RSMLM

RSMLM applies persistence-based clustering and persistent homology in R to segment and quantify biological nano-structures from single-molecule localization microscopy point clouds.

Key Features:

• Persistence-Based Clustering: Implements a segmentation protocol using persistence-based clustering to analyze densely packed structures across multiple spatial scales.
• Persistent Homology: Computes persistent homology to quantify topological features and preserved shapes in localization data.
• Flexibility and Versatility: Applicable to diverse biological contexts including receptor clustering in platelets, nuclear pore components, endocytic proteins, and microtubule networks.
• 2D and 3D Support: Supports analysis of both 2D and 3D localization microscopy data.
• Batch Processing: Enables batch processing of localization microscopy datasets.

Scientific Applications:

• Receptor Clustering: Analyzing receptor distribution and organization in platelets.
• Nuclear Pore Complexes: Investigating the structural components of nuclear pores.
• Endocytic Pathways: Studying proteins involved in endocytosis.
• Cytoskeletal Networks: Examining microtubule networks within cells.

Methodology:

Processes localization microscopy data represented as point clouds of spatial coordinates corresponding to single detection events; applies a persistence-based clustering segmentation protocol to segment densely packed structures and computes persistent homology to quantify topological features.