Yampolskaya Maria, Ikonomou Laertis, Mehta Pankaj
Department of Physics, Boston University, Boston, MA 02215, USA.
Department of Oral Biology, University at Buffalo School of Dental Medicine, Buffalo, NY 14215, USA Cell, Gene and Tissue Engineering Center, University at Buffalo, Buffalo, NY 14260, USA and Chemical and Biological Engineering Department, University at Buffalo, Buffalo, NY 14260, USA.
bioRxiv. 2025 Jun 4:2025.05.27.656406. doi: 10.1101/2025.05.27.656406.
Multicellular organisms develop a wide variety of highly-specialized cell types. The consistency and robustness of developmental cell fate trajectories suggests that complex gene regulatory networks effectively act as low-dimensional cell fate landscapes. A complementary set of works draws on the theory of dynamical systems to argue that cell fate transitions can be categorized into universal decision-making classes. However, the theory connecting geometric landscapes and decision-making classes to high-dimensional gene expression space is still in its infancy. Here, we introduce a phenomenological model that allows us to identify gene expression signatures of decision-making classes from single-cell RNA-sequencing time-series data. Our model combines low-dimensional gradient-like dynamical systems and high-dimensional Hopfield networks to capture the interplay between cell fate, gene expression, and signaling pathways. We apply our model to the maturation of alveolar cells in mouse lungs to show that the transient appearance of a mixed alveolar type 1/type 2 state suggests the triple cusp decision-making class. We also analyze lineage-tracing data on hematopoetic differentiation and show that bipotent neutrophil-monocyte progenitors likely undergo a heteroclinic flip bifurcation. Our results suggest it is possible to identify universal decision-making classes for cell fate transitions directly from data.
多细胞生物会发育出各种各样高度特化的细胞类型。发育细胞命运轨迹的一致性和稳健性表明，复杂的基因调控网络有效地充当了低维细胞命运景观。一系列互补的研究借鉴动力系统理论，认为细胞命运转变可分为通用的决策类别。然而，将几何景观和决策类别与高维基因表达空间联系起来的理论仍处于起步阶段。在此，我们引入了一个现象学模型，该模型使我们能够从单细胞RNA测序时间序列数据中识别决策类别的基因表达特征。我们的模型结合了低维梯度样动力系统和高维霍普菲尔德网络，以捕捉细胞命运、基因表达和信号通路之间的相互作用。我们将模型应用于小鼠肺部肺泡细胞的成熟过程，结果表明肺泡1型/2型混合状态的短暂出现表明其属于三重尖点决策类别。我们还分析了造血分化的谱系追踪数据，结果表明双能性中性粒细胞 - 单核细胞祖细胞可能经历了异宿翻转分岔。我们的结果表明，有可能直接从数据中识别细胞命运转变的通用决策类别。
