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.

Ensuring robust quality control (QC) remains a major challenge in quantitative proteomics, particularly in detecting and managing outliers. Deep learning offers powerful representational capacity for ultra-high-dimensional data but often suffers from overfitting in small-sample scenarios. To address this, we propose memory-constrained outlier detection (MCOD), a deep anomaly detection framework that directly processes MaxQuant outputs and achieves competitive recall at high precision levels, which suggests a reduced risk of overlooking true outliers. MCOD integrates two innovations: (i) a memory-constrained module (MC module) that mitigates over-representation of samples via prototype-based regularization, and (ii) an adaptive steady-aware regulator that dynamically adjusts the per-sample loss weights in the MC module according to the estimated overfitting risk. Across two simulation settings based on a human cervical cancer cell line (HeLa) proteomics dataset and two real-world cancer proteomics datasets, MCOD consistently outperformed 18 statistical, machine learning, and deep learning baselines, achieving superior area under the receiver operating characteristic curve and area under the precision-recall curve scores. Functional enrichment analyses on the two real-world datasets showed that MCOD performed favorably compared to the three domain-specific models. Furthermore, feature-level visualization provided insights into the rationale behind the model’s anomaly assignments. Collectively, MCOD establishes a robust and scalable framework for data QC in quantitative proteomics.

Mass spectrometry (MS) is a core analytical tool in proteomics, and the quality of the generated data directly determines the effectiveness of downstream analyses and the reliability of final research conclusions. While MS is also widely used in other omics applications, this study focuses on label-free quantitative proteomics, where samples are represented as protein-abundance matrices derived from MaxQuant. However, MS data are typically characterized by high dimensionality and substantial noise, posing serious challenges for quality control (QC). Existing QC methods have limited feature extraction capabilities and struggled to capture the key information embedded in the data, resulting in poor performance in identifying anomalous samples. Here, we propose the Multi-Scale Feature Fusion-based Autoencoder (MFF-AE). This deep learning-based anomaly detection model achieves precise identification of anomalous samples by integrating both global and local data features. The model consists of three modules: an autoencoder-based backbone network that efficiently embeds raw data into a low-dimensional semantic space, a local feature extraction and fusion module designed to capture and integrate multi-scale features within MS data, and a sample identification module that enhances discriminative representations to enable accurate anomaly detection. To evaluate the effectiveness of the proposed model, we conduct extensive experiments on a benchmark dataset with synthesized anomalies. Quantitative results on the benchmark dataset show that, compared with 15 baseline models from statistical learning, deep learning, and ensemble learning, our model consistently achieves the best performance across key metrics. Furthermore, through linear relationship analysis on real-world clinical datasets, the exclusion of outlier samples significantly increased the statistical significance and fold change in the identified differential proteins. Overall, the proposed model establishes a solid data foundation, paving the way for downstream mechanistic studies and target discovery.