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Enhanced spatial clustering of single-molecule localizations with graph neural networks

Author

Listed:
  • Jesús Pineda

    (University of Gothenburg)

  • Sergi Masó-Orriols

    (Universitat de Vic—Universitat Central de Catalunya (UVic-UCC)
    Institut de Recerca i Innovació en Ciències de la Vida i de la Salut a la Catalunya Central (IRIS-CC))

  • Montse Masoliver

    (Universitat de Vic—Universitat Central de Catalunya (UVic-UCC)
    Institut de Recerca i Innovació en Ciències de la Vida i de la Salut a la Catalunya Central (IRIS-CC))

  • Joan Bertran

    (Universitat de Vic—Universitat Central de Catalunya (UVic-UCC)
    Institut de Recerca i Innovació en Ciències de la Vida i de la Salut a la Catalunya Central (IRIS-CC))

  • Mattias Goksör

    (University of Gothenburg)

  • Giovanni Volpe

    (University of Gothenburg
    University of Gothenburg)

  • Carlo Manzo

    (Universitat de Vic—Universitat Central de Catalunya (UVic-UCC)
    Institut de Recerca i Innovació en Ciències de la Vida i de la Salut a la Catalunya Central (IRIS-CC))

Abstract

Single-molecule localization microscopy generates point clouds corresponding to fluorophore localizations. Spatial cluster identification and analysis of these point clouds are crucial for extracting insights about molecular organization. However, this task becomes challenging in the presence of localization noise, high point density, or complex biological structures. Here, we introduce MIRO (Multifunctional Integration through Relational Optimization), an algorithm that uses recurrent graph neural networks to transform the point clouds in order to improve clustering efficiency when applying conventional clustering techniques. We show that MIRO supports simultaneous processing of clusters of different shapes and at multiple scales, demonstrating improved performance across varied datasets. Our comprehensive evaluation demonstrates MIRO’s transformative potential for single-molecule localization applications, showcasing its capability to revolutionize cluster analysis and provide accurate, reliable details of molecular architecture. In addition, MIRO’s robust clustering capabilities hold promise for applications in various fields such as neuroscience, for the analysis of neural connectivity patterns, and environmental science, for studying spatial distributions of ecological data.

Suggested Citation

  • Jesús Pineda & Sergi Masó-Orriols & Montse Masoliver & Joan Bertran & Mattias Goksör & Giovanni Volpe & Carlo Manzo, 2025. "Enhanced spatial clustering of single-molecule localizations with graph neural networks," Nature Communications, Nature, vol. 16(1), pages 1-17, December.
    • Handle: RePEc:nat:natcom:v:16:y:2025:i:1:d:10.1038_s41467-025-65557-7
    DOI: 10.1038/s41467-025-65557-7
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    References listed on IDEAS

    as
    1. Steen W. B. Bender & Marcus W. Dreisler & Min Zhang & Jacob Kæstel-Hansen & Nikos S. Hatzakis, 2024. "SEMORE: SEgmentation and MORphological fingErprinting by machine learning automates super-resolution data analysis," Nature Communications, Nature, vol. 15(1), pages 1-13, December.
    2. Matthijs J. Warrens & Hanneke Hoef, 2022. "Understanding the Adjusted Rand Index and Other Partition Comparison Indices Based on Counting Object Pairs," Journal of Classification, Springer;The Classification Society, vol. 39(3), pages 487-509, November.
    3. Lawrence Hubert & Phipps Arabie, 1985. "Comparing partitions," Journal of Classification, Springer;The Classification Society, vol. 2(1), pages 193-218, December.
    4. David J. Williamson & Garth L. Burn & Sabrina Simoncelli & Juliette Griffié & Ruby Peters & Daniel M. Davis & Dylan M. Owen, 2020. "Machine learning for cluster analysis of localization microscopy data," Nature Communications, Nature, vol. 11(1), pages 1-10, December.
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