近日，阿德莱德表观遗传学中心Luciano G. Martelotto及其课题组的最新研究提出了基于成像的空间转录组学数据集的评估和可重复性的标准化度量。该项研究成果发表在2025年12月3日出版的《自然—生物技术》上。

在这项研究中，该课题组研究人员从几个全球站点的六种组织类型生成了空间试金石（ST）数据集，并进行了集中切片，在Xenium和CosMx平台上进行了分析。这些平台因其广泛的主题和独特的化学成分而被选中。小组评估了再现性、灵敏度、动态范围、信噪比、错误发现率、细胞类型注释和与单细胞分析的一致性。

本研究提供了ST标准化操作程序（STSOPs）和开放式软件SpatialQM，可以对所有技术指标的样本进行评估，并直接插入细胞注释。生成的基于成像的空间转录组学数据存储库包括254个空间配置文件，将公共和新生成的ST数据集合并到基于web的应用程序中，可以对基于成像的大量数据集进行分析和比较。最后，小组建立了最佳实践和指标来评估和整合基于成像的多组学数据，从单细胞到空间转录组学和空间蛋白质组学。

据介绍，空间转录组学缺乏评估基于成像的原位杂交技术的标准化指标。

附：英文原文

Title: Standardized metrics for assessment and reproducibility of imaging-based spatial transcriptomics datasets

Author: Plummer, Jasmine T., Dezem, Felipe Segato, Cook, David P., Park, Jiwoon, Zhang, Luke, Liu, Yutian, Maro, Maycon, DuBose, Hannah, Wani, Arjumand, Wise, Kellie, Roach, Michael, Harvey, Kate, Wang, Taopeng, Jensen, Kirk B., Morosini, Natalia, De Gregorio, Roberto, Alonso, Alicia, Houlihan, Shauna Lee, Schwartz, Robert E., Hissong, Erika, Snopkowski, Catherine, Wrana, Jeffrey L., Ryan, Natalie, Butler, Lisa M., Church, George, Swarbrick, Alexander, Mason, Christopher E., Martelotto, Luciano G.

Issue&Volume: 2025-12-03

Abstract: Spatial transcriptomics lacks standardized metrics for evaluating imaging-based in situ hybridization technologies across sites. In this study, we generated the Spatial Touchstone (ST) dataset from six tissue types across several global sites with centralized sectioning, analyzed on both Xenium and CosMx platforms. These platforms were selected for their widespread use and distinct chemistries. We assessed reproducibility, sensitivity, dynamic ranges, signal-to-noise ratio, false discovery rates, cell type annotation and congruence with single-cell profiling. This study offers ST standardized operating procedures (STSOPs) and an open-source software, SpatialQM, enabling evaluation of samples across all technical metrics and direct imputation of cell annotations. The generated imaging-based spatial transcriptomics data repository comprises 254 spatial profiles, incorporating both public and newly generated ST datasets in a web-based application, which enables analysis and comparison of user data against an extensive collection of imaging-based datasets. Finally, we establish best practices and metrics to evaluate and integrate imaging-based multi-omics data from single cells into spatial transcriptomics to spatial proteomics.

DOI: 10.1038/s41587-025-02811-9

Source: https://www.nature.com/articles/s41587-025-02811-9