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Biology subjects

Martinez-Val, A.

Publications and source records attributed to Martinez-Val, A..

4 recordsLinked to original sources

The proteomics and phosphoproteomics landscape of melanoma under T cell attack

Understanding how tumor cells interact with tumor-infiltrating lymphocytes (TILs) in the tumor microenvironment (TME) is crucial for identifying targetable immune checkpoints and predictive biomarkers for immunotherapy. While transcriptional responses have been characterized, protein-level changes remain largely unexplored. Here, we used a system reproducing the interaction of TILs with cancer cells occurring in the TME, by co-culturing patient-derived cancer cells with matched autologous TILs at sub-lethal ratios. Using this system, we profiled the early response that cancer (melanoma) cells and TILs activate following autologous T cell attack. To distinguish melanoma from TIL proteomes, we applied stable isotope labeling by amino acids in cell culture (SILAC) combined with Orbitrap Astral-based data-independent acquisition (DIA) mass spectrometry, enabling cell type-specific profiling of protein and phosphorylation dynamics without FACS sorting. This approach also captured the global newly synthesized proteome of the mixed cultures. Our analyses resolved interferon-{gamma}-dependent proteome changes occurring in melanoma cells, identified the cytotoxic and regulatory T-cell molecule (CRTAM) as a selective marker of reactive cytotoxic T lymphocytes, and revealed tumor-intrinsic kinase activation signatures. Among these, multiple DNA damage response-associated kinases were activated during immune attack, suggesting potential therapeutic vulnerabilities. Overall, this framework enables proteomic dissection of tumor-immune interactions and provides a resource for guiding biomarker discovery and therapeutic strategies to improve immunotherapy outcomes.

cancer biology↗

Integrative multi-layer workflow for quantitative analysis of post-translational modifications

Novel algorithms based on ultratolerant database searching have paved the way for comprehensive analysis of all possible post-translational modifications (PTM) that can be detected by mass spectrometry-based proteomics, obviating their prior knowledge. These tools together with novel quantitative statistical models allow hypothesis-free approaches to study the role and impact of PTM on biological systems. However, interpretation of this information from a pathophysiological perspective is challenging due to the huge amounts of PTM data, the existence of chemical, structural, and statistical artifacts and the lack of dedicated tools for their analysis. Here we propose a novel integrative workflow that automatically captures several layers of PTM-related information, including variations in trypsin efficiency, zonal changes, specific PTM changes and hypermodified regions, allowing advanced control of artefacts and coherent and comprehensive interpretation of PTM data. We show the performance of the new workflow by reanalyzing proteomics data from animal models of mitochondrial heteroplasmy and ischemia/reperfusion, revealing relevant PTM information not previously detectable, including consistent detection of novel oxidative modifications in Met and Cys residues from raw proteomics data. The workflow is available through the application PTM-compass.

bioinformatics↗

Protein aggregation capture assisted profiling of the thiol redox proteome

Oxidative damage is critical in various diseases, including cardiovascular and neurological conditions. Thiol redox reactions, acting as oxidative stress sensors, influence protein structure and function. Redox proteomics based on differential alkylation of reduced and oxidized Cys forms using mass spectrometry enables comprehensive analysis of thiol redox status in cells and tissues. We introduce PACREDOX, an innovative redox proteomics approach based on the Protein Aggregation Capture (PAC) protocol and we demonstrate its compatibility with library free data-independent acquisition (DIA). PACREDOX reduces preparation time and costs compared to traditional methods, such as FASILOX, while maintaining thiol and proteome coverage. To enable library-free DIA, we corrected in silico spectral libraries in DIA-NN using experimental retention time data from beta-methylthiol-modified peptides. PACREDOX with DIA quantified 4,000 protein groups and [~]45,000 modified peptides in myocardial tissue from a porcine model of atrial fibrillation, including over 8,000 cysteine-containing peptides, 30% of which were reversibly oxidized. Benchmarking PACREDOX and DIA against FASILOX in a myocardial infarction model reflects the potential and efficiency of this methodology to study oxidative damage. Overall, PACREDOX offers a high-throughput, cost-effective strategy for thiol redox proteome analysis, compatible with label-free quantitative workflows.

systems biology↗

Automated high-throughput biological sex identification from archaeological human dental enamel using targeted proteomics

Biological sex is key information for archaeological and forensic studies, which can be determined by proteomics. However, lack of a standardised approach for fast and accurate sex identification currently limits the reach of proteomics applications. Here, we introduce a streamlined mass spectrometry (MS)-based workflow for determination of biological sex using human dental enamel. Our approach builds on a minimally invasive sampling strategy by acid etching, a rapid online liquid chromatography (LC) gradient coupled to high-resolution parallel reaction monitoring assay allowing for a throughput of 200 samples-per-day with high quantitative performance enabling confident identification of both males and females. Additionally, we have developed a streamlined data analysis pipeline and integrated it into an R-Shiny interface for ease-of-use. The method was first developed and optimised using modern teeth and then validated in an independent set of deciduous teeth of known sex. Finally, the assay was successfully applied to archaeological material, enabling the analysis of over 300 individuals. We demonstrate unprecedented performance and scalability, speeding up MS analysis by tenfold compared to conventional proteomics-based sex identification methods. This work paves the way for large-scale archaeological or forensic studies enabling the investigation of entire populations rather than focusing on individual high-profile specimens.

biochemistry↗