Center for Interdisciplinary Cardiovascular Sciences, Cardiovascular Division , Brigham and Women's Hospital , Harvard Medical School, Boston , Massachusetts 02115 , United States.
Channing Division of Network Medicine , Brigham and Women's Hospital , Harvard Medical School, Boston , Massachusetts 02115 , United States.
J Proteome Res. 2019 Feb 1;18(2):775-781. doi: 10.1021/acs.jproteome.8b00615. Epub 2018 Oct 29.
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.
定量蛋白质组学实验，例如使用等重串联质量标签方法，有利于测量在响应一种或多种条件或刺激时蛋白质丰度在多个时间点的变化。其目的通常是确定哪些蛋白质在实验条件内和之间表现出相似的模式，因为具有共同丰度模式的蛋白质可能与给定的刺激具有共同的分子功能。为了便于识别和分析条件内和之间的共同丰度模式，我们之前受等重质量标签方法本身的启发开发了一种软件。具体来说，多个数据集在计算机上被标记并组合，以便随后在单个输出中分成多个亚组，描绘所有条件的变化，将典型的数据集之间的比较转换为数据集内的比较。我们的软件 XINA 的更新版本不仅提取了实验内和实验间的共同丰度分布，还整合了蛋白质-蛋白质相互作用数据库和综合资源，如 KEGG，分别推断相互作用者和分子功能，并生成直观的图形输出。在本报告中，我们比较了三个巨噬细胞培养实验中超过 5600 个独特蛋白质的动力学图谱，并通过直观的可视化证明，XINA 通过其共同丰度模式识别巨噬细胞激活的关键调节剂。
