OpenCell

OpenCell maps proteome-scale endogenous protein localization and interactions to characterize human cellular organization.

Key Features:

• Proteome-scale endogenous tagging: Implements proteome-scale endogenous tagging to label native proteins for systematic localization and interaction measurements.
• Integrated experimental modalities: Integrates genome engineering, confocal live-cell imaging, and mass spectrometry with data science to generate multi-modal datasets.
• Molecular and spatial network datasets: Produces comprehensive datasets that describe both molecular and spatial networks organizing the proteome.
• Unsupervised clustering: Applies unsupervised clustering techniques to delineate functional protein communities from generated datasets.
• Localization-based interaction inference: Derives functional information from protein localization patterns to predict molecular interactions.
• RNA-binding protein characterization: Identifies RNA-binding proteins as a distinct subgroup with unique interaction and localization properties.
• Proteome-wide cartography: Enables proteome-wide mapping of protein localization and interaction architectures in human cells.

Scientific Applications:

• Spatial proteomics mapping: Mapping protein localization across the human proteome to chart subcellular organization.
• Interaction network discovery: Inferring protein–protein interactions and molecular networks from localization and mass spectrometry data.
• Functional community annotation: Delineating functional communities of proteins via unsupervised clustering for biological discovery.
• RNA-binding protein analysis: Characterizing interaction and localization properties of RNA-binding proteins as a distinct subgroup.
• Mechanism discovery: Enabling discovery of novel cellular mechanisms and interaction motifs from localization patterns.

Methodology:

Generates datasets describing molecular and spatial networks and applies unsupervised clustering techniques to delineate functional protein communities.