Ultra-High-Resolution MS1-Based Quantification with Chimeric Spectra Deconvolution Enables In-Depth Quantitative...

Achieving in-depth, high-quality protein quantification is essential for proteomics in pharmaceutical and clinical investigations. Ultra-high-resolution (UHR) MS1-based proteomics quantification, recognized for its high sensitivity and stable signal intensity, shows great promise for high-quality proteomics quantification, especially in large cohorts. However, a substantial portion of MS1 quantitative features remain unidentified, primarily because the co-isolation of multiple peptides by the commonly used low-resolution quadrupole leads to chimeric spectra. Though excellent spectrum deconvolution methods have been developed, a strategy for confidently matching deconvoluted IDs to MS1-based quantitative features has been lacking. To address this challenge, we developed a quantitative strategy incorporating UHR-MS1 quantification with chimeric spectrum deconvolution via rigorous feature-ID matching, enabling accurate, selective MS1-based quantification of individual co-eluted peptides with close precursor m/z values. This method greatly enhances protein quantification depth while upholding high data quality. Compared to widely used MS1-based workflows, our pipeline, termed CHIonStar, demonstrated excellent reproducibility, accuracy, and precision, along with enhanced capacity to discover significantly altered proteins. This strategy was then applied to map the distribution of mouse brain proteins at the whole-tissue level, using the Micro-scaffold Assisted Spatial Proteomics (MASP) strategy. In addition to confirming the >5,000 proteins mapped by IonStar, CHIonStar generated ∼800 new high-quality protein maps, including novel markers of brain regions and an expanded repertoire of proteins associated with critical brain functions. The strategy developed here can be broadly applied to improve the quantitative depth and quality of MS1-based methods, especially in applications requiring high-quality quantification in large cohorts, such as spatial proteomics and clinical/pharmaceutical proteomics.