Clustering and Curation of Electropherograms: An Efficient Method for Analysing Large Cohorts of Glycomic Profiles in Tracking the Effects of Multivariate Parameters in Bioprocessing Operations

Clustering and Curation of Electropherograms: An Efficient Method for Analysing Large Cohorts of Glycomic Profiles in Tracking the Effects of Multivariate Parameters in Bioprocessing Operations
Title:
Clustering and Curation of Electropherograms: An Efficient Method for Analysing Large Cohorts of Glycomic Profiles in Tracking the Effects of Multivariate Parameters in Bioprocessing Operations
Other Titles:
Beilstein Journal of Organic Chemistry
Publication Date:
27 August 2020
Citation:
Walsh, I.; Choo, M. S. F.; Chiin, S. L.; Mak, A.; Tay, S. J.; Rudd, P. M.; Yuansheng, Y.; Choo, A.; Swan, H. Y.; Nguyen-Khuong, T. Beilstein J. Org. Chem. 2020, 16, 2087–2099. doi:10.3762/bjoc.16.176
Abstract:
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.
License type:
http://creativecommons.org/licenses/by/4.0/
Funding Info:
The authors thank the Agency for Science, Technology and Research (A*STAR), Singapore for supporting this study (SSF Project Grant A1818g0025).
Description:
ISSN:
1860-5397
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