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Updated: May 8, 2025
Isolation of Region-specific Microglia from One Adult Mouse Brain Hemisphere for Deep Single-cell RNA Sequencing
Published on: December 3, 2019
Deep Proteome Coverage of Microglia Using a Streamlined Data-Independent Acquisition-Based Proteomic Workflow: Method
Jessica Wohlfahrt1, Jennifer Guergues1, Stanley M Stevens1
1Department of Molecular Biosciences, University of South Florida, Tampa, FL 33620, USA.
Abstract:
As the primary innate immune cells of the brain, microglia play a key role in various homeostatic and disease-related processes. To carry out their numerous functions, microglia adopt a wide range of phenotypic states. The proteomic landscape represents a more accurate molecular representation of these phenotypes; however, microglia present unique challenges for proteomic analysis. This study implemented a streamlined liquid- and gas-phase fractionation method with data-dependent acquisition (DDA) and parallel accumulation-serial fragmentation (PASEF) analysis on a TIMS-TOF instrument to compile a comprehensive protein library obtained from adult-derived, immortalized mouse microglia with low starting material (10 µg). The empirical library consisted of 9140 microglial proteins and was utilized to identify an average of 7264 proteins/run from single-shot, data-independent acquisition (DIA)-based analysis microglial cell lysate digest (200 ng). Additionally, a predicted library facilitated the identification of 7519 average proteins/run from the same DIA data, revealing complementary coverage compared with the empirical library and collectively increasing coverage to approximately 8000 proteins. Importantly, several microglia-relevant pathways were uniquely identified with the empirical library approach. Overall, we report a simplified, reproducible approach to address the proteome complexity of microglia using low sample input and show the importance of library optimization for this phenotypically diverse cell type.
Insights
Researchers developed a streamlined proteomic method for analyzing microglia, the brain's immune cells. This approach efficiently identifies thousands of proteins from limited samples, aiding the study of microglial function in health and disease.
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.
