Quantification Quality Control Emerges as a Crucial Factor to Enhance Single-Cell Proteomics Data Analysis

Area of Science:

• Proteomics
• Biotechnology
• Computational Biology

Background:

• Single-cell proteomics (SCP) enables unbiased exploration of cellular heterogeneity.
• Challenges in SCP include high missing value rates and batch effects, hindering robust data analysis.
• Optimal data processing strategies for SCP remain underexplored.

Purpose of the Study:

• To develop and validate a novel quantification quality control for mass spectrometry (MS)-based single-cell proteomics.
• To enhance the identification of differentially expressed proteins (DEPs) by addressing missing values and batch effects.
• To improve the reliability and interpretability of SCP data analysis.

Main Methods:

• Integration of a novel quantification quality control with isobaric matching between runs (IMBR) and PSM-level normalization.
• Application of the refined pipeline to SCP datasets to assess protein and peptide quantification.
• Validation of identified DEPs using immunoblotting for functional relevance.

Main Results:

• The combined approach quantified an additional 12% of proteins and 19% of peptides, with over 90% valid values.
• Quantification quality control significantly reduced variations and q-values, leading to improved cell type separation.
• PSM-level normalization demonstrated comparable performance to protein-level methods while preserving original data profiles.
• Five out of six validated DEPs confirmed the pipeline's feasibility and biological relevance.

Conclusions:

• The developed pipeline, combining IMBR, cell quality control, and PSM-level normalization, is effective for SCP data analysis.
• This approach enhances protein identification, reduces quantification variability, and improves the accuracy of differential expression analysis.
• The refined pipeline offers a feasible and beneficial strategy for future single-cell proteomics studies.