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In this study, we developed a scalable workflow for the deep proteomic analysis of low- to ultralow-input samples by systematically optimizing a widely adopted Orbitrap and UHPLC platform to maximize sensitivity, precision, and throughput. This optimized workflow identified over 5600 proteins from 5 ng of peptides and 3400 proteins from 20 sorted cells, achieving a throughput of 30 analyses per day while maintaining deep proteome coverage and high quantitative reproducibility. Furthermore, by applying this method to spatially resolved proteomics, we identified over 6100 proteins from microscale regions of interest (ROIs) within a formalin-fixed, paraffin-embedded (FFPE) tissue. A data-driven normalization strategy was employed to correct for variable cellularity across tissue regions, effectively revealing intratumor heterogeneity and distinct molecular and functional signatures, including pathway activations not apparent in parallel spatial transcriptomic analysis. Ultimately, this accessible, high-performance method substantially lowers the instrumentation barrier for the deep proteomic profiling of trace-level biological samples.<\/jats:p>","DOI":"10.1021\/acs.analchem.6c02686","type":"journal-article","created":{"date-parts":[[2026,9,1]],"date-time":"2026-09-01T16:59:47Z","timestamp":1788281987000},"page":"25721-25740","source":"Crossref","is-referenced-by-count":0,"title":["An Instrumental\nOptimization of a Label-Free Proteomic\nMethod for Trace Protein Input"],"prefix":"10.1021","volume":"98","author":[{"given":"Dongyoon","family":"Shin","sequence":"first","affiliation":[{"name":"Bundang CHA Hospital , , ,","place":["Seongnam, Republic of Korea, 13520"]},{"name":"CHA University School of Medicine , , ,","place":["Seongnam, Republic of Korea, 13488"]}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Sumin","family":"Lee","sequence":"additional","affiliation":[{"name":"Meteor Biotech, Co. 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