Carbohydrate antigen 199 (CA199) is a serum biomarker which has certain value and significance in the diagnosis, prognosis, treatment, and postoperative monitoring of cancer. In this study, a lateral flow immunoassay based on europium (III) polystyrene time-resolved fluorescence microspheres (TRFM-based LFIA), integrated with a portable fluorescence reader, has been successfully establish for rapid and quantitative analysis of CA199 in human serum. Briefly, time-resolved fluorescence microspheres (TRFMs) were conjugated with antibody I (Ab1) against CA199 as detection probes, and antibody II (Ab2) was coated as capture element, and a “TRFMs-Ab1-CA199-Ab2” sandwich format would form when CA199 was detected by the TRFM-based LFIA. Under the optimal parameters, the detection limit of the TRFM-based LFIA for visible quantitation with the help of an ultraviolet light was 4.125 U/mL, which was four times lower than that of LFIA based on gold nanoparticles. Additionally, the fluorescence ratio is well linearly correlated with the CA199 concentration (0.00–66.0 U/mL) and logarithmic concentration (66.0–264.0 U/mL) for quantitative detection. Serum samples from 10 healthy people and 10 liver cancer patients were tested to confirm the performances of the point-of-care application of the TRFM-based LFIA, 20.0 U/mL of CA199 in human serum was defined as the threshold for distinguishing healthy people from liver cancer patients with an accuracy of about 60%. The establishment of TRFM-based LFIA will provide a sensitive, convenient, and efficient technical support for rapid screening of CA199 in cancer diagnosis and prognosis.

Neutralizing antibody (NAb) is a family of antibodies with special functions, which afford a degree of protection against infection and/or reduce the risk of clinically severe infection. Receptor binding domain (RBD) in the spike protein of SARS-CoV-2, a portion of the S1 subunit, can stimulate the immune system to produce NAb after infection and vaccination. The detection of NAb against SARS-CoV-2 is a simple and direct approach for evaluating a vaccine’s effectiveness. In this study, a direct, rapid, and point-of-care bicolor lateral flow immunoassay (LFIA) was developed for NAb against SARS-CoV-2 detection without sample pretreatment, and which was based on the principle of NAb-mediated blockage of the interaction between RBD and angiotensin-converting enzyme 2. In the bicolor LFIA, red and blue latex microspheres (LMs) were used to locate the test and control lines, leading to avoidance of erroneous interpretations of one-colored line results. Under the optimal conditions, NAb against SARS-CoV-2 detection carried out using the bicolor LFIA could be completed within 9 min, and the visible limit of detection was about 48 ng/mL. Thirteen serum samples were analyzed, and the results showed that the NAb levels in three positive serum samples were equal to, or higher than, 736 ng/mL. The LM-based bicolor LFIA allows one-step, rapid, convenient, inexpensive, and user-friendly determination of NAb against SARS-CoV-2 in serum.

Quality control in quantitative proteomics is a persistent challenge, particularly in identifying and managing outliers. Unsupervised learning models, which rely on data structure rather than predefined labels, offer potential solutions. However, without clear labels, their effectiveness might be compromised. Single models are susceptible to the randomness of parameters and initialization, which can result in a high rate of false positives. Ensemble models, on the other hand, have shown capabilities in effectively mitigating the impacts of such randomness and assisting in accurately detecting true outliers. Therefore, we introduced SEAOP, a Python toolbox that utilizes an ensemble mechanism by integrating multi-round data management and a statistics-based decision pipeline with multiple models. Specifically, SEAOP uses multi-round resampling to create diverse sub-data spaces and employs outlier detection methods to identify candidate outliers in each space. Candidates are then aggregated as confirmed outliers via a chi-square test, adhering to a 95% confidence level, to ensure the precision of the unsupervised approaches. Additionally, SEAOP introduces a visualization strategy, specifically designed to intuitively and effectively display the distribution of both outlier and non-outlier samples. Optimal hyperparameter models of SEAOP for outlier detection were identified by using a gradient-simulated standard dataset and Mann–Kendall trend test. The performance of the SEAOP toolbox was evaluated using three experimental datasets, confirming its reliability and accuracy in handling quantitative proteomics.

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.