Laboratory of Medical Immunology, Department of Laboratory Medicine, Radboud University Medical Center, Nijmegen, The Netherlands.
Translational Metabolic Laboratory, Department of Human Genetics, Radboud University Medical Center, Nijmegen, The Netherlands.
Clin Chem Lab Med. 2024 Jun 17;62(12):2507-2518. doi: 10.1515/cclm-2024-0306. Print 2024 Nov 26.
Minimal residual disease (MRD) status in multiple myeloma (MM) is an important prognostic biomarker. Personalized blood-based targeted mass spectrometry detecting M-proteins (MS-MRD) was shown to provide a sensitive and minimally invasive alternative to MRD-assessment in bone marrow. However, MS-MRD still comprises of manual steps that hamper upscaling of MS-MRD testing. Here, we introduce a proof-of-concept for a novel workflow using data independent acquisition-parallel accumulation and serial fragmentation (dia-PASEF) and automated data processing.
Using automated data processing of dia-PASEF measurements, we developed a workflow that identified unique targets from MM patient sera and personalized protein sequence databases. We generated patient-specific libraries linked to dia-PASEF methods and subsequently quantitated and reported M-protein concentrations in MM patient follow-up samples. Assay performance of parallel reaction monitoring (prm)-PASEF and dia-PASEF workflows were compared and we tested mixing patient intake sera for multiplexed target selection.
No significant differences were observed in lowest detectable concentration, linearity, and slope coefficient when comparing prm-PASEF and dia-PASEF measurements of serial dilutions of patient sera. To improve assay development times, we tested multiplexing patient intake sera for target selection which resulted in the selection of identical clonotypic peptides for both simplex and multiplex dia-PASEF. Furthermore, assay development times improved up to 25× when measuring multiplexed samples for peptide selection compared to simplex.
Dia-PASEF technology combined with automated data processing and multiplexed target selection facilitated the development of a faster MS-MRD workflow which benefits upscaling and is an important step towards the clinical implementation of MS-MRD.
多发性骨髓瘤（MM）中的微小残留病（MRD）状态是一个重要的预后生物标志物。个性化基于血液的靶向质谱法检测 M 蛋白（MS-MRD）已被证明是骨髓中 MRD 评估的一种敏感且微创的替代方法。然而，MS-MRD 仍然包括妨碍 MS-MRD 检测规模化的手动步骤。在这里，我们介绍了一种使用数据非依赖性采集-平行累积和串行片段化（dia-PASEF）和自动化数据处理的新概念工作流程的概念验证。
使用 dia-PASEF 测量的自动化数据处理，我们开发了一种从 MM 患者血清和个性化蛋白质序列数据库中识别独特靶标的工作流程。我们生成与 dia-PASEF 方法相关联的患者特异性文库，并随后定量和报告 MM 患者随访样本中的 M 蛋白浓度。比较了平行反应监测（prm）-PASEF 和 dia-PASEF 工作流程的分析性能，并测试了混合患者摄入血清进行多重靶标选择。
当比较患者血清系列稀释物的 prm-PASEF 和 dia-PASEF 测量值时，最低检测浓度、线性度和斜率系数没有观察到显著差异。为了提高测定开发时间，我们测试了混合患者摄入血清进行目标选择，这导致为单重和多重 dia-PASEF 选择了相同的克隆型肽。此外，与单重相比，测量多重样品进行肽选择时，测定开发时间最多提高了 25 倍。
Dia-PASEF 技术结合自动化数据处理和多重靶标选择，促进了更快的 MS-MRD 工作流程的开发，有利于规模化，并朝着 MS-MRD 的临床实施迈出了重要一步。
