4.8 Article

Improved Identification of Proteoforms in Top-Down Proteomics Using FAIMS with Internal CV Stepping

期刊

ANALYTICAL CHEMISTRY
卷 94, 期 8, 页码 3600-3607

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AMER CHEMICAL SOC
DOI: 10.1021/acs.analchem.1c05123

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  1. Deutsche Forschungsgemeinschaft (DFG) [SFB877, SPP2002]
  2. Cluster of Excellence Precision Medicine in Inflammation (PMI-), RTF-V

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In top-down proteomics, pre-fractionation is crucial for high confidence identification of proteoforms and increased proteome coverage. The use of field asymmetric ion mobility spectrometry (FAIMS) has shown to be beneficial, but only external compensation voltage (CV) stepping has been demonstrated. This study investigated the use of internal CV stepping for single-shot TD analysis and optimized MS parameters for different CVs. The optimized multi-CV method significantly increased the number of identified protein groups and proteoforms without increasing measurement time.
In top-down (TD) proteomics, prefractionation prior to mass spectrometric (MS) analysis is a crucial step for both the high confidence identification of proteoforms and increased proteome coverage. In addition to liquid-phase separations, gas-phase fractionation strategies such as field asymmetric ion mobility spectrometry (FAIMS) have been shown to be highly beneficial in TD proteomics. However, so far, only external compensation voltage (CV) stepping has been demonstrated for TD proteomics, i.e., single CVs were applied for each run. Here, we investigated the use of internal CV stepping (multiple CVs per acquisition) for single-shot TD analysis, which has huge advantages in terms of measurement time and the amount of sample required. In addition, MS parameters were optimized for the individual CVs since different CVs target certain mass ranges. For example, small proteoforms identified mainly with more negative CVs can be identified with lower resolution and number of microscans than larger proteins identified primarily via less negative CVs. We investigated the optimal combination and number of CVs for different gradient lengths and validated the optimized settings with the low-molecular-weight proteome of CaCo-2 cells obtained using a range of different sample preparation techniques. Compared to measurements without FAIMS, both the number of identified protein groups (+60-94%) and proteoforms (+46-127%) and their confidence were significantly increased, while the measurement time remained identical. In total, we identified 684 protein groups and 2675 proteoforms from CaCo-2 cells in less than 24 h using the optimized multi-CV method.

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