Department of Chemistry, Vanderbilt University, Nashville, Tennessee 37235, United States.
Department of Neurology, Vanderbilt University Medical Center, Nashville, Tennessee 37232, United States.
J Am Soc Mass Spectrom. 2023 Aug 2;34(8):1741-1752. doi: 10.1021/jasms.3c00179. Epub 2023 Jul 17.
Multiplexing enables the monitoring of hundreds to thousands of proteins in quantitative proteomics analyses and increases sample throughput. In most mass-spectrometry-based proteomics workflows, multiplexing is achieved by labeling biological samples with heavy isotopes via precursor isotopic labeling or isobaric tagging. Enhanced multiplexing strategies, such as combined precursor isotopic labeling and isobaric tagging (cPILOT), combine multiple technologies to afford an even higher sample throughput. Critical to enhanced multiplexing analyses is ensuring that analytical performance is optimal and that missingness of sample channels is minimized. Automation of sample preparation steps and use of quality control (QC) metrics can be incorporated into multiplexing analyses and reduce the likelihood of missing information, thus maximizing the amount of usable quantitative data. Here, we implemented QC metrics previously developed in our laboratory to evaluate a 36-plex cPILOT experiment that encompassed 144 mouse samples of various tissue types, time points, genotypes, and biological replicates. The evaluation focuses on the use of a sample pool generated from all samples in the experiment to monitor the daily instrument performance and to provide a means for data normalization across sample batches. Our results show that tracking QC metrics enabled the quantification of ∼7000 proteins in each sample batch, of which ∼70% had minimal missing values across up to 36 sample channels. Implementation of QC metrics for future cPILOT studies as well as other enhanced multiplexing strategies will help yield high-quality data sets.
多重检测能够实现数以百计到数千种蛋白质的定量蛋白质组学分析，并提高样本通量。在大多数基于质谱的蛋白质组学工作流程中，通过前体同位素标记或等重标记对生物样本进行重同位素标记，从而实现多重检测。增强型多重检测策略，如组合前体同位素标记和等重标记（cPILOT），将多种技术结合在一起，以提供更高的样本通量。增强型多重检测分析的关键是确保分析性能最佳，并且最小化样本通道的缺失。可以将样品制备步骤的自动化和质量控制（QC）指标纳入多重检测分析中，减少信息缺失的可能性，从而最大限度地提高可用定量数据量。在这里，我们实施了我们实验室之前开发的 QC 指标，以评估涵盖各种组织类型、时间点、基因型和生物学重复的 144 个小鼠样本的 36 重 cPILOT 实验。该评估侧重于使用来自实验中所有样本的样本池来监测日常仪器性能，并提供跨样本批次数据归一化的方法。我们的结果表明，跟踪 QC 指标能够在每个样本批次中定量约 7000 种蛋白质，其中多达 70%的蛋白质在多达 36 个样本通道中具有最小的缺失值。未来 cPILOT 研究以及其他增强型多重检测策略的 QC 指标实施将有助于生成高质量数据集。
