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通过质量评估和最佳实践分析工作流程优化Xenium原位数据效用。

Optimizing Xenium In Situ data utility by quality assessment and best-practice analysis workflows.

作者信息

Marco Salas Sergio, Kuemmerle Louis B, Mattsson-Langseth Christoffer, Tismeyer Sebastian, Avenel Christophe, Hu Taobo, Rehman Habib, Grillo Marco, Czarnewski Paulo, Helgadottir Saga, Tiklova Katarina, Andersson Axel, Rafati Nima, Chatzinikolaou Maria, Theis Fabian J, Luecken Malte D, Wählby Carolina, Ishaque Naveed, Nilsson Mats

机构信息

Science for Life Laboratory, Department of Biochemistry and Biophysics, Stockholm University, Stockholm, Sweden.

Institute of Computational Biology, Computational Health Center, Helmholtz Munich, Munich, Germany.

出版信息

Nat Methods. 2025 Apr;22(4):813-823. doi: 10.1038/s41592-025-02617-2. Epub 2025 Mar 13.

DOI:10.1038/s41592-025-02617-2
PMID:40082609
原文链接:https://pmc.ncbi.nlm.nih.gov/articles/PMC11978515/
Abstract

The Xenium In Situ platform is a new spatial transcriptomics product commercialized by 10x Genomics, capable of mapping hundreds of genes in situ at subcellular resolution. Given the multitude of commercially available spatial transcriptomics technologies, recommendations in choice of platform and analysis guidelines are increasingly important. Herein, we explore 25 Xenium datasets generated from multiple tissues and species, comparing scalability, resolution, data quality, capacities and limitations with eight other spatially resolved transcriptomics technologies and commercial platforms. In addition, we benchmark the performance of multiple open-source computational tools, when applied to Xenium datasets, in tasks including preprocessing, cell segmentation, selection of spatially variable features and domain identification. This study serves as an independent analysis of the performance of Xenium, and provides best practices and recommendations for analysis of such datasets.

摘要

Xenium原位平台是10x Genomics商业化推出的一款新型空间转录组学产品，能够在亚细胞分辨率下对数百个基因进行原位定位。鉴于市面上有众多空间转录组学技术，选择平台的建议和分析指南变得越来越重要。在此，我们研究了从多个组织和物种生成的25个Xenium数据集，将其可扩展性、分辨率、数据质量、能力和局限性与其他八种空间分辨转录组学技术及商业平台进行比较。此外，我们还对多个开源计算工具在应用于Xenium数据集时，在预处理、细胞分割、空间可变特征选择和域识别等任务中的性能进行了基准测试。本研究对Xenium的性能进行了独立分析，并为这类数据集的分析提供了最佳实践和建议。

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