
==== Front
Acta Biochim Biophys Sin (Shanghai)
Acta Biochim Biophys Sin (Shanghai)
ABBS
Acta Biochimica et Biophysica Sinica
1672-9145
1745-7270
Science Press

39099413
10.3724/abbs.2024123
Research Article
Deciphering disease through glycan codes: leveraging lectin microarrays for clinical insights
Deciphering disease through glycan codes
Yang Hangzhou 1
Lin Zihan 1
Wu Bo 1
Xu Jun 4
Tao Sheng-Ce 3 *
Zhou Shumin 2 *
1 Department of General Surgery Shanghai Jiao Tong University Affiliated Sixth People’s Hospital Shanghai 200233 China
2 Institute of Microsurgery on Extremities Shanghai Jiaotong University Affiliated Sixth People’s Hospital Shanghai 200233 China
3 Shanghai Center for Systems Biomedicine Key Laboratory of Systems Biomedicine (Ministry of Education) Shanghai Jiao Tong University 800 Dongchuan Road Shanghai 200240 China
4 Department of Orthopaedic Surgery Shanghai Jiao Tong University Affiliated Sixth People’s Hospital Shanghai 200233 China
† These authors contributed equally to this work.

Correspondence address. Tel: +86-13701669041; (S.C.T.) / Tel: +86-18502127296; E-mail: (S.Z.)taosc@sjtu.edu.cnzhoushumin_zw@126.com
1 8 2024
25 8 2024
56 8 11451155
16 4 2024
12 6 2024
© The Author(s) 2021.
2024
The Author(s)
0
https://creativecommons.org/licenses/by-nc/4.0/ This is an open access article distributed under the terms of the Creative Commons Attribution Non-Commercial License (https://creativecommons.org/licenses/by-nc/4.0/).

Glycosylation, a crucial posttranslational modification, plays a significant role in numerous physiological and pathological processes. Lectin microarrays, which leverage the high specificity of lectins for sugar binding, are ideally suited for profiling the glycan spectra of diverse and complex biological samples. In this review, we explore the evolution of lectin detection technologies, as well as the applications and challenges of lectin microarrays in analyzing the glycome profiles of various clinical samples, including serum, saliva, tissues, sperm, and urine. This review not only emphasizes significant advancements in the high-throughput analysis of polysaccharides but also provides insight into the potential of lectin microarrays for diagnosing and managing diseases such as tumors, autoimmune diseases, and chronic inflammation. We aim to provide a clear, concise, and comprehensive overview of the use of lectin microarrays in clinical settings, thereby assisting researchers in conducting clinical studies in glycobiology.

lectin microarray
clinical sample
glycomics
glycans
biomarkers
the grant from the Funding of Shanghai Sixth People HospitalNo. ynms202202 This work was supported by the grant from the Funding of Shanghai Sixth People Hospital (No. ynms202202).CitationH Yang, Z Lin, B Wu, J Xu, S Tao, S Zhou. Deciphering disease through glycan codes: leveraging lectin microarrays for clinical insights. Acta Biochim Biophys Sin, 2024, Vol.: fpage–lpage, https://doi.org/10.3724/abbs.2024123
Crossmark2024/7/16 14:24:40
AuthorMarkH Yang
AuthorMarkCiteH Yang, Z Lin, B Wu, J Xu, S Tao, S Zhou.
article-titleDeciphering disease through glycan codes: leveraging lectin microarrays for clinical insights
==== Body
pmcIntroduction

Glycosylation, a prevalent type of modification occurring during both cotranslational and posttranslational processes, plays a pivotal role in various cellular functions, including adhesion, recognition, molecular transport, clearance, and signal transduction [1]. Glycans exhibit remarkable structural diversity, encompassing variances in monosaccharide subunit linkages and branching, complex compositions of glycoconjugates resulted from interactions with polysaccharides, proteins, lipids, and other sugar moieties, as well as diverse glycosylation sites [2]. Changes in physiological states can lead to modifications in specific glycans, with the glycan chains of some IgG molecules undergoing significant alterations with advancing age [3]. Similarly, disease states significantly impact glycan biosynthesis, with changes in glycosylation patterns being more pronounced than alterations in other biological parameters under specific pathological conditions [4]. Therefore, the field of human glycomics is critical for advancing clinical research on diseases, with the precise identification of disease-specific glycoforms, holding the potential to greatly improve early disease diagnosis.

Over the past twenty years, techniques for examining glycosylation have primarily included capillary electrophoresis (CE) [ 5, 6], high-performance liquid chromatography (HPLC) [ 7, 8], mass spectrometry (MS) [9], and lectin assays [ 10, 11]. Capillary electrophoresis, powered by high-voltage direct current, offers benefits such as superior separation efficiency, minimal sample utilization, and partial structural insights. HPLC, known for its efficient and stable separation capabilities, coupled with high-sensitivity fluorescence detection, enables quantitative analysis of N-glycans. Mass spectrometry, a commonly employed detection method in glycan analysis, boasts high sensitivity, particularly for derivatized glycans, and enables complete elucidation of glycan composition through multistage MS data. However, prior to these analyses, enzymatic or chemical methods are typically used to detach glycans from glycoproteins, facilitating separate analysis and detection of glycan expression and alterations within samples. Lectins, naturally occurring substances initially discovered in plants but found across various organisms, are glycan-binding proteins capable of selectively interacting with specific sugar moieties of soluble sugars, glycoproteins, or glycolipids [12].

The History of Lectin Microarray Detection Technology

Lectin microarray technology, featuring an array of lectins with distinct glycan-binding capabilities affixed to a stable substrate, offers several advantages over traditional glycan analysis methods such as mass spectrometry. These advantages include simplicity, speed, high throughput, and high sensitivity, while eliminating the need for predetachment of glycans, which could alter their structure.

Compared to proteomics and genomics, glycomics research utilizing lectin microarray technology is a burgeoning field dedicated to investigating the biological relevance of glycan structures and their impact on the structure and function of covalently bound lipids and proteins. Lectin microarrays, which were introduced to the scientific community in 2005, have evolved over the past 19 years from a laboratory research tool to one with potential clinical application value [ 13- 15]. Lectin microarrays demonstrate significant potential in various fields, such as disease diagnosis and biomarker discovery, particularly in the treatment of autoimmune diseases and cancer. Additionally, lectin microarrays are often combined with mass spectrometry, proteomics, genomics, and other technologies to obtain comprehensive glycan analysis information, aiding in a deeper understanding of disease mechanisms [16]. The integration of multiple omics disciplines provides a clear advantage in translational cancer research, enhancing our understanding of the genetic landscape of tumors [ 17, 18].

In recent years, researchers have developed various novel surface chemical modification strategies to enhance the fixation efficiency and stability of lectins, including N-hydroxysuccinimide (NHS) esters, epoxy compounds, multifunctional surfaces, and novel cross-linkers [19]. The utilization of new substrate materials such as nanomaterials and two-dimensional materials has enhanced the loading capacity and functionality of lectin sensors [ 20, 21]. The advancement of lectin microarray technology is also attributed to the interdisciplinary fusion of multiple fields, such as biology, chemistry, physics, materials science, and computer science. Unlabeled glycosylation detection methods, such as surface plasmon resonance (SPR), optical microscopy, and MALDI-TOF MS, can be used to determine the intrinsic properties of sugars (dielectric, optical properties, mass, etc.) [22]. In addition to naturally extracted or isolated lectins from organisms, researchers have also designed and synthesized recombinant lectins through genetic engineering methods. Recombinant lectins can be functionally customized according to design requirements, offering wide application prospects in drug development, biosensor preparation, and other fields.

The progress in glycomics has lagged far behind that in genomics and proteomics, partly due to significant challenges in glycan analysis. In recent years, the automation level and high-throughput analysis capability of lectin microarrays have gradually improved. Shang et al. [23] developed an integrated and automated microfluidic lectin barcode platform that can significantly enhance the performance of antibody-lectin microarrays. The lectin microbead array developed by Hiroko et al. [24] can quantitatively characterize glycan alterations, enabling automated glycan analysis from sample pretreatment to detection.

The successive emergence of novel tools for glycomic analysis has made large-scale comparisons of glycomic data possible. Extensive glycan profile datasets and supportive databases such as GlyTouCan, UniCarb-DB, GlyGen, and UniCarbKB are also emerging. However, when analyzing large-scale glycomic datasets, the overlap between sample compounds is limited. The sparsity and lack of independence of the data may pose significant challenges in determining the origins of differential glycan sources [ 25, 26]. GlyCompare is a substructure-based method for glycan analysis developed by Bao et al. [27] to address key statistical issues in glycomics. GlycoNote is a versatile and reliable search engine with a robust quality control mode that enhances the reliability of output results, serving as a valuable data analysis tool in glycomic research [28]. In-depth analysis of glycan spectra remains challenging; for instance, false positive analysis of glycan fragments for specific glycoprotein samples is a crucial aspect of future glycan data analysis.

Currently, lectin microarrays have been utilized to detect changes in glycan profiles in various clinical samples, such as serum, urine, and saliva. Researchers obtained clinical samples from the observation and control groups, examined different glycan profiles associated with these diseases, and evaluated changes in lectin signal intensity between different groups ( Figure 1). Figure 1

Lectin microarrays employed for glycan spectrum analysis of clinical samples

Initially, researchers collected clinical biological samples such as blood, saliva, tissue, urine, semen, bronchoalveolar lavage fluid, and cervical-vaginal lavage fluid from study subjects and preprocessed them to obtain samples for testing. Subsequently, researchers utilized glass slides as substrates and employed lectin microarrays prepared with lectins from different species and/or commercial lectin microarrays to analyze the biological samples. Finally, leveraging the high specificity of lectins for sugar binding, researchers have analyzed the differences in the glycan spectra of the samples and their biological significance based on the varying fluorescence signal intensities on the lectin microarray.

Serum Glycomics Analysis in Clinical Diagnostics

The analysis of serum glycomics has become a cornerstone in clinical detection using lectin microarrays, aiding in early diagnosis, differential diagnosis, and disease prognosis monitoring. While initially focused on tumor-related areas, this application of lectin microarray technology has expanded to encompass various other diseases, with tumors remaining a priority in research endeavors ( Figure 2). Figure 2

Spectrum of diseases in serum glycomics analysis using lectin microarrays

Lectin detection in serum glycomics is widely utilized in research on tumors, inflammation, and immune system disorders. Researchers have compared the differences in the serum glycan spectra between the experimental group and the control group, revealing the significant potential value of this method for early diagnosis, differential diagnosis, prognosis monitoring, and other aspects of disease treatment.

Primarily, glycoproteins in human serum exhibit distinct glycan structures and have potential as biomarkers for cancers. For example, lectin microarrays have been employed for differentiating cancer from benign diseases and discriminating between different pathological types of cancer. First, the glycan patterns of serum GP73 demonstrate value in distinguishing hepatocellular carcinoma (HCC) from liver cirrhosis (LC), with the glycosylation of serum IgG and IgM showing potential advantages in enhancing the diagnostic accuracy of HCC [ 29, 30]. Specific lectins recognize N-glycans such as core fucose combined with Man-α6-Man glycans, tri/tetra-antennary and Neu5Ac-α6-bound, which have potential value in distinguishing prostate cancer from benign prostatic diseases [31]. Elevated core fucosylation has multiple biological implications, affecting epidermal growth factor receptor (EGFR) signaling. A reduction in core fucosylation reduces the phosphorylation of EGFR and the activity of downstream signaling pathways, which has important implications for cell proliferation and migration [32]. Significant changes in lectin fluorescence intensity (NFI) in early-stage cancer, with precise alterations in serum protein glycosylation patterns, provide important information for distinguishing different pathological types of lung cancer [ 33– 35]. The significant differences in the signal intensities of the lectins PWA and LEL and in the signal intensity of STL between the healthy control group and the gastric cancer group suggest that the specific high expression of the GlcNAc structure might serve as a potential early diagnostic marker for gastric cancer (the representative lectins used for glycan profiling are shown in Supplementary Table S1) [36]. Additionally, the combination of lectins (PHAE+HLsel) has the highest discriminatory potential in the diagnosis of colorectal cancer [37]. Elevated serum fucosylation of NRG1 may be associated with the BRAF V600E mutation, serving as a supplementary glycan biomarker for BRAF mutation-negative thyroid cancer [ 38, 39].

Cancer recurrence and distant metastasis are crucial factors in patient prognosis. Significant increases in the level of the lectin PHA-L reactive structure (β-1,6-GlcNAc branched N-glycan) in hepatocellular carcinoma patients with metastasis indicate potential biomarkers associated with metastasis [40]. Sera from metastatic and nonmetastatic breast cancer patients can be distinguished using lectin-binding profiles, suggesting potential diagnostic markers [41]. Studies have also shown that β-1,6-GlcNAc branching increases during tumor cell malignant transformation, promoting cancer development [ 42– 44]. A positive feedback mechanism of FUT8-mediated receptor core fucosylation promotes TGF-β signaling and EMT, stimulating breast cancer cell invasion and metastasis [ 45, 46].

In addition to key proteins involved in tumor cells, analyzing and measuring changes in glycomic extracellular vesicles associated with pancreatic cancer sera could offer an innovative diagnostic approach for this disease. A study revealed marked elevation in the signal intensities of six specific lectins, including DSA, STL, LEL, UDA, ACA, and ABA, in pancreatic cancer extracellular vesicles compared to those in healthy individuals [47].

In addition to tumor applications, recent research on serum protein glycosylation has shown promising advancements in immunology, inflammation, and various other diseases [ 48– 50]. For instance, Hashimoto’s thyroiditis (HT), an autoimmune thyroid disorder, is characterized by distinct glycosylation patterns of TgAb IgG and elevated levels of the serum glycoproteins LTF and MASP-1 bound to the lectin VVA [ 51– 53]. Changes in MMP-3 glycosylation can serve as specific biomarkers for rheumatoid arthritis (RA), and ACG and Jacalin can serve as markers of disease activity in RA [54]. SBA lectin has a greater affinity for serum IgG in RA patients, and different subgroups of RA exhibit distinct glycan profiles [55]. A rare case of monoclonal immunoglobulin deposition disease involved multiple glycan structural changes in IgA1 deposition and mesangial proliferation [56]. Therefore, an automated sandwich immunoassay system using lectin WFA and anti-IgA1 monoclonal antibodies has been established to detect abnormally glycosylated IgA1 [57].

Furthermore, the difference in the specific binding of lectin SNA-I to haptoglobin-related protein (HRP) can serve as a potential serum biomarker to distinguish between bacterial pneumonia and nonbacterial pneumonia [58]. Lectin Jacalin, which recognizes O-glycans, exhibits specific and strong reactivity toward the M-HBsAg required for Dane particle secretion, establishing a highly sensitive analytical method for determining the glycan profile of HBsAg on HBV particles [59].

In the past two decades, serum lectin microarray research has developed dramatically, with more types and functions of lectins emerging to meet different research needs, diagnoses, differentiation, and prognoses. However, abundant proteins and other biomolecules in serum samples can interfere with the specific binding of lectins to the glycoprotein of interest, increasing the background signal. The removal of highly abundant proteins allows for the detection of signals from glycoproteins present at lower concentrations, resulting in more reliable and accurate results [ 60, 61]. Lectin microarray technology is often combined with other analytical techniques, such as ELISA and mass spectrometry, for comprehensive analysis and accurate identification of complex samples.

Saliva Detection in Clinical Diagnostics

Proteins in saliva are predominantly synthesized and secreted by the salivary glands, with a small fraction originating from plasma. The significant overlap of 20%–30% in protein content between saliva and blood/plasma suggests that saliva is a promising clinical sample for diagnosing systemic diseases. To date, more than 3000 proteins have been identified in human saliva. Saliva sample testing offers an ideal noninvasive method for clinical diagnosis, being easy to collect and cost-effective [62].

Lectin detection via saliva glycan spectra holds significant potential for early diagnosis and treatment evaluation, not only in oral and digestive tract-related diseases but also in various other medical conditions ( Figure 3). Saliva sample testing represents an ideal noninvasive approach for oral and digestive tract-related diagnostics. Fang et al. [63] discovered that three lectins (AAL, PWM, and PHA-E+L) primarily recognize glycoproteins that are reduced in saliva from patients with oral lichen planus. Shu et al. [64] identified differences in the glycan expression profile of esophageal squamous cell carcinoma (ESCC) patients, suggesting that changes in salivary glycan types recognized by DSA could become potential noninvasive biomarkers for diagnosing ESCC patients. Additionally, Shu et al. [65] compared the salivary glycan components of patients with gastric cancer, atrophic gastritis, and healthy volunteers. They used 15 candidate lectins to construct diagnostic models for gastric cancer and atrophic gastritis and developed a saliva microarray detection method, providing new insights for the convenient diagnosis of gastric cancer. Yu et al. [66] used lectin microarrays to discover significant changes in glycosylation in the saliva of patients with type 2 diabetes mellitus (T2DM). They further utilized MALDI-TOF/TOF-MS affinity capture to obtain precise structural information on these glycans, providing new insights into the complex physiological changes in patients with T2DM. Ren et al. [67] developed a machine learning model based on salivary protein glycosylation data, with a support vector machine (SVM) showing the best diagnostic performance. This study also compared the differences in salivary glycan patterns before and after surgery, which may serve as potential prognostic biomarkers for patients with papillary thyroid carcinoma. Figure 3

Spectrum of diseases in saliva glycomic analysis using lectin microarrays

Researchers have applied lectin microarray technology to saliva glycomics analysis in both oncological and nononcological diseases, conducting exploratory work in biomarker discovery, early cancer detection and screening, disease prevention, and other areas.

In addition to the aforementioned studies, saliva detection has been applied in other diseases. Liu et al. [68] aimed to screen early breast cancer patients by analyzing changes in saliva glycomics. They constructed a diagnostic model based on glycan abundance, demonstrating good diagnostic capability. Furthermore, Han et al. [69] screened distinct glycan patterns in patients with diabetic nephropathy (DN) and nondiabetic renal disease (NDRD) using lectin microarrays. They identified lectins reflecting DN severity and prognosis, suggesting potential diagnostic and prognostic markers. Ding et al. [70] investigated changes in saliva glycan expression levels in response to H1N1 vaccine and avian influenza virus (AIV) strains, highlighting the significance of α2-3-linked terminal sialic acid levels, particularly in breastfeeding mothers postpartum.

Unlike serum, saliva contains a large and diverse population of oral microbiota, and salivary glycobiology plays a crucial role in the interactions between the oral host-microbiota, microbiota-host, and microbiota-microbiota. The role of salivary glycobiology in oral and systemic diseases is still being further explored and understood [71].

Urine Detection in Clinical Diagnostics

The detection of urinary glycan spectra using lectin microarrays primarily targets extracellular vesicles (EVs) and residual proteins. Urinary extracellular vesicles (uEVs), which are actively released by epithelial cells in the renal unit, contain protein and nucleic acid components, potentially serving as important disease biomarkers. Gerlach et al. [ 72, 73] compared the lectin microarray profiles of uEVs from individuals with polycystic kidney disease and healthy individuals, revealing complex glycan spectra on the surface of uEVs. These findings may offer valuable insights for the diagnosis and treatment of urological system diseases. However, the extraction and detection of target uEVs are challenging due to the complex composition of urine samples. Echevarria et al. [74] developed a bead-based method for lectin-based isolation of exosomes from urine and identified three lectins that show specific interactions with vesicles. Maria et al. [75] compared three different isolation methods for uEVs—ultracentrifugation (UC), size exclusion chromatography (SEC), and a commercial kit, highlighting the importance of standardizing uEV isolation techniques to ensure the analytical reproducibility necessary for their implementation in clinical settings.

In addition to studies on EVs, urine protein is also under consideration. Precise changes in glycan patterns in urine proteins can distinguish between diabetic nephropathy (DN) and nondiabetic renal disease (NDRD). The lectin DSA recognizes (β-1,4)-linked N-acetyl-D-glucosamine (GlcNAc), and its relative abundance in the urine of DN patients is significantly greater than that in the urine of NDRD patients [ 76, 77]. Additionally, Fetuin-A is a candidate marker for predicting the progression of diabetic nephropathy [78]. Yang et al. [79] conducted glycoproteomic analysis of urine proteins to detect pregnancy status in cynomolgus monkeys and Sprague-Dawley rats. This application provides new insights for the detection of human pregnancy-related diseases.

Sperm Detection in Reproductive Biology

Cell surface glycans, or glycocalyxes, also play a crucial role in influencing sperm motility, maturation, and fertilization. To address the issue of lectin-sperm binding glycan profiles, Xin et al. [80] developed a high-throughput lectin microarray for detecting mammalian sperm. Additionally, they analyzed the lectin binding profiles of homozygous DEFB126 mutations (del/del) compared to wild-type (wt/wt) sperm and identified six lectins as potential biomarkers for low fertility resulted from DEFB126 mutations [81]. Furthermore, the cryopreservation of semen is a commonly utilized technique in assisted reproductive technology and sperm banking; however, it has been shown to have detrimental effects on sperm quality. Xin et al. [82] discovered that ABA may serve as a potential biomarker for predicting the motility of cryopreserved sperm. Notably, Sun et al. [83] successfully developed human lectin arrays derived from yeast, marking a significant advancement in glycomics research with potential applications in human and mammalian studies.

Tissue Detection in Clinical Diagnostics

In addition to body fluids, lectin microarrays hold promise for glycan profiling in various tissue samples, including fresh tissue, pathological specimens, and autopsy samples. High-throughput standardization and in-depth analysis of tissue glycan spectra help in understanding disease-specific changes under different pathological conditions. Okatani et al. [84] optimized tissue histological staining processes and conducted cell type-specific glycomic analysis using laser dissection microscopy. Kobayashi et al. [85] reported glycomic profiles of clinical choriocarcinoma samples and iC3-1 cell lines, identifying upregulated glycan-related genes in iC3-1 cells and glycan alterations in choriocarcinoma tissues. Additionally, Zhou et al. [86] employed lectin microarray analysis to identify glycan alterations in triple-negative breast cancer (TNBC) cell lines, validating candidate lectins through flow cytometry and immunofluorescence experiments. They found a positive correlation between Ricinus communis agglutinin I (RCA-I) staining and TNBC cell metastatic ability. Matsuzawa et al. [87] compared the differences in glycosylation between recurrent and nonrecurrent triple-negative breast cancer patients using lectin microarrays. They found that TJA-II lectin exhibited increased specificity in the treatment of recurrent patients, which may aid in the development of new prognostic and therapeutic tools. Wagatsuma et al. [88] conducted differential glycoprotein analysis on crude extracts of nontumor and tumor regions of pancreatic ductal adenocarcinoma (PDAC) tissues. They combined this analysis with relevant genomic and transcriptomic multiomics public data and successfully demonstrated that basigin is a representative glycoprotein associated with PDAC. This study also highlighted the significant role of large public multiomics datasets in glycomics research. Additionally, Ogawa et al. [89] analyzed changes in gastric mucosal glycosylation before and after Helicobacter pylori eradication using lectin microarrays, discovering reversible alterations associated with H. pylori infection. Qin et al. [90] conducted high-throughput glycomics analysis on plasma samples from COVID-19 patients and postmortem tissues, revealing upregulated α2,6-sialylation in severe COVID-19 patient lung tissues.

Research on glycosylation changes in tissues faces various challenges. Despite significant advances in characterizing glycoproteins, glycolipids, and other components of cells and tissues over the past decade, most studies to date have only included fragmented glycan information or incomplete images of most cells and tissues. To overcome this limitation, Li et al. [91] developed a combined method for glycan detection based on an MS workflow, allowing for in-depth and comprehensive characterization of glycan spectra in cells and tissues. Additionally, the presence of multiple cell types in tissues poses challenges for specific labeling of different cell types in vivo using glycans. Fan et al. [92] developed genetically encoded metabolic glycan labeling (GeMGL), aiding in cell type-specific glycan imaging and glycoproteomics analysis in various tissues and disease models. Analyzing glycosylation changes in tissue samples during the development and progression of diseases requires consideration of the contributions and interferences from different cell types.

Exploring Specialized Clinical Samples with Lectin Microarrays

In addition to their common applications in detecting blood and urine samples, lectin microarrays can also be utilized for glycan analysis in specialized clinical samples. Cervical-vaginal fluid (CVF), which covers the vaginal epithelium, is an important immune medium that provides a barrier against infections. The glycosylation of proteins in the CVF plays a crucial role in its immune function. Wang et al. [93] investigated the glycosylation patterns of cervical-vaginal lavage (CVL) samples under different hormonal conditions. They suggested that the microbial community has a significant impact on glycan levels in CVF, potentially even more so than hormonal influences. Moreover, Liu et al. [94] examined the changes in glycosylation profiles of bronchoalveolar lavage fluid (BALF) in patients with three different pathological types of lung cancer. They devised a diagnostic model based on glycosylation abundance and reported that abnormal protein glycosylation changes in BALF could serve as a new biomarker for early-stage identification and diagnosis of lung cancer. The alteration of glycosylation in specialized clinical samples offers a fresh perspective for diagnosing related diseases. For instance, cerebrospinal fluid contains biological and neurological information, and glycoproteomic studies of cerebrospinal fluid have contributed to elucidating the pathogenesis of Alzheimer’s disease [ 95, 96]. Lectin microarrays also hold significant potential in glycan analysis of various clinical samples, such as amniotic fluid, tears, breast milk, and sweat, providing vast opportunities for application [ 97- 100].

Conclusions and Perspective

In conclusion, the integration of lectin detection technology with interdisciplinary fields has propelled collective advancements in glycobiology research ( Table 1). Lectin microarrays, as a pivotal tool, have significantly contributed to interpreting protein glycomics and hold great promise in clinical applications. The emphasis on high-quality sample collection underscores the importance of sample integrity in lectin detection results. Moreover, researchers increasingly rely on analyzing multiple clinical samples to garner comprehensive data on glycosylation changes, which is crucial for disease-specific diagnosis and understanding of pathological mechanisms. Table 1 Inter-group differential lectins presented in the spectrum of various diseases during glycomics detection process

Disease

	Sample source

	Number of samples

	Number of lectins

	Differentiated lectins

	Ref.

	
Papillary thyroid cancer

	Serum

	120

	14

	GSL2, BPL, NML, HHL, PHA-L and LEL

	[39]

	
Saliva

	79

	37

	PTL-I, SJA, LTL, PTL-II and MAL-I

	[67]

	
Hashimoto’s thyroiditis

	Serum

	47

	94

	LCA, VFA, MNA-M, SNA-I, PSA, LcH andPHA-L

	[52]

	
Serum

	53

	70

	VVA

	[53]

	
Breast cancer

	Saliva

	447

	37

	BS-I, NPA, PNA, PTL-II and MAL-I

	[68]

	
Triple-negative breast cancer

	Tissue

	30

	45

	TJA-II, ACA, WFA, and BPL

	[87]

	
Tissue

	212

	91

	RCA-I

	[86]

	
Choriocarcinoma

	Tissue

	4

	45

	SNA, SSA, TJA-I, RCA120, PHA-E, DSA, ACG, TxLC-I, UDA, Jacalin, WGA, PSA, LCA, AOL, NPA, GNA, BPL, WFA, ACA, HPA and MAH

	[85]

	
Gastric cancer

	Serum

	20

	50

	PWA, LEL, STL, EEL, RCA-II, RCA-I, VAL, DSA, PHA-L, UEA, CAL, CFL and GNL

	[36]

	
Saliva

	201

	37

	AAL, VVA, SBA and BS-I

	[65]

	
Helicobacter pylori-infected

	Tissue

	30

	37

	Jacalin and MPA

	[89]

	
Colorectal carcinoma

	Serum

	34

	16

	PHA-E and HL sel

	[37]

	
Hepatocellular carcinoma

	Serum

	148

	50

	AAL, ACL, Con A, DSA, ECL, LCA, LEL, NML, PSA and STL

	[29]

	
Serum

	207

	56

	EEL, MPL, TL and DSL

	[30]

	
Metastatic hepatocellular carcinoma

	Serum

	80

	50

	PHA-L

	[40]

	
Pancreatic cancer

	Serum

	215

	45

	ABA and ACA

	[47]

	
Pancreatic ductal adenocarcinoma

	Tissue

	14

	45

	MAL-I, ACG, SNA, SSA, TJA-I, PHA-E and STL

	[88]

	
Esophageal squamous cell carcinoma

	Saliva

	41

	37

	ECA, RCA120, BPL, jacalin, MAL-II, SNA,DSA, GSL-II, ConA, GNA, LCA, PSA and UEA-I

	[64]

	
Oral lichen planu

	Saliva

	58

	38

	AAL, PWM and PHA-E+L

	[63]

	
Prostate cancer

	Serum

	22

	17

	AAL and P-selectin

	[31]

	
IgA nephropathy

	Serum

	6

	43

	WFA

	[56]

	
Serum

	140

	1

	WFA

	[57]

	
Diabetic nephropathy

	Saliva

	181

	37

	AAL, LEL, LCA, VVA and NPA

	[69]

	
Urine

	57

	37

	SNA and STL

	[76]

	
Urine

	69

	19

	DSA

	[77]

	
Urine

	29

	45

	SNA, SSA and TJA-1

	[78]

	
Type 2 diabetes mellitus

	Saliva

	152

	37

	LEL, VVA, Jacalin, RCA120 and DSA

	[66]

	
Lung cancer

	Broncho-alveolar lavage fluid

	281

	37

	MAL-II, RCA120, ECA, HHL, GSL-I, DBA, DSA and AAL

	[94]

	
Serum

	48

	37

	BS-I

	[34]

	
Lung adenocarcinoma

	Serum

	40

	37

	AAL, GSL-I, SBA, Jacalin, UEA-I, DBA, HHL, MAL-I, BPL, PTL-I, SJA, GSL-I, BS-I, DSA, LTL, RCA-120, STL and ConA

	[35]

	
Serum

	48

	37

	AAL

	[34]

	
Lung squamous carcinoma

	Serum

	46

	37

	HHL, PHA-E+L, Jacalin, LTL, DBA, MAL-II, PTL-I, SJA, EEL, RCA-120, STL, BS-I, DSA, SBA, UEA-I and BPL

	[35]

	
Small cell lung cancers

	Serum

	90

	37

	RCA120, BS-I, UEA-I, WFA, LEL, LTL, SNA, PHA-E, Jacalin, LCA, STL, STL, GSL-I, NPA, HHL and DBA

	[36]

	
Serum

	48

	37

	PWM

	[34]

	
COVID-19

	Tissue

	44

	10

	diCBM40 and SNA

	[90]

	
Serum

	131

	43

	AIA, SLBR-H, SLBR-B, PHA-L and SNA

	[90]

	
Mycoplasma pneumonia

	Serum

	18

	91

	SNA-I

	[58]

	
Rheumatoid arthritis

	Serum

	464

	56

	SBA

	[55]

	
Pregnancy

	Urine

	/

	37

	PHA-E+L, DBA, GSL-II, BS-I and PSA

	[79]

	
Subfertility

	Sperm

	90

	91

	Jacalin/AIA, GHA, ACL, MPL, VVL and ABA

	[81]

	

The key strength of lectin microarrays lies in their ability to efficiently analyze precise changes in the glycan spectrum, enabling the identification of glycan patterns associated with diseases and facilitating early and accurate diagnosis. However, lectin microarrays also have limitations. The current library of mammalian lectins is limited, necessitating further exploration to develop commercially viable lectin microarrays sourced from mammals. Additionally, while lectin microarrays excel in analyzing glycan variances, identifying individual glycan structures remains challenging and often requires supplementary markers for improved diagnostic accuracy. Moreover, some commonly used lectins may fail to detect low-abundance glycan changes.

Integrating lectin microarrays with multidisciplinary approaches is essential for unraveling the complexities of the glycome. Combining lectin microarrays with efficient chromatographic separation methods and ultrasensitive mass spectrometry aids in analyzing glycans in complex mixtures. Furthermore, integrating lectin microarray-based glycomics with other omics disciplines allows for the exploration of interactions between bioactive substances, thereby deepening our understanding of disease mechanisms. High-throughput, automated glycan spectrum analysis and optimized data algorithms remain critical for advancing glycomics research. The introduction of artificial intelligence methods holds significant promise for efficiently analyzing glycan spectrum changes and constructing disease-specific diagnostic models.

In essence, the precise detection of glycan spectrum changes through lectin microarray technology has the potential to revolutionize clinical diagnostics, driving the advancement of precision medicine. This approach could lead to a deeper understanding of pathological mechanisms, the development of customized treatment plans, improved treatment outcomes, and reduced adverse reactions, thereby significantly impacting healthcare practices.

Supporting information

254TableS1

COMPETING INTERESTS

The authors declare that they have no conflict of interest.

Supplementary Data

Supplementary data is available at Acta Biochimica et Biphysica Sinica online.
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