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人群蛋白质组学在健康和疾病中的前景与挑战。

Promises and Challenges of populational Proteomics in Health and Disease.

机构信息

Human Genetics, Informatics and Predictive Sciences, Bristol-Myers Squibb, Cambridge, Massachusetts, USA.

Bioinformatics Core, Weill Cornell Medicine-Qatar, Education City, Doha, Qatar; Englander Institute for Precision Medicine, Weill Cornell Medicine, New York, New York, USA.

出版信息

Mol Cell Proteomics. 2024 Jul;23(7):100786. doi: 10.1016/j.mcpro.2024.100786. Epub 2024 May 17.

Abstract

Advances in proteomic assay technologies have significantly increased coverage and throughput, enabling recent increases in the number of large-scale population-based proteomic studies of human plasma and serum. Improvements in multiplexed protein assays have facilitated the quantification of thousands of proteins over a large dynamic range, a key requirement for detecting the lowest-ranging, and potentially the most disease-relevant, blood-circulating proteins. In this perspective, we examine how populational proteomic datasets in conjunction with other concurrent omic measures can be leveraged to better understand the genomic and non-genomic correlates of the soluble proteome, constructing biomarker panels for disease prediction, among others. Mass spectrometry workflows are discussed as they are becoming increasingly competitive with affinity-based array platforms in terms of speed, cost, and proteome coverage due to advances in both instrumentation and workflows. Despite much success, there remain considerable challenges such as orthogonal validation and absolute quantification. We also highlight emergent challenges associated with study design, analytical considerations, and data integration as population-scale studies are run in batches and may involve longitudinal samples collated over many years. Lastly, we take a look at the future of what the nascent next-generation proteomic technologies might provide to the analysis of large sets of blood samples, as well as the difficulties in designing large-scale studies that will likely require participation from multiple and complex funding sources and where data sharing, study designs, and financing must be solved.

摘要

蛋白质组学分析技术的进步显著提高了覆盖度和通量,使得近年来大规模基于人群的人类血浆和血清蛋白质组学研究数量有所增加。多重蛋白质分析方法的改进促进了数千种蛋白质在大动态范围内的定量,这是检测最低范围、潜在最与疾病相关的血液循环蛋白质的关键要求。在本观点中,我们探讨了如何利用人群蛋白质组数据集以及其他同时进行的组学测量来更好地理解可溶性蛋白质组的基因组和非基因组相关性,构建用于疾病预测的生物标志物面板等。我们讨论了质谱工作流程,因为由于仪器和工作流程的进步,它们在速度、成本和蛋白质组覆盖度方面与基于亲和力的阵列平台相比越来越具有竞争力。尽管取得了很大的成功,但仍然存在相当大的挑战,例如正交验证和绝对定量。我们还强调了与研究设计、分析考虑因素和数据集成相关的新兴挑战,因为人群规模的研究是分批进行的,并且可能涉及多年来收集的纵向样本。最后,我们展望了新兴的下一代蛋白质组学技术可能为大量血液样本分析提供的未来,以及设计可能需要来自多个复杂资金来源参与的大型研究的困难,并且必须解决数据共享、研究设计和融资问题。

https://cdn.ncbi.nlm.nih.gov/pmc/blobs/b444/11193116/d9d8b255e42d/ga1.jpg

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