A1 Vertaisarvioitu alkuperäisartikkeli tieteellisessä lehdessä
Structural Repetition Detector for multi-scale quantitative mapping of molecular complexes through microscopy
Tekijät: Mendes, Afonso; Saraiva, Bruno M.; Jacquemet, Guillaume; Mamede, Joao I.; Leterrier, Christophe; Henriques, Ricardo
Kustantaja: Springer Nature
Kustannuspaikka: BERLIN
Julkaisuvuosi: 2025
Journal: Nature Communications
Tietokannassa oleva lehden nimi: Nature Communications
Lehden akronyymi: NAT COMMUN
Artikkelin numero: 5767
Vuosikerta: 16
Sivujen määrä: 11
eISSN: 2041-1723
DOI: https://doi.org/10.1038/s41467-025-60709-1
Verkko-osoite: https://doi.org/10.1038/s41467-025-60709-1
Rinnakkaistallenteen osoite: https://research.utu.fi/converis/portal/detail/Publication/499251868
From molecules to organelles, cells exhibit recurring structural motifs across multiple scales. Understanding these structures provides insights into their functional roles. While super-resolution microscopy can visualise such patterns, manual detection in large datasets is challenging and biased. We present the Structural Repetition Detector (SReD), an unsupervised computational framework that identifies repetitive biological structures by exploiting local texture repetition. SReD formulates structure detection as a similarity-matching problem between local image regions. It detects recurring patterns without prior knowledge or constraints on the imaging modality. We demonstrate SReD's capabilities on various fluorescence microscopy images. Quantitative analyses of different datasets highlight SReD's utility: estimating the periodicity of spectrin rings in neurons, detecting Human Immunodeficiency Virus type-1 viral assembly, and evaluating microtubule dynamics modulated by End-binding protein 3. Our open-source plugin for ImageJ or FIJI enables unbiased analysis of repetitive structures across imaging modalities in diverse biological contexts.
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Julkaisussa olevat rahoitustiedot:
A.M. and R.H. acknowledge the support of the Gulbenkian Foundation (Fundação Calouste Gulbenkian), the European Research Council (ERC) under the European Union’s Horizon 2020 research and innovation programme (grant agreement No. 101001332) (to R.H.) and funding from the European Union through the Horizon Europe program (AI4LIFE project with grant agreement 101057970-AI4LIFE and RTSuperES project with grant agreement 101099654-RTSuperES to R.H.). Funded by the European Union. Views and opinions expressed are, however, those of the authors only and do not necessarily reflect those of the European Union. Neither the European Union nor the granting authority can be held responsible for them. This work was also supported by a European Molecular Biology Organization (EMBO) installation grant (EMBO-2020-IG-4734 to R.H.), a Chan Zuckerberg Initiative Visual Proteomics Grant (vpi-0000000044 with https://doi.org/10.37921/743590vtudfp to R.H.) and a Chan Zuckerberg Initiative Essential Open Source Software for Science (EOSS6-0000000260). This study was also supported by the Research Council of Finland (338537 to G.J.), the Sigrid Juselius Foundation (to G.J.), the Cancer Society of Finland (Syöpäjärjestöt; to G.J.), the Solutions for Health strategic funding to Åbo Akademi University (to G.J.), the InFLAMES Flagship Programme of the Academy of Finland (decision numbers: 337530, 337531, 357910 and 357911). This research was also supported by the National Institutes of Health (NIH) with grants K22AI140963, K61DA058348 and subcontract R01AI50998 (to J.I.M). C.L. acknowledges funding from the Agence National de la Recherche (ANR-20-CE13-0024 ‘ASHA’, ANR-21-CE42-0015-01 ‘5D-SURE’). C.L. acknowledges the INP NCIS imaging facility and Nikon Center of Excellence for Neuro-NanoImaging for service and expertise, with funding from Excellence Initiative of Aix-Marseille University, A*MIDEX, a French ‘Investissements d’Avenir’ program (AMX-19-IET-002) through the Marseille Imaging and NeuroMarseille Institute.
