A new Spatial Touchstone project delivers an open-source suite, including Spatial QM software, the Spatial Touchstone Portal for pre-screening, and Spatial Touchstone SOPs to unify tissue prep, data acquisition, and analysis.

STSOP formalizes end-to-end protocols from tissue preparation to data acquisition to promote consistency and reproducibility across laboratories.

Jasmine Plummer leads a collaboration with St. Jude, Weill Cornell Medicine, Harvard Wyss Institute, Lunenfeld-Tanenbaum Research Institute, and other institutions, supported by cancer and health foundations.

The open-source repository standardizes metrics and workflows, offering Spatial QM and a user-friendly portal to screen samples against the reference dataset before deeper analysis.

Co-authored by Jasmine Plummer and a global team, the project tackles the lack of quality metrics in spatial transcriptomics and defines benchmarks for normal transcript counts per cell across tissues.

Published in Nature Biotechnology on December 3, 2025, the work prioritizes democratizing access to robust protocols and tools for the broader research community.

The dataset covers healthy and diseased tissue across breast, prostate, colon, appendix, ileum, and pancreas, using two platforms with multiple replicates to establish baseline tissue representations.

Spatial Touchstone consolidates public spatial transcriptomics data with newly curated six-tissue datasets to standardize quality control and enable cross-institution comparisons, led by St. Jude.

Nature Biotechnology publication provides direct links to STP, STSOP, and Spatial QM tools for evaluating dataset performance across platforms.

In addition to cross-institution datasets, the project provides tissue-specific baseline expectations for spatial transcriptomics across two platforms and replicates.

A standardized framework with statistical metrics, quality control criteria, and reproducible protocols aims to improve cross-institution comparability and reduce wasted resources.

The initiative focuses on filling quality-control gaps in spatial transcriptomics to maximize ROI by ensuring analyses rely on reliable, standardized data.