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

Quantitative proteomics experiments, using for instance isobaric tandem mass tagging approaches, are conducive to measuring changes in protein abundance over multiple time points in response to one or more conditions or stimulations. The aim is often to determine which proteins exhibit similar patterns within and across experimental conditions, since proteins with coabundance patterns may have common molecular functions related to a given stimulation. In order to facilitate the identification and analyses of coabundance patterns within and across conditions, we previously developed a software inspired by the isobaric mass tagging method itself. Specifically, multiple data sets are tagged in silico and combined for subsequent subgrouping into multiple clusters within a single output depicting the variation across all conditions, converting a typical inter-data-set comparison into an intra-data-set comparison. An updated version of our software, XINA, not only extracts coabundance profiles within and across experiments but also incorporates protein–protein interaction databases and integrative resources such as KEGG to infer interactors and molecular functions, respectively, and produces intuitive graphical outputs. In this report, we compare the kinetics profiles of >5600 unique proteins derived from three macrophage cell culture experiments and demonstrate through intuitive visualizations that XINA identifies key regulators of macrophage activation via their coabundance patterns.