Jiang Na, Gao Yan, Xu Jia, Luo Fengting, Zhang Xiangyang, Chen Ruibing
School of Pharmaceutical Science and Technology, Tianjin University, Tianjin 300072, China.
Department of Clinical Laboratory, Tianjin Hospital, Tianjin 300142, China.
J Pharm Biomed Anal. 2022 Jul 15;216:114795. doi: 10.1016/j.jpba.2022.114795. Epub 2022 Apr 26.
Data independent acquisition (DIA) has emerged as a powerful proteomic technique with exceptional reproducibility and throughput, and has been widely applied to clinical sample analysis. DIA approaches normally rely on project-specific spectral libraries generated by data dependent acquisition (DDA), requiring extensive off-line fractionation and large amounts of input material. In this study, we aimed to explore the utility of DIA for the analysis of samples with limited quantities. We employed three software tools (DIA-NN, Spectronaut, and EncyclopeDIA) for data analysis and generated three types of libraries, including an experiment-specific library built by DDA analysis of off-line fractions, a FASTA sequence database, and a library generated by gas-phase fractionation (GPF), resulting in eight analysis pipelines. Then we systematically compared the performance of the eight strategies by analyzing the DIA data from HEK293T cell tryptic peptides with sample loads of 500 ng, 100 ng, 20 ng, and 4 ng. The results showed that DIA-NN with GPF-based libraries outperformed the others in protein identification and retention time calibration. Next, we further evaluated the optimized workflow by analyzing the proteome alteration in 5000 peripheral blood mononuclear cells (PBMCs) induced by lipopolysaccharide (LPS) stimulation. As a result, 3179 protein groups were quantified, and functional analysis revealed activation of multiple signaling pathways, e. g., endocytosis, NF-kappa B signaling, and T cell receptor signaling. The results showed the practicability of using DIA for scarce samples, and the established workflow of PBMC analysis could be easily adapted for biomarker discovery, immune status evaluation, and drug response monitoring, especially for diseases involved with dysfunction of the immune system.
数据非依赖型采集（DIA）已成为一种强大的蛋白质组学技术，具有出色的重现性和通量，并已广泛应用于临床样本分析。DIA方法通常依赖于由数据依赖型采集（DDA）生成的特定项目光谱库，这需要广泛的离线分级分离和大量的输入材料。在本研究中，我们旨在探索DIA在分析少量样本方面的效用。我们使用了三种软件工具（DIA-NN、Spectronaut和EncyclopeDIA）进行数据分析，并生成了三种类型的库，包括通过对离线级分进行DDA分析构建的特定实验库、一个FASTA序列数据库以及通过气相分级分离（GPF）生成的库，从而形成了八个分析流程。然后，我们通过分析来自HEK293T细胞胰蛋白酶肽段、样本上样量分别为500 ng、100 ng、20 ng和4 ng的DIA数据，系统地比较了这八种策略的性能。结果表明，基于GPF库的DIA-NN在蛋白质鉴定和保留时间校准方面优于其他方法。接下来，我们通过分析脂多糖（LPS）刺激诱导的5000个外周血单个核细胞（PBMC）中的蛋白质组变化，进一步评估了优化后的工作流程。结果定量了3179个蛋白质组，功能分析揭示了多种信号通路的激活，例如内吞作用、核因子κB信号通路和T细胞受体信号通路。结果表明了使用DIA分析稀缺样本的实用性，并且所建立的PBMC分析工作流程可以轻松适用于生物标志物发现、免疫状态评估和药物反应监测，特别是对于涉及免疫系统功能障碍的疾病。
