BoxCarmax: A High-Selectivity Data-Independent Acquisition Mass Spectrometry Method for the Analysis of Protein Turnover and Complex Samples.
Barbora SalovskaWenxue LiYi DiYansheng LiuPublished in: Analytical chemistry (2021)
The data-independent acquisition (DIA) performed in the latest high-resolution, high-speed mass spectrometers offers a powerful analytical tool for biological investigations. The DIA mass spectrometry (DIA-MS) combined with the isotopic labeling approach holds a particular promise for increasing the multiplexity of DIA-MS analysis, which could assist the relative protein quantification and the proteome-wide turnover profiling. However, the wide MS1 isolation windows employed in conventional DIA methods lead to a limited efficiency in identifying and quantifying isotope-labeled peptide pairs through peptide fragment ions. Here, we optimized a high-selectivity DIA-MS named BoxCarmax that supports the analysis of complex samples, such as those generated from Stable isotope labeling by amino acids in cell culture (SILAC) and pulse SILAC (pSILAC) experiments. BoxCarmax enables multiplexed acquisition at both MS1 and MS2 levels, through the integration of BoxCar and MSX features, as well as a gas-phase separation strategy. We found BoxCarmax significantly improved the quantitative accuracy in SILAC and pSILAC samples by mitigating the ratio suppression of isotope-peptide pairs. We further applied BoxCarmax to measure protein degradation regulation during serum starvation stress in cultured cells, revealing valuable biological insights. Our study offered an alternative and accurate approach for the MS analysis of protein turnover and complex samples.
Keyphrases
- mass spectrometry
- high resolution
- liquid chromatography
- gas chromatography
- multiple sclerosis
- ms ms
- amino acid
- high speed
- high performance liquid chromatography
- capillary electrophoresis
- protein protein
- atomic force microscopy
- big data
- tandem mass spectrometry
- induced apoptosis
- endothelial cells
- blood pressure
- single cell
- electronic health record
- binding protein
- oxidative stress
- machine learning
- single molecule
- cell death
- cell proliferation
- pet imaging
- positron emission tomography