Hrovatin Karin, Sikkema Lisa, Shitov Vladimir A, Heimberg Graham, Shulman Maiia, Oliver Amanda J, Mueller Michaela F, Ibarra Ignacio L, Wang Hanchen, Ramírez-Suástegui Ciro, He Peng, Schaar Anna C, Teichmann Sarah A, Theis Fabian J, Luecken Malte D
Department of Computational Health, Institute of Computational Biology, Helmholtz Zentrum München, Munich, Germany.
TUM School of Life Sciences Weihenstephan, Technical University of Munich, Freising, Germany.
Nat Methods. 2025 Jan;22(1):41-57. doi: 10.1038/s41592-024-02532-y. Epub 2024 Dec 13.
The rapid adoption of single-cell technologies has created an opportunity to build single-cell 'atlases' integrating diverse datasets across many laboratories. Such atlases can serve as a reference for analyzing and interpreting current and future data. However, it has become apparent that atlasing approaches differ, and the impact of these differences are often unclear. Here we review the current atlasing literature and present considerations for building and using atlases. Importantly, we find that no one-size-fits-all protocol for atlas building exists, but rather we discuss context-specific considerations and workflows, including atlas conceptualization, data collection, curation and integration, atlas evaluation and atlas sharing. We further highlight the benefits of integrated atlases for analyses of new datasets and deriving biological insights beyond what is possible from individual datasets. Our overview of current practices and associated recommendations will improve the quality of atlases to come, facilitating the shift to a unified, reference-based understanding of single-cell biology.
单细胞技术的迅速应用为构建整合众多实验室不同数据集的单细胞“图谱”创造了机会。此类图谱可作为分析和解读当前及未来数据的参考。然而,很明显图谱构建方法存在差异,且这些差异的影响往往并不明确。在此,我们回顾当前有关图谱构建的文献,并提出构建和使用图谱的注意事项。重要的是,我们发现不存在适用于所有情况的图谱构建方案,而是讨论特定背景下的注意事项和工作流程,包括图谱概念化、数据收集、整理与整合、图谱评估及图谱共享。我们还强调了整合图谱对于分析新数据集以及获得超越单个数据集所能提供的生物学见解的益处。我们对当前实践及相关建议的概述将提高未来图谱的质量,促进向基于统一参考的单细胞生物学理解的转变。