Evaluating and Optimizing Mass Spectrometry Proteomics Data to Deconvolve Cell-Type-Specific Protein Expression in...

Area of Science:

• Proteomics
• Bioinformatics
• Cancer Biology

Background:

• Intratumoral heterogeneity is key to tumor biology.
• Cell-type-specific protein expression is challenging to characterize using omics data.
• Bulk mass spectrometry (MS) proteomics offers potential for cell-type deconvolution but optimal quantification formats are unclear.

Purpose of the Study:

• To systematically evaluate MS proteomics quantification formats and preprocessing for resolving cell-type-specific protein expression.
• To identify optimal data transformation strategies for MS proteomics deconvolution.
• To develop a computational tool for analyzing MS proteomics data for cell-type specificity.

Main Methods:

• Leveraged large-cohort proteogenomics data.
• Evaluated label-free spectral counts, TMT MS1 intensities, and MS2 ratios.
• Applied data transformation techniques, including 'min-score', for deconvolution.
• Utilized coefficient of variation (CV) to assess deconvolution suitability.
• Developed the R package "ProTransDeconv".

Main Results:

• Label-free spectral counts are directly usable for deconvolution.
• TMT MS1 intensities and MS2 ratios require specific data transformations for effective deconvolution.
• A 'min-score' transformation significantly enhanced MS1 intensity-based deconvolution.
• Coefficient of variation (CV) identified as a reliable indicator for deconvolution suitability.
• Demonstrated utility in subtyping pancreatic cancer.

Conclusions:

• Specific MS proteomics quantification formats and transformations are crucial for accurate cell-type deconvolution.
• "ProTransDeconv" provides a comprehensive R package for analyzing bulk proteomics data.
• This work offers practical guidance for studying cell-type-specific protein dysregulation in tumors.