孤舟蓑笠翁 (2025-09-13 13:31):
#paper 【doi】10.1093/nar/gkaf805;【发表年份】2025年;【期刊】Nucleic Acids Research;【标题】The intrinsic dimension of gene expression during cell differentiation。【内容总结】作者想验证Waddington“细胞像球在崎岖高地上滚向不同山谷”的经典比喻是否能在单细胞转录组数据里找到几何证据,于是提出一个无需任何生物先验的“细胞潜能尺”——用基因表达空间的“内在维度”高低来判断细胞是多能还是已分化,方法核心是先给每个细胞在全部基因构成的高维空间里定位,再借用统计物理里估计流形维度的TWO-NN算法(数每个点与其第一、第二近邻的距离比值)算出局部本征维数,并通过等样本重采样把不同群体拉到同一基准,最后把数值线性压缩成0–1的ID-score;他们把这套流程应用到涵盖线虫、斑马鱼、小鼠、人类胚胎发育、器官发生、造血、水螅再生等30多个公开scRNA-seq数据集后发现:随着发育时间推进或细胞沿谱系走向终末,ID-score单调下降,多能干细胞>祖细胞>分化细胞,且能准确复现已知的胰腺内分泌、皮层、视网膜、血液等谱系层级,还能在UMAP/扩散图上自动挑出最“高维”的细胞作为轨迹根,与扩散伪时间呈-0.85相关,比传统熵或表达基因数更稳健;作者还用Hopfield模型做玩具实验,证明降温“冻结”系统时维度确实降低,进一步支持“分化=表达空间被约束”的物理图像,因此只需几何维度就能定量刻画细胞潜能,为发育、再生、重编程研究提供了一条简单、普适、无标记的计算捷径。
The intrinsic dimension of gene expression during cell differentiation
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Abstract:
Abstract Waddington’s epigenetic landscape has long served as a conceptual framework for understanding cell fate decisions. The landscape’s geometry encodes the molecular mechanisms that guide the gene expression profiles of uncommitted cells toward terminally differentiated cell types. In this study, we demonstrate that applying the concept of intrinsic dimension to single-cell transcriptomic data can effectively capture trends in expression trajectories, supporting this framework. This approach allows us to define a robust cell potency score without relying on prior biological information. By analyzing an extensive collection of datasets from various species, experimental protocols, and differentiation processes, we validate our method and successfully reproduce established hierarchies of cell type potency. Our work provides a direct link between geometric properties of single-cell expression profiles and the level of differentiation of a cell population.
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