首页> 美国卫生研究院文献>Beilstein Journal of Organic Chemistry >Clustering and curation of electropherograms: an efficient method for analyzing large cohorts of capillary electrophoresis glycomic profiles for bioprocessing operations
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Clustering and curation of electropherograms: an efficient method for analyzing large cohorts of capillary electrophoresis glycomic profiles for bioprocessing operations

机译:电泳图的聚类和策划:一种有效的方法用于分析用于生物处理操作的毛细管电泳型毛细管电泳型纤维谱的大衔接方法

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摘要

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.
机译:精确评估生物处理过程中的抗体糖基化需要大量糖类数据的高通量产生。这允许生物过程工程师识别控制糖基化临界质量属性的关键过程参数。在毛细管电泳激光诱导的荧光(CE-LIF)测量的协议中进行的进展增加了产生的N-糖基化值的大型数据集进行评估。由于存在内部和外部标准,凭借内部和外部标准,仍然存在速度,峰值拾取和峰值区域计算仍然是仍然存在的问题,以减少对定性分析的不对准,仍然存在快速和准确的定量问题。峰值拣选和区域计算问题通常是由于变化过程条件引入的波动导致异质峰形状。另外,具有共用聚糖的峰可以在某些过程条件下产生非高斯性质的峰值,而不是在其他过程中产生。在这里,我们描述了一种定量和定性愈合大队列CE-LIF族族数据的方法。对于甘草识别,使用了基于内部三重标准的先前报告的方法。为了确定Glycan相对数量,我们的方法使用聚类算法将高度异构的电泳图分为类似的组,使得可以手动定义峰值。然后使用开源软件来确定手动定义峰的峰面积。我们成功地将该半自动方法应用于来自生物反应器优化研究的单克隆抗体生物仿制物的数据集(含有391种糖型)。这种计算方法的关键优势在于,可以以高精度在糖类识别和定量中同时分析所有运行,并且该方法的规模没有理论限制。

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