Deep Learning-Based Event Classification of Mass Photometry Data for Optimal Mass Measurement at the Single-Molecule Level.

Journal: ACS nano
Published Date: Jan 18, 2026

Abstract

Mass photometry (MP) is a powerful technique for studying biomolecular structures, interactions, and dynamics in solution. It detects and quantifies small reflectivity changes at a glass-water interface during protein (un)binding, with signals typically averaged over 100 ms. However, particle motion at the point of single-molecule measurement can compromise key metrics such as mass resolution, sensitivity, and concentration. We present a three-dimensional convolutional residual network trained via supervised learning to classify landing events based on their spatiotemporal dynamics. By analyzing 3D event thumbnails, our method isolates optimal single-molecule measurements, eliminating cumulative histogram artifacts and improving resolving power by up to a factor of 2. Validated across diverse experimental data sets, including resolved and partially resolved samples, and varying masses, concentrations, and integration times, our approach delivers robust performance under (sub)optimal conditions. Our approach also provides measurement-level data-driven feedback, facilitating high quality MP measurements in challenging scenarios.

Authors

  • Kishwar Iqbal
    The Kavli Institute for Nanoscience Discovery, University of Oxford, Dorothy Crowfoot Hodgkin Building, South Parks Road, Oxford OX1 3QU, U.K.
  • Jan Christoph Thiele
    The Kavli Institute for Nanoscience Discovery, University of Oxford, Dorothy Crowfoot Hodgkin Building, South Parks Road, Oxford OX1 3QU, U.K.
  • Dominik Saman
    The Kavli Institute for Nanoscience Discovery, University of Oxford, Dorothy Crowfoot Hodgkin Building, South Parks Road, Oxford OX1 3QU, U.K.
  • Jack S Peters
    The Kavli Institute for Nanoscience Discovery, University of Oxford, Dorothy Crowfoot Hodgkin Building, South Parks Road, Oxford OX1 3QU, U.K.
  • Stephen Thorpe
    The Kavli Institute for Nanoscience Discovery, University of Oxford, Dorothy Crowfoot Hodgkin Building, South Parks Road, Oxford OX1 3QU, U.K.
  • Samuel Tusk
    The Kavli Institute for Nanoscience Discovery, University of Oxford, Dorothy Crowfoot Hodgkin Building, South Parks Road, Oxford OX1 3QU, U.K.
  • Jack Bardzil
    The Kavli Institute for Nanoscience Discovery, University of Oxford, Dorothy Crowfoot Hodgkin Building, South Parks Road, Oxford OX1 3QU, U.K.
  • Justin L P Benesch
    Department of Chemistry, University of Oxford, Oxford, UK.
  • Philipp Kukura
    Department of Chemistry, University of Oxford, Oxford OX1 3QZ, United Kingdom.

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