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

Ian Walsh,Terry Nguyen-Khuong,Pauline M Rudd,Shi Jie Tay,Ho Ying Swan,Yang Yuansheng,Sim Lyn Chiin,Amelia Mak,Andre Choo,Matthew S F Choo

doi:10.3762/bjoc.16.176

Ian Walsh, Terry Nguyen-Khuong + Show 8 more

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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.

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Glycosylation is important for the efficacy and function of a majority of the most dominant biologic drugs currently on the global market
This is difficult because glycosylation during fermentation occurs with a high degree of heterogeneity and is influenced by several factors including the host expression system and process parameters such as temperature shifts, pH, and the type of basal/feed media [7]. To understand how these environmental factors impact the glycosylation of a biologic, analytical methods are needed to assess how glycans behave under these diverse conditions. During this process development of antibody-based drugs, the N-glycosylation of an antibody can deviate from their expected glycomic profiles as a result of fluctuations in culture conditions and operating parameters
To the best of our knowledge, we are the first to apply this computational approach to a large set of capillary electrophoresis-laser-induced fluorescence (CE-LIF) glycomic data. The result of this new method is that large cohorts of bioreactor runs can be analyzed at once with high accuracy in quantitation and glycan identification. We demonstrate this approach through the high-throughput qualitative and quantitation of CE-LIF glycomic data, displaying glycan trends that exist in eleven in-house bioreactor culture conditions

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Glycosylation is important for the efficacy and function of a majority of the most dominant biologic drugs currently on the global market. The approach used to identify glycans was based on a triple standard GU calculation [10,11] and database matching whilst the quantitation used HappyTools calibration and area calculation [14]. Despite major misalignment of migration time and bracketing standards in the electropherograms, the variation of GU values for glycan peaks generally were within a very small range (Figure 1A and 1B) allowing for consistent database matching against a GU CE database (APTS fluorescent labelled) [10].

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