期刊
NATURE COMMUNICATIONS
卷 12, 期 1, 页码 -出版社
NATURE PORTFOLIO
DOI: 10.1038/s41467-021-25546-y
关键词
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资金
- National Research Foundation of Korea [NRF-2018R1A2A3075389, NRF-2016M3A9B6902871, NRF-2017K1A1A2013124, NRF-2021R1A2C200757311, NRF-2020R1C1C1013927]
- KAIST Key Research Institutes Project (Interdisciplinary Research Group)
- Bio and Medical Technology Development Program [NRF-2019M3E5D3073104]
- Institute for Basic Science from the Ministry of Science and ICT of Korea [IBS-R008-D1]
- National Research Foundation of Korea [2016M3A9B6902871] Funding Source: Korea Institute of Science & Technology Information (KISTI), National Science & Technology Information Service (NTIS)
The authors have developed a proximity labeling-based method to selectively label secreted proteins and combined it with proteomics to identify liver secretory proteins in mouse plasma.
Secretory proteins are an essential component of interorgan communication networks that regulate animal physiology. Current approaches for identifying secretory proteins from specific cell and tissue types are largely limited to in vitro or ex vivo models which often fail to recapitulate in vivo biology. As such, there is mounting interest in developing in vivo analytical tools that can provide accurate information on the origin, identity, and spatiotemporal dynamics of secretory proteins. Here, we describe iSLET (in situ Secretory protein Labeling via ER-anchored TurboID) which selectively labels proteins that transit through the classical secretory pathway via catalytic actions of Sec61b-TurboID, a proximity labeling enzyme anchored in the ER lumen. To validate iSLET in a whole-body system, we express iSLET in the mouse liver and demonstrate efficient labeling of liver secretory proteins which could be tracked and identified within circulating blood plasma. Furthermore, proteomic analysis of the labeled liver secretome enriched from liver iSLET mouse plasma is highly consistent with previous reports of liver secretory protein profiles. Taken together, iSLET is a versatile and powerful tool for studying spatiotemporal dynamics of secretory proteins, a valuable class of biomarkers and therapeutic targets. The in vivo identification of proteins secreted from a specific cell type or tissue remains challenging. Here, the authors develop a proximity labeling-based method to selectively label secreted proteins and combine it with proteomics to identify liver secretory proteins in mouse plasma.
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