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Chemical-nose/tongue technologies are emerging as promising analytical tools for glycan analysis. After briefly introducing the importance of glycans and their analytical methods, including the lectin microarray (LMA) as one of the gold standards, the fundamental principles underlying chemical noses/tongues are explained and various applications for monosaccharides and glycans are introduced. Then, the similarities and differences of these two approaches are discussed. While both technologies aim to comprehensively profile biospecimens based on ‘interaction patterns’ between multiple recognition probes and analytes, each has its own strengths. LMAs excel at specific, targeted analysis based on defined lectin–glycan interactions, whereas chemical nose/tongue offers greater flexibility and expandability in terms of system design, making it well-suited for discovering unknown glycan profiles and detecting broader differences in glycan mixtures. In the future, chemical-nose/tongue technologies may be applied to niche areas in glycan analysis and become powerful tools that complement LMA techniques.

ABSTRACT Multidrug-resistant (MDR) Pseudomonas aeruginosa infections pose a significant challenge to effective treatment. Although carbapenems were once considered the primary therapeutic option for MDR P. aeruginosa, the clinical use of these antibiotics has become increasingly limited, and the exploration of alternative antimicrobial strategies remains necessary. Bacterial surface glycans are critical in response to antibiotics and represent an attractive therapeutic target. However, the understanding of the role of glycan structures in bacterial resistance is currently quite limited. In this study, we used lectin microarrays to analyze the differences in glycan alterations between 53 drug-sensitive P. aeruginosa (DSPA) strains and 57 carbapenem-resistant P. aeruginosa (CRPA) strains obtained from clinical isolates, with the goal of identifying important glycopatterns associated with carbapenem resistance. The results revealed significant differences in the expression levels of glycan structures [e.g., Fucα1–6GlcNAc, α-d-Man, and Fucα1–3(Galβ1–4)GlcNAc] recognized by 20 lectins (e.g., LCA, PSA, and AAL) on the bacterial surface. Furthermore, we applied K-fold cross-validation to determine the optimal parameters and constructed the DSPA and CRPA models using gradient boosting decision tree (GBDT) algorithm. The GBDT model presented the best performance in an independent test cohort, demonstrating that the screened glycopatterns could serve as potential biomarkers for differentiating bacterial resistance. Our study is the first to apply lectin microarrays to monitor carbapenem resistance in clinical P. aeruginosa isolates, and we hope that the methodology used in this work will serve as an important tool for exploring the association of bacterial resistance with glycosylation mechanisms. IMPORTANCE Bacterial surface glycans are an attractive therapeutic target in response to antibiotics; however, current knowledge of the corresponding mechanisms is rather limited. Antimicrobial susceptibility testing, genome sequencing, and MALDI-TOF MS, commonly used in recent years to analyze bacterial resistance, are unable to rapidly and efficiently establish associations between glycans and resistance. The discovery of new antimicrobial strategies still requires the introduction of promising analytical methods. In this study, we applied lectin microarray technology and a machine-learning model to screen for important glycan structures associated with carbapenem-resistant P. aeruginosa. This work highlights that specific glycopatterns can be important biomarkers associated with bacterial antibiotic resistance, which promises to provide a rapid entry point for exploring new resistance mechanisms in pathogens.

Hepatocellular carcinoma (HCC) is difficult to diagnose early and progresses rapidly, making it one of the most deadly malignancies worldwide. This study aimed to evaluate whether salivary glycopattern changes combined with machine learning algorithms could help in the accurate diagnosis of HCC. Firstly, we detected the alteration of salivary glycopatterns by lectin microarrays in 118 saliva samples. Subsequently, we constructed diagnostic models for hepatic cirrhosis (HC) and HCC using three machine learning algorithms: Least Absolute Shrinkage and Selector Operation, Support Vector Machine (SVM), and Random Forest (RF). Finally, the performance of the diagnostic models was assessed in an independent validation cohort of 85 saliva samples by a series of evaluation metrics, including area under the receiver operator curve (AUC), accuracy, specificity, and sensitivity. We identified alterations in the expression levels of salivary glycopatterns in patients with HC and HCC. The results revealed that the glycopatterns recognized by 22 lectins showed significant differences in the saliva of HC and HCC patients and healthy volunteers. In addition, after Boruta feature selection, the best predictive performance was obtained with the RF algorithm for the construction of models for HC and HCC. The AUCs of the RF-HC model and RF-HCC model in the validation cohort were 0.857 (95% confidence interval [CI]: 0.780–0.935) and 0.886 (95% CI: 0.814–0.957), respectively. Detecting alterations in salivary protein glycopatterns with lectin microarrays combined with machine learning algorithms could be an effective strategy for diagnosing HCC in the future. [ABSTRACT FROM AUTHOR]

Extracellular vesicles (EVs) are released by all types of mammalian cells for cell–cell communication. In this study, surface glycans on EVs are compared in terms of their cell type, size, and isolation method to examine whether EV glycan profiles by lectin microarray can be used to define EV subpopulations. Moreover, EVs are glycoengineered with four distinctive surface glycan patterns and evaluated their cellular uptake efficiencies for potential drug delivery applications. Both similarities and differences in glycan patterns are identified on EVs obtained under each experimental condition. EV size‐ and isolation method‐dependent lectin‐binding patterns are observed. Moreover, cellular uptake behaviors of EVs are affected by EV glycan profiles and acceptor cells. The in vivo biodistribution of EVs is also dependent on their glycan profile. These results suggest that EV surface glycans are a potential novel indicator of EV heterogeneity, and glycoengineering is a useful approach to regulate cell–EV interactions for biomedical applications. [ABSTRACT FROM AUTHOR]

Abstract Many proteins in living organisms are glycosylated. As their glycan patterns exhibit protein‐, cell‐, and tissue‐specific heterogeneity, changes in the glycosylation levels could serve as useful indicators of various pathological and physiological states. Thus, the identification of glycoprotein biomarkers from specific changes in the glycan profiles of glycoproteins is a trending field. Lectin microarrays provide a new glycan analysis platform, which enables rapid and sensitive analysis of complex glycans without requiring the release of glycans from the protein. Recent developments in lectin microarray technology enable high‐throughput analysis of glycans in complex biological samples. In this review, we will discuss the basic concepts and recent progress in lectin microarray technology, the application of lectin microarrays in biomarker discovery, and the challenges and future development of this technology. Given the tremendous technical advancements that have been made, lectin microarrays will become an indispensable tool for the discovery of glycoprotein biomarkers.

Glycosylation is one of the most crucial posttranslational modifications of proteins, containing a remarkable amount of biological information. The alteration of glycosylation is closely associated with certain diseases. Exploring glyco-code in the development of diseases is a hot topic in recent years. Esophageal squamous cell carcinoma (ESCC) is the primary pathological histology in developing countries and a severe threat to human health. Although the glycan profiles in the blood samples of ESCC patients were analyzed using glycomic and glycoproteomic methods, the difference of salivary glycopatterns between healthy subjects and ESCC patients is not explicit yet. In the present study, ESCC patients (n = 16) and healthy volunteers (HVs, n = 25) were enrolled. The glycomic strategy combining lectin microarray and lectin blotting was employed to investigate and confirm the altered salivary glycopatterns. Datura stramonium (DSA) was selected to isolate the GlcNAc or Galβ1-4GlcNA-containing glycoproteins due to the distinct difference between ESCC patients and HVs. The N-glycans from DSA-enriched glycoproteins were released by PNGase F and further identified by MALDI-TOF/TOF-MS to obtain the precise structural information of the altered glycans. As a result, the glycopatterns recognized by 13 lectins (e.g., ECA, RCA120, and DSA) showed significant alterations in ESCC patients’ saliva. The ESCC patients showed higher levels of GalNAc and Gal, sialic acid, and GlcNAc expression profiles and lower levels of mannose and fucose expression profiles. The MALDI-TOF/TOF-MS results indicated that the proportion of the GlcNAc or Galβ1-4GlcNAc-containing N-glycans was increased in ESCC patients (79.04%) compared with HV (63.20%), which was consistent with the results of lectin microarrays. Our findings provide comprehensive information to understand the complex physiological changes in ESCC patients. And the altered salivary glycopatterns such as GlcNAc or Galβ1-4GlcNAc-containing N-glycans recognized by DSA might serve as potential biomarkers for the diagnosis of ESCC patients.

This is the first work focused on glycoprofiling of whole N- and O- glycome using lectins in an array format applied for analysis of serum samples from healthy individuals, benign prostate hyperplasia (BPH) patients, and prostate cancer (PCa) patients. Lectin microarray was prepared using traditional lectins with the incorporation of 2 recombinant bacterial lectins and 3 human lectins (17 lectins in total). Clinical validation of glycans as biomarkers was done in two studies: discrimination of healthy individuals with BPH patients vs. PCa patients (C vs. PCa) and discrimination of healthy individuals vs. BPH and PCa patients (H vs. PCond). Single lectins (17 lectins) and a combination of two lectins (136 binary lectin combinations) were applied in the clinical validation of glycan biomarkers providing 153 AUC values from ROC curves for both studies (C vs. PCa and H vs. PCond). Potential N- and O-glycans as biomarkers were identified and possible carriers of these glycans are shortly discussed. [ABSTRACT FROM AUTHOR]

Many proteins in living organisms are glycosylated. As their glycan patterns exhibit protein‐, cell‐, and tissue‐specific heterogeneity, changes in the glycosylation levels could serve as useful indicators of various pathological and physiological states. Thus, the identification of glycoprotein biomarkers from specific changes in the glycan profiles of glycoproteins is a trending field. Lectin microarrays provide a new glycan analysis platform, which enables rapid and sensitive analysis of complex glycans without requiring the release of glycans from the protein. Recent developments in lectin microarray technology enable high‐throughput analysis of glycans in complex biological samples. In this review, we will discuss the basic concepts and recent progress in lectin microarray technology, the application of lectin microarrays in biomarker discovery, and the challenges and future development of this technology. Given the tremendous technical advancements that have been made, lectin microarrays will become an indispensable tool for the discovery of glycoprotein biomarkers. [ABSTRACT FROM AUTHOR]

Mammalian hearts have regenerative potential restricted to early neonatal stage and lost within seven days after birth. Carbohydrates exclusive to cardiac neonatal tissue may be key regulators of regenerative potential. Although cell surface and extracellular matrix glycosylation are known modulators of tissue and cellular function and development, variation in cardiac glycosylation from neonatal tissue to maturation has not been fully examined. In this study, glycosylation of the adult rat cardiac ventricle showed no variability between the two strains analysed, nor were there any differences between the glycosylation of the right or left ventricle using lectin histochemistry and microarray profiling. However, in the Sprague-Dawley strain, neonatal cardiac glycosylation in the left ventricle differed from adult tissues using mass spectrometric analysis, showing a higher expression of high mannose structures and lower expression of complex N -linked glycans in the three-day-old neonatal tissue. Man 6 GlcNAc 2 was identified as the main high mannose N -linked structure that was decreased in adult while higher expression of sialylated N -linked glycans and lower core fucosylation for complex structures were associated with ageing. The occurrence of mucin core type 2 O -linked glycans was reduced in adult and one sulfated core type 2 O -linked structure was identified in neonatal tissue. Interestingly, O -linked glycans from mature tissue contained both N -acetylneuraminic acid (Neu5Ac) and N -glycolylneuraminic acid (Neu5Gc), while all sialylated N -linked glycans detected contained only Neu5Ac. As glycans are associated with intracellular communication, the specific neonatal structures found may indicate a role for glycosylation in the neonatal associated regenerative capacity of the mammalian heart. New strategies targeting tissue glycosylation could be a key contributor to achieve an effective regeneration of the mammalian heart in pathological scenarios such as myocardial infarction. Schematics showing the experimental flow of the study from lectin histochemistry to PGC LC-ESI-MS/MS analyses (spectra) and lectin microarrays (heat-maps) to identify the glycan structures present in adult and neonatal myocardial tissues. Unlabelled Image [ABSTRACT FROM AUTHOR]

Introduction: Glycoproteomics is an important subdiscipline of proteomics, focusing on the role of protein glycosylation in various biological processes. Protein glycosylation is the enzymatic addition of sugars or oligosaccharides to proteins. Altered glycosylation often occurs in the early stages of disease development, for example, certain tumor-associated glycans have been shown to be expressed in precursor lesions of different types of cancer, making them powerful early diagnostic markers. Lectin microarrays have become a powerful tool for both the study of glycosylation and the diagnosis of various diseases including cancer. Areas covered: This review will discuss the most useful features of lectin microarrays, such as their technological advances, their capability for parallel/high-throughput analysis for the important glycopatterns of glycoprotein, and an overview of their use for glycosylation analysis of various complex protein samples, as well as their diagnostic potential in various diseases. Expert opinion: Lectin microarrays have proved to be useful in studying multiple lectin–glycan interactions in a single experiment and, with the advances made in the field, hold a promise of enabling glycopatterns of diseases in a fast and efficient manner. Lectin microarrays will become increasingly powerful early diagnostic tool for a variety of conditions. [ABSTRACT FROM AUTHOR]

With the advantages of convenient, painless and non-invasive collection, saliva holds great promise as a valuable biomarker source for cancer detection, pathological assessment and therapeutic monitoring. Salivary glycopatterns have shown significant potential for cancer screening in recent years. However, the understanding of benign lesions at non-cancerous sites in cancer diagnosis has been overlooked. Clarifying the influence of benign lesions on salivary glycopatterns and cancer screening is crucial for advancing the development of salivary glycopattern-based diagnostics. In this study, 2885 samples were analyzed using lectin microarrays to identify variations in salivary glycopatterns according to the number, location, and type of lesions. By utilizing our previously published data of tumor-associated salivary glycopatterns, the performance of machine learning algorithm for cancer screening was investigated to evaluate the effect of adding benign disease cases to the control group. The results demonstrated that both the location and number of lesions had discernible effects on salivary glycopatterns. And it was also revealed that incorporating a broad range of benign diseases into the controls improved the classifier's performance in distinguishing cancer cases from controls. This finding holds guiding significance for enhancing salivary glycopattern-based cancer screening and facilitates their practical implementation in clinical settings. [ABSTRACT FROM AUTHOR]

Abstract There is a high desire for novel targets/biomarkers to diagnose and treat colorectal cancer (CRC). Here, an approach starting from a polyacrylamide hydrogel–based lectin microarray is presented to screen the high expression of glycans on the CRC cell surface and to identify new lectin biomarkers for CRC. Three common CRC cell lines (SW480, SW620, and HCT116) and one normal colon cell line (NCM460) are profiled on the microarray with 27 lectins. The experimental results reveal that CRC cells highly express the glycans with d‐galactose, d‐glucose, and/or sialic acid residues, and Uelx Europaeus Agglutinin‐I (UEA‐I) exhibits reasonable specificity with SW480 cells. After conjugation of UEA‐I with silica‐coated NaGdF4:Yb3+, Er3+@NaGdF4 upconversion nanoparticles, the follow‐up in vitro and in vivo experiments provide further evidence on that UEA‐I can serve as tumor‐targeting molecule to diagnose SW480 tumor by multimodal imaging including upconversion luminescence imaging, T1‐weighted magnetic resonance imaging, and X‐ray computed tomography imaging.

Abstract: There is a high desire for novel targets/biomarkers to diagnose and treat colorectal cancer (CRC). Here, an approach starting from a polyacrylamide hydrogel–based lectin microarray is presented to screen the high expression of glycans on the CRC cell surface and to identify new lectin biomarkers for CRC. Three common CRC cell lines (SW480, SW620, and HCT116) and one normal colon cell line (NCM460) are profiled on the microarray with 27 lectins. The experimental results reveal that CRC cells highly express the glycans with d‐galactose, d‐glucose, and/or sialic acid residues, and Uelx Europaeus Agglutinin‐I (UEA‐I) exhibits reasonable specificity with SW480 cells. After conjugation of UEA‐I with silica‐coated NaGdF4:Yb3+, Er3+@NaGdF4 upconversion nanoparticles, the follow‐up in vitro and in vivo experiments provide further evidence on that UEA‐I can serve as tumor‐targeting molecule to diagnose SW480 tumor by multimodal imaging including upconversion luminescence imaging, T1‐weighted magnetic resonance imaging, and X‐ray computed tomography imaging. [ABSTRACT FROM AUTHOR]

Macrophages are the primary host target cells of Mycobacterium tuberculosis ( M.tb ). However, little is known about the changes of membrane glycopatterns of macrophages in response to M. tb infection. Using lectin microarrays we compared the differential expression of glycopatterns of macrophages upon stimulation with the heat-inactivated virulent M.tb H37Rv or attenuate M.tb H37Ra . We found that widespread alteration of macrophage membrane glycopatterns were induced by the heat-inactivated virulent M. tb H37Rv, as shown by the significantly changed binding abilities of 11 lectins (sugar binding proteins) among 40 lectins tested. The binding ability of the lectin ABA to macrophages showed the greatest increase after virulent M. tb H37Rv treatment, which suggests that the expression of N-acetyl- d -lactosamine (ABA binding ligand Galβ1-3GalNAc, O -link glycan) is mainly increased on macrophages during virulent M.tb infection. Addition of ABA blocked the attachment/engulfment of M. tb H37Rv, but not H37Ra, to macrophages. Further, increased glycosylated CD44, one of ABA-binding glycoproteins on macrophages, was identified by pull-down assays with ABA-agarose, followed by mass spectrometry and western blotting. ABA directly binds with Galβ1-3GalNAc-glycosylated CD44 on macrophage, and inhibits M. tb mannose-capped lipoarabinomannan (ManLAM) binding to glycosylated CD44. Moreover, ABA increases IL-6, but reduces IL-10 production of ManLAM-treated macrophages and inhibits M. tb H37Rv-induced necrosis in macrophages. Our study will help to reveal the mechanism of pathogenicity and virulence of M. tb from a new perspective and provide a potential new diagnostic and therapeutic strategy for tuberculosis based on glycopatterns, ABA and its ligand Galβ1-3GalNAc-glycosylated CD44 target molecule on macrophage. [ABSTRACT FROM AUTHOR]

This study was performed to monitor the glycoform distribution of a recombinant antibody fusion protein expressed in CHO cells over the course of fed-batch bioreactor runs using high-throughput methods to accurately determine the glycosylation status of the cell culture and its product. Three different bioreactors running similar conditions were analysed at the same five time-points using the advanced methods described here. N-glycans from cell and secreted glycoproteins from CHO cells were analysed by HILIC-UPLC and MS, and the total glycosylation (both N- and O-linked glycans) secreted from the CHO cells were analysed by lectin microarrays. Cell glycoproteins contained mostly high mannose type N-linked glycans with some complex glycans; sialic acid was α-(2,3)-linked, galactose β-(1,4)-linked, with core fucose. Glycans attached to secreted glycoproteins were mostly complex with sialic acid α-(2,3)-linked, galactose β-(1,4)-linked, with mostly core fucose. There were no significant differences noted among the bioreactors in either the cell pellets or supernatants using the HILIC-UPLC method and only minor differences at the early time-points of days 1 and 3 by the lectin microarray method. In comparing different time-points, significant decreases in sialylation and branching with time were observed for glycans attached to both cell and secreted glycoproteins. Additionally, there was a significant decrease over time in high mannose type N-glycans from the cell glycoproteins. A combination of the complementary methods HILIC-UPLC and lectin microarrays could provide a powerful and rapid HTP profiling tool capable of yielding qualitative and quantitative data for a defined biopharmaceutical process, which would allow valuable near 'real-time' monitoring of the biopharmaceutical product. [ABSTRACT FROM AUTHOR]

Abstract: The review assesses current status and attempts to forecast trends in the development of lectin biorecognition technology. The progressive trend is characterized scientometrically and reflects the current transient situation, when standard low-throughput lectin-based techniques are being replaced by a novel microarray-based techniques offering high-throughput of detection. The technology is still in its infancy (validation phase), but already shows promise as an efficient tool to decipher the enormous complexity of the glycocode that influences physiological status of the cell. Further enhancement in robustness and flexibility of lectin microarrays is predicted by using recombinant and artificial lectins that will render production of lectin microarrays cost-effective and more affordable. Mass spectrometry is expected to play an important role to characterize the binding profile of new lectins. Differences in glycan recognition by lectins and anti-carbohydrate antibodies are given on a molecular basis, and strong and weak points of both biorecognition molecules in diagnosis are briefly discussed. [Copyright &y& Elsevier]