​​With continuous advancements in mass spectrometry (MS), improved separation efficiency in liquid chromatography (LC), and increasingly sophisticated bioinformatics pipelines, MS-based proteomics has achieved large-scale, high-depth protein identification. It has become an essential platform for elucidating disease mechanisms, discovering drug targets, and developing clinical biomarkers [1]. However, a critical bottleneck remains at the sample preparation stage efficient extraction of proteins from complex biological samples and the generation of high-quality peptide digests which directly determines the sensitivity and reliability of downstream MS analysis [2].

Traditional sample preparation methods, such as in-solution digestion, in-gel digestion, and filter-aided sample preparation (FASP), are often labor-intensive and suffer from significant surface adsorption and high sample loss (>30%), resulting in limited proteome coverage (typically <60%). In addition, these workflows require relatively large amounts of starting material (>100 μg) and prolonged digestion times (12–24 hours), posing major challenges for low-input clinical samples and high-throughput studies.