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.

Immobilization of vitronectin-binding heparan sulfates onto surfaces to support human pluripotent stem cells.

Functionalizing medical devices with polypeptides to enhance their performance has become important for improved clinical success. The extracellular matrix (ECM) adhesion protein vitronectin (VN) is an effective coating, although the chemistry used to attach VN often reduces its bioactivity. In vivo, VN binds the ECM in a sequence-dependent manner with heparan sulfate (HS) glycosaminoglycans. We reasoned therefore that sequence-based affinity chromatography could be used to isolate a VN-binding HS fraction (HS9) for use as a coating material to capture VN onto implant surfaces. Binding avidity and specificity of HS9 were confirmed by enzyme-linked immunosorbent assay (ELISA) and surface plasmon resonance (SPR)-based assays. Plasma polymerization of allylamine (AA) to tissue culture-treated polystyrene (TCPS) was then used to capture and present HS9 as determined by radiolabeling and ELISA. HS9-coated TCPS avidly bound VN, and this layered surface supported the robust attachment, expansion, and maintenance of human pluripotent stem cells. Compositional analysis demonstrated that 6-O- and N-sulfation, as well as lengths greater than three disaccharide units (dp6) are critical for VN binding to HS-coated surfaces. Importantly, HS9 coating reduced the threshold concentration of VN required to create an optimally bioactive surface for pluripotent stem cells. We conclude that affinity-purified heparan sugars are able to coat materials to efficiently bind adhesive factors for biomedical applications. © 2017 Wiley Periodicals, Inc. J Biomed Mater Res Part B: Appl Biomater, 106B: 1887-1896, 2018.