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Related Experiment Video

Updated: Aug 22, 2025

Identification of Protein Interaction Partners in Mammalian Cells Using SILAC-immunoprecipitation Quantitative Proteomics
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Identification of Protein Interaction Partners in Mammalian Cells Using SILAC-immunoprecipitation Quantitative Proteomics

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Using SILAC to Develop Quantitative Data-Independent Acquisition (DIA) Proteomic Methods.

Ellen P Casavant1, Jason Liang1, Sumedh Sankhe1

  • 1Genentech, South San Francisco, CA, USA.

Methods in Molecular Biology (Clifton, N.J.)
|November 12, 2022
PubMed
Summary
This summary is machine-generated.

This study presents a new protocol and script for optimizing quantitative data-independent acquisition (DIA) proteomics, crucial for biomarker discovery. The method uses stable isotope labeling of amino acids in culture (SILAC) for reliable protein quantitation in clinical samples.

Keywords:
Computational analysisData-independent acquisitionProteins quantitationSILAC

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Area of Science:

  • Biochemistry
  • Proteomics
  • Biotechnology

Background:

  • Proteins are vital for biological systems, and their identification and quantification offer insights into cellular processes.
  • Quantitative proteomics enables unbiased surveying of numerous proteins, surpassing traditional antibody-based methods.
  • Data-dependent acquisition (DDA) methods in proteomics have limitations in quantitation due to stochasticity.

Purpose of the Study:

  • To address the computational intensity and lack of guidelines for quantitative data-independent acquisition (DIA) proteomics.
  • To present a protocol and script workflow for optimizing DIA methods.
  • To facilitate the discovery and assessment of biomarkers in clinical samples using DIA proteomics.

Main Methods:

  • Development of a protocol for cell growth and labeling using stable isotope labeling of amino acids in culture (SILAC).
  • Integration of peptide digestion, preparation, and DIA method optimization steps.
  • Creation of a novel script workflow for computational analysis, data visualization, and identification of quantitation thresholds.

Main Results:

  • A comprehensive protocol for quantitative DIA method optimization using SILAC is provided.
  • Novel computational tools and data visualization methods are introduced for analyzing DIA datasets.
  • Guidelines for identifying linear abundance ranges and high-confidence quantitation thresholds are described.

Conclusions:

  • The presented protocol and script workflow enhance the optimization of quantitative DIA methods.
  • This approach improves the reliability and efficiency of protein quantitation in complex biological samples.
  • The developed tools support biomarker discovery and clinical applications of proteomics.