Han Yourui, Chen Bolin, Bi Zhongwen, Zhang Jianjun, Hu Youpeng, Bian Jun, Kang Ruiming, Shang Xuequn
School of Computer Science, Northwestern Polytechnical University, Xi'an, 710072, China.
Key Laboratory of Big Data Storage and Management, Northwestern Polytechnical University, Ministry of Industry and Information Technology, Xi'an, 710072, China.
Interdiscip Sci. 2025 Jan 7. doi: 10.1007/s12539-024-00686-z.
The Waddington landscape was initially proposed to depict cell differentiation, and has been extended to explain phenomena such as reprogramming. The landscape serves as a concrete representation of cellular differentiation potential, yet the precise representation of this potential remains an unsolved problem, posing significant challenges to reconstructing the Waddington landscape. The characterization of cellular differentiation potential relies on transcriptomic signatures of known markers typically. Numerous computational models based on various energy indicators, such as Shannon entropy, have been proposed. While these models can effectively characterize cellular differentiation potential, most of them lack corresponding dynamical interpretations, which are crucial for enhancing our understanding of cell fate transitions. Therefore, from the perspective of cell migration and proliferation, a feasible framework was developed for calculating the dynamically interpretable energy indicator to reconstruct Waddington landscape based on sparse autoencoders and the reaction diffusion advection equation. Within this framework, typical cellular developmental processes, such as hematopoiesis and reprogramming processes, were dynamically simulated and their corresponding Waddington landscapes were reconstructed. Furthermore, dynamic simulation and reconstruction were also conducted for special developmental processes, such as embryogenesis and Epithelial-Mesenchymal Transition process. Ultimately, these diverse cell fate transitions were amalgamated into a unified Waddington landscape.
沃丁顿景观最初被提出用于描绘细胞分化，并已扩展到解释诸如重编程等现象。该景观作为细胞分化潜能的具体表征，但这种潜能的精确表示仍然是一个未解决的问题，给重建沃丁顿景观带来了重大挑战。细胞分化潜能的表征通常依赖于已知标志物的转录组特征。已经提出了许多基于各种能量指标（如香农熵）的计算模型。虽然这些模型可以有效地表征细胞分化潜能，但它们中的大多数缺乏相应的动力学解释，而动力学解释对于增强我们对细胞命运转变的理解至关重要。因此，从细胞迁移和增殖的角度出发，开发了一个可行的框架，用于基于稀疏自动编码器和反应扩散对流方程计算可动态解释的能量指标，以重建沃丁顿景观。在此框架内，对典型的细胞发育过程，如造血和重编程过程进行了动态模拟，并重建了它们相应的沃丁顿景观。此外，还对特殊的发育过程，如胚胎发生和上皮-间质转化过程进行了动态模拟和重建。最终，这些不同的细胞命运转变被整合到一个统一的沃丁顿景观中。
