德国亥姆霍兹中心Fabian J. Theis和Heiko Lickert共同合作，近期取得重要工作进展。他们研究提出，科学家利用moscot技术实现细胞时空谱图绘制。相关研究成果2025年1月22日在线发表于《自然》杂志上。

据介绍，单细胞基因组技术能够跨时间和空间维度对数百万个细胞进行多组学分析。然而，实验上的限制阻碍了在细胞自然的时间动态过程以及其原生空间组织微环境中对细胞进行全面测量。最优传输已成为解决这些限制的有力工具，并有助于恢复细胞的原始环境。然而，大多数最优传输应用无法整合多组学信息，也难以应用于大规模单细胞图谱分析。

研究人员推出了多组学单细胞最优传输（moscot）方法，这是一个可扩展的单细胞基因组最优传输框架，支持所有应用中的多组学分析。研究人员展示了moscot能够高效重建来自20个时间点的小鼠胚胎中170万个细胞的发育轨迹。为了展示moscot在空间分析方面的能力，研究人员通过将单细胞图谱中的多组学信息映射到小鼠肝脏样本中，丰富了空间转录组数据集，并对小鼠大脑的多个冠状切面进行了对齐。研究人员还提出了moscot.spatiotemporal方法，该方法利用跨空间和时间维度的基因表达数据，揭示了小鼠胚胎发生的时空动态。

此外，研究人员利用基因表达和染色质可及性的配对测量数据，解析了一份此前未发表的小鼠胰腺发育时间序列数据集中δ细胞和ε细胞的内分泌谱系关系。通过在人类诱导多能干细胞胰岛细胞分化模型中对NEUROD2作为ε祖细胞调节因子进行实验验证，证实了这一研究结果。Moscot将以开源软件的形式提供，并配有详尽的文档说明。

附：英文原文

Title: Mapping cells through time and space with moscot

Author: Klein, Dominik, Palla, Giovanni, Lange, Marius, Klein, Michal, Piran, Zoe, Gander, Manuel, Meng-Papaxanthos, Laetitia, Sterr, Michael, Saber, Lama, Jing, Changying, Bastidas-Ponce, Aime, Cota, Perla, Tarquis-Medina, Marta, Parikh, Shrey, Gold, Ilan, Lickert, Heiko, Bakhti, Mostafa, Nitzan, Mor, Cuturi, Marco, Theis, Fabian J.

Issue&Volume: 2025-01-22

Abstract: Single-cell genomic technologies enable the multimodal profiling of millions of cells across temporal and spatial dimensions. However, experimental limitations hinder the comprehensive measurement of cells under native temporal dynamics and in their native spatial tissue niche. Optimal transport has emerged as a powerful tool to address these constraints and has facilitated the recovery of the original cellular context1,2,3,4. Yet, most optimal transport applications are unable to incorporate multimodal information or scale to single-cell atlases. Here we introduce multi-omics single-cell optimal transport (moscot), a scalable framework for optimal transport in single-cell genomics that supports multimodality across all applications. We demonstrate the capability of moscot to efficiently reconstruct developmental trajectories of 1.7million cells from mouse embryos across 20 time points. To illustrate the capability of moscot in space, we enrich spatial transcriptomic datasets by mapping multimodal information from single-cell profiles in a mouse liver sample and align multiple coronal sections of the mouse brain. We present moscot.spatiotemporal, an approach that leverages gene-expression data across both spatial and temporal dimensions to uncover the spatiotemporal dynamics of mouse embryogenesis. We also resolve endocrine-lineage relationships of delta and epsilon cells in a previously unpublished mouse, time-resolved pancreas development dataset using paired measurements of gene expression and chromatin accessibility. Our findings are confirmed through experimental validation of NEUROD2 as a regulator of epsilon progenitor cells in a model of human induced pluripotent stem cell islet cell differentiation. Moscot is available as open-source software, accompanied by extensive documentation.

DOI: 10.1038/s41586-024-08453-2

Source: https://www.nature.com/articles/s41586-024-08453-2