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

Area of Science:

• Proteomics and Mass Spectrometry
• Biochemistry and Molecular Biology
• Neuroscience and Spatial Biology

Background:

• High-quality protein quantification is crucial for pharmaceutical and clinical research.
• Ultra-high-resolution MS1-based proteomics offers sensitivity but struggles with unidentified quantitative features due to chimeric spectra from co-isolated peptides.
• Existing deconvolution methods lack a strategy for confidently linking identified spectra to MS1 quantitative data.

Purpose of the Study:

• To develop a novel quantitative strategy that integrates ultra-high-resolution MS1 quantification with chimeric spectrum deconvolution.
• To enable accurate and selective MS1-based quantification of individual co-eluted peptides, even with close precursor m/z values.
• To enhance protein quantification depth and data quality for large cohort studies.

Main Methods:

• Developed CHIonStar, a quantitative strategy combining UHR-MS1 quantification with chimeric spectrum deconvolution.
• Implemented rigorous feature-ID matching to confidently link deconvoluted spectra to MS1 quantitative features.
• Applied the CHIonStar strategy with Micro-scaffold Assisted Spatial Proteomics (MASP) for whole-tissue mouse brain protein mapping.

Main Results:

• CHIonStar demonstrated superior reproducibility, accuracy, and precision compared to existing MS1 workflows.
• The method significantly enhanced the discovery of altered proteins.
• Applied to mouse brain mapping, CHIonStar confirmed over 5,000 proteins and identified ~800 new high-quality protein maps, including novel regional markers.

Conclusions:

• The developed strategy effectively addresses the challenge of unidentified MS1 quantitative features caused by chimeric spectra.
• CHIonStar significantly improves the depth and quality of MS1-based proteomics quantification.
• This approach is broadly applicable to large-scale proteomics, spatial biology, and clinical/pharmaceutical applications.