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Published on: December 16, 2021
SILAC Mass Spectrometry Profiling: A Psychiatric Disorder Perspective.
Daniella Duque-Guimarães1,2, Thomas Prates Ong1,3, Juliana de Almeida-Faria1,4
1University of Cambridge Metabolic Research Laboratories and MRC Metabolic Diseases Unit, Wellcome Trust-MRC Institute of Metabolic Science, Addenbrooke's Hospital, Cambridge, UK.
Stable isotope labelling by amino acids in cell culture (SILAC) enables proteomic profiling to identify and quantify newly synthesized proteins. This method is applicable to cell models relevant to psychiatric disorders like schizophrenia, affective disorders, and autism spectrum conditions.
Area of Science:
- Biochemistry
- Proteomics
- Cell Biology
Background:
- Stable isotope labelling by amino acids in cell culture (SILAC) is a quantitative proteomic technique.
- Accurate identification and quantification of newly synthesized proteins are crucial for understanding cellular processes.
Purpose of the Study:
- To describe a detailed protocol for identifying and quantifying newly synthesized proteins using SILAC.
- To highlight the applicability of the SILAC methodology to cell systems relevant to neuropsychiatric disorders.
Main Methods:
- Utilizing SILAC for differential labeling of cell populations.
- Employing mass spectrometry for the identification and quantification of labeled peptides.
- Analyzing proteomic data to determine changes in newly synthesized proteins.
Main Results:
- Successful identification and quantification of newly synthesized proteins in cultured cells.
- Demonstration of the protocol's effectiveness in various cell systems.
- Potential for application in studying disease mechanisms and drug responses.
Conclusions:
- The described SILAC protocol provides a robust method for proteomic profiling of newly synthesized proteins.
- This technique is valuable for research into schizophrenia, affective disorders, and autism spectrum conditions.
- The methodology facilitates the investigation of cellular responses to pharmacological stimuli in relevant disease models.
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