Stable isotope labeling by amino acids in cultured primary neurons
Stable isotope labeling by amino acids in cultured primary neurons
Cultured primary neurons are a well-established model for the study of neuronal function. Conventional stable isotope labeling with amino acids in cell culture (SILAC) requires nearly complete metabolic labeling of proteins and therefore is difficult to apply to cultured primary neurons, which do not divide in culture. Here we describe a protocol that utilizes a multiplex SILAC labeling strategy for primary cultured neurons. In this strategy, two different sets of heavy amino acids are used for labeling cells for the different experimental conditions. This allows for a straightforward SILAC quantitation using partially labeled cells because the two cell populations are always equally labeled.
57-64
Zhang, Guoan
fbb6a65f-dba6-43b8-aa9b-9a95f8c8a979
Deinhardt, Katrin
5f4fe23b-2317-499f-ba6d-e639a4885dc1
Neubert, Thomas A
1bc0908c-3b8e-490a-a680-5779601d2618
8 June 2014
Zhang, Guoan
fbb6a65f-dba6-43b8-aa9b-9a95f8c8a979
Deinhardt, Katrin
5f4fe23b-2317-499f-ba6d-e639a4885dc1
Neubert, Thomas A
1bc0908c-3b8e-490a-a680-5779601d2618
Abstract
Cultured primary neurons are a well-established model for the study of neuronal function. Conventional stable isotope labeling with amino acids in cell culture (SILAC) requires nearly complete metabolic labeling of proteins and therefore is difficult to apply to cultured primary neurons, which do not divide in culture. Here we describe a protocol that utilizes a multiplex SILAC labeling strategy for primary cultured neurons. In this strategy, two different sets of heavy amino acids are used for labeling cells for the different experimental conditions. This allows for a straightforward SILAC quantitation using partially labeled cells because the two cell populations are always equally labeled.
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Published date: 8 June 2014
Organisations:
Biomedicine
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Local EPrints ID: 373263
URI: http://eprints.soton.ac.uk/id/eprint/373263
ISSN: 1064-3745
PURE UUID: 2d1ad130-5e20-4460-b2d2-bec0480b5852
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Date deposited: 13 Jan 2015 13:35
Last modified: 15 Mar 2024 03:45
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Author:
Guoan Zhang
Author:
Thomas A Neubert
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