A three-stage search strategy combining database reduction and retention time filtering to improve the sensitivity of low-input and single-cell proteomic analysis

Abstract

When performing proteome profiling of low-input and single-cell samples, achieving deep protein coverage is very challenging due to the sensitivity limitation of current proteomic methods. Herein, we introduce a three-stage search strategy that combines the advantages of database reduction and Δ retention time (ΔRT) filtering. The strategy improves peptide/protein identification and reproducibility by retaining more correct identifications and filtering out incorrect identifications. The raw data were first merged and searched against a Uniprot database with a relaxed false discovery rate (FDR) of 40% to identify the possible detectable proteins. The identified proteins were then used as a new database to search the raw data against with a tighter FDR of 10%. After this, the results were filtered using ΔRT (the difference between the measured and predicted RT) to reduce the incorrect identifications and maintain the FDR below 1%. This strategy resulted in over 30% improvement in proteome coverage for single-cells and samples of similar size. The reproducibility of identification and quantification was also enhanced for the low-input samples. Moreover, the 50% higher number of differential proteins found in the two types of single neurons indicates the application potential of this strategy.