PHLOWER利用单细胞多模态数据来推断复杂的、多分支的细胞分化轨迹，这一成果由亚琛工业大学Ivan G. Costa研究组经过不懈努力而取得。相关论文于2025年10月23日发表在《自然—方法学》杂志上。

为了应对这些挑战，该课题组人员提出了PHLOWER（用于从细胞分化流推断轨迹的霍奇拉普拉斯分解），它利用简单复合体上霍奇分解的谐波分量来从单细胞多模态数据推断轨迹嵌入。这些细胞分化的自然表征有助于估计其潜在的分化树。研究团队通过多分支分化树和主题肾类器官多模态和空间单细胞数据的基准测试来评估PHLOWER。这证明了PHLOWER在复杂树的推断和识别肾类器官中调节脱靶细胞的转录因子方面的能力。因此，PHLOWER可以通过利用多模态数据来推断复杂的分支轨迹和预测转录调控因子。

据悉，计算轨迹分析是从单细胞数据推断分化树的关键计算任务。一个开放的挑战是从多模态数据中预测复杂和多分支树。

附：英文原文

Title: PHLOWER leverages single-cell multimodal data to infer complex, multi-branching cell differentiation trajectories

Author: Cheng, Mingbo, Jansen, Jitske, Reimer, Katharina C., Grande, Vincent P., Nagai, James S., Li, Zhijian, Kieling, Paul, Grasshoff, Martin, Kuppe, Christoph, Schaub, Michael T., Kramann, Rafael, Costa, Ivan G.

Issue&Volume: 2025-10-23

Abstract: Computational trajectory analysis is a key computational task for inferring differentiation trees from this single-cell data. An open challenge is the prediction of complex and multi-branching trees from multimodal data. To address these challenges, we present PHLOWER (decomposition of the Hodge Laplacian for inferring trajectories from flows of cell differentiation), which leverages the harmonic component of the Hodge decomposition on simplicial complexes to infer trajectory embeddings from single-cell multimodal data. These natural representations of cell differentiation facilitate the estimation of their underlying differentiation trees. We evaluate PHLOWER through benchmarking with multi-branching differentiation trees and using kidney organoid multimodal and spatial single-cell data. These demonstrate the power of PHLOWER in both the inference of complex trees and the identification of transcription factors regulating off-target cells in kidney organoids. Thus, PHLOWER enables inference of complex branching trajectories and prediction of transcriptional regulators by leveraging multimodal data.

DOI: 10.1038/s41592-025-02870-5

Source: https://www.nature.com/articles/s41592-025-02870-5