Comparison of Database Searching Programs for the Analysis of Single-Cell Proteomics Data

Single-cell proteomics is emerging as an important subfield in the proteomics and mass spectrometry communities, with potential to reshape our understanding of cell development, cell differentiation, disease diagnosis, and the development of new therapies. Compared with significant advancements in the hardware that is used in single-cell proteomics, there has been little work comparing the effects of using different software packages to analyze single-cell proteomics datasets. To this end, seven popular proteomics programs were compared here, applying them to search three single-cell proteomics datasets generated by three different platforms. The results suggest that MSGF+, MSFragger, and Proteome Discoverer are generally more efficient in maximizing protein identifications, that MaxQuant is better suited for the identification of low-abundance proteins, that MSFragger is superior in elucidating peptide modifications, and that Mascot and X!Tandem are better for analyzing long peptides. Furthermore, an experiment with different loading amounts was carried out to investigate changes in identification results and to explore areas in which single-cell proteomics data analysis may be improved in the future. We propose that this comparative study may provide insight for experts and beginners alike operating in the emerging subfield of single-cell proteomics.

Automated summary

Single-cell proteomics is an important subfield with potential to reshape our understanding of cell development, differentiation, disease diagnosis, and therapy. This study compared seven popular proteomics programs on three different datasets, finding that MSGF+, MSFragger, and Proteome Discoverer are more efficient for maximizing protein identifications. MaxQuant is better for low-abundance proteins, MSFragger is superior in elucidating peptide modifications, and Mascot and X!Tandem are better for analyzing long peptides. The study also explored improvements in single-cell proteomics data analysis. Overall, this comparative study provides insight for both experts and beginners in single-cell proteomics.