Data analysis is a critical part of quantitative proteomics studies in interpreting biological questions. Numerous computational tools for protein quantification, imputation and differential expression (DE) analysis were generated in the past decade and the search for optimal tools is still going on. Moreover, due to the rapid development of RNA sequencing (RNA-seq) technology, a vast number of DE analysis methods were created for that purpose. The applicability of these newly developed RNA-seq-oriented tools to proteomics data remains in doubt. In order to benchmark these analysis methods, a proteomics dataset consisting of proteins derived from humans, yeast and drosophila, in defined ratios, was generated in this study. Based on this dataset, DE analysis tools, including microarray- and RNA-seq-based ones, imputation algorithms and protein quantification methods were compared and benchmarked. Furthermore, applying these approaches to two public datasets showed that RNA-seq-based DE tools achieved higher accuracy (ACC) in identifying DEPs. This study provides useful guidelines for analyzing quantitative proteomics datasets. All the methods used in this study were integrated into the Perseus software, version 2.0.3.0, which is available at https://www.maxquant.org/perseus.

Benchmarking differential expression, imputation and quantification methods for proteomics data

Quantitative proteomics reveals common and unique molecular mechanisms underlying beneficial effects of caffeine and trigonelline on human hepatocytes.

Mutations in KRAS are common drivers of human cancers and are often those with the poorest overall prognosis for patients. A recently developed compound, MRTX1133, has shown promise in inhibiting the activity of KRASG12D mutant proteins, which is one of the main drivers of pancreatic cancer. To better understand the mechanism of action of this compound, I performed both proteomics and metabolomics on four KRASG12D mutant pancreatic cancer cell lines. To obtain increased granularity in the proteomic observations, single-cell proteomics was successfully performed on two of these lines. Following quality filtering, a total of 1498 single cells were analyzed. From these cells, 3140 total proteins were identified with approximately 953 proteins quantified per cell. At 48 h of treatment, two distinct populations of cells can be observed based on the level of effectiveness of the drug in decreasing the total abundance of the KRAS protein in each respective cell, with results that are effectively masked in the bulk cell analysis. All mass spectrometry data and processed results are publicly available at www.massive.ucsd.edu at accessions PXD039597, PXD039601, and PXD039600.

Metabolomic, Proteomic, and Single-Cell Proteomic Analysis of Cancer Cells Treated with the KRASG12D Inhibitor MRTX1133

Genetic germline variants of PPP2R5D (encoding: phosphoprotein phosphatase 2 regulatory protein 5D) result in PPP2R5D-related disorder (Jordan's Syndrome), which is characterized by intellectual disability, hypotonia, seizures, macrocephaly, autism spectrum disorder, and delayed motor skill development. The disorder originates from de novo single nucleotide mutations, generating missense variants that act in a dominant manner. Pathogenic mutations altering 13 different amino acids have been identified, with the E198K variant accounting for ∼40% of reported cases. However, the generation of a heterozygous E198K variant cell line to study the molecular effects of the pathogenic mutation has been challenging. Here, we use CRISPR-PRIME genomic editing to introduce a transition (c.592G>A) in a single PPP2R5D allele in HEK293 cells, generating E198K-heterozygous lines to complement existing E420K variant lines. We generate global protein and phosphorylation profiles of WT, E198K, and E420K cell lines and find unique and shared changes between variants and WT cells in kinase- and phosphatase-controlled signaling cascades. We observed ribosomal protein S6 (RPS6) hyperphosphorylation as a shared signaling alteration, indicative of increased ribosomal protein S6-kinase activity. Treatment with rapamycin or an RPS6-kinase inhibitor (LY2584702) suppressed RPS6 phosphorylation in both, suggesting upstream activation of mTORC1/p70S6K. Intriguingly, our data suggests ERK-dependent activation of mTORC1 in both E198K and E420K variant cells, with additional AKT-mediated mTORC1 activation in the E420K variant. Thus, although upstream activation of mTORC1 differs between PPP2R5D-related disorder genotypes, inhibition of mTORC1 or RPS6 kinases warrants further investigation as potential therapeutic strategies for patients. 

Quantitative proteomics and phosphoproteomics of PP2A-PPP2R5D variants reveal deregulation of RPS6 phosphorylation via converging signaling cascades

http://www.mcponline.org/article/S1535947623001251/pdf

Inducible protein degradation as a strategy to identify Phosphoprotein Phosphatase 6 substrates in RAS-mutant colorectal cancer cells.

The Perseus software provides a comprehensive framework for the statistical analysis of large-scale quantitative proteomics data, also in combination with other omics dimensions. Rapid developments in proteomics technology and the ever-growing diversity of biological studies increasingly require the flexibility to incorporate computational methods designed by the user. Here, we present the new functionality of Perseus to integrate self-made plugins written in C#, R, or Python. The user-written codes will be fully integrated into the Perseus data analysis workflow as custom activities. This also makes language-specific R and Python libraries from CRAN (cran.r-project.org), Bioconductor (bioconductor.org), PyPI (pypi.org), and Anaconda (anaconda.org) accessible in Perseus. The different available approaches are explained in detail in this article. To facilitate the distribution of user-developed plugins among users, we have created a plugin repository for community sharing and filled it with the examples provided in this article and a collection of already existing and more extensive plugins. © 2020 The Authors. Basic Protocol 1: Basic steps for R plugins Support Protocol 1: R plugins with additional arguments Basic Protocol 2: Basic steps for python plugins Support Protocol 2: Python plugins with additional arguments Basic Protocol 3: Basic steps and construction of C# plugins Basic Protocol 4: Basic steps of construction and connection for R plugins with C# interface Support Protocol 4: Advanced example of R Plugin with C# interface: UMAP Basic Protocol 5: Basic steps of construction and connection for python plugins with C# interface Support Protocol 5: Advanced example of python plugin with C# interface: UMAP Support Protocol 6: A basic workflow for the analysis of label-free quantification proteomics data using perseus.

Expanding the Perseus Software for Omics Data Analysis With Custom Plugins.

https://doi.org/10.1016/j.mcpro.2022.100460

MS-based Detection of Pathogen Antigen Through Immunoprecipitation of Its Proteolytic Peptides

ProteoPlotter: An Executable Proteomics Visualization Tool Compatible with Perseus.

We introduce Metis, a new plugin for the Perseus software aimed at analyzing quantitative multi-omics data based on metabolic pathways. Data from different omics types are connected through reactions of a genome-scale metabolic-pathway reconstruction. Metabolite concentrations connect through the reactants, while transcript, protein, and protein post-translational modification (PTM) data are associated through the enzymes catalyzing the reactions. Supported experimental designs include static comparative studies and time-series data. As an example for the latter, we combine circadian mouse liver multi-omics data and study the contribution of cycles of phosphoproteome and metabolome to enzyme activity regulation. Our analysis resulted in 52 pairs of cycling phosphosites and metabolites connected through a reaction. The time lags between phosphorylation and metabolite peak show non-uniform behavior, indicating a major contribution of phosphorylation in the modulation of enzymatic activity. 

https://doi.org/10.1016/j.crmeth.2022.100198

Perseus plugin “Metis” for metabolic-pathway-centered quantitative multi-omics data analysis for static and time-series experimental designs

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Expanding the Perseus Software for Omics Data Analysis With Custom Plugins.

MS-based Detection of Pathogen Antigen Through Immunoprecipitation of Its Proteolytic Peptides

ProteoPlotter: An Executable Proteomics Visualization Tool Compatible with Perseus.

Perseus plugin “Metis” for metabolic-pathway-centered quantitative multi-omics data analysis for static and time-series experimental designs