Standardization of PGC-LC-MS-based glycomics for sample specific glycotyping

Ashwood, Christopher; Pratt, Brian; MacLean, Brendan; Gundry, Rebekah L.; Packer, Nicolle H.

doi:10.1039/c9an00486f

Cited by 73 publications

(61 citation statements)

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“…8,27 Working in negative ionization mode has key advantages to address these challenges due to its high ionization efficiency especially for sialylated glycans, stability of sialic acids and no migration of fucose. More importantly, negative ionization mode fragmentation allows in-depth structural identification of glycan structures, 14,16,[28][29][30] since additional structural information can be obtained from cross-ring fragments providing diagnostic ions for the characterization of glycan linkages. 28 MS/MS spectra can be made publicly available by exporting from the DataAnalysis software using Glycoworkbench workspace and uploading to Unicarb DR repository following a standardized bioinformatics infrastructure, 31 serving as an open resource for automated N-and O-glycan identification via spectral matching.…”

Section: Resultsmentioning

confidence: 99%

“…In summary, we present an integrated approach for N-and O-glycomics from purified proteins and complex biological samples combining sequential release of N-and O-glycans based on protein immobilization on PVDF membrane filter plates in 96-well format and structural elucidation based on PGC nano-LC-ESI-MS/MS using negative electrospray ionization, inspired by the methodology developed by Packer and coworkers. 13,14 Here, we provide a method with high repeatability for combined N-and O-glycomics of biological samples, in which glycans were prepared in a higher-throughput and less time consuming manner as compared to previous workflows. 13,14 Still, this method requires multiple manual handling steps and several sample transfers between different 96-well plates.…”

Section: Discussionmentioning

confidence: 99%

“…13,14 Here, we provide a method with high repeatability for combined N-and O-glycomics of biological samples, in which glycans were prepared in a higher-throughput and less time consuming manner as compared to previous workflows. 13,14 Still, this method requires multiple manual handling steps and several sample transfers between different 96-well plates. The transfer of this sample preparation method to a liquid-handling robotic workstation should be feasible.…”

Section: Discussionmentioning

confidence: 99%

“…The approach is based on methodology developed by Packer and coworkers. 13,14 who have pioneered the analysis of glycan alditols by porous graphitized carbon nano-liquid chromatography coupled to a tandem mass spectrometer (PGC nano-LC-ESI-MS/MS) using negative electrospray ionization. 13 Powerful separation capacity enabling discrimination between glycan isomers is a major advantage of PGC chromatography.…”

Section: Introductionmentioning

confidence: 99%

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Development of a 96-well plate sample preparation method for integratedN- andO-glycomics using porous graphitized carbon liquid chromatography-mass spectrometry

et al. 2020

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Section: Resultsmentioning

confidence: 99%

Section: Discussionmentioning

confidence: 99%

Section: Discussionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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Development of a 96-well plate sample preparation method for integratedN- andO-glycomics using porous graphitized carbon liquid chromatography-mass spectrometry

et al. 2020

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“…Due to the sparsity and non-independence of glycoprofile, clustering and comparing different glycoprofiles can be challenging 23 . We tested this by clustering glycoprofiles from a panel of different Erythropoietin (EPO) glycoforms, each produced in different glycoengineered CHO cell lines.…”

Section: Resultsmentioning

confidence: 99%

Correcting for sparsity and non-independence in glycomic data through a systems biology framework

Bao

Kellman

Chiang

et al. 2019

Preprint

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23Glycans are fundamental cellular building blocks, involved in many organismal functions. 24Advances in glycomics are elucidating the roles of glycans, but it remains challenging to 25 properly analyze large glycomics datasets, since the data are sparse (each sample often has only a 26 few measured glycans) and detected glycans are non-independent (sharing many intermediate 27 biosynthetic steps). We address these challenges with GlyCompare, a glycomic data analysis 28 approach that leverages shared biosynthetic pathway intermediates to correct for sparsity and 29 non-independence in glycomics. Specifically, quantities of measured glycans are propagated to 30 intermediate glycan substructures, which enables direct comparison of different glycoprofiles 31 and increases statistical power. Using GlyCompare, we studied diverse N-glycan profiles from 32 glycoengineered erythropoietin. We obtained biologically meaningful clustering of mutant cell 33 glycoprofiles and identified knockout-specific effects of fucosyltransferase mutants on tetra-34 antennary structures. We further analyzed human milk oligosaccharide profiles and identified 35 novel impacts that the mother's secretor-status on fucosylation and sialylation. Our substructure-36 oriented approach will enable researchers to take full advantage of the growing power and size of 37 glycomics data. 38 39 40 the rapid generation of many glycoprofiles with detailed glycan composition 7-10 , exposing the 54 complex and heterogeneous glycosylation patterns on lipids and proteins 11,12 . Large glycoprofile 55 datasets and supporting databases are also emerging, including GlyTouCan 13 , UnicarbDB 14 , 56GlyGen and UniCarbKB 15 . 57These new technologies and databases provide opportunities to examine global trends in 58 glycan function and their association with disease. However, the rapid and accurate comparison 59 of glycoprofiles can be challenging with the size, sparsity and heterogeneity of such datasets. 60Indeed, in any one glycoprofile, only a few glycans may be detected among the thousands of 61 possible glycans 16 . Thus, if there is a major perturbation to glycosylation in a dataset, few 62 glycans, if any, may overlap between samples. However, these non-overlapping glycans may 63 4 only differ in their synthesis by as few as one enzymatic step. Thus, it can be difficult to know 64 which glycans to compare. Furthermore, since glycans often share substantial portions of their 65 biosynthetic pathways with each other, statistical methods that assume independence (e.g., t-66 tests, ANOVA, etc) are inappropriate for glycomics. Here we address these challenges by 67 proposing glycan substructures, or intermediates, as the appropriate functional units for 68 meaningful glycoprofile comparisons, since each substructure can capture one step in the 69 complex process of glycan synthesis. Thus, using substructures for comparison, we account for 70 the shared dependencies across glycans. 71Previous work has investigated the similarity across glycans using glycan motifs, ...

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Object classification in analytical chemistry via data‐driven discovery of partial differential equations

Padgett

Geldiyev

Gautam

et al. 2021

Comp and Math Methods

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Glycans are one of the most widely investigated biomolecules, due to their roles in numerous vital biological processes. However, few system-independent, LC-MS/MS (liquid chromatography tandem mass spectrometry) based studies have been developed with this particular goal. Standard approaches generally rely on normalized retention times as well as m/z-mass to charge ratios of ion values. Due to these limitations, there is need for quantitative characterization methods which can be used independently of m/z values, thus utilizing only normalized retention times. As such, the primary goal of this article is to construct an LC-MS/MS based classification of the glycans derived from standard glycoproteins and human blood serum using a glucose unit index as the reference frame in the space of compound parameters. For the reference frame, we develop a closed-form analytic formula via the Green's function of a relevant convection-diffusion-absorption equation used to model composite material transport. The aforementioned equation is derived from an Einstein-Brownian motion paradigm, which provides a physical interpretation of the time-dependence at the point of observation for molecular transport in the experiment. The necessary coefficients are determined via a data-driven learning procedure. The methodology is presented in an abstractly and validated via comparison with experimental mass spectrometer data. K E Y W O R D Sdata-driven PDE, Einstein approach, learning algorithms INTRODUCTIONThe biological significance of glycans is evident from the numerous studies demonstrating their roles in living systems. These molecules alone, as well as in conjunction with other biomolecules, participate in important biological functions. For example, glycosylation is one of the major post-translational modifications 1,2 and is known to mediate a broad range of biological processes such as cell recognition, 3 cell signaling, 3,4 immune response, 5 and protein stability. 6 Furthermore, aberrations in glycosylation patterns are found to be related to various diseases, including cancers. 3,[7][8][9][10][11] Glycans display high structural complexity owing to the presence of diverse monosaccharide composition, different linkages, and various branching options. 12 Tandem mass spectrometry (MS) has emerged as an effective technique

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Standardization of PGC-LC-MS-based glycomics for sample specific glycotyping

Cited by 73 publications

References 50 publications

Development of a 96-well plate sample preparation method for integratedN- andO-glycomics using porous graphitized carbon liquid chromatography-mass spectrometry

Development of a 96-well plate sample preparation method for integratedN- andO-glycomics using porous graphitized carbon liquid chromatography-mass spectrometry

Correcting for sparsity and non-independence in glycomic data through a systems biology framework

Object classification in analytical chemistry via data‐driven discovery of partial differential equations

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