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

Understanding intratumoral heterogeneity is essential
for elucidating
tumor biology. Compared to RNA expression, omics-level characterization
of cell-type-specific protein expression remains a technical challenge.
Bulk mass spectrometry (MS) provides abundant proteomics resources
to infer cell-type specificity via data deconvolution; however, it
is unclear which proteomic quantification formats are optimal, as
they differ from the data types for which most deconvolution methods
were designed. Here, leveraging recently generated large-cohort proteogenomics
data, we systematically evaluated different MS proteomics quantification
formats and preprocessing strategies to resolve cell-type-specific
protein expression. Our results indicate that while label-free spectral
counts can be used directly, TMT MS1 intensities and MS2 ratios are
less suitable and require appropriate data transformation. We demonstrate
that a ‘min-score’ transformation significantly improves
MS1 intensity-based deconvolution, providing useful insights for subtyping
pancreatic cancer. Moreover, we identified the coefficient of variation
(CV) as a robust statistical indicator of deconvolution suitability.
Finally, we developed “ProTransDeconv”, an R package
integrating data transformation, deconvolution, and quality checks
for major MS proteomics data formats. This work provides practical
guidance for deconvolving bulk proteomics to study cell-type-specific
protein-level dysregulation.