Plasma proteomics holds immense potential for clinical research and biomarker discovery, serving as a non-invasive "liquid biopsy" for tissue sampling. Mass spectrometry (MS)-based proteomics, thanks to improvement in speed and robustness, emerges as an ideal technology for exploring the plasma proteome for its unbiased and highly specific protein identification and quantification. Despite its potential, plasma proteomics is still a challenge due to the vast dynamic range of protein abundance, hindering the detection of less abundant proteins. Different approaches can help overcome this challenge. Conventional depletion methods face limitations in cost, throughput, accuracy, and off-target depletion. Nanoparticle-based enrichment shows promise in compressing dynamic range, but cost remains a constraint. Enrichment strategies for extracellular vesicles (EVs) can enhance plasma proteome coverage dramatically, but current methods are still too laborious for large series. Neat plasma remains popular for its cost-effectiveness, time efficiency, and low volume requirement. We used a test set of 33 plasma samples for all evaluations. Samples were digested using S-Trap and analyzed on Evosep One and nanoElute coupled to a timsTOF Pro using different elution gradients and ion mobility ranges. Data were mainly analyzed using library-free searches using DIA-NN. This study explores ways to improve proteome coverage in neat plasma both in MS data acquisition and MS data analysis. We demonstrate the value of sampling smaller hydrophilic peptides, increasing chromatographic separation, and using library-free searches. Additionally, we introduce the EV boost approach, that leverages on the extracellular vesicle fraction to enhance protein identification in neat plasma samples. Globally, our optimized analysis workflow allows the quantification of over 1000 proteins in neat plasma with a 24SPD throughput. We believe that these considerations can be of help independently of the LC–MS platform used.

中文翻译：

---

纯血浆蛋白质组学：从最坏的情况中获得最好的结果

血浆蛋白质组学在临床研究和生物标志物发现方面具有巨大的潜力，可作为组织取样的非侵入性“液体活检”。基于质谱 (MS) 的蛋白质组学由于速度和稳健性的提高，成为探索血浆蛋白质组的理想技术，以实现公正且高度特异性的蛋白质鉴定和定量。尽管具有潜力，但血浆蛋白质组学仍然是一个挑战，因为蛋白质丰度的动态范围很大，阻碍了丰度较低的蛋白质的检测。不同的方法可以帮助克服这一挑战。传统的去除方法面临成本、通量、准确性和脱靶去除方面的限制。基于纳米颗粒的富集在压缩动态范围方面显示出希望，但成本仍然是一个限制。细胞外囊泡（EV）的富集策略可以显着提高血浆蛋白质组覆盖率，但目前的方法对于大规模系列来说仍然过于费力。纯等离子体因其成本效益、时间效率和低体积要求而仍然很受欢迎。我们使用包含 33 个血浆样本的测试集进行所有评估。使用 S-Trap 消解样品，并使用不同的洗脱梯度和离子淌度范围在与 timsTOF Pro 耦合的 Evosep One 和 nanoElut 上进行分析。主要使用 DIA-NN 进行无库搜索来分​​析数据。本研究探讨了在 MS 数据采集和 MS 数据分析中提高纯血浆中蛋白质组覆盖率的方法。我们展示了对较小的亲水性肽进行采样、增加色谱分离以及使用无库搜索的价值。此外，我们还引入了 EV 增强方法，该方法利用细胞外囊泡部分来增强纯血浆样品中的蛋白质识别。在全球范围内，我们优化的分析工作流程允许以 24SPD 的通量对纯血浆中的 1000 多种蛋白质进行定量。我们相信，无论所使用的 LC-MS 平台如何，这些考虑因素都会有所帮助。