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Published on: February 27, 2020
A fully automated FAIMS-DIA mass spectrometry-based proteomic pipeline
Luke Reilly1, Erika Lara1, Daniel Ramos1
1Center for Alzheimer's and Related Dementias (CARD), National Institute on Aging and National Institute of Neurological Disorders and Stroke, National Institutes of Health, Bethesda, MD, USA.
This study introduces a streamlined proteomics pipeline for deep cellular proteome coverage. The automated workflow, using FAIMS-DIA and Spectronaut, identifies over 9,000 proteins in iPSC-derived neurons with high accuracy and reproducibility.
Area of Science:
- Proteomics
- Mass Spectrometry
- Cellular Biology
Background:
- Deep proteome coverage is crucial for understanding cellular function.
- High-throughput and scalable proteomics methods are needed for comprehensive analysis.
- Data-independent acquisition (DIA) offers advantages for proteome profiling.
Purpose of the Study:
- To develop and validate a standardized, high-throughput proteomics pipeline.
- To optimize DIA strategies for deep cellular proteome coverage.
- To evaluate the performance of FAIMS-DIA and Spectronaut for proteomic analysis.
Main Methods:
- An integrated, automated sample preparation platform.
- Data-independent acquisition (DIA) coupled with High-Field Asymmetric Waveform Ion Mobility Spectrometry (FAIMS).
- Library-free DIA database search using Spectronaut.
Main Results:
- Single compensation voltage (CV) at -35 V with FAIMS-DIA achieved the deepest proteome coverage.
- Spectronaut demonstrated superior performance in direct-DIA database searching, quantifying the most proteins.
- The optimized FAIMS-DIA method identified >9,000 proteins in iPSC-derived neurons with <10% missing values, showing high reproducibility and accuracy.
Conclusions:
- The developed "off-the-shelf" proteomics pipeline enables deep and scalable cellular proteome analysis.
- FAIMS-DIA with a single CV setting and Spectronaut is a powerful combination for high-throughput proteomics.
- This method offers superior performance compared to existing DIA strategies for complex biological samples.
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