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

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 sup

SUBMITTER: Hugelier S 

PROVIDER: S-EPMC11466814 | biostudies-literature | 2024 Oct

REPOSITORIES: biostudies-literature