mini-Complexome Profiling, an FDR-controlled workflow for global targeted Detection of protein complexes (Heart Data)

Co-fractionation mass spectrometry experiments (CF-MS) using native-like protein separations such as Blue Native electrophoresis (BNE) or size exclusion chromatography (SEC) enable a global characterization of protein networks, and a better understanding of cellular functions. They do, however, pose significant requirements on sample amounts, on wet lab and on instrument time, which frequently renders statistically useful replication or comparative experiments between multiple biological states non-feasible. Here we present a fast workflow called mini-Complexome Profiling (mCP) for global targeted detection of annotated protein-protein complexes. It comprises mild extraction of complexes, fractionation by BNE and analysis by data independent acquisition mass spectrometry (DIA-MS). Of note, BNE fractionation into 35 fractions is achieved using commercial mini-gels, with minimal requirements on sample amounts. Complexes are detected using a novel, bespoke R package with a controlled false discovery rate (FDR) approach. The tool is available to the community on a Github repository (https://github.com/hugoagno3/mCP ).

采用蓝色天然电泳（Blue Native electrophoresis, BNE）或尺寸排阻色谱（size exclusion chromatography, SEC）等类天然蛋白质分离策略的共分级质谱（Co-fractionation mass spectrometry, CF-MS）实验，可实现蛋白质组网络的全局表征，并加深对细胞功能的理解。然而，此类实验对样品用量、湿实验操作及仪器上机时间均提出了较高要求，往往难以开展具备统计学效力的重复实验，或在多种生物状态间开展对比实验。本文报道一种名为微型复合物组谱分析（mini-Complexome Profiling, mCP）的快速工作流程，用于注释蛋白质复合物的全局靶向检测。该流程包含复合物的温和提取、经BNE完成的分级分离，以及基于数据非依赖性采集质谱（data independent acquisition mass spectrometry, DIA-MS）的分析。值得注意的是，借助商业化微型凝胶，可将BNE分级分离为35个组分，且对样品用量的要求极低。复合物的检测可通过一款全新定制的R包完成，该方法采用可控错误发现率（false discovery rate, FDR）策略。本工具已通过GitHub仓库（https://github.com/hugoagno3/mCP）向科研社区开放。