Publications by authors named "Hannah Hyun-Sook Kim"
Article Synopsis
- Single-molecule localization microscopy (SMLM) helps visualize very small subcellular structures clearly, but the tools to analyze these images efficiently are lacking.
- The new tool called Enhanced Classification of Localized Point clouds by Shape Extraction (ECLiPSE) uses machine learning to automatically classify the structures seen in SMLM images by examining their shapes, ensuring accurate measurements.
- ECLiPSE has been tested successfully with both unsupervised and supervised methods, showing high accuracy, and is being used to study protein aggregates linked to neurodegenerative diseases and differences in healthy versus depolarized mitochondria.
Cryo-electron tomography can uniquely probe the native cellular environment for macromolecular structures. Tomograms feature complex data with densities of diverse, densely crowded macromolecular complexes, low signal-to-noise, and artifacts such as the missing wedge effect. Post-processing of this data generally involves isolating regions or particles of interest from tomograms, organizing them into related groups, and rendering final structures through subtomogram averaging.
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