An Inflection Point in High-Throughput Proteomics with Orbitrap Astral: Analysis of Biofluids, Cells, and Tissues.
Nathan G HendricksSantosh D BhosaleAngel J KeoseyanJosselin OrtizAleksandr StotlandSaeed SeyedmohammadChi D L NguyenJonathan T BuiAnnie MoradianSusan M MockusJennifer E Van EykPublished in: Journal of proteome research (2024)
This Technical Note presents a comprehensive proteomics workflow for the new combination of Orbitrap and Astral mass analyzers across biofluids, cells, and tissues. Central to our workflow is the integration of Adaptive Focused Acoustics (AFA) technology for cells and tissue lysis to ensure robust and reproducible sample preparation in a high-throughput manner. Furthermore, we automated the detergent-compatible single-pot, solid-phase-enhanced sample Preparation (SP3) method for protein digestion. The synergy of these advanced methodologies facilitates a robust and high-throughput approach for cell and tissue analysis, an important consideration in translational research. This work disseminates our platform workflow, analyzes the effectiveness, demonstrates the reproducibility of the results, and highlights the potential of these technologies in biomarker discovery and disease pathology. For cells and tissues (heart, liver, lung, and intestine) proteomics analysis by data-independent acquisition mode, identifications exceeding 10,000 proteins can be achieved with a 24 min active gradient. In 200 ng injections of HeLa digest across multiple gradients, an average of more than 80% of proteins have a CV less than 20%, and a 45 min run covers ∼90% of the expressed proteome. This complete workflow allows for large swaths of the proteome to be identified and is compatible with diverse sample types.
Keyphrases
- high throughput
- induced apoptosis
- cell cycle arrest
- mass spectrometry
- gene expression
- electronic health record
- systematic review
- oxidative stress
- randomized controlled trial
- high resolution
- small molecule
- stem cells
- mesenchymal stem cells
- deep learning
- big data
- climate change
- pi k akt
- ms ms
- label free
- data analysis
- anaerobic digestion