XINA: A Workflow for the Integration of Multiplexed Proteomics Kinetics Data with Network Analysis

Area of Science:

• Proteomics
• Systems Biology
• Bioinformatics

Background:

• Quantitative proteomics, particularly isobaric tandem mass tagging, measures protein abundance changes over time and conditions.
• Identifying proteins with similar abundance patterns suggests shared molecular functions and roles in biological responses.
• Analyzing coabundance patterns across multiple experiments is crucial for understanding complex cellular processes.

Purpose of the Study:

• To introduce an updated software, XINA, for analyzing protein coabundance patterns within and across quantitative proteomics experiments.
• To enhance the identification of proteins with similar kinetic profiles and infer their functional relationships.
• To demonstrate XINA's capability in identifying key regulators of macrophage activation through integrated data analysis.

Main Methods:

• Development and application of the XINA software for in silico tagging and combining of multiple proteomics datasets.
• Integration of protein-protein interaction databases and KEGG pathway information within XINA.
• Comparative analysis of kinetic profiles for over 5600 unique proteins from three macrophage cell culture experiments.

Main Results:

• XINA successfully extracts coabundance profiles within and across experiments.
• The software integrates interaction and pathway data to infer protein functions and relationships.
• Intuitive visualizations generated by XINA highlight key regulators of macrophage activation based on coabundance patterns.

Conclusions:

• XINA provides a powerful tool for analyzing complex quantitative proteomics data.
• The software facilitates the discovery of functionally related proteins and biological pathway insights.
• XINA effectively identifies key regulators in cellular processes like macrophage activation through coabundance profiling.