Decoupling Quantification and Identification for Label-Free Top-Down Proteomics Improves Proteome Coverage
Quantifying proteoforms is essential for understanding biological processes. The most widely used quantification approach in top-down proteomics (TDP) is label-free quantification (LFQ), where quantification and identification compete for acquisition time. We identified missing values, insufficient fragmentation, and undersampling of low-abundance proteoforms as major factors limiting conventional TDP-LFQ workflows. Our proposed workflow decouples quantification and identification through MS1-only quantification followed by targeted data-dependent MS2 identification. The MS1-only acquisition more than doubled the number of mass features quantified reliably and improved quantification precision. Focusing targeted MS2 acquisition on significantly differentially abundant features enables optimization of MS2 parameters and increases identification coverage by 50%. The workflow was validated across different chromatographic conditions, MS acquisition setups, and sample preparation strategies. In Escherichia coli grown on glucose or acetate, the increased depth enabled by this workflow revealed biological insights inaccessible to a previously published bottom-up proteomics dataset. Overall, decoupling quantification and identification improves quantitative depth and proteoform coverage in TDP-LFQ and provides a flexible strategy for high-sensitivity and high-throughput proteoform quantification.