Scholarlink

<p>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.</p>

错误：搜索内容不能为空，请输入英文关键词
错误：关键词超出字数限制，请精简
高级检索

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

  • 期刊论文

  • 出版模式：

    开放获取

  • 发表日期：

    2025年12月3日

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

摘要

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.