A magnetic bead-based workflow for streamlined and quantitative mass spectrometry sample preparation in cancer proteomics

Background: Efficient and reproducible sample preparation is essential for robust mass spectrometry (MS)-based proteomics, particularly in oncology applications where input materials can be limited. We developed a Magnetic Particle-based Sample Preparation (MPSP) platform leveraging magnetic bead-based protein capture from diverse lysis conditions, enabling integration with automated workflows and downstream proteomic analysis. Methods: Human K562 cells and other cancer cell lines were lysed using various buffers. Proteins were captured via magnetic beads and organic solvent-induced precipitation, followed by on-bead digestion with optimized protease combinations (e.g., trypsin, Lys-C, Arg-C). Peptide recovery and digestion efficiency were benchmarked against precipitation and filter-based methods. The workflow was automated using an Agilent AssayMAP Bravo system, and performance was assessed using LC-MS/MS, SDS-PAGE, and high-pH reversed-phase HPLC fractionation. Application-specific adaptations included quantitative PROTAC analysis and phospho/enrichment strategies. Results: MPSP enabled efficient surfactant removal with minimal protein loss, achieving higher protein identifications (5,794 proteins) than precipitation (5,413) or filter-based (5,567) methods. The approach was compatible across bead surface chemistries and cell lines (HEK, HeLa, K562) and demonstrated scalability to low-input samples (<10,000 cells). Digestion optimization via Design of Experiments (DOE) established ideal parameters: 1:16 enzyme-to-substrate ratio, 16-hour digestion at 40°C, yielding improved peptide recovery (94% digestion efficiency). MPSP reduced sample prep time by ≥1 day and supported quantitative accuracy in PROTAC screens, enabling BRD4 ubiquitination detection and enhanced peptide enrichment using K-ε-GG, pY antibodies, and IMAC/TiO₂. Automated exosome processing using MPSP identified 697 proteins versus 256 by manual methods, with a 2,861-protein overlap. Conclusions: MPSP offers a scalable, automation-compatible solution for cancer proteomics sample preparation. It supports robust quantitation, high sensitivity, and reproducibility across diverse sample types and workflows. This platform is positioned to enhance throughput and analytical depth in translational and discovery oncology research.