‡College of Pharmaceutical Sciences, Zhejiang University, Hangzhou 310058, China; §School of Pharmaceutical Sciences, Chongqing University, Chongqing 401331, China; ¶Department of Bioinformatics, Chongqing Medical University, Chongqing 400016, China.
‡College of Pharmaceutical Sciences, Zhejiang University, Hangzhou 310058, China.
Mol Cell Proteomics. 2019 Aug;18(8):1683-1699. doi: 10.1074/mcp.RA118.001169. Epub 2019 May 16.
The label-free proteome quantification (LFQ) is multistep workflow collectively defined by quantification tools and subsequent data manipulation methods that has been extensively applied in current biomedical, agricultural, and environmental studies. Despite recent advances, in-depth and high-quality quantification remains extremely challenging and requires the optimization of LFQs by comparatively evaluating their performance. However, the evaluation results using different criteria (precision, accuracy, and robustness) vary greatly, and the huge number of potential LFQs becomes one of the bottlenecks in comprehensively optimizing proteome quantification. In this study, a novel strategy, enabling the discovery of the LFQs of simultaneously enhanced performance from thousands of workflows (integrating 18 quantification tools with 3,128 manipulation chains), was therefore proposed. First, the feasibility of achieving simultaneous improvement in the precision, accuracy, and robustness of LFQ was systematically assessed by collectively optimizing its multistep manipulation chains. Second, based on a variety of benchmark datasets acquired by various quantification measurements of different modes of acquisition, this novel strategy successfully identified a number of manipulation chains that simultaneously improved the performance across multiple criteria. Finally, to further enhance proteome quantification and discover the LFQs of optimal performance, an online tool (https://idrblab.org/anpela/) enabling collective performance assessment (from multiple perspectives) of the entire LFQ workflow was developed. This study confirmed the feasibility of achieving simultaneous improvement in precision, accuracy, and robustness. The novel strategy proposed and validated in this study together with the online tool might provide useful guidance for the research field requiring the mass-spectrometry-based LFQ technique.
无标记蛋白质组定量（LFQ）是一个多步骤的工作流程，由定量工具和后续的数据处理方法共同定义，已广泛应用于当前的生物医学、农业和环境研究中。尽管最近取得了进展，但深入和高质量的定量仍然极具挑战性，需要通过比较评估其性能来优化 LFQ。然而，使用不同标准（精度、准确性和稳健性）进行评估的结果差异很大，而潜在的 LFQ 数量众多成为全面优化蛋白质组定量的瓶颈之一。在这项研究中，提出了一种新策略，可以从数千个工作流程（整合 18 种定量工具和 3128 种操作链）中发现同时具有增强性能的 LFQ。首先，通过共同优化其多步操作链，系统评估了 LFQ 的精度、准确性和稳健性同时得到提高的可行性。其次，基于各种定量测量获得的各种基准数据集，以及不同采集模式的，该新策略成功识别了许多能够在多个标准上同时提高性能的操作链。最后，为了进一步增强蛋白质组定量并发现最佳性能的 LFQ，开发了一个在线工具（https://idrblab.org/anpela/），能够从多个角度对整个 LFQ 工作流程的性能进行集体评估。本研究证实了同时提高精度、准确性和稳健性的可行性。本研究提出并验证的新策略以及在线工具可能为需要基于质谱的 LFQ 技术的研究领域提供有用的指导。
