Time series proteome profiling to study endoplasmic reticulum stress response.

Area of Science:

• Proteomics
• Cellular Biology
• Biochemistry

Background:

• Time series profiling provides insights into protein expression dynamics and biochemical pathways.
• Historically, protein-level time series data generation has been limited by the lack of robust and reproducible methods.
• mRNA-level profiling (e.g., microarrays) is established, but protein-level dynamics remain less understood.

Purpose of the Study:

• To establish a robust methodology for generating time series data at the protein level.
• To monitor protein expression dynamics within the endoplasmic reticulum (ER) compartment.
• To investigate cellular responses to ER stress inducers.

Main Methods:

• Utilized Stable Isotope Labeling by Amino acids in Cell culture (SILAC) strategy.
• Employed Sodium Dodecyl Sulfate-Polyacrylamide Gel Electrophoresis (SDS-PAGE) for protein separation.
• Applied Liquid Chromatography-Tandem Mass Spectrometry (LC-MS/MS) for protein identification and quantification.
• Leveraged the GeneSpring GX analysis platform for data processing and visualization.

Main Results:

• Successfully monitored expression levels of proteins within the ER compartment over time.
• Demonstrated the ability to track protein changes in human primary fibroblast cells under ER stress (induced by tunicamycin and thapsigargin).
• Enabled data normalization, statistical analysis for significant changes, and cross-dataset comparisons using the GeneSpring GX platform.

Conclusions:

• The developed methodology enables reproducible, time-resolved protein expression profiling.
• This approach overcomes previous limitations in generating protein-level time series data.
• Facilitates deeper understanding of cellular responses, particularly in the context of ER stress and protein dynamics.