Abstract
Plasma proteomics is expanding across platforms and cohorts, and integrating these data for AI demands comparability at the protein level, not merely concordant associations1,2. Affinity and MS platforms use distinct probes (antibodies, aptamers, or peptides) and signal readouts, yielding contradictory cross-platform results3,4. Without a known quantitative truth, we cannot distinguish biology from measurement distortion, leaving no gold standard for integration. Here we introduce Plasmix—a plasma reference suite with predefined male:female ratios (M, 1:0; Y, 3:1; P, 1:1; X, 1:3; F, 0:1)—and show that preserving this quantitative titration gradient, not just technical repeatability, predicts cross-platform concordance and identifies protein measurements suitable for integration. Profiling Plasmix across five platforms (Olink, SomaScan, NULISA, AAgAtlas, and MS-DIA) and 12 protocols across 17 batches, we found discordance is dominated by signal generation, not sample identity, and platforms distort signals in a protein-specific manner. Crucially, proteins retaining the titration response showed stronger agreement in an independent cohort; anchoring to the Plasmix midpoint (P) via sample-to-reference ratios reduced distortions, extending harmonizable coverage by 10–20%. Plasmix thus provides a physical ruler to benchmark accuracy, identifying genuinely integrable measurements before pooling datasets or training AI models—a critical bottleneck for plasma proteomics.
Authors and Affiliations
Yaqing Liu¹, Haiyan Wang¹˒², Yutong Zhang¹, Renquan Lu³, Weihong Xu⁴, Wanwan Hou¹, Yuyang Zhu¹, Xixi Li¹, Qingwang Chen¹, Yuechen Gao¹, Suhui Zhang⁴, Qiaochu Chen¹, Zhiyin An¹, Jiaqi Yu⁵˒⁶, Wantao Ying⁵˒⁶, Xiaobo Yu⁷, Jianhong Wu⁸, Ziquan Fan⁸, Xiao Yao⁹, Zhihui Li¹⁰, Hongmei Shan¹¹, Tiannan Guo¹², Zihong Ye¹³, Xiaoping Yu¹³, Gang Liu¹⁴, Ying Yu¹, Zhiyu Peng⁴, Chen Ding¹, Qiang Tian⁴, Rui Zhang¹⁵, Jinming Li¹⁵, Xiang Fang¹⁶, Fuchu He⁷˒¹⁷, Li Jin¹, Xingdong Chen¹˒¹⁸˒¹⁹, Leming Shi¹˒³˒⁴˒¹⁹*, and Yuanting Zheng¹⁴˒²⁰*
¹ State Key Laboratory of Genetics and Development of Complex Phenotypes, School of Life Sciences and Human Phenome Institute, Fudan University, Shanghai, China
² Department of Neurology, Zhongshan Hospital, Fudan University, Shanghai, China
³ Cancer Institute, Shanghai Cancer Center, Fudan University, Shanghai, China
⁴ International Human Phenome Institutes (Shanghai), Shanghai, China
⁵ State Key Laboratory of Medical Proteomics, National Center for Protein Sciences (Beijing), Academy of Military Medical Sciences, Beijing, China
⁶ Beijing Proteome Research Center, Beijing, China
⁷ State Key Laboratory of Medical Proteomics, National Center for Protein Sciences (Beijing), Research Unit of Proteomics Driven Cancer Precision Medicine (Chinese Academy of Medical Sciences), Beijing 102206, China
⁸ Thermo Fisher Scientific, Inc., Shanghai, China
⁹ Alamar Biosciences, Inc., Fremont, California, USA
¹⁰ Illumina, Inc., San Diego, California, USA
¹¹ Olink Proteomics AB, Uppsala, Sweden
¹² State Key Laboratory of Medical Proteomics, School of Medicine, Westlake University, Hangzhou, China
¹³ Key Laboratory of Microbiological Metrology, Measurement & Bio-product Quality Security, State Administration for Market Regulation; China Jiliang University, Hangzhou, China
¹⁴ Key Laboratory of Bioanalysis and Metrology, State Administration for Market Regulation, Shanghai Institute of Measurement and Testing Technology Co., Ltd., Shanghai, China
¹⁵ National Center for Clinical Laboratories, Institute of Geriatric Medicine, Chinese Academy of Medical Sciences, Beijing Hospital, Beijing, China
¹⁶ National Institute of Metrology, Beijing, China
¹⁷ International Academy of Phronesis Medicine (Guangdong), 510320, Guangdong, China
¹⁸ School of Public Health, Fudan University, Shanghai, China
¹⁹ Fudan University Taizhou Institute of Health Sciences, Taizhou, Jiangsu, China
²⁰ Department of Pathology, Zhongshan Hospital, Fudan University, Shanghai, China