Moonsu Bok (Center for Proteogenome Research, Department of Chemistry, Korea University)

Moonsu Bok (Center for Proteogenome Research, Department of Chemistry, Korea University)

Kwonhee Bok (Center for Proteogenome Research, Department of Chemistry, Korea University)

Chaewon Kang (Center for Proteogenome Research, Department of Chemistry, Korea University)

Jiwon Hong (Center for Proteogenome Research, Department of Chemistry, Korea University)

Taewon Ko (Center for Proteogenome Research, Department of Chemistry, Korea University)

Su-jin Kim (Center for Proteogenome Research, Department of Chemistry, Korea University)

Jingi Bae (Center for Proteogenome Research, Department of Chemistry, Korea University)

Hokeun Kim (Center for Proteogenome Research, Department of Chemistry, Korea University)

Sang-Won Lee (Center for Proteogenome Research, Department of Chemistry, Korea University)

 Plasma proteins serve as critical indicators for disease diagnosis, prognostic evaluation, and therapeutic monitoring, and their analysis represents the largest segment of the in vitro diagnostics market. However, the wide dynamic range of plasma protein concentrations poses a significant analytical challenge, often obscuring low-abundance species such as tissue leakage proteins and signaling molecules.
 

 To address this issue, we utilized a nanoparticle-based plasma sample preparation strategy via the ProteographOne™ platform (Seer, Inc.), which capitalizes on the spontaneous formation of protein coronas on engineered nanoparticles. This process preferentially enriches low-abundance proteins while depleting high-abundance species, thereby compressing the dynamic range of the plasma proteome. The workflow is fully automated and supports high-throughput processing of up to 81 samples (including four internal controls) per run within 4 hours and 44 minutes, minimizing hands-on time and sample loss.

 Subsequent proteomic analysis was performed using our proprietary dual nanoLC-MS/MS system operating with mutually independent channels and a 24-minute gradient, optimized for both analytical speed and proteome depth. On average, approximately 8,500 protein groups were identified per plasma sample using DIA-NN analysis. This streamlined and scalable platform enables rapid and comprehensive plasma proteome profiling, facilitating the discovery of novel biomarkers relevant to disease initiation, progression, and therapeutic response.