SpatialVista as a unified ecosystem for high-performance visualization and exploration of 3D spatial transcriptomics data

The emergence of spatial transcriptomics has fundamentally reshaped the understanding of gene expression within intact tissues by enabling molecular profiles to be localized to precise cellular and anatomical contexts¹. More recently, three-dimensional spatial transcriptomics (3D-ST) technologies have extended this paradigm beyond isolated tissue sections, allowing cellular organization to be reconstructed across entire tissue volumes^2,3,4. This technological shift opens up new opportunities to investigate spatial gradients, long-range cellular interactions and disease-associated architectures⁵ that are inherently 3D, particularly in complex organs such as the brain⁶.

To address these limitations, we developed SpatialVista (https://yanglab.westlake.edu.cn/spatialvista), a unified visualization ecosystem designed for the integrated exploration of 3D-ST data throughout the entire analytical workflow. SpatialVista provides a shared set of high-performance visualization capabilities accessible through complementary interfaces, enabling consistent exploration of 3D-ST data across every stage of the analytical pipeline (Fig. 1a).

This is a preview of subscription content, access

Access options

• Purchase on SpringerLink
• Instant access to the full article PDF.

Prices may be subject to local taxes which are calculated during checkout

Subjects

Code availability

The SpatialVista online browser, desktop application and documentation are freely available at https://yanglab.westlake.edu.cn/spatialvista. The Jupyter/Python widget andl-vista-py under a BSD 3-Clause license

References

1. Moses, L. & Pachter, L. Nat. Methods19, 534–546 (2022).

   Article 
   CAS 
   PubMed 
   Google Scholar 

2. Schott, M. et al. Cell187, 3953–3972.e26 (2024).

3. Sui, X. et al. Nat. Methods22, 2574–2584 (2025).

4. Kern, C. et al. Preprint at bioRxivhttps://doi.org/10.1101/2025.11.02.686137 (2025).

5. Song, L., Chen, W., Hou, J., Guo, M. & Yang, J. Nature641, 932–941 (2025).

   Article 
   CAS 
   PubMed 
   PubMed Central 
   Google Scholar 

6. Zhang, M. et al. Nature624, 343–354 (2023).

7. Keller, M. S. et al. Nat. Methods22, 63–67 (2025).

8. Solorzano, L., Partel, G. & Wählby, C. Bioinformatics36, 4363–4365 (2020).

9. CZI Cell Science Program. Nucleic Acids Res.53, D886–D900 (2025).

10. Qiu, X. et al. Cell187, 7351–7373.e61 (2024).

11. Sofroniew, N. et al. Zenodohttps://doi.org/10.5281/zenodo.20709435 (2026).

12. Wolf, F. A., Angerer, P. & Theis, F. J. Genome Biol.19, 15 (2018).

    Article 
    PubMed 
    PubMed Central 
    Google Scholar 

Acknowledgements

We thank Y. Wang and D. Yi for helpful discussions and the Westlake University High-Performance Computing Center for their assistance in computing. This work was supported by the National Natural Science Foundation of China (32595482, U23A20165), the National Key R&D Program of China (2024YFC3405800, 2024YFC3405802), the “Pioneer” and “Leading Goose” R&D Programs of Zhejiang (2024SSYS0032) and the New Cornerstone Science Foundation.

Authors and Affiliations

Ethics declarations

Competing interests

The authors declare no competing interests.

Peer review

Peer review information

Nature Genetics thanks the anonymous reviewers for their contribution to the peer review of this work.

Supplementary information

Supplementary Notes 1–6, Supplementary Figures 1–4, Supplementary Tables 1 and 2 and Supplementary References

Rights and permissions

About this article

Cite this article

Wei, W., Li, L., Song, L. et al. SpatialVista as a unified ecosystem for high-performance visualization and exploration of 3D spatial transcriptomics data.
Nat Genet (2026). https://doi.org/10.1038/s41588-026-02696-7

• Version of record:14 August 2026

• DOI
  :https://doi.org/10.1038/s41588-026-02696-7