Clustering and curation of electropherograms: an efficient method for analyzing large cohorts of capillary electrophoresis glycomic profiles for bioprocessing operations.

The accurate assessment of antibody glycosylation during bioprocessing requires the high-throughput generation of large amounts of glycomics data. This allows bioprocess engineers to identify critical process parameters that control the glycosylation critical quality attributes. The advances made in protocols for capillary electrophoresis-laser-induced fluorescence (CE-LIF) measurements of antibody N-glycans have increased the potential for generating large datasets of N-glycosylation values for assessment. With large cohorts of CE-LIF data, peak picking and peak area calculations still remain a problem for fast and accurate quantitation, despite the presence of internal and external standards to reduce misalignment for the qualitative analysis. The peak picking and area calculation problems are often due to fluctuations introduced by varying process conditions resulting in heterogeneous peak shapes. Additionally, peaks with co-eluting glycans can produce peaks of a non-Gaussian nature in some process conditions and not in others. Here, we describe an approach to quantitatively and qualitatively curate large cohort CE-LIF glycomics data. For glycan identification, a previously reported method based on internal triple standards is used. For determining the glycan relative quantities our method uses a clustering algorithm to 'divide and conquer' highly heterogeneous electropherograms into similar groups, making it easier to define peaks manually. Open-source software is then used to determine peak areas of the manually defined peaks. We successfully applied this semi-automated method to a dataset (containing 391 glycoprofiles) of monoclonal antibody biosimilars from a bioreactor optimization study. The key advantage of this computational approach is that all runs can be analyzed simultaneously with high accuracy in glycan identification and quantitation and there is no theoretical limit to the scale of this method.

Characterization of a GDP-Fucose Transporter and a Fucosyltransferase Involved in the Fucosylation of Glycoproteins in the Diatom Phaeodactylum tricornutum.

Although Phaeodactylum tricornutum is gaining importance in plant molecular farming for the production of high-value molecules such as monoclonal antibodies, little is currently known about key cell metabolism occurring in this diatom such as protein glycosylation. For example, incorporation of fucose residues in the glycans N-linked to protein in P. tricornutum is questionable. Indeed, such epitope has previously been found on N-glycans of endogenous glycoproteins in P. tricornutum. Meanwhile, the potential immunogenicity of the α(1,3)-fucose epitope present on plant-derived biopharmaceuticals is still a matter of debate. In this paper, we have studied molecular actors potentially involved in the fucosylation of the glycoproteins in P. tricornutum. Based on sequence similarities, we have identified a putative P. tricornutum GDP-L-fucose transporter and three fucosyltransferase (FuT) candidates. The putative P. tricornutum GDP-L-fucose transporter coding sequence was expressed in the Chinese Hamster Ovary (CHO)-gmt5 mutant lacking its endogenous GDP-L-fucose transporter activity. We show that the P. tricornutum transporter is able to rescue the fucosylation of proteins in this CHO-gmt5 mutant cell line, thus demonstrating the functional activity of the diatom transporter and its appropriate Golgi localization. In addition, we overexpressed one of the three FuT candidates, namely the FuT54599, in P. tricornutum and investigated its localization within Golgi stacks of the diatom. Our findings show that overexpression of the FuT54599 leads to a significant increase of the α(1,3)-fucosylation of the diatom endogenous glycoproteins.

Combining Glucose Units, m/z, and Collision Cross Section Values: Multiattribute Data for Increased Accuracy in Automated Glycosphingolipid Glycan Identifications and Its Application in Triple Negative Breast Cancer.

Glycan head-groups attached to glycosphingolipids (GSLs) found in the cell membrane bilayer can alter in response to external stimuli and disease, making them potential markers and/or targets for cellular disease states. To identify such markers, comprehensive analyses of glycan structures must be undertaken. Conventional analyses of fluorescently labeled glycans using hydrophilic interaction high-performance liquid chromatography (HILIC) coupled with mass spectrometry (MS) provides relative quantitation and has the ability to perform automated glycan assignments using glucose unit (GU) and mass matching. The use of ion mobility (IM) as an additional level of separation can aid the characterization of closely related or isomeric structures through the generation of glycan collision cross section (CCS) identifiers. Here, we present a workflow for the analysis of procainamide-labeled GSL glycans using HILIC-IM-MS and a new, automated glycan identification strategy whereby multiple glycan attributes are combined to increase accuracy in automated structural assignments. For glycan matching and identification, an experimental reference database of GSL glycans containing GU, mass, and CCS values for each glycan was created. To assess the accuracy of glycan assignments, a distance-based confidence metric was used. The assignment accuracy was significantly better compared to conventional HILIC-MS approaches (using mass and GU only). This workflow was applied to the study of two Triple Negative Breast Cancer (TNBC) cell lines and revealed potential GSL glycosylation signatures characteristic of different TNBC subtypes.