Improved Data Acquisition Settings on Q Exactive HF-X and Fusion Lumos Tribrid Orbitrap-Based Mass Spectrometers for Proteomic Analysis of Limited Samples.
Michal GregušAntonius KollerSomak RayAlexander R IvanovPublished in: Journal of proteome research (2024)
Deep proteomic profiling of complex biological and medical samples available at low nanogram and subnanogram levels is still challenging. Thorough optimization of settings, parameters, and conditions in nanoflow liquid chromatography-tandem mass spectrometry (MS)-based proteomic profiling is crucial for generating informative data using amount-limited samples. This study demonstrates that by adjusting selected instrument parameters, e.g., ion injection time, automated gain control, and minimally altering the conditions for resuspending or storing the sample in solvents of different compositions, up to 15-fold more thorough proteomic profiling can be achieved compared to conventionally used settings. More specifically, the analysis of 1 ng of the HeLa protein digest standard by Q Exactive HF-X Hybrid Quadrupole-Orbitrap and Orbitrap Fusion Lumos Tribrid mass spectrometers yielded an increase from 1758 to 5477 (3-fold) and 281 to 4276 (15-fold) peptides, respectively, demonstrating that higher protein identification results can be obtained using the optimized methods. While the instruments applied in this study do not belong to the latest generation of mass spectrometers, they are broadly used worldwide, which makes the guidelines for improving performance desirable to a wide range of proteomics practitioners.
Keyphrases
- mass spectrometry
- liquid chromatography tandem mass spectrometry
- liquid chromatography
- ultra high performance liquid chromatography
- tandem mass spectrometry
- simultaneous determination
- high resolution
- label free
- ms ms
- high resolution mass spectrometry
- gas chromatography
- healthcare
- high performance liquid chromatography
- electronic health record
- primary care
- solid phase extraction
- multiple sclerosis
- heart failure
- big data
- cell proliferation
- protein protein
- artificial intelligence
- small molecule
- patient reported outcomes
- general practice
- ionic liquid
- acute heart failure
- data analysis