Umney, O., Slaney, H., Williams, C.J.M. et al. (3 more authors) (2025) ClusterNet: Classifying Single-Molecule Localization Microscopy Datasets with Graph-Based Deep Learning of Supra-Cluster Structure. Small Science. e202500255. ISSN: 2688-4046
Abstract
Single-molecule localization microscopy (SMLM) data can reveal differences in protein organization between different disease types or samples. Classification of samples is an important task that allows for automated recognition and grouping of data by sample type for downstream analysis. However, methods for classifying structures larger than single clusters of localizations in SMLM point-cloud datasets are not well developed. A graph-based deep learning pipeline is presented for classification of SMLM point-cloud data over a field of view of any size. The pipeline combines features of individual clusters (calculated from their constituent localizations) with the structure formed by the positions of multiple clusters (supracluster structure). This method outperforms previous classification results on a model open-source DNA-PAINT dataset, with 99% accuracy. It is also applied to a challenging new SMLM dataset from colorectal cancer tissue. Explainability tools Uniform Manifold Approximation and Projection and SubgraphX allow exploration of the influence of spatial features and structures on classification results, and demonstrate the importance of supracluster structure in classification.
Metadata
| Item Type: | Article |
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| Authors/Creators: |
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| Copyright, Publisher and Additional Information: | © 2025 The Author(s). Small Science published by Wiley-VCH GmbH. This is an open access article under the terms of the Creative Commons Attribution License, which permits use, distribution and reproduction in any medium, provided the original work is properly cited. |
| Keywords: | classification; deep learning; direct stochastic optical reconstruction microscopy; DNA-PAINT; graph neural network; point cloud; single-molecule localization microscopy |
| Dates: |
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| Institution: | The University of Leeds |
| Academic Units: | The University of Leeds > Faculty of Biological Sciences (Leeds) > School of Molecular and Cellular Biology (Leeds) |
| Funding Information: | Funder Grant number Wellcome Trust 223125/Z/21/Z |
| Date Deposited: | 30 Sep 2025 13:49 |
| Last Modified: | 29 Oct 2025 14:01 |
| Status: | Published online |
| Publisher: | Wiley |
| Identification Number: | 10.1002/smsc.202500255 |
| Related URLs: | |
| Open Archives Initiative ID (OAI ID): | oai:eprints.whiterose.ac.uk:232276 |

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