Data-Independent Immunopeptidomics Discovery of Low-Abundant Bacterial Epitopes

Mass spectrometry-based immunopeptidomics is a powerful
approach
to uncover peptides presented by human leukocyte antigen (HLA) molecules
that can guide vaccine design and immunotherapies. While data-dependent
acquisition (DDA) has been the standard for navigating through the
complexity associated with nonenzymatic immunopeptide database searches,
data-independent acquisition (DIA) is increasingly adopted in immunopeptidomics
research. In this work, we compare diaPASEF to conventional ddaPASEF
in terms of global immunopeptidome profiling and bacterial epitope
discovery of the model intracellular pathogen Listeria monocytogenes. We show that DIA
spectrum-centric workflows that search pseudo-MS/MS spectra complement
DDA analysis by uncovering additional human and bacterial immunopeptides.
Furthermore, we leveraged DIA-NN for generating and searching proteome-wide
predicted HLA class I peptide spectral libraries, scoring approximately
150 million immunopeptide peptide precursors. This approach outperformed
other spectrum-based methods in the identification of MHC class I
peptides and recovered low-abundant peptide precursors missed by other
methods. Taken together, our results demonstrate how both DIA spectrum-
and peptide-centric immunopeptidomics analysis are promising strategies
to identify low-abundant immunopeptides.

基于质谱的免疫肽组学（mass spectrometry-based immunopeptidomics）是一种强有力的研究手段，可用于揭示人类白细胞抗原（HLA）分子呈递的肽段，这类肽段可为疫苗设计与免疫治疗提供关键指导。尽管数据依赖型采集（data-dependent acquisition, DDA）长期以来是应对非酶解免疫肽数据库搜索相关复杂体系分析的标准方法，但数据非依赖型采集（data-independent acquisition, DIA）在免疫肽组学研究中的应用正日益广泛。本研究以模型胞内致病菌单核细胞增生李斯特菌（Listeria monocytogenes）为研究对象，对比了diaPASEF与传统ddaPASEF在全局免疫肽组分析及细菌表位发现方面的性能。研究结果显示，以谱图为中心的DIA工作流通过检索伪MS/MS谱图，可作为DDA分析的有效补充，挖掘出更多人类与细菌来源的免疫肽段。此外，本研究借助DIA-NN生成并检索了全蛋白质组预测的HLA I类肽段谱库，对约1.5亿个免疫肽前体进行了打分。该方法在MHC I类肽段鉴定方面优于其他基于谱图的分析方法，且能找回其他方法遗漏的低丰度肽前体。综上，本研究结果表明，以谱图为中心与以肽段为中心的免疫肽组学分析策略，均是鉴定低丰度免疫肽段的极具潜力的方案。