Centre for Healthy Brain Ageing, School of Psychiatry, University of New South Wales, Sydney, NSW 2052, Australia.
Bioanalytical Mass Spectrometry Facility, Mark Wainwright Analytical Centre, University of New South Wales, Wallace Wurth Building (C27), Sydney, NSW 2052, Australia.
J Proteome Res. 2021 Feb 5;20(2):1261-1279. doi: 10.1021/acs.jproteome.0c00670. Epub 2021 Jan 20.
Human plasma is one of the most widely used tissues in clinical analysis, and plasma-based biomarkers are used for monitoring patient health status and/or response to medical treatment to avoid unnecessary invasive biopsy. Data-driven plasma proteomics has suffered from a lack of throughput and detection sensitivity, largely due to the complexity of the plasma proteome and in particular the enormous quantitative dynamic range, estimated to be between 9 and 13 orders of magnitude between the lowest and the highest abundance protein. A major challenge is to identify workflows that can achieve depth of plasma proteome coverage while minimizing the complexity of the sample workup and maximizing the sample throughput. In this study, we have performed intensive depletion of high-abundant plasma proteins or enrichment of low-abundant proteins using the Agilent multiple affinity removal liquid chromatography (LC) column-Human 6 (Hu6), the Agilent multiple affinity removal LC column-Human 14 (Hu14), and ProteoMiner followed by sodium dodecyl sulfate-polyacrylamide gel electrophoresis (SDS PAGE) and C18 prefractionation techniques. We compared the performance of each of these fractionation approaches to identify the method that satisfies requirements for analysis of clinical samples and to include good plasma proteome coverage in combination with reasonable sample output. In this study, we report that one-dimensional (1D) gel-based prefractionation allows parallel sample processing and no loss of proteome coverage, compared with serial chromatographic separation, and significantly accelerates analysis time, particularly important for large clinical projects. Furthermore, we show that a variety of methodologies can achieve similarly high plasma proteome coverage, allowing flexibility in method selection based on project-specific needs. These considerations are important in the effort to accelerate plasma proteomics research so as to provide efficient, reliable, and accurate diagnoses, population-based health screening, clinical research studies, and other clinical work.
人血浆是临床分析中应用最广泛的组织之一，基于血浆的生物标志物用于监测患者的健康状况和/或对治疗的反应，以避免不必要的有创活检。数据驱动的血浆蛋白质组学受到通量和检测灵敏度的限制，主要是由于血浆蛋白质组的复杂性，特别是巨大的定量动态范围，估计在最低和最高丰度蛋白之间有 9 到 13 个数量级。一个主要的挑战是确定能够实现血浆蛋白质组深度覆盖的工作流程，同时最大限度地减少样品处理的复杂性并使样品通量最大化。在这项研究中，我们使用 Agilent 多重亲和去除液相色谱（LC）柱-Human 6（Hu6）、Agilent 多重亲和去除 LC 柱-Human 14（Hu14）和 ProteoMiner 进行了高强度的高丰度血浆蛋白的耗尽或低丰度蛋白质的富集，随后进行十二烷基硫酸钠-聚丙烯酰胺凝胶电泳（SDS PAGE）和 C18 预分级技术。我们比较了这些分馏方法的性能，以确定满足分析临床样品要求的方法，并结合合理的样品输出，实现良好的血浆蛋白质组覆盖。在这项研究中，我们报告一维（1D）凝胶基预分级允许并行样品处理，与串行色谱分离相比，不会损失蛋白质组覆盖，并且显著加快了分析时间，对于大型临床项目尤为重要。此外，我们表明，多种方法可以实现类似的高血浆蛋白质组覆盖，允许根据项目特定需求灵活选择方法。这些考虑因素对于加速血浆蛋白质组学研究以提供高效、可靠和准确的诊断、基于人群的健康筛查、临床研究和其他临床工作非常重要。
