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Neuropeptide Characterization Workflow from Sampling to Data-Independent Acquisition Mass Spectrometry
Samuel Okyem1, Yanqi Tan1, Elena Romanova1
1Department of Chemistry, University of Illinois Urbana-Champaign; Beckman Institute for Advanced Science and Technology, University of Illinois Urbana-Champaign.
Journal of Visualized Experiments : Jove
|August 25, 2025
Summary
Analyzing brain neuropeptides is challenging due to low abundance and complex processing. This study presents a mass spectrometry workflow using data-dependent acquisition (DDA) and data-independent acquisition (DIA) for improved neuropeptide detection and quantification in rat brains.
Area of Science:
- Neuroscience
- Biochemistry
- Analytical Chemistry
Background:
- Endogenous neuropeptides are crucial for brain function, regulating behavior, stress, pain, and homeostasis.
- Analyzing neuropeptides is technically challenging due to their low abundance, rapid degradation, variable processing, and sparse signals in mass spectrometry.
- Existing methods struggle with the dynamic range and post-translational modifications of neuropeptides.
Purpose of the Study:
- To develop and validate a comprehensive mass spectrometry (MS)-based workflow for analyzing neuropeptides in Rattus norvegicus brain tissue.
- To enhance the sensitivity, accuracy, and reproducibility of neuropeptide detection and quantification.
- To provide a robust platform for studying neuropeptide expression and function in complex biological samples.
Main Methods:
- Utilized a timsTOF mass spectrometry platform employing both data-dependent acquisition (DDA) and data-independent acquisition (DIA).
- Implemented optimized sample preparation protocols including dissection, peptide extraction, and clean-up.
- Performed nano liquid chromatography (LC)-MS with ion mobility gas-phase fractionation for improved detection.
- Leveraged DDA-generated spectral libraries for DIA-based quantification in Skyline for high-confidence MS2-level measurements.
Main Results:
- The integrated DDA-DIA workflow significantly increased neuropeptide coverage in rat brain tissue.
- Enhanced quantitative reproducibility was achieved compared to conventional methods.
- High-confidence MS2-level measurements were enabled through spectral library support.
Conclusions:
- The developed workflow offers a robust and sensitive platform for comprehensive neuropeptide analysis in complex brain tissue.
- This method overcomes previous technical limitations in neuropeptide detection and quantification.
- The protocol facilitates deeper insights into the roles of neuropeptides in brain function and disease.
