Department of Physiology, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA, USA.
Biochemistry and Molecular Biophysics Graduate Group, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA, USA.
Nat Methods. 2024 Oct;21(10):1909-1915. doi: 10.1038/s41592-024-02414-3. Epub 2024 Sep 10.
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.
单分子定位显微镜（SMLM）已经广泛用于可视化亚细胞细胞器和结构的形态，具有纳米级空间分辨率。然而，用于自动定量和分类 SMLM 图像的分析工具却落后了。在这里，我们介绍了增强的通过形状提取进行局部点云分类（ECLiPSE），这是一个专门设计用于通过二维或三维 SMLM 捕获的细胞结构进行分类的自动化机器学习分析管道。ECLiPSE 利用了一整套形状描述符，其中大多数是直接从定位中提取的，以在对单个结构进行特征描述时最小化偏差。ECLiPSE 已经在数据集上通过无监督和监督分类进行了验证，包括各种细胞结构，实现了接近完美的准确性。我们将二维 ECLiPSE 应用于分类与神经退行性疾病相关的形态上不同的蛋白质聚集体。此外，我们还使用三维 ECLiPSE 来识别健康和去极化线粒体之间的相关生物学差异。ECLiPSE 将增强我们在各种生物学背景下研究细胞结构的方式。
