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:

Unravelling cellular interactions using flow cytometry

We present a cost-effective ultra-high-throughput cytometry-based framework for the detection of physical interactions between cells, along with the characterization of complex cellular landscapes. Application of our approach can offer a systems-level understanding of immunity and facilitate study of the kinetics, mode of action and personalized response prediction of immunotherapies.

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: Cytometry-based cellular interaction mapping.

References

  1. Nakandakari-Higa, S. et al. Universal recording of immune cell interactions in vivo. Nature 627, 399–406 (2024). This paper reports uLIPSTIC, a universal proximity-based labelling method to capture transient cell–cell interactions.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  2. Giladi, A. & Cohen, M. et al. Dissecting cellular crosstalk by sequencing physically interacting cells. Nat. Biotechnol. 38, 629–637 (2020). The authors present PIC-seq, a framework for sorting of physically interacting cells followed by single-cell RNA sequencing and deconvolution of the contributing cell types.

    Article  CAS  PubMed  Google Scholar 

  3. Schraivogel, D. et al. High-speed fluorescence image-enabled cell sorting. Science 375, 315–320 (2022). Schraivogel and colleagues developed a method that combines multicolor fluorescence-activated cell sorting and image data at unprecedented throughput.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  4. Saeys, Y., Van Gassen, S. & Lambrecht, B. N. Computational flow cytometry: helping to make sense of high-dimensional immunology data. Nat. Rev. Immunol. 16, 449–462 (2016). This review is an excellent introduction to the computational analysis of high-parameter flow cytometry data, approaching it like other single-cell omics data.

    Article  CAS  PubMed  Google Scholar 

  5. Paul, S. et al. Cancer therapy with antibodies. Nat. Rev. Cancer 24, 399–426 (2024). This review article presents an overview of current immunotherapeutic strategies such as bispecific antibodies, detailing their mechanisms of action.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

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: Vonficht, D. et al. Ultra-high-scale cytometry-based cellular interaction mapping. Nat. Methods https://doi.org/10.1038/s41592-025-02744-w (2025).

Rights and permissions

Reprints and permissions

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

Unravelling cellular interactions using flow cytometry. Nat Methods 22, 1770–1771 (2025). https://doi.org/10.1038/s41592-025-02743-x

Download citation

  • Published:

  • Version of record:

  • Issue date:

  • DOI: https://doi.org/10.1038/s41592-025-02743-x

Search

Quick links

Nature Briefing: Translational Research

Sign up for the Nature Briefing: Translational Research newsletter — top stories in biotechnology, drug discovery and pharma.

Get what matters in translational research, free to your inbox weekly. Sign up for Nature Briefing: Translational Research