A high-throughput method was developed for the automated enrichment of newly synthesized proteins (NSPs), which are labeled metabolically by substituting methionine with the "click-able" analogue azidohomoalanine (AHA). A suitable conjugate containing a dibenzocyclooctyne (DBCO) group allows the specific selection of NSPs by a fast 1 h click chemistry-based reaction with AHA. Through an automated pipetting platform, the samples are loaded into streptavidin cartridges for the selective binding of the NSPs by means of a biotin bait contained in the conjugate. The enriched proteins are eluted by a reproducible chemical cleavage of the 4,4-dimethyl-2,6-dioxocyclohexylidene (Dde) group in the conjugate, which increases selectivity. The NSPs can be collected and digested in the same well plate, and the resulting peptides can be subsequently loaded for automated cleanup, followed by mass spectrometry analysis. The proposed automated method allows for the robust and effective enrichment of samples in 96-well plates in a period of 3 h. Our developed enrichment method was comprehensively evaluated and then applied to the proteomics analysis of the melanoma A375 cell secretome, after treatment with the cytokines interferon α (IFN-α) and γ (IFN-γ), resulting in the quantification of 283 and 263 proteins, respectively, revealing intricate tumor growth-supportive and -suppressive effects.

Acquired resistance to MAPK inhibitors limits the clinical efficacy in melanoma treatment. We and others have recently shown that BRAF inhibitor (BRAFi)-resistant melanoma cells can develop a dependency on the therapeutic drugs to which they have acquired resistance, creating a vulnerability for these cells that can potentially be exploited in cancer treatment. In drug-addicted melanoma cells, it was shown that this induction of cell death was preceded by a specific ERK2-dependent phenotype switch; however, the underlying molecular mechanisms are largely lacking. To increase the molecular understanding of this drug dependency, we applied a mass spectrometry-based proteomic approach on BRAFi-resistant BRAFMUT 451Lu cells, in which ERK1, ERK2, and JUNB were silenced separately using CRISPR-Cas9. Inactivation of ERK2 and, to a lesser extent, JUNB prevents drug addiction in these melanoma cells, while, conversely, knockout of ERK1 fails to reverse this phenotype, showing a response similar to that of control cells. Our analysis reveals that ERK2 and JUNB share comparable proteome responses dominated by reactivation of cell division. Importantly, we find that EMT activation in drug-addicted melanoma cells upon drug withdrawal is affected by silencing ERK2 but not ERK1. Moreover, transcription factor (regulator) enrichment shows that PIR acts as an effector of ERK2 and phosphoproteome analysis reveals that silencing of ERK2 but not ERK1 leads to amplification of GSK3 kinase activity. Our results depict possible mechanisms of drug addiction in melanoma, which may provide a guide for therapeutic strategies in drug-resistant melanoma.

Neuronal development is a complex multistep process that shapes neurons by progressing though several typical stages, including axon outgrowth, dendrite formation, and synaptogenesis. Knowledge of the mechanisms of neuronal development is mostly derived from the study of animal models. Advances in stem cell technology now enable us to generate neurons from human induced pluripotent stem cells (iPSCs). Here we provide a mass spectrometry-based quantitative proteomic signature of human iPSC-derived neurons, i.e., iPSC-derived induced glutamatergic neurons and iPSC-derived motor neurons, throughout neuronal differentiation. Tandem mass tag 10-plex labeling was carried out to perform proteomic profiling of cells at different time points. Our analysis reveals significant expression changes (FDR < 0.001) of several key proteins during the differentiation process, e.g., proteins involved in the Wnt and Notch signaling pathways. Overall, our data provide a rich resource of information on protein expression during human iPSC neuron differentiation.

Intestinal ischemia-reperfusion (IR) injury is a severe clinical condition, and unraveling its pathophysiology is crucial to improve therapeutic strategies and reduce the high morbidity and mortality rates. Here, we studied the dynamic proteome and phosphoproteome in the human intestine during ischemia and reperfusion, using liquid chromatography-tandem mass spectrometry (LC-MS/MS) analysis to gain quantitative information of thousands of proteins and phosphorylation sites, as well as mass spectrometry imaging (MSI) to obtain spatial information. We identified a significant decrease in abundance of proteins related to intestinal absorption, microvillus, and cell junction, whereas proteins involved in innate immunity, in particular the complement cascade, and extracellular matrix organization increased in abundance after IR. Differentially phosphorylated proteins were involved in RNA splicing events and cytoskeletal and cell junction organization. In addition, our analysis points to mitogen-activated protein kinase (MAPK) and cyclin-dependent kinase (CDK) families to be active kinases during IR. Finally, matrix-assisted laser desorption ionization time-of-flight (MALDI-TOF) MSI presented peptide alterations in abundance and distribution, which resulted, in combination with Fourier-transform ion cyclotron resonance (FTICR) MSI and LC-MS/MS, in the annotation of proteins related to RNA splicing, the complement cascade, and extracellular matrix organization. This study expanded our understanding of the molecular changes that occur during IR in the human intestine and highlights the value of the complementary use of different MS-based methodologies.

The increased speed and sensitivity in mass spectrometry-based proteomics has encouraged its use in biomedical research in recent years. Large-scale detection of proteins in cells, tissues, and whole organisms yields highly complex quantitative data, the analysis of which poses significant challenges. Standardized proteomic workflows are necessary to ensure automated, sharable, and reproducible proteomics analysis. Likewise, standardized data processing workflows are also essential for the overall reproducibility of results. To this purpose, we developed PaDuA, a Python package optimized for the processing and analysis of (phospho)proteomics data. PaDuA provides a collection of tools that can be used to build scripted workflows within Jupyter Notebooks to facilitate bioinformatics analysis by both end-users and developers.