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芯片蛋白质组学——微流控技术与蛋白质组学的交汇

Proteomics-on-a-Chip - Microfluidics meets proteomics.

作者信息

Zhang Haoqing, Zhao Lei, Brodský Jan, Migliaccio Ludovico, Gablech Imrich, Neužil Pavel, You Minli

机构信息

The Key Laboratory of Biomedical Information Engineering of the Ministry of Education, School of Life Science and Technology, Xi'an Jiaotong University, Xi'an, Shaanxi, 710049, PR China; Bioinspired Engineering and Biomechanics Center (BEBC), Xi'an Jiaotong University, Xi'an, 710049, PR China; TFX Group-Xi'an Jiaotong University Institute of Life Health, Xi'an 710049, PR China.

The Key Laboratory of Biomedical Information Engineering of the Ministry of Education, School of Life Science and Technology, Xi'an Jiaotong University, Xi'an, Shaanxi, 710049, PR China; Bioinspired Engineering and Biomechanics Center (BEBC), Xi'an Jiaotong University, Xi'an, 710049, PR China.

出版信息

Biosens Bioelectron. 2025 Apr 1;273:117122. doi: 10.1016/j.bios.2024.117122. Epub 2025 Jan 9.

DOI:10.1016/j.bios.2024.117122
PMID:39813764
Abstract

Proteomics provides an understanding of biological systems by enabling the detailed study of protein expression profiles, which is crucial for early disease diagnosis. Microfluidic-based proteomics enhances this field by integrating complex proteome analysis into compact and efficient systems. This review focuses on developing microfluidic chip structures for proteomics, covering on-chip sample pretreatment, protein extraction, purification, and identification in recent years. Furthermore, our work aims to inspire researchers to select proper methodologies in designing novel, efficient assays for proteomics applications by analyzing trends and innovations in this field.

摘要

蛋白质组学通过对蛋白质表达谱进行详细研究,为理解生物系统提供了途径,这对疾病的早期诊断至关重要。基于微流控技术的蛋白质组学通过将复杂的蛋白质组分析整合到紧凑高效的系统中,进一步推动了该领域的发展。本文综述聚焦于近年来用于蛋白质组学的微流控芯片结构的发展,涵盖芯片上的样品预处理、蛋白质提取、纯化及鉴定。此外,我们的工作旨在通过分析该领域的发展趋势和创新成果,启发研究人员在设计新颖、高效的蛋白质组学应用检测方法时选择合适的方法。

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引用本文的文献

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Progress and trends on machine learning in proteomics during 1997-2024: a bibliometric analysis.1997 - 2024年蛋白质组学中机器学习的进展与趋势:文献计量分析
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