Academic Journal
Ten quick tips for spatial transcriptomics analysis.
| Τίτλος: | Ten quick tips for spatial transcriptomics analysis. |
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| Συγγραφείς: | Kurogi N; Graduate School of Medicine, The University of Tokyo, Tokyo, Japan., Shimbara K; BaraPhaSilico, Ibaraki, Japan., Koreeda T; CLINIC FOR Group, Tokyo, Japan., Tsuyuzaki K; Data Science Core, Chiba University, Chiba, Japan. |
| Πηγή: | PLoS computational biology [PLoS Comput Biol] 2026 Sep 15; Vol. 22 (9), pp. e1014757. Date of Electronic Publication: 2026 Sep 15 (Print Publication: 2026). |
| Τύπος έκδοσης: | Journal Article; Review; Research Support, Non-U.S. Gov't |
| Γλώσσα: | English |
| Στοιχεία περιοδικού: | Publisher: Public Library of Science Country of Publication: United States NLM ID: 101238922 Publication Model: eCollection Cited Medium: Internet ISSN: 1553-7358 (Electronic) Linking ISSN: 1553734X NLM ISO Abbreviation: PLoS Comput Biol Subsets: MEDLINE |
| Imprint Name(s): | Original Publication: San Francisco, CA : Public Library of Science, [2005]- |
| Ιατρικοί όροι (MeSH): | Spatial Transcriptomics*/methods , Gene Expression Profiling*/methods, Computational Biology/methods ; Software ; Animals ; Humans ; Proteomics |
| Περίληψη: | 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. (Copyright: © 2026 Kurogi et al. This is an open access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.) |
| Competing Interests: | The authors have declared that no competing interests exist. |
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| Grant Information: | JSPS KAKENHI; JST A-STEP |
| Entry Date(s): | Date Created: 20260915 Date Completed: 20260915 Latest Revision: 20260922 |
| Update Code: | 20260922 |
| PubMed Central ID: | PMC13577581 |
| DOI: | 10.1371/journal.pcbi.1014757 |
| PMID: | 42743262 |
| Βάση Δεδομένων: | MEDLINE |
| ISSN: | 1553-7358 |
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| DOI: | 10.1371/journal.pcbi.1014757 |