Isobaric matching between runs and novel PSM-level normalization in MaxQuant strongly improve reporter ion-based quantification - mouse tissue dataset

Isobaric labeling has the promise of combining high sample multiplexing with precise quantification. However, normalization issues and the missing value problem of complete n-plexes hamper quantification across more than one n-plex. Here we applied two novel algorithms implemented in MaxQuant that substantially improve the data analysis with multiple n-plexes. First, isobaric matching between runs (IMBR) makes use of the three-dimensional MS1 features to transfer identifications from identified to unidentified MS/MS spectra between LC-MS runs in order to utilize reporter ion intensities in unidentified spectra for quantification. On typical datasets, we observe a significant gain in quantifiable n-plexes MS/MS spectra that can be used for quantification. Second, we introduce a novel PSM-level normalization, applicable to data with and without common reference channel. It is a weighted median-based method, in which the weights reflect the number of ions that were used for fragmentation. This dataset is TMT 8-plex samples without any referene channels.

同位素标记（isobaric labeling）具备将高样本多重化能力与精确定量相结合的潜力。然而，归一化问题与完整n重标记组（n-plex）的缺失值问题，阻碍了跨多个n重标记组的定量分析流程。
本研究应用了两种集成于MaxQuant软件的新型算法，可显著优化多n重标记组的数据分析工作。其一，运行间同位素匹配（isobaric matching between runs, IMBR）借助三维MS1特征，实现不同液相色谱-质谱（LC-MS）运行批次间已鉴定与未鉴定串联质谱（MS/MS）谱图的鉴定结果迁移，从而可利用未鉴定谱图中的报告离子强度开展定量分析。在典型数据集上，该方法可显著提升可用于定量的n重标记组串联质谱谱图的数量。
其二，本研究提出一种全新的肽谱匹配（Peptide Spectrum Match, PSM）水平归一化方法，该方法可适用于带有或不带通用参考通道的数据集。其本质为基于加权中位数的算法，其中权重反映了用于肽段碎裂的离子数目。
本数据集为不含任何参考通道的TMT 8重标记（Tandem Mass Tag, TMT）样本。