美国宾夕法尼亚大学Melike Lakadamyali等研究人员，合作开发出一种用于二维和三维单分子定位显微镜数据结构和形态分析的多功能分类技术ECLiPSE。2024年9月10日出版的《自然—方法学》发表了这项成果。

研究人员介绍了增强型局部点云形状提取分类技术（ECLiPSE），这是一种专门设计用于分类通过二维或三维单分子定位显微镜（SMLM）捕获的细胞结构的自动化机器学习分析流程。ECLiPSE利用了一整套全面的形状描述符，其中大多数直接从定位数据中提取，以在表征单个结构时尽量减少偏差。

ECLiPSE已在包括各种细胞结构的数据集上通过无监督和有监督分类进行验证，达到近乎完美的准确率。研究人员应用二维ECLiPSE分类与神经退行性疾病相关的形态各异的蛋白质聚集体。此外，研究人员还使用三维ECLiPSE来识别健康和去极化线粒体之间的相关生物学差异。ECLiPSE将提升人们在多种生物学背景下对细胞结构的研究。

据悉，SMLM已广泛用于以纳米级空间分辨率可视化细胞器和亚细胞结构的形态。然而，自动量化和分类SMLM图像的分析工具尚未跟上发展。

附：英文原文

Title: ECLiPSE: a versatile classification technique for structural and morphological analysis of 2D and 3D single-molecule localization microscopy data

Author: Hugelier, Siewert, Tang, Qing, Kim, Hannah Hyun-Sook, Gyparaki, Melina Theoni, Bond, Charles, Santiago-Ruiz, Adriana Naomi, Porta, Slvia, Lakadamyali, Melike

Issue&Volume: 2024-09-10

Abstract: Single-molecule localization microscopy (SMLM) has gained widespread use for visualizing the morphology of subcellular organelles and structures with nanoscale spatial resolution. However, analysis tools for automatically quantifying and classifying SMLM images have lagged behind. Here we introduce Enhanced Classification of Localized Point clouds by Shape Extraction (ECLiPSE), an automated machine learning analysis pipeline specifically designed to classify cellular structures captured through two-dimensional or three-dimensional SMLM. ECLiPSE leverages a comprehensive set of shape descriptors, the majority of which are directly extracted from the localizations to minimize bias during the characterization of individual structures. ECLiPSE has been validated using both unsupervised and supervised classification on datasets, including various cellular structures, achieving near-perfect accuracy. We apply two-dimensional ECLiPSE to classify morphologically distinct protein aggregates relevant for neurodegenerative diseases. Additionally, we employ three-dimensional ECLiPSE to identify relevant biological differences between healthy and depolarized mitochondria. ECLiPSE will enhance the way we study cellular structures across various biological contexts.

DOI: 10.1038/s41592-024-02414-3

Source: https://www.nature.com/articles/s41592-024-02414-3