Mendes Afonso, Saraiva Bruno M, Jacquemet Guillaume, Mamede João I, Leterrier Christophe, Henriques Ricardo
Optical Cell Biology group, Instituto Gulbenkian de Ciência, Oeiras, Portugal.
Turku Bioimaging, University of Turku and Åbo Akademi University, Turku, Finland.
Res Sq. 2024 Oct 15:rs.3.rs-5182329. doi: 10.21203/rs.3.rs-5182329/v1.
From molecules to organelles, cells exhibit recurring structural motifs across multiple scales. Understanding these structures provides insights into their functional roles. While superresolution 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 three datasets highlight SReD's utility: estimating the periodicity of spectrin rings in neurons, detecting HIV-1 viral assembly, and evaluating microtubule dynamics modulated by EB3. Our open-source ImageJ and Fiji plugin enables unbiased analysis of repetitive structures across imaging modalities in diverse biological contexts.
从分子到细胞器，细胞在多个尺度上展现出反复出现的结构基序。了解这些结构有助于深入了解它们的功能作用。虽然超分辨率显微镜可以可视化此类模式，但在大型数据集中进行手动检测具有挑战性且存在偏差。我们提出了结构重复检测器（SReD），这是一个无监督计算框架，通过利用局部纹理重复来识别重复性生物结构。SReD将结构检测表述为局部图像区域之间的相似性匹配问题。它无需先验知识或对成像方式的约束即可检测反复出现的模式。我们展示了SReD在各种荧光显微镜图像上的能力。对三个数据集的定量分析突出了SReD的实用性：估计神经元中血影蛋白环的周期性、检测HIV-1病毒组装以及评估由EB3调节的微管动力学。我们的开源ImageJ和Fiji插件能够在不同生物背景下对跨成像方式的重复性结构进行无偏差分析。
