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将 LOPIT 与差速超速离心相结合进行高分辨率空间蛋白质组学研究。

Combining LOPIT with differential ultracentrifugation for high-resolution spatial proteomics.

机构信息

Cambridge Centre for Proteomics, Department of Biochemistry, University of Cambridge, 80 Tennis Court Road, Cambridge, CB2 1GA, UK.

Department of Genetics, University of Cambridge, 20 Downing Place, Cambridge, CB2 3EJ, UK.

出版信息

Nat Commun. 2019 Jan 18;10(1):331. doi: 10.1038/s41467-018-08191-w.

DOI:10.1038/s41467-018-08191-w
PMID:30659192
原文链接:https://pmc.ncbi.nlm.nih.gov/articles/PMC6338729/
Abstract

The study of protein localisation has greatly benefited from high-throughput methods utilising cellular fractionation and proteomic profiling. Hyperplexed Localisation of Organelle Proteins by Isotope Tagging (hyperLOPIT) is a well-established method in this area. It achieves high-resolution separation of organelles and subcellular compartments but is relatively time- and resource-intensive. As a simpler alternative, we here develop Localisation of Organelle Proteins by Isotope Tagging after Differential ultraCentrifugation (LOPIT-DC) and compare this method to the density gradient-based hyperLOPIT approach. We confirm that high-resolution maps can be obtained using differential centrifugation down to the suborganellar and protein complex level. HyperLOPIT and LOPIT-DC yield highly similar results, facilitating the identification of isoform-specific localisations and high-confidence localisation assignment for proteins in suborganellar structures, protein complexes and signalling pathways. By combining both approaches, we present a comprehensive high-resolution dataset of human protein localisations and deliver a flexible set of protocols for subcellular proteomics.

摘要

蛋白质定位的研究得益于利用细胞分馏和蛋白质组学分析的高通量方法有了很大的进展。同位素标记细胞器蛋白质的超微定位(hyperLOPIT)是该领域中一种成熟的方法。它可以实现细胞器和亚细胞区室的高分辨率分离,但相对耗时且资源密集。作为一种更简单的替代方法,我们在此开发了同位素标记细胞器蛋白质的差速离心法(LOPIT-DC),并将该方法与基于密度梯度的 hyperLOPIT 方法进行了比较。我们证实,使用差速离心可以获得分辨率高达亚细胞器和蛋白质复合物级别的图谱。hyperLOPIT 和 LOPIT-DC 产生非常相似的结果,有助于鉴定同工型特异性定位,并对亚细胞器结构、蛋白质复合物和信号通路中的蛋白质进行高置信度定位分配。通过结合这两种方法,我们提供了一个全面的人类蛋白质定位的高分辨率数据集,并提供了一套用于亚细胞蛋白质组学的灵活方案。

https://cdn.ncbi.nlm.nih.gov/pmc/blobs/a2ea/6338729/4dca47a4febc/41467_2018_8191_Fig7_HTML.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/a2ea/6338729/a6b95f494ec5/41467_2018_8191_Fig1_HTML.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/a2ea/6338729/77432380be5f/41467_2018_8191_Fig2_HTML.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/a2ea/6338729/93d0f49d1710/41467_2018_8191_Fig3_HTML.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/a2ea/6338729/cd3c679a1ff6/41467_2018_8191_Fig4_HTML.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/a2ea/6338729/4ac64c48a19f/41467_2018_8191_Fig5_HTML.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/a2ea/6338729/cad30b2c784f/41467_2018_8191_Fig6_HTML.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/a2ea/6338729/4dca47a4febc/41467_2018_8191_Fig7_HTML.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/a2ea/6338729/a6b95f494ec5/41467_2018_8191_Fig1_HTML.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/a2ea/6338729/77432380be5f/41467_2018_8191_Fig2_HTML.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/a2ea/6338729/93d0f49d1710/41467_2018_8191_Fig3_HTML.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/a2ea/6338729/cd3c679a1ff6/41467_2018_8191_Fig4_HTML.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/a2ea/6338729/4ac64c48a19f/41467_2018_8191_Fig5_HTML.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/a2ea/6338729/cad30b2c784f/41467_2018_8191_Fig6_HTML.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/a2ea/6338729/4dca47a4febc/41467_2018_8191_Fig7_HTML.jpg

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3
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4
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5
Tumor-derived exosomes and their application in cancer treatment.肿瘤衍生的外泌体及其在癌症治疗中的应用。
J Transl Med. 2025 Jul 8;23(1):751. doi: 10.1186/s12967-025-06814-7.
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