Deep Proteome Coverage of Microglia Using a Streamlined Data-Independent Acquisition-Based Proteomic Workflow: Method...

Area of Science:

• Neuroscience
• Immunology
• Proteomics

Background:

• Microglia are essential innate immune cells in the brain, crucial for homeostasis and disease.
• Microglial functions are linked to diverse phenotypic states, best represented by their proteomes.
• Proteomic analysis of microglia is challenging due to their complexity and limited sample availability.

Purpose of the Study:

• To develop a streamlined, reproducible proteomic method for analyzing microglia.
• To create a comprehensive microglial protein library using limited starting material.
• To enhance protein identification and coverage in microglial cell lysates.

Main Methods:

• Implemented a streamlined liquid- and gas-phase fractionation method.
• Utilized data-dependent acquisition (DDA) and parallel accumulation-serial fragmentation (PASEF) on a TIMS-TOF instrument.
• Generated an empirical protein library from immortalized mouse microglia (10 µg input) and compared it with a predicted library for data-independent acquisition (DIA) analysis (200 ng input).

Main Results:

• An empirical library identified 9140 microglial proteins.
• DIA analysis with the empirical library identified an average of 7264 proteins/run.
• Combined empirical and predicted libraries increased coverage to ~8000 proteins, with unique pathways identified by the empirical approach.

Conclusions:

• A simplified, reproducible proteomic strategy effectively analyzes microglial proteome complexity with low sample input.
• Library optimization is critical for maximizing protein identification in phenotypically diverse microglia.
• This method facilitates deeper understanding of microglial roles in neurological health and disease.