Ten quick tips for spatial transcriptomics analysis
4 affiliations
- 1Graduate School of Medicine, The University of Tokyo, Tokyo, Japan
- 2BaraPhaSilico, Ibaraki, Japan
- 3CLINIC FOR Group, Tokyo, Japan
- 4Data Science Core, Chiba University, Chiba, Japan
Abstract
Spatial transcriptomics (ST) enables genome-wide gene expression profiling while retaining spatial context within tissue sections. Since the foundational work by Ståhl et al. in 2016, the field has expanded rapidly, with diverse platforms now spanning sequencing-based (e.g., Visium, Visium HD, Slide-seq, Stereo-seq, and Seq-Scope) and imaging-based (e.g., MERFISH, Xenium, and CosMx SMI) approaches. The breadth of platforms, data structures, and computational tools, however, can be daunting for newcomers. Here, we present ten quick tips spanning the entire ST research workflow: whether ST suits a given biological question, how to select a platform aligned with study objectives, how to understand and process ST data, and which software tools to employ for analysis and visualization. We further discuss interpreting spatial patterns in biological context, integrating complementary modalities such as single-cell RNA sequencing and spatial proteomics, and leveraging public datasets and sharing results. Finally, we highlight current limitations of ST, particularly the challenge of reconstructing three-dimensional tissue architecture from serial tissue sections. This review provides biologists, bioinformaticians, and clinician-scientists with a concise, platform-neutral roadmap for incorporating ST into research, from experimental design to biological discovery. Author summary: Spatial transcriptomics (ST) lets researchers measure gene expression while preserving the tissue location of each measurement. Where a cell sits, and which cells surround it, shape how it behaves—information that is lost when tissue is dissociated. Despite the growing popularity of ST, there is currently no concise, practical guide for researchers new to the field, for whom the diversity of platforms, data formats, and computational tools can be overwhelming. Here, we distill essential knowledge into ten quick tips covering the entire ST research workflow—from deciding whether ST is the right approach for a given biological question, through selecting an appropriate experimental platform, to analyzing and interpreting the resulting data. We also discuss integrating ST with other omics data and highlight current limitations that users should be aware of. Our goal is to help researchers across disciplines—including oncology, neuroscience, and developmental biology—design rigorous ST experiments, avoid common pitfalls, and generate reproducible, biologically meaningful insights.
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BibTeX
@article{kurogi2026quick,
title = {{Ten quick tips for spatial transcriptomics analysis}},
author = {Nagomi Kurogi and Koki Shimbara and Tatsuya Koreeda and Koki Tsuyuzaki},
journal = {PLOS Computational Biology},
year = {2026},
month = sep,
doi = {10.1371/journal.pcbi.1014757},
url = {https://journals.plos.org/ploscompbiol/article?id=10.1371/journal.pcbi.1014757},
note = {CC BY 4.0},
}
RIS
TY - JOUR
TI - Ten quick tips for spatial transcriptomics analysis
AU - Nagomi Kurogi
AU - Koki Shimbara
AU - Tatsuya Koreeda
AU - Koki Tsuyuzaki
PY - 2026
DA - 2026/09/15
JO - PLOS Computational Biology
DO - 10.1371/journal.pcbi.1014757
UR - https://journals.plos.org/ploscompbiol/article?id=10.1371/journal.pcbi.1014757
AB - Spatial transcriptomics (ST) enables genome-wide gene expression profiling while retaining spatial context within tissue sections. Since the foundational work by Ståhl et al. in 2016, the field has expanded rapidly, with diverse platforms now spanning sequencing-based (e.g., Visium, Visium HD, Slide-seq, Stereo-seq, and Seq-Scope) and imaging-based (e.g., MERFISH, Xenium, and CosMx SMI) approaches. The breadth of platforms, data structures, and computational tools, however, can be daunting for newcomers. Here, we present ten quick tips spanning the entire ST research workflow: whether ST suits a given biological question, how to select a platform aligned with study objectives, how to understand and process ST data, and which software tools to employ for analysis and visualization. We further discuss interpreting spatial patterns in biological context, integrating complementary modalities such as single-cell RNA sequencing and spatial proteomics, and leveraging public datasets and sharing results. Finally, we highlight current limitations of ST, particularly the challenge of reconstructing three-dimensional tissue architecture from serial tissue sections. This review provides biologists, bioinformaticians, and clinician-scientists with a concise, platform-neutral roadmap for incorporating ST into research, from experimental design to biological discovery. Author summary: Spatial transcriptomics (ST) lets researchers measure gene expression while preserving the tissue location of each measurement. Where a cell sits, and which cells surround it, shape how it behaves—information that is lost when tissue is dissociated. Despite the growing popularity of ST, there is currently no concise, practical guide for researchers new to the field, for whom the diversity of platforms, data formats, and computational tools can be overwhelming. Here, we distill essential knowledge into ten quick tips covering the entire ST research workflow—from deciding whether ST is the right approach for a given biological question, through selecting an appropriate experimental platform, to analyzing and interpreting the resulting data. We also discuss integrating ST with other omics data and highlight current limitations that users should be aware of. Our goal is to help researchers across disciplines—including oncology, neuroscience, and developmental biology—design rigorous ST experiments, avoid common pitfalls, and generate reproducible, biologically meaningful insights.
SN - 1553-7358
ER -