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Lozano-Juarez, S.

Publications and source records attributed to Lozano-Juarez, S..

2 recordsLinked to original sources

The Human Cell Line Phosphoproteome Atlas: A Deep Empirical Resource Revealing Kinase Activity Landscapes

Protein phosphorylation orchestrates cellular signaling and controls diverse biological processes, with its dysregulation driving diseases, notably cancer. Comprehensive, high-throughput phosphoproteomics remains limited by detection sensitivity, data completeness, and computational bottlenecks, especially in low-input settings. We present the deepest empirical human phosphoproteome resource to date, regrouping over 200,000 class I phosphosites across 33 diverse human cell lines, and demonstrate that this spectral library dramatically improves single-shot phosphoproteomics with 30-fold faster data processing compared to library-free approaches and enhanced confidence in phosphosite localization even from minimal sample input. Integrating proteome and phosphoproteome data, we developed a combined kinase activity score, revealing cell line- and cancer-specific signaling vulnerabilities, many correlating with drug sensitivity. This resource accelerates deep, reproducible phosphoproteomics, enabling systematic functional mapping of cellular signaling networks, and empowers precision oncology by highlighting actionable kinase targets in diverse cell states.

biochemistry↗

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↗