Triple-Internal Standard Based Glycan Structural Assignment Method for Capillary Electrophoresis Analysis of Carbohydrates

Járvás, Gábor; Szigeti, Márton; Chapman, J F; Guttman, András

doi:10.1021/acs.analchem.6b03596

Cited by 37 publications

(40 citation statements)

References 20 publications

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“…1, Trace A, featuring 14 peaks of interest (peak area ࣙ1%) including multisialo (1-4), monosialo (5)(6)(7)(8), and neutral glycans (9)(10)(11)(12)(13)(14). Structural elucidation of these carbohydrates were based on their calculated GU values following the triple internal standard approach of Jarvas et al [32] in conjunction with searching the NIBRT database (https://glycobase.nibrt.ie) and also on previously published literature data [33]. In cases of any ambiguities, exoglycosidase-based carbohydrate sequencing was applied (data not shown).…”

Section: Comparison Of the Serum N-glycome Profile Between The Healthmentioning

confidence: 99%

Capillary electrophoresis analysis of N‐glycosylation changes of serum paraproteins in multiple myeloma

et al. 2017

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Multiple myeloma (MM) is an immedicable malignancy of the human plasma cells producing abnormal antibodies (also referred to as paraproteins) leading to kidney problems and hyperviscosity syndrome. In this paper, we report on the N-glycosylation analysis of paraproteins from total human serum as well as the fragment crystallizable region (F ) and fragment antigen binding (F ) κ/λ light chain fractions of papain digested immunoglobulins from multiple myeloma patients. CE-LIF detection was used for the analysis of the N-glycans after endoglycosidase (PNGase F) mediated sugar release and fluorophore labeling (APTS). While characteristic N-glycosylation pattern differences were found between normal control and untreated, treated and remission stage multiple myeloma patient samples at the global serum level, less distinctive changes were observed at the immunoglobulin level. Principal component analysis adequately differentiated the four groups (control and three patient groups) on the basis of total serum N-glycosylation analysis. 12 N-glycan features showed statistically significant differences (p <0.05) among various stages of the disease in comparison to the control at the serum level, while only six features were identified with similar significance at the immunoglobulin level, including the analysis of the partitioned F fragment as well as the F κ and F λ chains.

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Section: Comparison Of the Serum N-glycome Profile Between The Healthmentioning

confidence: 99%

Capillary electrophoresis analysis of N‐glycosylation changes of serum paraproteins in multiple myeloma

et al. 2017

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“… 85 Traditionally, a complete glycan ladder is used to estimate the relative size of the sample using the migration time of the standard oligomer and the analytes to address an experimentally observed change in ladder migration associated with a helical turn that occurs in maltooligosaccharide structures above a degree of polymerization of 7. 85 However, as shown in Figure 6 , the approach is simplified to only three co-injected size standards to accurately index migration time without the drawbacks of ladder and sample overlap due to comigration. 85 Additionally, one standard also serves to normalize variations in the sample injection.…”

Section: Methods To Identify Glycansmentioning

confidence: 99%

“…The representative electropherogram of maltooligomers in the lower trace is aligned with DP2, DP3, and DP15 in the upper panel. Reprinted with permission from ref ( 85 ). Copyright 2016 American Chemical Society.…”

Section: Methods To Identify Glycansmentioning

confidence: 99%

“… 56 As shown in eq 2 , the glucose index of the specified glycan analyte, GU x where G N is the number of maltose in the adjacent maltooligosaccharide ladder peak, t X , t N , and t N +1 are the migration times of the target glycan and the N and N + 1 number of maltooligomers, respectively. The use of a glycan ladder and GU values was further improved with the built-in database GUcal for high throughput analysis, 56 , 85 reported as . 86 The program automatically calculates the GU values of peaks in the electropherogram upon loading the ASCII file of both standard maltooligosaccharide ladder and sample traces.…”

Section: Methods To Identify Glycansmentioning

confidence: 99%

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Capillary Electrophoresis Separations of Glycans

et al. 2018

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Capillary electrophoresis has emerged as a powerful approach for carbohydrate analyses since 2014. The method provides high resolution capable of separating carbohydrates by charge-to-size ratio. Principle applications are heavily focused on N-glycans, which are highly relevant to biological therapeutics and biomarker research. Advances in techniques used for N-glycan structural identification include migration time indexing and exoglycosidase and lectin profiling, as well as mass spectrometry. Capillary electrophoresis methods have been developed that are capable of separating glycans with the same monosaccharide sequence but different positional isomers, as well as determining whether monosaccharides composing a glycan are alpha or beta linked. Significant applications of capillary electrophoresis to the analyses of N-glycans in biomarker discovery and biological therapeutics are emphasized with a brief discussion included on carbohydrate analyses of glycosaminoglycans and mono-, di-, and oligosaccharides relevant to food and plant products. Innovative, emerging techniques in the field are highlighted and the future direction of the technology is projected based on the significant contributions of capillary electrophoresis to glycoscience from 2014 to the present as discussed in this review.

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“…The migration and/or retention time of each point of the profile is converted to glucose units (GU) using a glucose homo-oligomer ladder injected prior and after the samples. A virtual ladder, by using the recently introduced triple internal standard approach, is also appropriate for GU value calculation [76]. The electropherogram and/or chromatogram is than fractionally divided into different GU value sections, based on the major oligosaccharide classes in the profile (neutral, sialylated, HM, and hybrid).…”

Section: Introducing the Glycosimilarity Indexmentioning

confidence: 99%

The glycosylation aspects of biosimilarity

Guttman¹

2018

J Bioanal Biomed

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Teaser: This review summarizes the critical quality attributes of biosimilarity, also introducing the glycosimilarity index as an additional parameter to prove similarity between innovator and biosimilar products. Highlights The critical quality attributes of biosimilarity are reviewed  Regulatory and statistical considerations are summarized.  Importance of glycosylation analysis during biosimilar production is detailed.  Analytical methods to track N-glycosylation patterns are reviewed.  Glycosimilarity index is introduced.The recent expiration of several protein therapeutics opened the door for biosimilar development. Biosimilars are biologic medical products that are similar but not identical copies of already-authorized protein therapeutics. Critical quality attributes (CQA), such as post-translational modifications of recombinant biotherapeutics, are important for the clinical efficacy and safety of both the innovative biologics and their biosimilar counterparts. Here, we summarize biosimilarity CQAs, considering the regulatory guidelines and the statistical aspects (e.g., biosimilarity index) and then discuss glycosylation as one of the important attributes of biosimilarity. Finally, we introduced the 'Glycosimilarity Index', which is based on the averaged biosimilarity criterion.

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Triple-Internal Standard Based Glycan Structural Assignment Method for Capillary Electrophoresis Analysis of Carbohydrates

Cited by 37 publications

References 20 publications

Capillary electrophoresis analysis of N‐glycosylation changes of serum paraproteins in multiple myeloma

Capillary electrophoresis analysis of N‐glycosylation changes of serum paraproteins in multiple myeloma

Capillary Electrophoresis Separations of Glycans

The glycosylation aspects of biosimilarity

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