SubCellBarCode

SubCellBarCode maps protein subcellular localization across human cancer cell lines using quantitative mass spectrometry (MS) data and computational analysis.

Key Features:

• Comprehensive fractionation protocol: Includes subcellular fractionation through MS sample preparation and retains all generated cell fractions to maximize data completeness.
• Quantitative MS-data analysis: Performs quantitative mass spectrometry data analysis adaptable to various experimental conditions and employs machine learning-based classification for protein localization.
• Multiple fractionation approaches: Supports high-resolution isoelectric focusing, high-pH reverse-phase fractionation, and direct analysis by long-gradient liquid chromatography-MS without pre-fractionation.
• Robust classification system: Classifies proteins into 15 distinct subcellular compartments and four neighborhoods for detailed spatial mapping.
• Visualization and differential localization analysis: Provides visualization and supports analysis of treatment-induced relocalization, condition-dependent localization changes, and cell type–specific patterns.
• R package implementation: Implements the dry-lab component as an R package in Bioconductor.

Scientific Applications:

• Cancer research: Enables proteome-wide spatial localization studies in human cancer cell lines to investigate molecular mechanisms and potential therapeutic targets.
• Protein relocalization studies: Detects and analyzes treatment-induced and condition-dependent protein relocalization across experimental conditions.
• Comparative cell-type localization: Characterizes cell type–specific localization patterns across different cell lines.

Methodology:

Computational methods comprise quantitative MS data analysis, machine learning-based classification into 15 compartments and four neighborhoods, visualization and differential localization analyses, implemented in an R package (Bioconductor).