Skip to main content

Thank you for visiting nature.com. You are using a browser version with limited support for CSS. To obtain the best experience, we recommend you use a more up to date browser (or turn off compatibility mode in Internet Explorer). In the meantime, to ensure continued support, we are displaying the site without styles and JavaScript.

  • Research Briefing
  • Published:

Predicting cellular responses with conditional diffusion models

Modeling cellular responses to developmental and chemical cues is essential for understanding disease progression and informing therapeutic strategies, yet it often demands extensive experimental screening. We have developed a conditional diffusion model called Squidiff, which enables the in silico prediction of single-cell transcriptomic responses to both developmental signals and perturbations.

This is a preview of subscription content, access via your institution

Access options

Buy this article

USD 39.95

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

Fig. 1: Conceptual framework of Squidiff for modeling cellular transcriptomic responses.

References

  1. Quake, S. R. The cellular dogma. Cell 187, 6421–6423 (2024). A commentary article on redefining the cellular dogma through single-cell genomics.

    Article  PubMed  Google Scholar 

  2. Bunne, C. et al. How to build the virtual cell with artificial intelligence: priorities and opportunities. Cell 187, 7045–7063 (2024). A perspective article on building the virtual cell using artificial intelligence.

    Article  PubMed  PubMed Central  Google Scholar 

  3. Preechakul, K. et al. Diffusion autoencoders: toward a meaningful and decodable representation. Proc. IEEE/CVF Conf. on Computer Vision and Pattern Recognition 10619–10629 (IEEE, 2022). A conference article on integrating autoencoder and diffusion models to generate images.

  4. Tavakol, D. N. et al. Modeling and countering the effects of cosmic radiation using bioengineered human tissues. Biomaterials. 301, 122267 (2023). A research article on modeling and mitigating cosmic radiation with bioengineered tissues.

    Article  PubMed  PubMed Central  Google Scholar 

  5. Klein, D. et al. CellFlow enables generative single-cell phenotype modeling with flow matching. Preprint at bioRxiv https://doi.org/10.1101/2025.04.11.648220 (2025). A preprint on using a flow matching technique to model and predict cellular phenotypes under various perturbations.

Download references

Additional information

Publisher’s note Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.

This is a summary of: He, S. et al. Squidiff: predicting cellular development and responses to perturbations using a diffusion model. Nat. Methods https://doi.org/10.1038/s41592-025-02877-y (2025).

Rights and permissions

Reprints and permissions

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

Predicting cellular responses with conditional diffusion models. Nat Methods 23, 24–25 (2026). https://doi.org/10.1038/s41592-025-02878-x

Download citation

  • Published:

  • Version of record:

  • Issue date:

  • DOI: https://doi.org/10.1038/s41592-025-02878-x

Search

Quick links

Nature Briefing

Sign up for the Nature Briefing newsletter — what matters in science, free to your inbox daily.

Get the most important science stories of the day, free in your inbox. Sign up for Nature Briefing