Clustering and curation of electropherograms: an efficient method for analyzing large cohorts of capillary electrophoresis glycomic profiles for bioprocessing operations
| Autor: | Shi Jie Tay, Ian Walsh, Andre Choo, Sim Lyn Chiin, Matthew S F Choo, Terry Nguyen-Khuong, Amelia Mak, Pauline M. Rudd, Yang Yuansheng, Ho Ying Swan |
|---|---|
| Jazyk: | angličtina |
| Rok vydání: | 2020 |
| Předmět: |
Divide and conquer algorithms
glycosylation data analysis capillary electrophoresis peak picking 01 natural sciences Full Research Paper lcsh:QD241-441 Glycomics 03 medical and health sciences Capillary electrophoresis lcsh:Organic chemistry Bioprocess lcsh:Science Cluster analysis electropherogram 030304 developmental biology 0303 health sciences Chemistry 010401 analytical chemistry Organic Chemistry 0104 chemical sciences process development Electropherogram Identification (information) lcsh:Q monoclonal antibodies Critical quality attributes Biological system clustering |
| Zdroj: | Beilstein Journal of Organic Chemistry Beilstein Journal of Organic Chemistry, Vol 16, Iss 1, Pp 2087-2099 (2020) |
| ISSN: | 1860-5397 |
| Popis: | 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. |
| Databáze: | OpenAIRE |
| Externí odkaz: |
|