Intelligent Data Acquisition Blends Targeted and Discovery Methods

A mass spectrometry (MS) method is
described here that can reproducibly
identify hundreds of peptides across multiple experiments. The method
uses intelligent data acquisition to precisely target peptides while
simultaneously identifying thousands of other, nontargeted peptides
in a single nano-LC–MS/MS experiment. We introduce an online
peptide elution order alignment algorithm that targets peptides based
on their relative elution order, eliminating the need for retention-time-based
scheduling. We have applied this method to target 500 mouse peptides
across six technical replicate nano-LC–MS/MS experiments and
were able to identify 440 of these in all six, compared with only
256 peptides using data-dependent acquisition (DDA). A total of 3757
other peptides were also identified within the same experiment, illustrating
that this hybrid method does not eliminate the novel discovery advantages
of DDA. The method was also tested on a set of mice in biological
quadruplicate and increased the number of identified target peptides
in all four mice by over 80% (826 vs 459) compared with the standard
DDA method. We envision real-time data analysis as a powerful tool
to improve the quality and reproducibility of proteomic data sets.

本文报道了一种质谱（mass spectrometry，MS）方法，可在多组实验中重复鉴定数百种肽段。该方法采用智能数据采集技术，能够精准靶向目标肽段，同时在单次纳升液相色谱-串联质谱（nano-LC–MS/MS）实验中同步鉴定数千种非靶向肽段。本研究提出一种在线肽段洗脱顺序对齐算法，可基于肽段的相对洗脱顺序实现靶向筛选，无需依赖保留时间进行调度。我们将该方法应用于6次技术重复的纳升液相色谱-串联质谱实验，靶向500种小鼠肽段，最终在全部6次实验中均鉴定出其中440种；而采用数据依赖采集（data-dependent acquisition，DDA）方法仅能鉴定出256种肽段。同一实验中还额外鉴定出总计3757种其他肽段，表明这种混合方法并未削弱DDA的新型发现优势。该方法还在4只小鼠的生物学重复实验中进行了测试，与标准DDA方法相比，全部4只小鼠的靶向肽段鉴定数量均提升了80%以上（826 vs 459）。我们认为实时数据分析可作为提升蛋白质组数据集质量与重现性的有力工具。