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.

定量蛋白质组学（Quantitative proteomics）实验——例如采用同重同位素串联质量标签（isobaric tandem mass tagging）技术——可用于检测在一种或多种处理条件或刺激因素作用下，多个时间点的蛋白质丰度变化情况。此类实验的核心目标通常是筛选出在实验条件内部及跨实验条件间呈现相似丰度模式的蛋白质，因为具有共丰度模式的蛋白质往往与特定刺激存在共同的分子功能关联。为便于识别与分析条件内及跨条件的共丰度模式，我们团队此前基于同重同位素质量标记方法的原理开发了一款软件。具体而言，该软件可对多组数据集进行计算机模拟（in silico）标记并合并，随后在单份输出结果中将其划分为多个聚类，以展示所有实验条件间的差异，从而将典型的数据集间比较转化为数据集内比较。我们开发的这款软件的更新版本——XINA——不仅可提取实验内及跨实验的共丰度特征谱，还分别整合了蛋白质-蛋白质相互作用数据库与整合资源（如京都基因与基因组百科全书（KEGG））以推断相互作用蛋白及分子功能，并生成直观的可视化输出结果。本研究报告中，我们对源自3组巨噬细胞培养实验的5600余种独特蛋白质的动力学谱进行了比较，并通过直观可视化结果证明，XINA可通过共丰度模式筛选出巨噬细胞活化的关键调控因子。