A facile, controllable and environmentally friendly method for fabricating a novel polysaccharide–silica composite stationary phase (SiO₂@(HA-CS)₁₂) was developed in this report. Two natural polysaccharides (hyaluronan acid and chitosan) were controllably coated on the silica surface using a layer-by-layer assembly technique, and then the polysaccharide shell was chemically cross-linked to improve the stability. The column efficiency of the SiO₂@(HA-CS)₁₂ column reached 74 000 plates per m in HILIC mode and 20 100 plates per m in IEC mode, which indicates great potential for separating polar and charged samples.

This study presents an advanced approach for the comprehensive analysis of low-abundance proteins in soybean seeds, addressing challenges posed by high-abundance storage proteins. We compared the effectiveness of Data-Dependent Acquisition (DDA), Data-Independent Acquisition (DIA), and BoxCar mass spectrometry techniques to identify low-abundance proteins in two types of soybean seeds: High-Oil and High-Protein seeds. The results indicate that the DIA method, and particularly the BoxCar methods, significantly improve the detection of low-abundance proteins compared to DDA, offering deeper insights into soybean seed biology. Specifically, BoxCar-based analysis revealed distinct proteomic differences between High-Oil and High-Protein seeds, highlighting more active metabolic processes in High-Oil seeds. Additionally, several key proteins were identified and annotated as uniquely expressed in either High-Oil or High-Protein seeds. These findings emphasize the importance of advanced proteomic techniques, such as BoxCar, in deepening our understanding of soybean seed biology and supporting breeding strategies to improve nutritional qualities.

Efficient and reproducible protein extraction is a critical prerequisite for high-quality proteomic and glycoproteomic analyses. In this study, four commonly used lysis buffers, sodium dodecyl sulfate (SDS), guanidine hydrochloride (GuHCl), urea (UA), and mammalian protein extraction reagent (MPER), were systematically evaluated within an integrated proteomic and N-glycoproteomic workflow. Using HeLa and HEK293T cells as model systems, we assessed buffer performance in terms of protein and intact N-glycopeptide identification depth, quantitative reproducibility, subcellular coverage, and glycan type distribution. Across both cell lines, SDS consistently achieved the deepest proteome and N-glycoproteome coverage, yielding the highest numbers of identified proteins, N-glycopeptides, glycoproteins, and glycosylation sites. Quantitative analysis demonstrated that SDS provided superior reproducibility, with approximately 85% of quantified proteins exhibiting coefficients of variation below 5%. Subcellular localization analysis at the global proteome level showed that SDS enabled more comprehensive extraction of proteins from multiple cellular compartments, including the nucleus, cytoplasm, mitochondria, and plasma membrane, indicating reduced extraction bias toward specific subcellular regions. Consistently, subcellular localization analysis of identified glycoproteins revealed enhanced coverage of membrane-associated compartments, particularly the plasma membrane, endoplasmic reticulum, Golgi apparatus, and lysosome. In addition, the analysis of glycan type classification for intact N-glycopeptides revealed that the SDS lysis buffer demonstrated the most comprehensive identification capability for glycopeptides with multiple glycosylation modifications in both cell lines. MPER and UA showed a highly consistent distribution across various glycosylation types, whereas the guanidine hydrochloride method was comparatively least effective. Overall, these results establish SDS as a robust lysis buffer for comprehensive, reproducible, and quantitatively stable proteomic and N-glycoproteomic analyses, providing practical guidance for buffer selection in quantitative glycosylation-focused studies.