英国惠康基因组园区Sarah A. Teichmann团队近期取得重要工作进展，他们研究开发了单细胞轨迹的基因水平比对方法。相关研究成果2024年9月19日在线发表于《自然—方法学》杂志上。

据介绍，单细胞数据分析可以推断细胞群的动态变化，例如跨时间、空间或响应扰动，从而得出伪时间轨迹。目前比较轨迹的方法通常使用动态规划，但受到存在确定性匹配等假设的限制。

研究人员描述了Genes2Genes，这是一个用于对齐单细胞轨迹的贝叶斯信息理论动态规划框架。它能够捕获参考和查询轨迹之间单个基因的顺序匹配和不匹配，突出显示不同的比对模式集群。在真实世界和模拟数据集中，它准确地推断出排列，并证明了其在疾病细胞状态轨迹分析中的实用性。在一项概念验证应用中，Genes2Genes发现，体外分化的T细胞在缺乏TNF信号相关基因表达的情况下，与体内未成熟状态相匹配。

总之，这一研究表明精确的轨迹对齐可以精确定位与体内系统的差异，从而指导体外培养条件的优化。

附：英文原文

Title: Gene-level alignment of single-cell trajectories

Author: Sumanaweera, Dinithi, Suo, Chenqu, Cujba, Ana-Maria, Muraro, Daniele, Dann, Emma, Polanski, Krzysztof, Steemers, Alexander S., Lee, Woochan, Oliver, Amanda J., Park, Jong-Eun, Meyer, Kerstin B., Dumitrascu, Bianca, Teichmann, Sarah A.

Issue&Volume: 2024-09-19

Abstract: Single-cell data analysis can infer dynamic changes in cell populations, for example across time, space or in response to perturbation, thus deriving pseudotime trajectories. Current approaches comparing trajectories often use dynamic programming but are limited by assumptions such as the existence of a definitive match. Here we describe Genes2Genes, a Bayesian information-theoretic dynamic programming framework for aligning single-cell trajectories. It is able to capture sequential matches and mismatches of individual genes between a reference and query trajectory, highlighting distinct clusters of alignment patterns. Across both real world and simulated datasets, it accurately inferred alignments and demonstrated its utility in disease cell-state trajectory analysis. In a proof-of-concept application, Genes2Genes revealed that T cells differentiated in vitro match an immature in vivo state while lacking expression of genes associated with TNF signaling. This demonstrates that precise trajectory alignment can pinpoint divergence from the in vivo system, thus guiding the optimization of in vitro culture conditions.

DOI: 10.1038/s41592-024-02378-4

Source: https://www.nature.com/articles/s41592-024-02378-4