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

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 Delta retention time (Delta RT) 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 Delta RT (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.

Automated summary

Achieving deep protein coverage in proteome profiling of low-input and single-cell samples is challenging. A three-stage search strategy combining database reduction and Delta retention time (Delta RT) filtering improved peptide/protein identification and reproducibility. This strategy resulted in over 30% improvement in proteome coverage for single-cells and similar size samples, with enhanced reproducibility and higher differential protein discovery.