普林斯顿大学Benjamin J. Raphael团队近日取得一项新成果。经过不懈努力，他们的研究发现从谱系追踪数据推断细胞分化图。2025年12月8日出版的《自然—方法学》杂志发表了这项成果。

在这里，该课题组研究人员引入了一个定量框架来评估细胞分化图，并开发了一种称为Carta的算法，该算法可以从单细胞谱系追踪数据中推断出最佳分化图。Carta的关键观点是在图谱的复杂性和谱系树上未观察到的细胞类型转换的数量之间进行权衡。课题组人员发现，在哺乳动物躯干发育和母位造血模型中，Carta发现了其他方法未揭示的重要发育特征，包括细胞类型的趋同分化、祖细胞分化动力学和新的中间祖细胞。

据介绍，在发育过程中，细胞通过越来越受限制的细胞类型分层分化，这一过程可以通过细胞分化图进行总结。最近的技术在规模上描绘谱系和细胞类型，但是现有的方法从这些数据推断细胞分化图依赖于启发式模型和对发育过程的限制性假设。

附：英文原文

Title: Inferring cell differentiation maps from lineage tracing data

Author: Sashittal, Palash, Zhang, Richard Y., Law, Benjamin K., Schmidt, Henri, Strzalkowski, Alexander, Bolondi, Adriano, Chan, Michelle M., Raphael, Benjamin J.

Issue&Volume: 2025-12-08

Abstract: During development, cells differentiate through a hierarchy of increasingly restricted cell types, a process that is summarized by a cell differentiation map. Recent technologies profile lineages and cell types at scale, but existing methods to infer cell differentiation maps from these data rely on heuristic models with restrictive assumptions about the developmental process. Here we introduce a quantitative framework to evaluate cell differentiation maps and develop an algorithm, called Carta, that infers an optimal differentiation map from single-cell lineage tracing data. The key insight in Carta is to balance the tradeoff between the complexity of the map and the number of unobserved cell type transitions on the lineage tree. We show that, in models of mammalian trunk development and mouse hematopoiesis, Carta identifies important features of development that are not revealed by other methods, including convergent differentiation of cell types, progenitor differentiation dynamics and new intermediate progenitors.

DOI: 10.1038/s41592-025-02903-z

Source: https://www.nature.com/articles/s41592-025-02903-z