细胞重编程可以提供对疾病机制的有价值的见解,并有可能为再生医学提供新的工具。然而,这仍然是一个效率低下且往往不完整的过程。然而,实验表明,几乎所有的体细胞最终都会产生多能状态,尽管延迟不同,只要重编程转录因子的表达得以维持。此外,似乎可以鉴定出特定的细胞亚群,这些细胞亚群显示出增强的重编程为多能状态的倾向。已经提出,初始随机过程负责该初始引发,随后是引导引发的细胞进入多能状态的确定性过程。这里,我们提出了细胞重编程的人口转移观点,这解释了这些观察结果,并调和了这一过程的随机性和确定性。根据这个观点,一小群细胞,其状态更接近多能状态,并处于预先存在的能量有利轨迹中,将最初选择重新编程。此外,通过保持重编程因子的异位表达,其他细胞进入这些途径作为转录和表观遗传随机变异的结果。因此,越来越多的细胞达到多能状态,细胞群分布向这种状态转移。重要的是,额外的扰动可以改变表观遗传景观,允许细胞更多地访问重编程轨迹,从而提高重编程效率。对导致最终细胞状态的初始细胞亚群和状态途径的了解应该使我们能够设计替代的扰动策略来提高重编程效率和保真度。
Cellular reprogramming can offer valuable insight into disease mechanism and has the potential to provide novel tools for regenerative medicine. Yet it remains an inefficient and often incomplete process. However, experiments show that almost all somatic cells eventually give rise to the pluripotent state, albeit at different latencies, as long as expression of reprogramming transcription factors is maintained. Furthermore, it appears that specific subpopulations of cells can be identified that show enhanced propensities to be reprogrammed to the pluripotent state. It has been proposed that an initial stochastic process is responsible for this initial priming that is followed by a deterministic process that directs the primed cells into the pluripotent state. Here, we propose a population shift view of cellular reprogramming, which explains these observations and reconciles the stochastic and deterministic nature of this process. According to this view, a small population of cells, whose states are closer to the pluripotent state and reside in pre-existing energetically favorable trajectories, will be initially selected for reprogramming. Moreover, by maintaining ectopic expression of reprogramming factors, other cells enter these pathways as a result of transcriptional and epigenetic stochastic variations. Consequently, increasing numbers of cells reach the pluripotent state, and the cell population distribution shifts toward this state. Importantly, additional perturbations can change the epigenetic landscape, allowing cells more access to the reprogramming trajectories, thereby increasing reprogramming efficiency. Knowledge of the initial cellular subpopulations and pathways of states that lead to the final cellular state should allow us to design alternative perturbation strategies to improve reprogramming efficiency and fidelity.