A simplified perchloric acid workflow with neutralization (PCA N) for democratizing deep plasma proteomics at population scale.

IF 5.5 2区 生物学 Q1 BIOCHEMICAL RESEARCH METHODS
Vincent Albrecht, Johannes B Müller-Reif, Vincenth Brennsteiner, Matthias Mann
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引用次数: 0

Abstract

Large scale plasma proteomics studies offer tremendous potential for biomarker discovery but face significant challenges in balancing analytical depth, throughput and cost-effectiveness. We present an optimized perchloric acid-based workflow with neutralization - PCA-N - that addresses these limitations. By introducing a neutralization step following protein precipitation, PCA-N enables direct enzymatic digestion without additional purification steps, reducing sample volume requirements to only 5 μL of plasma while maintaining deep plasma proteome coverage. The streamlined protocol allows preparation of over 10,000 samples per day using 384-well formats at costs comparable to undepleted plasma analysis (NEAT). Rigorous validation according to the recently introduced CLSI C64 guideline demonstrated that despite somewhat higher technical variability compared to NEAT, PCA-N maintained excellent biological resolution and reproducibility. We confirmed the workflow's exceptional stability through analysis of over 1,700 quality control samples systematically interspersed among more than 40,000 plasma samples measured continuously over 353 days. Technical performance remained consistent across multiple instruments, sample preparation batches and nearly a year of measurements. Compared to NEAT plasma proteomics, PCA-N doubled the proteomic depth while maintaining comparable reagent costs and throughput. The minimal sample requirements, operational simplicity while using only common laboratory chemicals and exceptional scalability positions PCA-N as an attractive approach for population-level plasma proteomics, democratizing access to deep plasma proteomics analysis.

一个简化的高氯酸工作流程与中和(PCA N)民主化在人口规模的深层血浆蛋白质组学。
大规模血浆蛋白质组学研究为生物标志物的发现提供了巨大的潜力,但在平衡分析深度、通量和成本效益方面面临着重大挑战。我们提出了一个优化的高氯酸为基础的工作流程与中和- PCA-N -解决这些限制。通过引入蛋白质沉淀后的中和步骤,PCA-N无需额外的纯化步骤即可实现直接酶切,将样品体积要求降低至仅5 μL血浆,同时保持较深的血浆蛋白质组覆盖。简化的方案允许每天使用384孔格式制备超过10,000个样品,成本与未耗尽血浆分析(NEAT)相当。根据最近推出的CLSI C64指南进行的严格验证表明,尽管与NEAT相比,PCA-N的技术可变性更高,但仍保持了出色的生物分辨率和可重复性。我们通过分析1700多个质量控制样本,系统地穿插在超过353天连续测量的40,000多个血浆样本中,证实了该工作流程的卓越稳定性。技术性能在多个仪器、样品制备批次和近一年的测量中保持一致。与NEAT血浆蛋白质组学相比,PCA-N的蛋白质组学深度增加了一倍,同时保持了相当的试剂成本和吞吐量。最小的样品要求,操作简单,同时只使用普通的实验室化学品和卓越的可扩展性使PCA-N成为人群水平血浆蛋白质组学的一种有吸引力的方法,使深入血浆蛋白质组学分析大众化。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
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来源期刊
Molecular & Cellular Proteomics
Molecular & Cellular Proteomics 生物-生化研究方法
CiteScore
11.50
自引率
4.30%
发文量
131
审稿时长
84 days
期刊介绍: The mission of MCP is to foster the development and applications of proteomics in both basic and translational research. MCP will publish manuscripts that report significant new biological or clinical discoveries underpinned by proteomic observations across all kingdoms of life. Manuscripts must define the biological roles played by the proteins investigated or their mechanisms of action. The journal also emphasizes articles that describe innovative new computational methods and technological advancements that will enable future discoveries. Manuscripts describing such approaches do not have to include a solution to a biological problem, but must demonstrate that the technology works as described, is reproducible and is appropriate to uncover yet unknown protein/proteome function or properties using relevant model systems or publicly available data. Scope: -Fundamental studies in biology, including integrative "omics" studies, that provide mechanistic insights -Novel experimental and computational technologies -Proteogenomic data integration and analysis that enable greater understanding of physiology and disease processes -Pathway and network analyses of signaling that focus on the roles of post-translational modifications -Studies of proteome dynamics and quality controls, and their roles in disease -Studies of evolutionary processes effecting proteome dynamics, quality and regulation -Chemical proteomics, including mechanisms of drug action -Proteomics of the immune system and antigen presentation/recognition -Microbiome proteomics, host-microbe and host-pathogen interactions, and their roles in health and disease -Clinical and translational studies of human diseases -Metabolomics to understand functional connections between genes, proteins and phenotypes
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