Shi_2020_Proteomics__e2000175

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, we present a fraction prediction algorithm-assisted two-dimensional LC-based parallel reaction monitoring-mass spectrometry (FRACPRED-2D-PRM) approach for measuring low-abundance proteins in human plasma. Plasma digests were separated by the first dimension high-pH RP-LC with data-dependent acquisition (DDA). We then used the FRACPRED algorithm to predict the retention time of undetectable target peptides according to those of other abundant plasma peptides during the first dimension separation. Fractions predicted to contain target peptides were analyzed by the second dimension low-pH nano RP-LC PRM. We demonstrated the accuracy and robustness of fraction prediction with the FRACPRED algorithm by measuring two low-abundance proteins, aldolase B and carboxylesterase 1, in human plasma. The FRACPRED-2D-PRM proteomics approach demonstrated markedly improved efficiency and sensitivity over conventional 2D-LC proteomics assays. We expect that this approach will be widely used in the study of low-abundance proteins in plasma and other complex biological samples. This article is protected by copyright. All rights reserved.