PURPOSE: Lectins are valuable tools for detecting specific glycans in biological samples, but the interpretation of the measurements can be ambiguous due to the complexities of lectin specificities. Here, we present an approach to improve the accuracy of interpretation by converting lectin measurements into quantitative predictions of the presence of various glycan motifs. EXPERIMENTAL DESIGN: The conversion relies on a database of analyzed glycan array data that provides information on the specificities of the lectins for each of the motifs. We tested the method using measurements of lectin binding to glycans on glycan arrays and then applied the method to predicting motifs on the protein mucin 1 (MUC1) expressed in eight different pancreatic cancer cell lines. RESULTS: The combined measurements from several lectins were more accurate than individual measurements for predicting the presence or absence of motifs on arrayed glycans. The analysis of MUC1 revealed that each cell line expressed a unique pattern of glycoforms, and that the glycoforms significantly differed between MUC1 collected from conditioned media and MUC1 collected from cell lysates. CONCLUSIONS AND CLINICAL RELEVANCE: This new method could provide more accurate analyses of glycans in biological sample and make the use of lectins more practical and effective for a broad range of researchers.
PURPOSE: Lectins are valuable tools for detecting specific glycans in biological samples, but the interpretation of the measurements can be ambiguous due to the complexities of lectin specificities. Here, we present an approach to improve the accuracy of interpretation by converting lectin measurements into quantitative predictions of the presence of various glycan motifs. EXPERIMENTAL DESIGN: The conversion relies on a database of analyzed glycan array data that provides information on the specificities of the lectins for each of the motifs. We tested the method using measurements of lectin binding to glycans on glycan arrays and then applied the method to predicting motifs on the protein mucin 1 (MUC1) expressed in eight different pancreatic cancer cell lines. RESULTS: The combined measurements from several lectins were more accurate than individual measurements for predicting the presence or absence of motifs on arrayed glycans. The analysis of MUC1 revealed that each cell line expressed a unique pattern of glycoforms, and that the glycoforms significantly differed between MUC1 collected from conditioned media and MUC1 collected from cell lysates. CONCLUSIONS AND CLINICAL RELEVANCE: This new method could provide more accurate analyses of glycans in biological sample and make the use of lectins more practical and effective for a broad range of researchers.
Authors: Glòria Tabarés; Catherine M Radcliffe; Sílvia Barrabés; Manel Ramírez; R Núria Aleixandre; Wolfgang Hoesel; Raymond A Dwek; Pauline M Rudd; Rosa Peracaula; Rafael de Llorens Journal: Glycobiology Date: 2005-09-21 Impact factor: 4.313
Authors: Thiruvengadam Arumugam; Vijaya Ramachandran; Keith F Fournier; Huamin Wang; Lauren Marquis; James L Abbruzzese; Gary E Gallick; Craig D Logsdon; David J McConkey; Woonyoung Choi Journal: Cancer Res Date: 2009-07-07 Impact factor: 12.701
Authors: Bryan S Reatini; Elliot Ensink; Brian Liau; Jessica Y Sinha; Thomas W Powers; Katie Partyka; Marshall Bern; Randall E Brand; Pauline M Rudd; Doron Kletter; Richard Drake; Brian B Haab Journal: Anal Chem Date: 2016-11-15 Impact factor: 6.986
Authors: Matthew B West; Katie Partyka; Christa L Feasley; Kevin A Maupin; Indiwari Goppallawa; Christopher M West; Brian B Haab; Marie H Hanigan Journal: BMC Biotechnol Date: 2014-12-06 Impact factor: 2.563