Proteomics in ischemic stroke: a decade of advances in LC-MS/MS technology

Proteomics has emerged as a powerful approach for elucidating the complex pathophysiology of ischemic stroke, which remains the leading cause of global disability and mortality. This review brings together the major advances in LC-MS/MS technology between 2015 and 2025, with a clear focus on data-independent acquisition and parallel reaction monitoring. These approaches now allow deep, reproducible proteomic profiling in both animal models and human samples. Over the past decade LC-MS/MS technology has changed dramatically. Automated sample preparation platforms, high-throughput liquid chromatography systems (Evosep One and µPAC), advanced ion-mobility separation techniques (timsTOF and cyclic IM), and next-generation mass analyzers (Orbitrap Astral and ZenoTOF) have fundamentally transformed the field. The shift from traditional data-dependent acquisition to more robust data-independent acquisition and parallel reaction monitoring workflows resulted in superior proteome coverage, enhanced quantitative accuracy, and a substantial reduction in missing values. In animal models, studies based on these workflows have revealed dynamic protein changes in brain tissue, urine, and extracellular vesicles, which are linked to neuroinflammation, oxidative stress, glutamate signaling, and neuroprotection. In human studies using plasma, serum, cerebrospinal fluid, urine, sputum, and thrombi, researchers have identified promising diagnostic biomarkers. These include panels capable of distinguishing ischemic stroke from hemorrhage, predicting recurrence risk, and effectively monitoring treatment response. This review examines the critical role of protein post-translational modifications and evaluates current bioinformatics tools (such as DIA-NN, Spectronaut, and Skyline) that are now essential for robust data processing and multi-omics integration. These substantial advances in LC-MS/MS have significantly deepened our mechanistic understanding of ischemic stroke and accelerated the discovery of clinically useful biomarkers. Translating these findings into precision diagnostics and personalized therapies will require continued standardization, rigorous validation in large cohorts, and closer integration with other omics technologies.