotypical descriptions, global analysis methods now can connect phenotypes with underlying molecular processes. These methods characterize cell states with fine molecular resolution and open the door to understand how cell states can evolve and transition into each other. In 1940, Waddington suggested that cells move through a landscape of mountains and valleys as rolling marbles from one (meta)stable state to another 1 . This now famous model appeals through its intuitive nature but leaves open why the marbles roll into certain valleys and whether they can revert to an initial state. Recent efforts have applied computational models to understand cell state transitions, generated by

Dynamic regulation of RAS and RAS signaling

“…Accomplishing this task will require a close collaboration between biology and computational modeling which can systematically analyze the dynamic systems behavior [ 146 , 147 ]. Recent advances in computational and mathematical modeling now allow us to investigate RAS pathway dynamics in great detail [ 138 , 148 ], and a new method to assess and control cell states will facilitate the reconstruction and mechanistic analysis of RAS-dependent networks [ 149 ].…”

Predicting Anti-Cancer Drug Combination Responses with a Temporal Cell State Network Model

“…Studies have also confirmed the factors behind certain other cell state transitions-for instance, the transcriptomic factors and signaling molecules in different epithelial to mesenchymal transitions 30,[83][84][85] . Cell state transition networks have been identified for multiple cancer types [15][16][17][18][19]21,22,[86][87][88] , generally by combining single cell measurements (e.g. single cell RNAseq), with perturbation time courses, such as enriching for one cell state and then observing the fractional composition dynamics.…”

“…One major driver is tumor heterogeneity; cells in different "states" that have different drug sensitivities. Cell states are often either defined by their histology or transcriptomics (through for example single cell RNAseq experiments) [15][16][17][18][19][20][21] , and it is becoming appreciated that cells can transition between such states in development-like networks, sometimes called cell state networks 17,22 . Such plasticity between cell states can contribute to drug resistance [23][24][25] , and combinations of drugs targeting different pathways and factors involving phenotype transition have been proposed to prevent such resistance 23 .…”

Cell state‐directed therapy – epigenetic modulation of gene transcription demonstrated with a quantitative systems pharmacology model of temozolomide

“…It is well‐appreciated that cancers and particularly glioblastoma multiforme (GBM) possesses a high degree of intratumoral heterogeneity (ITH) wherein cells of different types or states can transition to one another creating a plastic environment capable of resisting internal and external stresses, such as a hypoxic environment or drug therapy 1–4 . Efforts to characterize and understand GBM ITH are intimately linked to multi‐omic analyses that have indicated major cell states and their relevance to disease progression, yet how to therapeutically approach cellular plasticity is a new endeavor 5–7 . In an analysis of melanoma, drug therapy converted transient transcriptional states in rare persister cells via epigenetic reprogramming to stably resistant cell states 2 .…”