From Understanding the Development Landscape of the Canonical Fate-Switch Pair to Constructing a Dynamic Landscape for Two-Step Neural Differentiation

AbstractRecent progress in stem cell biology, notably cell fate conversion, calls for novel theoretical understanding for cell differentiation. The existing qualitative concept of Waddington’s “epigenetic landscape” has attracted particular attention because it captures subsequent fate decision points, thus manifesting the hierarchical (“tree-like”) nature of cell fate diversification. Here, we generalized a recent work and explored such a developmental landscape for a two-gene fate decision circuit by integrating the underlying probability landscapes with different parameters (corresponding to distinct developmental stages). The change of entropy production rate along the parameter changes indicates which parameter changes can represent a normal developmental process while other parameters’ change can not. The transdifferentiation paths over the landscape under certain conditions reveal the possibility of a direct and reversible phenotypic conversion. As the intensity of noise increases, we found that the landscape becomes flatter and the dominant paths more straight, implying the importance of biological noise processing mechanism in development and reprogramming. We further extended the landscape of the one-step fate decision to that for two-step decisions in central nervous system (CNS) differentiation. A minimal network and dynamic model for CNS differentiation was firstly constructed where two three-gene motifs are coupled. We then implemented the SDEs (Stochastic Differentiation Equations) simulation for the validity of the network and model. By integrating the two landscapes for the two switch gene pairs, we constructed the two-step development landscape for CNS differentiation. Our work provides new insights into cellular differentiation and important clues for better reprogramming strategies.摘要：近年来干细胞生物学领域的研究进展，尤其是细胞命运转换方向的突破，亟需针对细胞分化的新型理论阐释。沃丁顿表观遗传景观（Waddington’s epigenetic landscape）这一既有定性概念已受到学界广泛关注，因其能够捕捉后续的命运决策节点，从而清晰展现细胞命运分化的层级化（“树状”）本质。本研究推广了一项近期研究成果，通过将底层概率景观与不同参数（对应不同发育阶段）相结合，针对双基因命运决策环路探究了此类发育景观。沿参数变化的熵产生率（entropy production rate）的变化可揭示：哪些参数的变化能够表征正常发育过程，而其余参数的变化则无法实现这一点。特定条件下景观上的转分化路径（transdifferentiation paths），揭示了直接且可逆的表型转化的可能性。随着噪声强度升高，我们发现景观变得更为平缓，优势路径也更为平直，这暗示了生物噪声处理机制在发育与重编程过程中的重要性。我们进一步将单步命运决策的景观拓展至中枢神经系统（Central Nervous System, CNS）分化中的两步决策景观。本研究首先构建了中枢神经系统分化的最小网络与动力学模型，其中耦合了两个三基因基序。随后通过随机微分方程（Stochastic Differentiation Equations, SDEs）开展仿真，验证了该网络与模型的有效性。通过整合两个开关基因对对应的景观，我们构建了中枢神经系统分化的两步发育景观。本研究为细胞分化领域提供了全新的认知视角，也为优化重编程策略提供了重要线索。