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Best practice mass photometry: a guide to optimal single-molecule mass measurement

Nature Protocols (2025)Cite this article

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Abstract

Mass photometry (MP) has emerged as a powerful approach to study quaternary biomolecular structure, dynamics and interactions. The capabilities of the method ultimately hinge on the ability to accurately measure the tiny optical contrast generated by individual molecules at a glass–water interface, which enables mass-resolved quantification of biomolecular mixtures. Ideally, this capability is limited only by photon shot noise, but in practice depends on additional parameters and details of the assay. Here, we focus on the key factors affecting MP performance and present simple steps that can be taken to achieve optimal MP measurements in terms of mass resolution, quantitative detection limit, reproducibility and analyte concentration range without compromising the speed and simplicity of the technique. Each sample takes <10 min to analyse, with an additionial 2 h if amination of the glass surface is desired.

Key points

  • The protocol optimizes the interplay between surface and sample preparation, experimental approach and analysis parameters to maximize sensitivity and resolution when performing MP measurements.

  • All steps can be implemented into MP workflows with less than a day’s work, and many require only simple considerations at the time of the measurement.

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Fig. 1: Principles of mass photometry.
Fig. 2: Measurement reproducibility.
Fig. 3: Shot noise and how it affects quantitative detection and mass resolution.
Fig. 4: Noise contributions beyond shot noise.
Fig. 5: Accurate identification and quantification of binding events.
Fig. 6: Effect of concentration and surface chemistry on precision, accuracy and quantification.
Fig. 7: Effect of concentration and averaging window on quantification of oligomer ratios.
Fig. 8: Flow diagram for the preparation, measurement and analysis of an MP experiment.
Fig. 9: Titration measurement of SARS-CoV-2 antibodies.

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Data availability

The datasets for all figures are available at https://doi.org/10.5281/zenodo.15800728. All data are shared under the CC BBY 4.0 license.

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Acknowledgements

This work was funded by the European Research Council (ERC) Consolidator Grant PHOTOMASS 819593 (to P.K. and K.I.); the Engineering and Physical Research Council (EPSRC) Leadership Fellowship EP/T03419X/1 (to P.K. and J.C.T.); the Biotechnology and Biological Sciences Research Council BB/W00349X/1 (to P.K. and S.T.); the Wellcome Trust grant number 218514/Z/19/Z (to R.v.W.); UK Research and Innovation (UKRI) under the UK government’s Horizon Europe funding guarantee through project Marie Skłodowska-Curie Actions (MSCA) Postdoctoral Fellowship NanoMassCreator (101062868) EP/X025713/1 (to J.K.); a Clarendon scholarship, a Menasseh Ben Israel scholarship and a Kingsgate scholarship (to D.L.); and an EPRSC Doctoral Training Partnership (to J.B.). For the purpose of Open Access, the author has applied a CC BY public copyright license to any Author Accepted Manuscript (AAM) version arising from this submission. The authors thank K. Zouboulis, F. Naughton-Allen and A. O’Shea for feedback on the manuscript and discussion, and M. Kushwah for providing the Dyn1 ΔPRD protein.

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Author notes

  1. These authors contributed equally: Jiří Kratochvíl, Raman van Wee, Jan Christoph Thiele, Dan Loewenthal, Jack Bardzil, Kishwar Iqbal.

Authors and Affiliations

  1. The Kavli Institute for Nanoscience Discovery, University of Oxford, Oxford, UK

    Jiří Kratochvíl, Raman van Wee, Jan Christoph Thiele, Dan Loewenthal, Jack Bardzil, Kishwar Iqbal, Justin L. P. Benesch, Stephen Thorpe & Philipp Kukura

  2. Physical and Theoretical Chemistry Laboratory, Department of Chemistry, University of Oxford, Oxford, UK

    Jiří Kratochvíl, Raman van Wee, Jan Christoph Thiele, Dan Loewenthal, Jack Bardzil, Kishwar Iqbal, Justin L. P. Benesch, Stephen Thorpe & Philipp Kukura

  3. Department of Physiology, Anatomy and Genetics, University of Oxford, Oxford, UK

    Raman van Wee

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Contributions

All authors agree that their authorship orders can be exchanged on their CVs. All authors contributed to the conceptualization. Data acquisition, analysis, interpretation and writing of corresponding protocol sections were as follows: Fig. 1, J.C.T.; Figs. 2, 3d, 4g–i, D.L.; Fig. 3a–c,e–g, J.B.; Fig. 4a–f, R.v.W.; Fig. 5, K.I.; Figs. 6, 7 and 9, J.K.; Fig. 8, S.T.; writing of the introduction, S.T.; review and editing of the final manuscript, S.T. and P.K.; supervision, S.T. and P.K.

Corresponding authors

Correspondence to Stephen Thorpe or Philipp Kukura.

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Competing interests

P.K. is a nonexecutive director, shareholder of and consultant to Refeyn Ltd. J.L.P.B is an academic founder, shareholder, and advisor to Refeyn Ltd. The other authors declare no competing interests.

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Nature Protocols thanks Francesca Vallese, Grzegorz Piszczek, Alexander Buell and the other, anonymous, reviewer(s) for their contribution to the peer review of this work.

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Key references

Asor, R. et al. Proc. Natl. Acad. Sci. USA 121, e2403260121 (2024): https://doi.org/10.1073/pnas.2403260121

Sonn-Segev, A. et al. Nat. Commun. 11, 1772 (2020): https://doi.org/10.1038/s41467-020-15642-w

Olerinyova, A. et al. Chem. 7, 224–236 (2021): https://doi.org/10.1016/j.chempr.2020.11.011

Young, G. et al. Science 360, 423–427 (2018): https://doi.org/10.1126/science.aar5839

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Kratochvíl, J., van Wee, R., Thiele, J.C. et al. Best practice mass photometry: a guide to optimal single-molecule mass measurement. Nat Protoc (2025). https://doi.org/10.1038/s41596-025-01255-4

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