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Optimized protocol for the identification of lipid droplet proteomes using proximity labeling proteomics in cultured human cells

Lipid droplets are endoplasmic reticulum-derived neutral lipid storage organelles that play critical roles in cellular lipid and energy homeostasis. Here, we present a protocol for the identification of high-confidence lipid droplet proteomes in a cell culture model. This approach overcomes limitati...

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Detalles Bibliográficos
Autores principales: Peterson, Clark W.H., Deol, Kirandeep K., To, Milton, Olzmann, James A.
Formato: Online Artículo Texto
Lenguaje:English
Publicado: Elsevier 2021
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8190507/
https://www.ncbi.nlm.nih.gov/pubmed/34151299
http://dx.doi.org/10.1016/j.xpro.2021.100579
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author Peterson, Clark W.H.
Deol, Kirandeep K.
To, Milton
Olzmann, James A.
author_facet Peterson, Clark W.H.
Deol, Kirandeep K.
To, Milton
Olzmann, James A.
author_sort Peterson, Clark W.H.
collection PubMed
description Lipid droplets are endoplasmic reticulum-derived neutral lipid storage organelles that play critical roles in cellular lipid and energy homeostasis. Here, we present a protocol for the identification of high-confidence lipid droplet proteomes in a cell culture model. This approach overcomes limitations associated with standard biochemical fractionation techniques, employing an engineered ascorbate peroxidase (APEX2) to biotinylate endogenous lipid droplet proteins in living cells for subsequent purification and identification by proteomics. For complete details on the use and execution of this protocol, please refer to Bersuker et al. (2018).
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spelling pubmed-81905072021-06-17 Optimized protocol for the identification of lipid droplet proteomes using proximity labeling proteomics in cultured human cells Peterson, Clark W.H. Deol, Kirandeep K. To, Milton Olzmann, James A. STAR Protoc Protocol Lipid droplets are endoplasmic reticulum-derived neutral lipid storage organelles that play critical roles in cellular lipid and energy homeostasis. Here, we present a protocol for the identification of high-confidence lipid droplet proteomes in a cell culture model. This approach overcomes limitations associated with standard biochemical fractionation techniques, employing an engineered ascorbate peroxidase (APEX2) to biotinylate endogenous lipid droplet proteins in living cells for subsequent purification and identification by proteomics. For complete details on the use and execution of this protocol, please refer to Bersuker et al. (2018). Elsevier 2021-06-05 /pmc/articles/PMC8190507/ /pubmed/34151299 http://dx.doi.org/10.1016/j.xpro.2021.100579 Text en © 2021 The Authors https://creativecommons.org/licenses/by-nc-nd/4.0/This is an open access article under the CC BY-NC-ND license (http://creativecommons.org/licenses/by-nc-nd/4.0/).
spellingShingle Protocol
Peterson, Clark W.H.
Deol, Kirandeep K.
To, Milton
Olzmann, James A.
Optimized protocol for the identification of lipid droplet proteomes using proximity labeling proteomics in cultured human cells
title Optimized protocol for the identification of lipid droplet proteomes using proximity labeling proteomics in cultured human cells
title_full Optimized protocol for the identification of lipid droplet proteomes using proximity labeling proteomics in cultured human cells
title_fullStr Optimized protocol for the identification of lipid droplet proteomes using proximity labeling proteomics in cultured human cells
title_full_unstemmed Optimized protocol for the identification of lipid droplet proteomes using proximity labeling proteomics in cultured human cells
title_short Optimized protocol for the identification of lipid droplet proteomes using proximity labeling proteomics in cultured human cells
title_sort optimized protocol for the identification of lipid droplet proteomes using proximity labeling proteomics in cultured human cells
topic Protocol
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8190507/
https://www.ncbi.nlm.nih.gov/pubmed/34151299
http://dx.doi.org/10.1016/j.xpro.2021.100579
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