Department of Clinical Pharmacy, University of Michigan, Ann Arbor, MI, 48109-1065, USA.
Department of Pharmaceutical Sciences, University of Michigan, Ann Arbor, MI, 48109-1065, USA.
Proteomics. 2020 Dec;20(24):e2000175. doi: 10.1002/pmic.202000175. Epub 2020 Nov 3.
Multidimensional fractionation-based enrichment methods improve the sensitivity of proteomic analysis for low-abundance proteins. However, a major limitation of conventional multidimensional proteomics is the extensive labor and instrument time required for analyzing many fractions obtained from the first dimension separation. Here, a fraction prediction algorithm-assisted 2D LC-based parallel reaction monitoring-mass spectrometry (FRACPRED-2D-PRM) approach for measuring low-abundance proteins in human plasma is presented. Plasma digests are separated by the first dimension high-pH RP-LC with data-dependent acquisition (DDA). The FRACPRED algorithm is then usedto predict the retention times of undetectable target peptides according to those of other abundant plasma peptides during the first dimension separation. Fractions predicted to contain target peptides are analyzed by the second dimension low-pH nano RP-LC PRM. The accuracy and robustness of fraction prediction with the FRACPRED algorithm are demonstrated by measuring two low-abundance proteins, aldolase B and carboxylesterase 1, in human plasma. The FRACPRED-2D-PRM proteomics approach demonstrates markedly improved efficiency and sensitivity over conventional 2D-LC proteomics assays. It is expected that this approach will be widely used in the study of low-abundance proteins in plasma and other complex biological samples.
基于多维分馏的富集方法可提高低丰度蛋白质的蛋白质组学分析的灵敏度。然而，传统多维蛋白质组学的一个主要局限性是，从第一维分离中获得的许多馏分的分析需要大量的劳动力和仪器时间。这里提出了一种基于二维液相色谱平行反应监测质谱（FRACPRED-2D-PRM）的用于测量人血浆中低丰度蛋白质的馏分预测算法辅助方法。血浆消化物通过第一维高 pH RP-LC 与数据依赖采集（DDA）进行分离。然后，根据第一维分离过程中其他丰富的血浆肽的保留时间，使用 FRACPRED 算法预测不可检测靶肽的保留时间。根据第二维低 pH 纳升 RP-LC PRM 分析预测含有靶肽的馏分。通过测量人血浆中的两种低丰度蛋白质醛缩酶 B 和羧酸酯酶 1，证明了 FRACPRED 算法的馏分预测的准确性和稳健性。与传统二维液相色谱蛋白质组学分析相比，FRACPRED-2D-PRM 蛋白质组学方法显著提高了效率和灵敏度。预计该方法将广泛应用于血浆和其他复杂生物样品中低丰度蛋白质的研究。
