数据独立的免疫组学 发现低丰富的细菌表皮
Patrick Willems1,2, An Staes1,2,3, Laia Miret-Casals1,2
1VIB-UGent Center for Medical Biotechnology, VIB, Ghent 9052, Belgium.
Journal of proteome research
|October 30, 2025
概括
数据独立获取 (DIA) 免疫组学,特别是diaPASEF,增强了由人类白细胞抗原 (HLA) 分子呈现的人类和细菌的发现,有助于疫苗设计.
科学领域:
- 免疫学 免疫学 免疫学
- 蛋白质组学是指蛋白质组学.
- 质谱测量质量谱测量
背景情况:
- 基于质谱的免疫组学识别了由人类白细胞抗原 (HLA) 分子呈现的,这对于疫苗设计和免疫疗法至关重要.
- 数据依赖获取 (DDA) 已经成为标准,但数据独立获取 (DIA) 正在在免疫学中获得引力.
研究的目的:
- 为了将diaPASEF (DIA) 与常规的 ddaPASEF (DDA) 进行免疫组概况和细菌表位发现.
- 评估DIA以频谱为中心的工作流程,以发现额外的人类和细菌免疫.
- 评估DIA-NN在生成和搜索HLA类I光谱库中的性能.
主要方法:
- 使用*Listeria monocytogenes*作为模型进行免疫形状分析的diaPASEF和ddaPASEF的比较.
- 使用DIA以频谱为中心的工作流程,搜索伪MS/MS频谱.
- 采用DIA-NN用于全蛋白体预测的HLA类I光谱库生成和搜索.
主要成果:
- 通过识别额外的人类和细菌免疫来补充DDA.
- 基于DIA-NN的光谱库搜索超越了其他基于光谱的方法来识别MHC I类.
- 其他方法遗漏的低丰富性前体被使用DIA方法恢复.
结论:
- 无论是以DIA频谱为中心的还是以为中心的免疫组学都是识别低丰富性免疫的有效策略.
- 基于DIA的方法为免疫组分析和表位发现提供了增强的能力.
- 这些发现支持DAI在疫苗和免疫疗法开发的免疫学研究中的更广泛采用.
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