Benchmarking informatics workflows for data-independent acquisition single-cell proteomics

Area of Science:

• Proteomics
• Mass Spectrometry
• Bioinformatics

Background:

• Single-cell proteomics using data-independent acquisition mass spectrometry (DIA MS) is rapidly advancing.
• The impact of various data analysis strategies on single-cell proteomic outcomes remains under-investigated.

Purpose of the Study:

• To establish a framework for benchmarking data analysis strategies in DIA-based single-cell proteomics.
• To systematically evaluate different software tools, searching strategies, and informatic workflow combinations.

Main Methods:

• Developed a benchmarking framework for DIA-based single-cell proteomics.
• Compared popular DIA data analysis software and searching strategies.
• Evaluated informatic workflow combinations: sparsity reduction, missing value imputation, normalization, batch effect correction, and differential expression analysis.

Main Results:

• Comprehensive comparison of DIA data analysis software and searching strategies.
• Systematic evaluation of informatic workflow method combinations.
• Benchmarking performed on simulated and real single-cell samples with spike-in.

Conclusions:

• Provides recommendations for data analysis in DIA-based single-cell proteomics.
• Highlights the importance of selecting appropriate data analysis strategies for reliable results.
• Facilitates improved data interpretation in single-cell proteomic studies.