PO.CH02.01 · 化学
用于癌症蛋白质组学中简化定量质谱样本制备的磁珠工作流程
A magnetic bead-based workflow for streamlined and quantitative mass spectrometry sample preparation in cancer proteomics
作者与单位 Authors & Affiliations
摘要 Abstract
中文摘要
背景：高效且可重复的样本制备对于稳健的基于质谱（MS）的蛋白质组学至关重要，特别是在输入材料可能有限的肿瘤学应用中。我们开发了一个基于磁颗粒的样本制备（MPSP）平台，利用磁珠从多样的裂解条件中捕获蛋白，从而实现与自动化工作流程和下游蛋白质组学分析的整合。
方法：使用多种缓冲液裂解人K562细胞和其他癌症细胞系。通过磁珠和有机溶剂诱导的沉淀捕获蛋白，随后用优化的蛋白酶组合（如trypsin、Lys-C、Arg-C）进行珠上酶解。将肽段回收率和酶解效率与沉淀法和基于滤膜的方法进行基准比较。该工作流程使用Agilent AssayMAP Bravo系统实现自动化，并使用LC-MS/MS、SDS-PAGE和高pH反相HPLC分级评估性能。特定应用的适配包括定量PROTAC分析和磷酸化/富集策略。
结果：MPSP实现了高效的表面活性剂去除且蛋白损失最小，获得了比沉淀法（5,413种）或基于滤膜的方法（5,567种）更高的蛋白鉴定数（5,794种蛋白）。该方法在各种珠表面化学性质和细胞系（HEK、HeLa、K562）之间兼容，并展示了对低输入样本（<10,000个细胞）的可扩展性。通过实验设计（DOE）优化酶解确立了理想参数：1:16的酶底物比、40°C下16小时酶解，产生了改善的肽段回收率（94%酶解效率）。MPSP将样本制备时间缩短了≥1天，并支持PROTAC筛选中的定量准确性，实现了BRD4泛素化检测，并使用K-ε-GG、pY抗体以及IMAC/TiO₂增强肽段富集。使用MPSP的自动化外泌体处理鉴定出697种蛋白，而手动方法为256种，两者重叠2,861种蛋白。
结论：MPSP为癌症蛋白质组学样本制备提供了一个可扩展、兼容自动化的解决方案。它支持在多样样本类型和工作流程中的稳健定量、高灵敏度和可重复性。该平台有望提升转化和发现肿瘤学研究中的通量和分析深度。
查看英文原文 English abstract
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.
利益披露 Disclosure
M. Rosenblatt, None..
Z. Zeng, None..
A. Deshpande, None..
M. Urh, None..
W. Zhou, None.