The sum being greater than the parts is evident in every aspect of chemistry and biology. This explains how building blocks like amino acids give rise to proteins that can guide essential cellular processes, or how cells come together to make up complex tissues and organs. Very often, the coming together of two molecules involves the formation of covalent bonds between their functional groups. This process, called bioconjugation, is a natural phenomenon that lies at the base of complex biological structures. More importantly, science can utilize this process to combine two molecules to generate multifunctional tools. Bioconjugation is a widely used technique today with applications to drug development, diagnostics, protein-based therapeutics, and nanotechnology applications. 

The idea of machines emulating human intelligence to perform tasks, make decisions, and improve their learning patterns was introduced to computer science in the 1950s [1]. Today, artificial intelligence (AI) is a highly-trending topic and a prominent part of our lives, from chatbots to digital phone assistants to smart homes. Its integration into our routine aside, AI plays a central role in life sciences, mainly biotechnology and bioinformatics, with the common goal of interpreting complex biological processes. AI algorithms are widely used to analyze big omics  data to identify drug targets as well as to predict the activity of drug candidates on their targets.

Given that post-translational modifications, such as glycosylation, add a new layer of complexity to analyzing protein-protein and protein-drug interactions, the application of bioinformatics to glycobiology is necessary to understand and may predict  the role of glycans in various forms of cellular behavior.

Early Implementation of AI in Glycobiology

The implementation of AI for glycomics began in the 1990s with mass spectrometry pipelines, where machine learning algorithms were applied to predict glycopeptide fragment intensities [2]. With the increased emphasis on protein glycosylation patterns, researchers wanted to characterize glycosylation sites in more detail by studying the amino acid sequence of N-glycosylation and the lesser-studied O-glycosylation. Although it was known that glycan linkage occurred at the oxygen of a serine or a threonine, the role of the neighboring amino acids on O-glycosylation   not been elucidated.

During the era of first-generation AI tools, datasets of glycosylation sites have been collected from proteins in tissue samples and biopsies, which were made available on databases such as UniPep [3] and N-GlycositeAtlas[4]. In addition, artificial neural network tools, such as NetNGlyc [5] and YinOYang [6] were developed to predict new N- and O- glycosylation sites using the known glycan data as training sets. Between 2005 and 2015, the predictive power of neural networks was improved through support vector machines and random forest algorithms. Based on these algorithms, software solutions like GlycoMine [7] used a multilayered prediction based on amino acid sequence, and structural and functional features of glycans to improve glycosylation site prediction.

Advancements In Machine Learning Algorithms for Glycosylation Analysis

 Today, the influence of AI on glycobiology continues to expand with the combination of genomics, transcriptomics, and proteomics, as well as computational methods, which greatly enhance site prediction and glycan profiling. For example, Moon et al. developed a random forest algorithm that takes steric and electronic parameters of glycan stereoisomers to accurately predict the selective binding of a particular isomer [8]. Antonakoudis et al. used artificial neural networks in a systems-based approach, where a stoichiometric model was developed to predict glycosylation enzyme fluxes and the subsequent glycan abundances [9].

Meanwhile, other platforms, such as Glycowork, focused on processing broad glycan data to reveal organism-specific glycan profiles [10].  

Besides site prediction and profiling, AI tools contributed to a better understanding of the complex relationship between glycans and cellular phenotypes. Qin et al. introduced an algorithm that uses single-cell SUGAR-seq data to predict the genes that led to N-glycan branching and the effect of different branches on T-cell subtypes in mouse models [12]. Interestingly, these genes were not uncovered in differential expression analysis between cell subtypes, which highlights the value of deep learning in phenotypic analysis.

Another exciting tool is GlyCompareCT, which – as its name suggests – compares the composition and abundance of glycan motifs in different datasets by decomposing them into glycan substructures [13]. This allows users to generate the complete set of motifs from the substructures. The Python-based nature of GlyCompareCT makes it a user-friendly tool that can be run via command-line.

Challenges and Future Directions in Glycoinformatics

While the multitude of glycoinformatics tools can contribute to our understanding of glycosylation, more work is needed to integrate next-generation machine learning into glycobiology. In particular, deep learning tools are instrumental when working with large and unstructured data sets. AlphaFold [14] is one of the pioneering projects that employs deep learning to predict protein structure, including its possible folded states. That said, the platform can only process protein sequences, thus lacking the foresight for glycosylation and other post-translational modifications.

More recently, deep learning methods began to be used for deducing glycosyltransferase structure and function from sequence data. Taujale et al. developed a workflow that used supervised deep learning to infer the folding state of glycosyltransferases from their protein sequences, which allowed them to predict their sugar donor specificities [15]. Subsequently, novel tools, such as GlyNet [16], SweetTalk [17], and glyBERT [18], began to emerge, with improved predictive value for the synthesis of branched and non-linear glycans. The same tools could also be applied to predict protein glycosylation sites [19].

One of the main challenges in glycobiology is the lack of broad glycomics data, which obscures the discovery of novel glycan structures. Next-generation AI models can overcome this issue by incorporating new features in addition to glycan structure. These features can be extracted from omics data that provide information about the upstream (e.g., precursor monosaccharides) and downstream processes (impact on signaling pathways). Since several glycans can share common synthetic steps or exhibit similar downstream effects, this knowledge can significantly enhance the scope of predicted glycans [20].

Finally, the consortium of machine learning tools can be leveraged to understand host-pathogen interactions. In particular, the ability to foresee cross-species transmission can help circumvent t he impact of future pandemics. Firstly, evaluating similar glycan structures across different species can reveal the host receptor-glycan interactions that allow viral entry to see which organisms are susceptible to viral invasion. It can also shed light on how pathogens use glycosylation to mimic host glycans to evade immune response. Furthermore, the combination of input, such as glycan similarity and phylogenetic distance – between humans and the animal studied – can inform us about the likelihood of pathogenic mutations that enable host switching towards humans. Preliminary models, such as SweetNet, leverage next-generation machine learning tools such as graph convolutional neural networks to identify glycan receptors on influenza and rotavirus while revealing binding specificities [21]. This approach can be extrapolated to several other viral proteins to explain how they are transmitted in humans.

Conclusion

Continuous development of AI models and integration of multi-omics could be invaluable for addressing various questions in glycobiology. These include but are not limited to glycosyltransferase structures, glycosylation sites on proteins, the impact of complex glycans on cellular function, pathogen-host interactions, and immuno-oncology (i.e., tumor microenvironment).  The collection of novel insights gained from AI models will help researchers conduct more targeted studies to understand the role of glycosylation in health and disease.

There are currently many open-source software tools and databases on glycoinformatics. The Glycoinformatics Consortium (GLIC) webinar series is a great place to learn about some of these tools, particularly for storing and processing glycan array data. The most noteworthy microarray databases and processing tools include CarbArrayART [22], Glycan Array Dashboard (GLAD) [23], CarboGrove [24], and the Glycan Array Data Repository [25]. In addition, LectinOracle [26] and Glycowork [10] are promising deep learning-based tools to predict protein glycan interactions. A review article by Li et al. perfectly summarizes the collection of additional resources for the computational evaluation of glycosylation [27].

“To learn more about glycans and lectins and how they can be utilized in your workflow to push forward immunology research, check out our Exploring the World of Glycobiology ebook. For other resources and tips and tricks, stay tuned to the SpeakEasy Science blog.  “

1. McCarthy, J., et al., A proposal for the dartmouth summer research project on artificial intelligence, august 31, 1955. AI magazine, 2006. 27(4): p. 12-12.
2. Elias, J.E., et al., Intensity-based protein identification by machine learning from a library of tandem mass spectra. Nature biotechnology, 2004. 22(2): p. 214-219.
3. Zhang, H., et al., UniPep-a database for human N-linked glycosites: a resource for biomarker discovery. Genome biology, 2006. 7(8): p. 1-12.
4. Sun, S., et al., N-GlycositeAtlas: a database resource for mass spectrometry-based human N-linked glycoprotein and glycosylation site mapping. Clinical proteomics, 2019. 16(1): p. 1-11.
5. Gupta, R., E. Jung, and S. Brunak, NetNGlyc 1.0 Server. Center for biological sequence analysis, technical university of Denmark available from: http://www. cbs. dtu dk/services/NetNGlyc, 2004.
6. Gupta, R. and S. Brunak, Prediction of glycosylation across the human proteome and the correlation to protein function, in Biocomputing 2002. 2001, World Scientific. p. 310-322.
7. Li, F., et al., GlycoMine: a machine learning-based approach for predicting N-, C-and O-linked glycosylation in the human proteome. Bioinformatics, 2015. 31(9): p. 1411-1419.
8. Moon, S., et al., Predicting glycosylation stereoselectivity using machine learning. Chemical Science, 2021. 12(8): p. 2931-2939.
9. Antonakoudis, A., et al., Synergising stoichiometric modelling with artificial neural networks to predict antibody glycosylation patterns in Chinese hamster ovary cells. Computers & Chemical Engineering, 2021. 154: p. 107471.
10. Thomès, L., R. Burkholz, and D. Bojar, Glycowork: A Python package for glycan data science and machine learning. Glycobiology, 2021. 31(10): p. 1240-1244.
11. Bojar, D., et al., A useful guide to lectin binding: machine-learning directed annotation of 57 unique lectin specificities. ACS chemical biology, 2022. 17(11): p. 2993-3012.
12. Qin, R., L.K. Mahal, and D. Bojar, Deep learning explains the biology of branched glycans from single-cell sequencing data. Iscience, 2022. 25(10).
13. Zhang, Y., et al., Preparing glycomics data for robust statistical analysis with GlyCompareCT. STAR protocols, 2023. 4(2): p. 102162.
14. Varadi, M., et al., AlphaFold Protein Structure Database: massively expanding the structural coverage of protein-sequence space with high-accuracy models. Nucleic acids research, 2022. 50(D1): p. D439-D444.
15. Taujale, R., et al., Deep evolutionary analysis reveals the design principles of fold A glycosyltransferases. Elife, 2020. 9: p. e54532.
16. Carpenter, E.J., et al., GlyNet: a multi-task neural network for predicting protein–glycan interactions. Chemical Science, 2022. 13(22): p. 6669-6686.
17. Bojar, D., et al., Deep-learning resources for studying glycan-mediated host-microbe interactions. Cell Host & Microbe, 2021. 29(1): p. 132-144. e3.
18. Dai, B., D.E. Mattox, and C. Bailey-Kellogg, Attention please: modeling global and local context in glycan structure-function relationships. bioRxiv, 2021: p. 2021.10. 15.464532.
19. Taherzadeh, G., et al., SPRINT-Gly: predicting N-and O-linked glycosylation sites of human and mouse proteins by using sequence and predicted structural properties. Bioinformatics, 2019. 35(20): p. 4140-4146.
20. Bao, B., et al., Correcting for sparsity and interdependence in glycomics by accounting for glycan biosynthesis. Nature communications, 2021. 12(1): p. 4988.
21. Burkholz, R., J. Quackenbush, and D. Bojar, Using graph convolutional neural networks to learn a representation for glycans. Cell Reports, 2021. 35(11).
22. Akune, Y., et al., CarbArrayART: a new software tool for carbohydrate microarray data storage, processing, presentation, and reporting. Glycobiology, 2022. 32(7): p. 552-555.
23. Mehta, A.Y. and R.D. Cummings, GLAD: GLycan Array Dashboard, a visual analytics tool for glycan microarrays. Bioinformatics, 2019. 35(18): p. 3536-3537.
24. Klamer, Z.L., et al., CarboGrove: a resource of glycan-binding specificities through analyzed glycan-array datasets from all platforms. Glycobiology, 2022. 32(8): p. 679-690.
25. York, W.S., et al., GlyGen: computational and informatics resources for glycoscience. Glycobiology, 2020. 30(2): p. 72-73.
26. Lundstrøm, J., et al., LectinOracle: A Generalizable Deep Learning Model for Lectin–Glycan Binding Prediction. Advanced Science, 2022. 9(1): p. 2103807.
27. Li, H., A.W. Chiang, and N.E. Lewis, Artificial intelligence in the analysis of glycosylation data. Biotechnology Advances, 2022. 60: p. 108008.

Multiplexing has revolutionized immunostaining workflows. It allows the simultaneous detection of multiple targets in the same tissue section and empowers scientists to gather more data while sparing precious samples. But issues with cross-reactivity between reagents can hinder the ability to perform simultaneous double or triple staining. When these issues arise, scientists can use the VectaPlex™ Antibody Removal Kit to do multiple rounds of labeling and perform multiplexing experiments.

Unique advantages of VectaPlex

VectaPlex is a game changer for scientists performing experiments that require antibody removal. Most current methods to achieve this goal are aggressive and can damage the tissue. But the VectaPlex kit comprises gentle reagents that effectively remove antibodies while preserving tissue integrity. In only 25 minutes, scientists can have the tissue section ready for another round of staining. VectaPlex enhances multiplexing workflows by integrating the following advantages:

• Convenience. VectaPlex Antibody Removal Kit comes with convenient ready-to-use reagents. This seamless approach eliminates the need for complex protocols and saves precious time and resources.
• Tissue integrity. VectaPlex formulation is tailored for formalin-fixed paraffin-embedded tissue sections, ensuring their integrity. Reagents effectively remove antibodies from tissue sections at room temperature, eliminating the need for microwave or pressure cookers. Keeping specimens from high-temperature incubations further contributes to optimal tissue preservation. VectaPlex is suitable for even the most sensitive tissues, like lung, skin, and breast, guaranteeing repeated use without quality compromise. 
• Efficiency. VectaPlex’s gentle approach to antibody removal secures consistent results. Reagents in the kit promote efficient antibody removal, minimizing interference in the staining and enhancing signal specificity. Complete removal of antibodies at each step of multiple rounds of labeling translates into more reliable and reproducible results.
• Enhanced multiplexing. The combination of tissue integrity with effective antibody removal allows for repeated rounds of staining without compromising the quality of the sample. VectaPlex allows up to 6 rounds of staining, empowering scientists to maximize the potential of multiplexing experiments, investigate a wide range of targets within the same sample, and dive deeper into biological processes.
• Flexibility. VectaPlex is compatible with both immunofluorescence and tyramide signal amplification workflows, offering unparalleled versatility.

Real-world benefits for the user

VectaPlex directly addresses common challenges scientists face in the laboratory. Cross-reactivity between staining reagents often gets in the way of successful multiplexing experiments. But VectaPlex tackles this issue head-on. Scientists can perform up to 6 rounds of multiple labeling and answer more complex research questions than those they would address with concomitant double or triple labeling.

Limited tissue sample is another factor that often hinders data gathering. But VectaPlex combines efficient antibody removal with tissue preservation to offer a practical solution to this problem. Scientists can use VectaPlex to target multiple proteins and answer different research questions even when tissue samples are scarce.

VectaPlex also streamlines time-intensive multiplex optimization. Ready-to-use reagents eliminate the need for cumbersome preparation steps. Each round of antibody removal only takes 25 minutes, simplifying the process and saving precious time.

Finally, VectaPlex protects precious samples from unintentional human errors. If the antibodies can be easily removed, incubating tissue sections with the incorrect antibody no longer renders the specimen unusable. With VectaPlex, scientists can easily fix unintended mistakes and restart the staining workflow without damaging the sample.

The VectaPlex Antibody Removal Kit brings an innovative and reliable solution to multiplexing workflows that require antibody removal. By simplifying workflows, preserving tissue integrity, and delivering consistent results, VectaPlex empowers scientists to gather more data, even in the face of cross-reactivity issues or with limited or sensitive tissue sections. For a more efficient and reliable multiplexing experience, consider VectaPlex as a practical solution to recurring challenges.

For additional insights and resources on enhancing your multiplexing processes, check out the VectaPlex product page, Immunohistochemistry Multiplexing Guide, the Immunofluorescence Resource Guide, and other pieces on the blog.

The body’s autoimmune response has been leveraged by cancer researchers to propel immunotherapy tools into clinical use. Although the main focus has been on the ability of T-cells to fight cancer cells, the involvement of tumor-infiltrating B-cells is also becoming evident. Tumor-infiltrating B lymphocytes (TIL-Bs) are produced in much higher amounts in various cancers than in healthy tissues, which highlights their positive prognostic value (1). However, their exact role in cancer remains controversial, with the demonstration of both positive and negative impacts.

While they play a critical role in the recruitment of other immune cells and the production of tertiary lymphoid structures to enhance antitumor immunity, some B-cell types have also been shown to promote inflammation and suppress the immune response (2). The conflicting evidence of TIL-B action in cancer points to the need to better understand how B-cells develop and interact with their targets. 

One way B-cells promote immune response is through the production of antibodies for antigen recognition. It is interesting to note that many of these glycoantigens, such as MUC1 (3) and ganglioside D3 (4), are aberrantly glycosylated in cancer. That’s why investigating their interactions with glycans can enlighten us about the role of glycosylation in the development, differentiation, maturation, and survival of B-cells. 

B-cell development 

Germinal center (GC) B-cell development and differentiation are prerequisites for adaptive immune response through B-cell-mediated antibody production. Initially, naive B-cells found in the spleen, tonsil, and primary lymph nodes are exposed to the antigens on the surface of dendritic cells, which leads to their activation.

Activated B-cells form clusters of germinal centers in primary follicles, where they proliferate, mature, and differentiate. More specifically, they undergo a process called somatic hypermutation, defined as the introduction of random mutations to the B-cell receptor regions. Thus, the body generates a library of B-cells with highly diverse receptors with an affinity to various antigens.

These B-cells are filtered according to their interactions with the antigens displayed on the surface of follicular dendritic cells. Depending on the antigens presented, the B-cells undergo class switch recombination to differentiate into plasma cells or memory B-cells, which are responsible for the secretion of antibodies in the bloodstream. 

As we zoom into each step of this process, we encounter glycan-lectin interactions that promote or regulate B-cell development at various points. 

Galectins modulate B-cell development 

Recent studies uncovered the role of galectins in regulating the adaptive immune response. It was shown that galectins can interact with the surface glycans of both innate and adaptive immune cells. Galectin interactions play a multifaceted role in modulating B-cell activation and germinal center-type differentiation essential for maintaining immune response (5).    

Gal-1 was implicated as a promoter of B-cell receptor-mediated signaling, which increased B-cell activation and proliferation (6). On the other hand, Gal-1 was also shown to interact with immune receptors, such as CD45—which is involved in B-cell activation and survival, to modulate activation (7). Similarly, Gal-3 and Gal-9 reduced activation and GC development rates in splenic and tonsillar B-cells, respectively (8–9).  

Furthermore, Galectins are one of the key players in the differentiation fate of B-cells. For example, Gal-1 and Gal-8 can be effective promoters of B-cell differentiation, driving the early stages of immunoglobulin production (10) or establishing plasma cell homeostasis (11). In other studies, Gal-1 was shown to induce apoptosis in active B-cells to terminate immune response (12).

Further research also revealed that the modulatory activity of galectins was dependent on the surface glycan profiles of B-cells. For example, Giovannone et al. demonstrated the role of the B-cell N-glycan composition on Gal-9.

Although Gal-9 strongly bound naive memory B-cells to blunt their activity and exhibited strong binding, GC B-cells exhibited diminished Gal-9 binding due to the I-branching of their surface glycoproteins (9). Thus, a bidirectional relationship between the glycan profiles of B-cells and the level of suppression through galectin binding becomes clear.  

O-linked glycans and B-cell differentiation 

The dynamic nature of GC B-cell glycan profiles was further investigated to uncover the role of glycan remodeling in B-cell differentiation.  

O-glycan modifications were previously identified in T cells, where altered T-antigen expression on the CD8 glycoprotein was associated with changes in T-cell activity. The T-antigen levels of T cells at different maturity levels were detected by peanut agglutinin (PNA) binding.

Following this strategy, Giovannone et al. exposed GC B-cells to PNA to find that α2,3 sialyltransferase, ST3GAL1, was downregulated, which altered the B-cell receptor-type tyrosine phosphatase CD45 (13). Furthermore, using a series of plant lectins, the research team demonstrated that ST3GAL1 overexpression nullified PNA binding in differentiated GC B-cells and shifted the glycan composition of CD45.

Indeed, the cells exhibited a shift from extended core-2 O-glycans to truncated α2,3-sialylated T-antigen, as determined by binding affinity studies with plant lectins from Vector Laboratories, such as Maackia amurensis lectin-II (MAL-II), Jacalin, Phaseolus vulgaris leucoagglutinin (PHA-L), and Sambucus nigra agglutinin (SNA). Overall, the study established a framework of alterations in GC B-cell glycosylation and glycan profiles at different stages of differentiation.   

N-linked glycans and B-cell maturation 

Negative selection is an obstacle before cell maturation, whereby the binding of high-affinity self-antigens to B-cells can promote cell death. As previously mentioned, N-glycan branching on B-cells is a determining factor for whether or not galectins can bind and inhibit their maturation. This raises more questions about the influence of N-glycans on B-cell survival.

To that end, Mortales et al. provided further evidence that N-glycan branching is required for mature B-cell development (14). The team employed a flow cytometry protocol to detect glycan expression profiles on bone marrow B-cells stained with fluorophore-conjugated Phaseolus vulgaris leukoagglutinin (L-PHA) from Vector Laboratories. B-cells with branched N-glycans on CD19 displayed heightened signaling through their antigen receptors, stimulating positive selection and maturation. In contrast, suppressed N-glycan branching in pre-B-cells promoted a negative cell selection and eventual apoptosis.  

Interestingly, N-glycan branching can also create a seesaw effect between innate and adaptive immune responses in specific conditions. Another study by Mortales et al. showed that branching reduced pro-inflammatory innate responses by triggering the endocytosis of toll-like receptors TLR2 and TLR4, fostering adaptive immunity through increased B-cell receptor signaling in multiple sclerosis (15). 

Sialic Acid and B-cell survival 

Sialic acids are abundant on B-cell surfaces. In particular, sialoglycans have previously been associated with B-cell signaling and migration through interacting with Sialic acid binding immunoglobulin-type lectins (Siglecs). To better elucidate the role of sialic acid-siglec interactions, Linder et al. blocked sialic acid synthesis in mouse models, expecting a dampening of cell migration.

However, sialoglycan deficiency also influenced the B-cell population, unlike sialylated glycoprotein expression, which had negligible influence on cell fate. In contrast, sialoglycan deficiency caused an increase in apoptotic signals by caspase 3 and 8, giving rise to B-cell deficient mice (16). The study highlights the importance of these results for cancer cells, emphasizing the protective roles of sialoglycans against cancer cell apoptosis and implicating them as potential therapeutic targets. 

Conclusion and future directions 

The growing pool of evidence agrees on the regulatory role of glycosylation in B-cell development, differentiation, maturation, and survival. Lectin interactions with B-cell surface glycans are determining factors for the route B-cell development will follow. Therefore, future studies must connect glycomics with other omics data to improve the breadth of knowledge on glycosylation and adaptive immune response.  

Live-cell imaging has proven to be critical in evaluating the role of glycans in morphology and cellular interactions. However, the commonly used enzyme labeling methods for glycan detection in live cells still lack specificity and localization while potentially exerting cytotoxicity on the cells.

Click chemistry tools that recently gained recognition with the Nobel Prize in Chemistry 2022 hold immense potential to simplify glycoprotein labeling. More specifically, glycan epitopes of proteins are replaced with derivatives of naturally occurring monosaccharides that can bind reporter molecules through straightforward click reactions. Thus, bioconjugation of clickable monosaccharides to reporters offers non-invasive, quick, and robust glycan labeling with higher specificity (17). We will cover click chemistry-aided bioconjugation methods in more detail in future blog posts. 

Another exciting future direction in glycobiology is the integration of machine learning-aided statistical and bioinformatics tools. Thanks to the wide range of experimental data available on web interfaces, genome-wide data acquisition has been made possible.

Thus, the variety of transcriptomic and metabolomic data can be correlated to glycomics to demonstrate a holistic view of the relationship between glycans and B-cell development (18). This can be especially helpful in unlocking the multifaceted roles of B-cells in cancer progression and immune responses, which will inform the development of more effective immunotherapy strategies. 

To learn more about glycans and lectins and how they can be utilized in your workflow to push forward immunology research, check out our Exploring the World of Glycobiology ebook. For other resources and tips and tricks, stay tuned to the SpeakEasy Science blog.   

1. Laumont CM, et al. 2022. Tumour-Infiltrating B Cells: Immunological Mechanisms, Clinical Impact and Therapeutic Opportunities. Nature Reviews Cancer. 
2. Wei X, et al. 2015. Regulatory B Cells Contribute to the Impaired Antitumor Immunity in Ovarian Cancer Patients. Tumor Biology. 
3. Pavoni E, et al. 2007. Tumor-Infiltrating B Lymphocytes as an Efficient Source of Highly Specific Immunoglobulins Recognizing Tumor Cells. BMC Biotechnology. 
4. Kotlan B, et al. 2005. Novel Ganglioside Antigen Identified by B Cells in Human Medullary Breast Carcinomas: The Proof of Principle Concerning the Tumor-Infiltrating B Lymphocytes. The Journal of Immunology. 
5. Liu FT, et al. 2023. The Role of Galectins in Immunity and Infection. Nature Reviews Immunology. 
6. Gauthier L, et al. 2002. Galectin-1 is a Stromal Cell Ligand of the Pre-B Cell Receptor (BCR) Implicated in Synapse Formation Between Pre-B and Stromal Cells and in Pre-BCR Triggering. Proceedings of the National Academy of Sciences. 
7. Yu X, et al. 2006. Interaction of the B Cell-Specific Transcriptional Coactivator OCA-B and Galectin-1 and a Possible Role in Regulating BCR-Mediated B Cell Proliferation. Journal of Biological Chemistry. 
8. Beccaria CG, et al. 2018. Galectin-3 Deficiency Drives Lupus-Like Disease by Promoting Spontaneous Germinal Centers Formation via IFN-γ. Nature Communications. 
9. Giovannone N, et al. 2018. Galectin-9 Suppresses B Cell Receptor Signaling and is Regulated by I-Branching of N-Glycans. Nature Communications. 
10. Tsai CM, et al. 2008. Galectin-1 Promotes Immunoglobulin Production During Plasma Cell Differentiation. The Journal of Immunology. 
11. Anginot A, et al. 2013. Galectin 1 Modulates Plasma Cell Homeostasis and Regulates the Humoral Immune Response. The Journal of Immunology. 
12. Tabrizi SJ, et al. 2009. T Cell Leukemia/Lymphoma 1 and Galectin-1 Regulate Survival/Cell Death Pathways in Human Naive and IgM+ Memory B Cells Through Altering Balances in Bcl-2 Family Proteins. The Journal of Immunology. 
13. Giovannone N, et al. 2018. Human B Cell Differentiation is Characterized by Progressive Remodeling of O-linked Glycans. Frontiers in Immunology. 
14. Mortales CL, et al. 2020. N-Glycan Branching is Required for Development of Mature B Cells. The Journal of Immunology. 
15. Mortales CL, et al. 2020. N-Glycan Branching Decouples B Cell Innate and Adaptive Immunity to Control Inflammatory Demyelination. Iscience. 
16. Linder AT, et al. 2022. Sialic Acids on B Cells are Crucial for Their Survival and Provide Protection Against Apoptosis. Proceedings of the National Academy of Sciences. 
17. Wu ZL, et al. 2015. Glycoprotein Labeling with Click Chemistry (GLCC) and Carbohydrate Detection. Carbohydrate Research. 
18. Vicente MM, et al. 2023. Glycome Dynamics in T and B Cell Development: Basic Immunological Mechanisms and Clinical Applications. Trends in Immunology.