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

Abstract

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 introduce 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. Second, we introduce a novel PSM-level normalization, applicable to data with and without common reference channel. It is a weighted medianbased method, in which the weights reflect the number of ions that were used for fragmentation. On a typical dataset, we observe complete removal of batch effects and dominance of the biological sample grouping after normalization. Furthermore, we provide many novel processing and normalization options in Perseus, the companion software for the downstream analysis of quantitative proteomics results. All novel tools and algorithms are available with the regular MaxQuant and Perseus releases, which are downloadable at http://maxquant.org.

The rise of single‐cell proteomics

“…MBR was recently extended from label‐free analysis to isobaric labeled experiments and is integrated in the newest MaxQuant version. Isobaric MBR transfers identifications after recalibration of mass and RT via 3D MS1 features, to MS/MS spectra that were previously not identified and then uses their reporter ion intensities for quantification 89 . MBR is exceptionally effective in boosting identification numbers and overcome stochastic sampling in DDA.…”

Analyzing Ribosome Remodeling in Health and Disease

“…[104] This problem makes it more difficult to compare RP abundance across different conditions. Advances relying on matching peptide intensity readouts, that is, MaxLFQ, [105] and on enhanced peptide identifications via DIA [94][95][96] or DDA methods incorporating retention time information [77,106,107] can mitigate the missing value problem when using label-free approaches.…”

Expanding the Perseus Software for Omics Data Analysis With Custom Plugins

“…The samples for the example of UMAP analysis can be downloaded at PRIDE (PXD003710) (Bailey, McDevitt, Westphall, Pagliarini, & Coon, 2014). Additionally, the MaxQuant (Cox & Mann, 2008;Sinitcyn, Rudolph, & Cox, 2018;Yu, Kiriakidou, & Cox, 2020) proteinGroup table of this dataset is also provided at https:// github.com/ JurgenCox/ perseus-plugin-programming/ tree/ master/ dataset. The values are normalized and transformed by logarithm, and the unreliable protein groups (reversed, only identified by site, contaminant, containing more than 30%…”

“…MaxQuant is one of the most commonly used software applications for mass‐spectrometry‐based proteomics data analysis. It can support numerous types of labeling strategies and MS platforms (Cox et al., 2014; Tyanova, Mann, & Cox, 2014; Yu et al., 2020). Moreover, different quantification methods, false‐discovery rate control, and visualization are also provided in MaxQuant.…”