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

Area of Science:

• Proteomics
• Mass Spectrometry
• Computational Biology

Background:

• Single-cell proteomics is a rapidly advancing field with significant implications for understanding cellular processes and diseases.
• Despite hardware improvements, a lack of comparative studies on software for single-cell proteomics data analysis hinders progress.

Purpose of the Study:

• To systematically compare the performance of seven popular proteomics software packages for analyzing single-cell proteomics datasets.
• To identify the strengths and weaknesses of different software tools in terms of protein identification, low-abundance protein detection, peptide modification elucidation, and long peptide analysis.

Main Methods:

• Seven widely used proteomics software packages (MSGF+, MSFragger, Proteome Discoverer, MaxQuant, Mascot, X!Tandem, and one other) were applied to analyze three distinct single-cell proteomics datasets.
• Datasets were generated using three different technological platforms to ensure comprehensive evaluation.
• An additional experiment varying sample loading amounts was conducted to assess software performance under different conditions.

Main Results:

• MSGF+, MSFragger, and Proteome Discoverer demonstrated superior performance in maximizing protein identifications.
• MaxQuant showed an advantage in identifying low-abundance proteins.
• MSFragger proved most effective for identifying peptide modifications.
• Mascot and X!Tandem were found to be better suited for the analysis of longer peptides.

Conclusions:

• The choice of software significantly impacts the outcomes of single-cell proteomics data analysis.
• This comparative study provides valuable insights for selecting appropriate tools, benefiting both novice and experienced researchers in the field.
• Further improvements in single-cell proteomics data analysis can be achieved by addressing the identified areas of software performance variation.