alexa Development of a data-mining system for differential profiling of cell glycoproteins based on lectin microarray | Open Access Journals
ISSN: 0974-276X
Journal of Proteomics & Bioinformatics
Like us on:
Make the best use of Scientific Research and information from our 700+ peer reviewed, Open Access Journals that operates with the help of 50,000+ Editorial Board Members and esteemed reviewers and 1000+ Scientific associations in Medical, Clinical, Pharmaceutical, Engineering, Technology and Management Fields.
Meet Inspiring Speakers and Experts at our 3000+ Global Conferenceseries Events with over 600+ Conferences, 1200+ Symposiums and 1200+ Workshops on
Medical, Pharma, Engineering, Science, Technology and Business

Development of a data-mining system for differential profiling of cell glycoproteins based on lectin microarray

Atsushi Kuno1#, Yoko Itakura1#, Masashi Toyoda2, Yoriko Takahashi3, Masao Yamada4, Akihiro Umezawa2, and Jun Hirabayashi1*
1Research Center for Medical Glycoscience (RCMG), National Institute of Advanced   Industrial Science and   Technology (AIST), 1-1-1, Umezono, Tsukuba, Ibaraki 305-8568,   Japan
2Department of Reproductive Biology and Pathology, National Research Institute for Child   Health and
  Development, 2-10-1, Okura, Setagaya, Tokyo, 157-8535, Japan
3Bioscience Group, Mitsui Knowledge Industry Co., Ltd., Hitotsubashi SI bldg., 3-26,   Kandanishikicho,
  Chiyoda-ku, Tokyo 101-0054, Japan
4Glycomics Research Laboratory, Moritex Corporation, 1-3-3, Azamino-Minami, Aoba-ku,   Yokohama City,   Kanagawa 225-0012, Japan
Corresponding Author : Dr. Jun Hirabayashi
Research Center for Medical Glycoscience
National Institute of Advanced, Industrial Science and Technology 
AIST Tsukuba Central 2, 1-1-1, Umezono
Tsukuba, Ibaraki 305-8568, Japan
Tel        : +81-29-861-3124,
Fax      : +81-29-861-3125,
E-mail :
Received April 15, 2008; Accepted May 14, 2008; Published May 20, 2008
Citation: Atsushi K, Yoko I, Masashi T, Yoriko T, Masao Y, et al. (2008) Development of a Data-mining System for Differential Profiling of Cell Glycoproteins Based on Lectin Microarray. J Proteomics Bioinform 1:068-072. doi:10.4172/jpb.1000011
Copyright: © 2008 Atsushi K, et al. This is an open-access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.
Related article at
DownloadPubmed DownloadScholar Google

Visit for more related articles at Journal of Proteomics & Bioinformatics


Lectin microarray is an emerging technique enabling multiplex glycan profiling in a direct, rapid and sensitive manner. So far, there has been no robust system available for efficient data-mining to realize differential profiling, which is an effective approach to biomarker investigation. In the present paper, we describe a practical strategy for proteomics-based glycan-related biomarker discovery, with an example of mice embryonal carcinoma and embryonic stem cells and their differentiated forms with retinoic acid. Data were processed by the microarray system using a max-normalization procedure after a gain-merging process, followed by principal component analysis.

Differential glycan profiling; Biomarker discovery; Lectin microarray; Principal component analysis
EC cells: Embryonal Carcinoma cells; ES cells: Embryonic Stem cells; MS: Mass Spectrometry; PCA: Principal Component Analysis; TBSTx: Tris-buffered Saline containing 0.1% Triton X-100.
Cell surface dynamics are characterized by altered glycosylation in the development and differentiation stages. Drastic glycosylation change has also been proposed for tumor progression and metastasis. For instance, cell surface sialylation and 1-6 branching of N-linked oligosaccharides are strongly correlated with differentiation of embryonal carcinoma cells and metastatic potential of cancer cells (Dennis et al., 1982; Dennis et al., 1987; Heffernan et al., 1993). Therefore, it is highly likely that finding of novel cell differentiation- related or tumor-specific glycoproteins with significant structural changes will become reliable biomarkers. From these points of view, proteomics-based biomarker discoveries have now been complemented by extensive glyco-technologies, such as chemical capturing targeting N-linked glycoproteins (Zhang et al., 2003; Nishimura et al., 2005) and affinity capturing with the use of various glycan-binding proteins, i.e., lectins (dashed arrows in Fig. 1A) (Kaji et al., 2003).
One of the successful reports involving the concept of glycoproteomics includes the discovery of GP73, a novel glycoprotein discovered as a serological biomarker candidate for liver cancer (Block et al., 2005; Drake et al.. 2006). Traditionally, serial lectin affinity chromatography (Cummings et al., 1982) has been a procedure for enrichment of particular glycoproteins with a target glycan structure of either N- or O-glycosylation (Madera et al., 2005; Qiu et al., 2005). In this case, selection of a highly-effective set of lectins is essential for success in the biomarker discovery(dashed arrows in Fig. 1A). If a systematic data-mining procedure which follows differential glycan analysis were to be available, it would facilitate the design of an optimal set of lectins (bold arrows in Fig. 1A).
Lectin microarray is an emerging technology enabling an ultrasensitive measuring of multiplex lectin-glycan interaction analysis (Angeloni et al., 2005; Pilobello et al., 2005; Kuno et al., 2005). Taking advantage of the merits of this technology, i.e., sensitive detection and simple manipulation, an increasing number of studies using lectin microarray report that cell-surface glycans are closely associated with the functions, states and relation to diseases of individual cells (Ebe et al., 2006; Pilobello et al., 2007; Tateno et al., 2007). Among biological interests in glycans, a current trend is the focus on glycan-related biomarkers. However, there is no established strategy and optimized protocols for cell glycoprotein profiling, in particular regarding data-mining procedures. In this study, we describe logistic processes for differential cell glycoprotein profiling including data-mining as an alternative approach to conventional proteomics-based biomarker discovery. Key points of the strategy for cell glycoprotein profiling include: (1) fitting the protein concentrations in the appropriate range between 0.2 and 0.5 g/ml to obtain robust and reproducible signal patterns, (2) a gain-merging technique to expand the dynamic range of the lectin-glycan interaction signals, and (3) the max-normalization procedure using the merged data for normalization. The data thus processed were found to be useful for systematic determination of the best set of lectins among more than 40 probe candidate lectins immobilized on the microarray (bold arrows in Figure. 1A). A model study focused on regenerative medicine is described for mice embryonal carcinoma and embryonic stem cells as well as their differentiated forms with retinoic acid.
Results and Discussion
Optimization of lectin microarray manipulations. For improved proteomics-based biomarker discovery, cell glycoproteins are proposed as targets. Glycosylation change is analyzed by a highsensitivity, robust, and reproducible method using lectin microarray, if the evanescent-field fluorescence-assisted detection method is adopted. However, the previous protocol for cell glycoprotein analysis has not fulfilled the recent requirements for detailed cell profiling and biomarker discovery (Ebe et al., 2006). To address these issues, we first established a strict protocol for differential analysis of cell glycoproteins using mouse embryonal carcinoma cells (mouse teratocarcinoma cell line F9) as a model. The analyte (i.e., glycoprotein) was focused on hydrophobic, raft-associated membrane-bound proteins isolated using a CelLytic MEM Protein Extraction kit (Sigma, St. Louis, MO), because we found the proteins to be analyzed showed the highest signal-to-noise ratio. A small aliquot of the obtained protein (200 ng from approximately 1 x 103 cells) was labeled with Cy3-succimidyl ester (designated as Cy3-labeled glycoprotein). Various concentrations of the Cy3- labeled glycoprotein solution (60 ml, 0.02~1.0 mg/ml) were then subjected to the lectin microarray analysis. Due to the specificity of the CCD camera, a gain value should be set so that the observed fluorescence intensities of almost all positive-spots on the glass slide fall within the range 1,000 to 40,000, which provides a dynamic range with sufficient linearity. Each glass slide was successively scanned under different gain conditions. A dosedependent increment of signal intensity was observed on most of the positive-spots
(Fig. 1B). However, we could not confirm satisfactory linearity for all of the spots under a single gain condition. For instance, the signals of some positive-spots (e.g., GSL-I, ECA, SBA, LCA, ConA, TJA-II, and PSA) were kept below 1,000 under the lower gain (80) condition as shown in the top of Fig. 1B. Under the higher gain (100) condition, the intensities of four lectins (DSA, STL, WGA, and LEL) were above the upper limit 40,000, at protein concentrations of 0.2 mg/ml or more (the bottom of Fig. 1B). Such uneven linearity could cause inappropriate interpretation of the data. A useful data optimization procedure needed to be introduced to solve this basic problem.
Data-processing by gain-merging and max-normalization. Provided the intensities of all positive-spots are kept within the acceptable dynamic range (1,000 to 40,000), signal patterns of each analyte should be theoretically the same even under different gain conditions, i.e., higher gain intensity (IntH i) over lower gain intensity (IntL i) ratios for lectin i should be almost the same value. To ensure high-reproducibility, the dynamic range was expanded by a “gain-merging” procedure. An outline of the procedure (Fig. 2A) is as follows: a slide glass is scanned under two different gain conditions; higher gain to “rescue” weak signals (e.g., lectin f in Fig. 2A) below 1,000 (IntH (lectin f)) and lower gain to “suppress” excessively strong signals (e.g., lectin d) over 40,000 (IntL (lectin d)). At this point, selection of appropriate “merging”-lectins is important (lectins a, b, and e in the case of Fig. 2A), the signal intensities of which fall within the range 1,000 to 40,000 under both higher and lower gain conditions. With these selected merging lectins, a “Factor (F)” is determined as the average of higher/lower ratios calculated for individual merging lectins by eq (1).
F = Average (IntH i / IntL i )                                          ...eq (1)
The gain-merging procedure is completed by replacement of the over-range intensities (>40,000) obtained under the higher gain condition (e.g., IntH (lectin c)) with theoretical intensities (IntT (lectin c)) by eq (2).
IntT (lectin c) = IntL (lectin c) x F                                      ...eq (2)
For other lectins with no over-range under the higher gain condition, signal intensities obtained under the higher gain condition are used with no modification. During this process, all the resultant intensities of positive-spots were within the expanded dynamic range, from 1,000 to 40,000 x F. When 1.0 μg/ml of F9 cell proteins were subjected to analysis (Fig. 1B), all 34 positive lectins fell within the merged dynamic range (1,000~132,000) after the gain-merging procedure (F =3.3), whereas 85% (29 lectins under the lower gain (80) condition) and 76% (26 lectins under the higher gain (100) condition) of positive lectins were within the original dynamic range (1,000~40,000), respectively.
Using the merged data, a normalization procedure was developed to simplify and stabilize the subsequent differential glycoprotein analysis. Considering the difficulty in selecting a universal lectin, to assure the same level of signal intensities, we selected a practical procedure to calculate the relative intensity in comparison with the strongest intensity among the positive-spots under the given conditions, i.e., max-normalization. The max-normalized data of F9 cells thus processed gave similar signal patterns provided that protein concentrations were maintained within the range 0.2 to 0.5 μg/ml (Fig. 2B).
A similar observation has also been made for the differentiated forms with retinoic acid (F9-RA) (Fig. 2B). These results suggest that the procedure of max-normalization following gain-merging contribute to the establishment of high-reproducible cell glycoprotein profiling with extremely simple and systematic manipulations.
Principal component analysis: We next examined whether or not a statistical analysis of the data could actually determine the best set of lectins, which should be useful for an efficient enrichment of relevant glycoproteins associated with glycosylation change induced by retinoic acid treatment. For this purpose, principal component analysis (PCA) using a web-based NIA array analysis tool (; Chapman et al., 2001; Sharov et al., 2005), was chosen and applied to the above processed lectin microarray data of F9 cells (four different preparations) as well as F9-RA (three different preparations). For the sake of comparison, we also analyzed mouse embryonic stem cells (mES) (four different preparations) and their differentiated forms (mES-RA) (two different preparations). The lectin microarray data processed according to the developed procedures gave two principal components (PCs). The 2D-biplot format thus obtained clearly divided the above 13 preparations into four independent clusters; i.e., F9, F9-RA, mES and mES-RA (the upper left of Fig. 2C). The result also revealed double negative-correlation with the PC1 and PC2, i.e., signal enhancement with retinoic acid, for three probe lectins (αGalNAc binders, DBA and HPA, and β1-6 branching binder, PHA(L); the upper left of Fig. 2C).
Importantly, the normalized intensities of these lectins were relatively low (i.e., 0~0.03; Fig. 2B), which the method could have failed to detect without the use of the rescue process using the gain-merging procedure (<1,000 under the lower gain condition) (see the PCA of the raw data without gain-merging processing in the bottom of Fig. 2C). This observation clearly indicates a practical merit of such a datamining procedure for the investigation of novel glycan-related biomarkers, which are expected to be fairly minor components in clinical samples.
A lectin microarray-based data-mining system for differential profiling of cell glycoproteins has been developed by adopting maxnormalization following gain-merging processes. This highly-reproducible analysis with simple and systematic manipulations should provide the basis of a robust and logistic strategy for the discovery of proteomics-based glycan-related biomarkers.
We thank N. Uchiyama, Y. Kubo, and J. Murakami for supplying the lectin microarray. We also thank A. Matsuda for critical discussion concerning the preparation of protein solution. This work was supported in part by a grant for New Energy and Industrial Technology Development Organization (NEDO) in Japan.

  1. Angeloni S, Ridet JL, Kusy N, Gao H, Crevoisier F, et al. (2005) Glycoprofiling with micro- arrays of glycoconjugates and lectins. Glycobiology 15: 31- 41. »  CrossRef  »  PubMed  »  Google Scholar

  2. Block TM, Comunale MA, Lowman M, Steel LF, Romano PR, et al. (2005) Use of targeted glycoproteomics to identify serum glycoproteins that correlate with liver cancer in woodchucksand humans. Proc Natl Acad Sci USA 102: 779-784. »  CrossRef  »  PubMed  »  Google Scholar

  3. Chapman S, Schenk P, Kazan K, Manners J (2001) Using biplots to interpret gene expression patterns in plants. Bioinformatics 18: 202-204. »  CrossRef  »  PubMed   

  4. Cummings R, Kornfeld S (1982) Fractionation of asparagineslinked oligosaccharides by serial lectin-agarose affinity chromatography. J Biol Chem 257: 11235-11240. »  CrossRef  »   Google Scholar

  5. Dennis JW, Waller C, Timpl R, Schirrmacher V (1982) Surface sialic acid reduces attachment of metastatic tumour cells to collagen type IV and fibronectin. Nature 300: 274-276. »  CrossRef  »  PubMed  »  Google Scholar

  6. Dennis JW, Laferte S, Waghorne C, Breitman ML, Kerbel RS (1987) Beta1-6 branching of Asn-linked oligosaccharides is directly associated with metastasis. Science 236: 582-585. »  CrossRef  »  PubMed  »  Google Scholar

  7. Drake RR, Schwegler EE, Malik G, Diaz J, Block T, et al. (2006) Lectin capture strategies combined with mass spectrometry for the discovery of serum glycoprotein biomarkers. Mol Cell Proteomics 5: 1957-1967. »  CrossRef  »  PubMed  »  Google Scholar

  8. Ebe Y, Kuno A, Uchiyama N, Koseki-Kuno S, Yamada M, et al. (2006) Application of lectin microarray to crude samples: differential glycan profiling of Lec mutants. J Biochem (Tokyo) 139: 323-327. »  CrossRef  »  PubMed  »  Google Scholar

  9. Heffernan M, Lotan R, Amos B, Palcic M, Takano R, et al. (1993) Branching β1-6N-acetylglucosaminetransferases and polylactosamine expression in mouse F9 teratocarcinoma cells and differentiated counterparts. J Biol Chem 268: 1242-1251. »  CrossRef  »  PubMed  »  Google Scholar

  10. Kaji H, Saito H, Yamauchi Y, Shinkawa T, Taoka M, et al. (2003) Lectin affinity capture, isotope-coded tagging and mass spectrometry to identify N-linked glycoproteins. Nat Biotech 21: 667-672.  »  CrossRef  »  PubMed  »    Google Scholar

  11. Kuno A, Uchiyama N, Koseki-Kuno S, Ebe Y, Takashima S, et al. (2005) Evanescent-field fluorescence-assisted lectin microarray: a new strategy for glycan profiling. Nat Methods 2: 851-856. »  CrossRef  »  PubMed  »  Google Scholar

  12. Madera M, Mechref Y, Novotny MV (2005) Combining lectin microcolumns with high-resolution separation techniques for enrichment of glycoproteins and glycopeptides. Anal Chem 77: 4081- 4090.   »  CrossRef  »  PubMed  »  Google Scholar

  13. Nishimura SI, Niikura K, Kurogochi M, Matsushita T, Fumoto M, et al. (2005) High-throughput protein glycomics: Combined use of chemoselective glycoblotting and MALDI-TOF/TOF mass spectrometry. Angew Chem Int Ed 44: 91-96.  

  14. Pilobello KT, Krishnamoorthy L, Slawek D, Mahal LK (2005) Development of a lectin microarray for the rapid analysis of protein glycopatterns. ChemBioChem 6: 985-989. »  CrossRef  »  PubMed  »  Google Scholar

  15. Pilobello KT, Slawek DE, and Mahal LK (2007) A ratiometric lectin microarray approach to analysis of the dynamic mammalian glycome. Proc Natl Acad Sci USA 104: 11534-11539. »  CrossRef  »  PubMed  »  Google Scholar

  16. Qiu R, Regnier FE (2005) Use of multidimensional lectin affinity chromatography in differential glycoproteomics. Anal Chem 77: 2802-2809. »  CrossRef  »  PubMed  »  
    Google Scholar

  17. Sharov AA, Dudekula DB, Ko MSH (2005) A web-based tool for principal component and significance analysis of microarray data. Bioinformatics 21: 2548-2549. »  CrossRef  »  PubMed  »  Google Scholar

  18. Tateno H, Uchiyama N, Kuno A, Togayachi A, Sato T, (2007) A novel strategy for mammalian cell surface glycome profiling using lectin microarray. Glycobiology 17: 1138-1146. »  CrossRef  »  PubMed  »  Google Scholar

  19. Zhang H, Li XJ, Martin DB, Aebersold R (2003) Identification and quantification of N-linked glycoproteins using hydrazide chemistry, stable isotope labeling and mass spectrometry. Nat Biotech 21: 660-666.  »  CrossRef  »  PubMed  »  Google Scholar

Select your language of interest to view the total content in your interested language
Post your comment

Share This Article

Relevant Topics

Recommended Conferences

  • 8th International Conference on Proteomics and Bioinformatics
    May 22-24, 2017 Osaka, Japan
  • 9th International Conference on Bioinformatics
    October 23-24, 2017 Paris, France
  • 9th International Conference and Expo on Proteomics
    October 23-25, 2017 Paris, France

Article Usage

  • Total views: 11483
  • [From(publication date):
    May-2008 - May 01, 2017]
  • Breakdown by view type
  • HTML page views : 7730
  • PDF downloads :3753

Post your comment

captcha   Reload  Can't read the image? click here to refresh

Peer Reviewed Journals
Make the best use of Scientific Research and information from our 700 + peer reviewed, Open Access Journals
International Conferences 2017-18
Meet Inspiring Speakers and Experts at our 3000+ Global Annual Meetings

Contact Us

Agri, Food, Aqua and Veterinary Science Journals

Dr. Krish

1-702-714-7001 Extn: 9040

Clinical and Biochemistry Journals

Datta A

1-702-714-7001Extn: 9037

Business & Management Journals


1-702-714-7001Extn: 9042

Chemical Engineering and Chemistry Journals

Gabriel Shaw

1-702-714-7001 Extn: 9040

Earth & Environmental Sciences

Katie Wilson

1-702-714-7001Extn: 9042

Engineering Journals

James Franklin

1-702-714-7001Extn: 9042

General Science and Health care Journals

Andrea Jason

1-702-714-7001Extn: 9043

Genetics and Molecular Biology Journals

Anna Melissa

1-702-714-7001 Extn: 9006

Immunology & Microbiology Journals

David Gorantl

1-702-714-7001Extn: 9014

Informatics Journals

Stephanie Skinner

1-702-714-7001Extn: 9039

Material Sciences Journals

Rachle Green

1-702-714-7001Extn: 9039

Mathematics and Physics Journals

Jim Willison

1-702-714-7001 Extn: 9042

Medical Journals

Nimmi Anna

1-702-714-7001 Extn: 9038

Neuroscience & Psychology Journals

Nathan T

1-702-714-7001Extn: 9041

Pharmaceutical Sciences Journals

John Behannon

1-702-714-7001Extn: 9007

Social & Political Science Journals

Steve Harry

1-702-714-7001 Extn: 9042

© 2008-2017 OMICS International - Open Access Publisher. Best viewed in Mozilla Firefox | Google Chrome | Above IE 7.0 version