Improved identification of proteoforms using FAIMS with internal CV stepping in top-down proteomics

In top-down (TD) proteomics, fractionation prior to mass spectrometric (MS) analysis is a crucial step for the high confidence identification of proteoforms and increased sample depth. 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, the need for multiple injections using different compensation voltages (CV) leads to a huge increase in measurement time and the amount of sample required. Therefore, we here investigated for the first time the use of internal CV stepping for single shot TD analysis, i.e., the application of multiple CVs per acquisition. In addition, MS parameters were optimized for the individual CVs since different CVs target certain mass ranges. For example, small proteoforms identified mainly with lower CVs can be identified with a lower resolution and number of microscans than larger proteins identified mainly with higher 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 by 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 2,675 proteoforms from CaCo-2 cells in less than 24 hours using the optimized multi-CV method.

在自上而下蛋白质组学（top-down (TD) proteomics）中，质谱（mass spectrometric (MS)）分析前的分级处理是实现高置信度蛋白质型（proteoforms）鉴定并提升样品覆盖深度的关键步骤。除液相分离策略外，诸如场非对称离子迁移谱（field asymmetric ion mobility spectrometry (FAIMS)）这类气相分级手段已被证实可显著助力自上而下蛋白质组学研究。然而，采用不同补偿电压（compensation voltages (CV)）开展多次进样的需求，会大幅延长检测时长并增加样品消耗量。因此，本研究首次探索了单次进样自上而下分析中的内部CV步进应用，即单次采集过程中施加多种补偿电压。此外，针对不同CV优化了质谱参数，这是由于不同CV会靶向特定的质量范围。例如，主要在低CV下被鉴定的小分子蛋白质型，相较于主要在高CV下检测的大分子蛋白质，可采用更低的分辨率与微扫描次数完成分析。本研究探究了适配不同梯度时长的最优CV组合与数量，并通过采用不同样品制备技术获取的Caco-2细胞低分子量蛋白质组，对优化后的参数设置进行了验证。相较于未使用FAIMS的检测组，本方法不仅使鉴定得到的蛋白质群组（+60%~94%）与蛋白质型（+46%~127%）的数量及其置信度均得到显著提升，同时检测时长保持不变。借助优化后的多CV方法，本研究在24小时内从Caco-2细胞中共鉴定到684个蛋白质群组与2675个蛋白质型。