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Quantitative Label-Free Single-Cell Proteomics on the Orbitrap Astral MS.
Valdemaras Petrosius1, Pedro Aragon-Fernandez1, Tabiwang N Arrey2
1Department of Biotechnology and Biomedicine, Technical University of Denmark, Kgs Lyngby, Denmark.
Enhanced Orbitrap Astral mass spectrometry enables deeper proteome profiling from single-cell proteomics (scp-MS) samples. This advancement improves sensitivity and quantitative accuracy for cellular phenotype insights.
Area of Science:
- Proteomics
- Mass Spectrometry
- Cellular Biology
Background:
- Single-cell proteomics by mass spectrometry (scp-MS) offers deep biological insights but faces sensitivity limitations.
- Current instrumentation struggles with the low signal from single-cell samples, approaching detection boundaries.
Purpose of the Study:
- To evaluate the Orbitrap Astral mass spectrometer's enhanced sensitivity for deep proteome profiling in low-input and single-cell samples.
- To optimize data acquisition methods for maximum sensitivity in scp-MS.
- To assess the quantitative accuracy of measurements from low-input samples.
Main Methods:
- Conducted a comprehensive survey of data acquisition parameters to identify optimal settings for sensitivity.
- Performed quantitative accuracy assessments against known values.
- Generated and analyzed scp-MS data from cultured cell lines and primary bone marrow samples.
- Explored protein covariation to assess the capture of protein complex information.
Main Results:
- The Orbitrap Astral demonstrated enhanced capacity for deep proteome profiling from low-input to single-cell levels.
- Optimized acquisition methods significantly improved sensitivity.
- Quantitative measurements showed good accuracy.
- Differences in proteome coverage were observed across different sample types.
- Protein covariation analysis successfully captured information on known protein complexes.
Conclusions:
- The Orbitrap Astral mass spectrometer significantly advances scp-MS capabilities by enhancing sensitivity and quantitative accuracy.
- This technology facilitates deeper proteome coverage and provides insights into cellular phenotypes and protein complexes from low-input samples.
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