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Highly Multiplex Targeted Proteomics Enabled by Real-Time Chromatographic Alignment

机译:通过实时色谱对准使能高度多重靶向蛋白质组学

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摘要

Targeted mass spectrometry methods produce high-quality quantitative data in terms of limits of detection and dynamic range, at the cost of a substantial compromise in throughput compared to methods such as data independent and data dependent acquisition. The logistical and experimental issues inherent to maintaining assays of even several hundred targets are significant. Prominent among these issues is the drift in analyte retention time as liquid chromatography (LC) columns wear, forcing targeted scheduling windows to be much larger than LC peak widths. If these problems could be solved, proteomics assays would be capable of targeting thousands of peptides in an hour-long experiment, enabling large cohort studies to be performed without sacrificing sensitivity and specificity. We describe a solution in the form of a new method for real-time chromatographic alignment and demonstrate its application to a 56 min LC-gradient HeLa digest assay with 1489 targets. The method is based on the periodic acquisition of untargeted survey scans in a reference experiment and alignment to those scans during subsequent experiments. We describe how the method enables narrower scheduled retention time windows to be used. The narrower scheduling windows enables more targets to be included in the assay or proportionally more time to be allocated to each target, improving the sensitivity. Finally, we point out how the procedure could be improved and how much additional target multiplexing could be gained in the future.
机译:目标质谱方法在检测和动态范围的限制方面产生高质量的定量数据,与吞吐量的实质性妥协相比,与诸如数据无关和数据相关的采集等方法相比,吞吐量的实质性妥协。维持甚至数百个目标的检测中固有的后固定的物流和实验问题是显着的。在这些问题中突出是分析物保留时间的漂移作为液相色谱(LC)柱磨损,迫使靶向调度窗口大于LC峰宽度。如果可以解决这些问题,蛋白质组学测定将能够在一小时的实验中靶向数千种肽,使得能够进行大规模的队列研究,而不会牺牲敏感性和特异性。我们描述了一种以实时色谱对准的新方法形式的解决方案,并证明其应用于具有1489个靶标的56分钟的LC梯度HeLa消化测定。该方法基于在后续实验期间对参考实验中未标准调查扫描的周期性收购,并对这些扫描进行对准。我们描述了如何使用该方法的方法来使用较窄的预定保留时间窗口。较窄的调度Windows使得更多的目标能够包括在测定中或按比例地将时间分配给每个目标,从而提高灵敏度。最后,我们指出了如何改进该过程以及将来可以获得多少目标复用。

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