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Annual Review of Pathology: Mechanisms of Disease

Volume 20, 2025

Challenges and Opportunities in the Clinical Translation of High-Resolution Spatial Transcriptomics

  • Tancredi Massimo Pentimalli1,2, Nikos Karaiskos1 and Nikolaus Rajewsky1,2,3,4,5,6
  • 1Laboratory for Systems Biology of Regulatory Elements, Berlin Institute for Medical Systems Biology, Max Delbrück Center for Molecular Medicine in the Helmholtz Association, Berlin, Germany; email: [email protected], [email protected], [email protected] 2Charité – Universitätsmedizin Berlin, Berlin, Germany 3German Center for Cardiovascular Research (DZHK), Berlin, Germany 4NeuroCure Cluster of Excellence, Berlin, Germany 5German Cancer Consortium (DKTK), Berlin, Germany 6National Center for Tumor Diseases, Berlin, Germany
  • Vol. 20:405-432 (Volume publication date January 2025)
  • First published as a Review in Advance on October 30, 2024
  • Copyright © 2025 by the author(s).
    This work is licensed under a Creative Commons Attribution 4.0 International License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited. See credit lines of images or other third-party material in this article for license information.

Abstract

Pathology has always been fueled by technological advances. Histology powered the study of tissue architecture at single-cell resolution and remains a cornerstone of clinical pathology today. In the last decade, next-generation sequencing has become informative for the targeted treatment of many diseases, demonstrating the importance of genome-scale molecular information for personalized medicine. Today, revolutionary developments in spatial transcriptomics technologies digitalize gene expression at subcellular resolution in intact tissue sections, enabling the computational analysis of cell types, cellular phenotypes, and cell–cell communication in routinely collected and archival clinical samples. Here we review how such molecular microscopes work, highlight their potential to identify disease mechanisms and guide personalized therapies, and provide guidance for clinical study design. Finally, we discuss remaining challenges to the swift translation of high-resolution spatial transcriptomics technologies and how integration of multimodal readouts and deep learning approaches is bringing us closer to a holistic understanding of tissue biology and pathology.

Keyword(s): artificial intelligence, cell–cell communication, disease mechanism, molecular pathology, personalized medicine, spatial transcriptomics
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2025-01-24
2025-06-20
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Challenges and Opportunities in the Clinical Translation of High-Resolution Spatial Transcriptomics
Annual Review of Pathology: Mechanisms of Disease 20, 405 (2025); https://doi.org/10.1146/annurev-pathmechdis-111523-023417
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