Antibody glycan quality predicted from CHO cell culture media markers and machine learning.

N-glycosylation can have a profound effect on the quality of mAb therapeutics. In biomanufacturing, one of the ways to influence N-glycosylation patterns is by altering the media used to grow mAb cell expression systems. Here, we explore the potential of machine learning (ML) to forecast the abundances of N-glycan types based on variables related to the growth media. The ML models exploit a dataset consisting of detailed glycomic characterisation of Anti-HER fed-batch bioreactor cell cultures measured daily under 12 different culture conditions, such as changes in levels of dissolved oxygen, pH, temperature, and the use of two different commercially available media. By performing spent media quantitation and subsequent calculation of pseudo cell consumption rates (termed media markers) as inputs to the ML model, we were able to demonstrate a small subset of media markers (18 selected out of 167 mass spectrometry peaks) in a Chinese Hamster Ovary (CHO) cell cultures are important to model N-glycan relative abundances (Regression - correlations between 0.80-0.92; Classification - AUC between 75.0-97.2). The performances suggest the ML models can infer N-glycan critical quality attributes from extracellular media as a proxy. Given its accuracy, we envisage its potential applications in biomaufactucuring, especially in areas of process development, downstream and upstream bioprocessing.

An Integrative Glycomic Approach for Quantitative Meat Species Profiling.

It is estimated that food fraud, where meat from different species is deceitfully labelled or contaminated, has cost the global food industry around USD 6.2 to USD 40 billion annually. To overcome this problem, novel and robust quantitative methods are needed to accurately characterise and profile meat samples. In this study, we use a glycomic approach for the profiling of meat from different species. This involves an O-glycan analysis using LC-MS qTOF, and an N-glycan analysis using a high-resolution non-targeted ultra-performance liquid chromatography-fluorescence-mass spectrometry (UPLC-FLR-MS) on chicken, pork, and beef meat samples. Our integrated glycomic approach reveals the distinct glycan profile of chicken, pork, and beef samples; glycosylation attributes such as fucosylation, sialylation, galactosylation, high mannose, α-galactose, Neu5Gc, and Neu5Ac are significantly different between meat from different species. The multi-attribute data consisting of the abundance of each O-glycan and N-glycan structure allows a clear separation between meat from different species through principal component analysis. Altogether, we have successfully demonstrated the use of a glycomics-based workflow to extract multi-attribute data from O-glycan and N-glycan analysis for meat profiling. This established glycoanalytical methodology could be extended to other high-value biotechnology industries for product authentication.

Combining Glucose Units, m/z, and Collision Cross Section Values: Multiattribute Data for Increased Accuracy in Automated Glycosphingolipid Glycan Identifications and Its Application in Triple Negative Breast Cancer.

Glycan head-groups attached to glycosphingolipids (GSLs) found in the cell membrane bilayer can alter in response to external stimuli and disease, making them potential markers and/or targets for cellular disease states. To identify such markers, comprehensive analyses of glycan structures must be undertaken. Conventional analyses of fluorescently labeled glycans using hydrophilic interaction high-performance liquid chromatography (HILIC) coupled with mass spectrometry (MS) provides relative quantitation and has the ability to perform automated glycan assignments using glucose unit (GU) and mass matching. The use of ion mobility (IM) as an additional level of separation can aid the characterization of closely related or isomeric structures through the generation of glycan collision cross section (CCS) identifiers. Here, we present a workflow for the analysis of procainamide-labeled GSL glycans using HILIC-IM-MS and a new, automated glycan identification strategy whereby multiple glycan attributes are combined to increase accuracy in automated structural assignments. For glycan matching and identification, an experimental reference database of GSL glycans containing GU, mass, and CCS values for each glycan was created. To assess the accuracy of glycan assignments, a distance-based confidence metric was used. The assignment accuracy was significantly better compared to conventional HILIC-MS approaches (using mass and GU only). This workflow was applied to the study of two Triple Negative Breast Cancer (TNBC) cell lines and revealed potential GSL glycosylation signatures characteristic of different TNBC subtypes.

LC/MS-based Intact IgG and Released Glycan Analysis for Bioprocessing Applications.

Robust plate based antibody glycan analysis platforms are urgently needed for biopharmaceutical development and manufacturing as well as for clinical biomarker research. A 96-well plate based workflow has been developed to analyze both intact IgG antibodies and released N-glycans using an Orbitrap Fusion Mass Spectrometer and an LC/MS method on the Waters UNIFI platform. Here, such a workflow including protein A purification, PNGaseF digestion, 2-AB labeling, and SPE clean-up is described. The measured IgG glycan profile is consistent with that obtained from non-plate based method and commercial kit and has the advantage of less hands-on time. Also the application of the workflow in cell culture monitoring and clonal selection work is demonstrated. Apart from checking the major glycan structure changes among clones, post translational modifications (PTMs) such as C-terminal lysine residue clipping and N-terminal pyroglutamic acid formation can also be deduced from the workflow.

Rapid characterization of protein productivity and production stability of CHO cells by matrix-assisted laser desorption/ionization time-of-flight mass spectrometry.

The biopharmaceutical industry has been in pursuit of strategies which can isolate stable and high-producing cell lines. The whole cell mass spectrometry method by matrix-assisted laser desorption/ionization time-of-flight (MALDI-TOF) is a rapid and simple method for cell characterization based on the differences in the fingerprints of the mass spectra. This work describes how the method was evaluated for the application of screening for stable and high-producing clones from a panel of recombinant Chinese hamster ovary (CHO) cell lines. Detectable m/z values and their relative intensities were collected and processed by partial least squares (PLS). To reduce the errors introduced by the preparation method and spectra noise, high intensity preliminary data was selected and the number of variables introduced was validated by leave-one-out cross-validation. The differences in recombinant protein productivity and titer were revealed by PLS regression with promising results. Partial least-squares discriminant analysis (PLS-DA) was applied to differentiate stable and unstable cell lines as traditional stability testing would require several months involving numerous continuous passages. Results confirmed that the whole cell MALDI-TOF method can be a powerful method for routine monitoring of bioprocesses and study can be further developed by extending the number of the cell lines tested to establish a recombinant cell line database.

Rapid characterization of high/low producer CHO cells using matrix-assisted laser desorption/ionization time-of-flight.

An intact-cell mass spectrometry (ICM) method using matrix-assisted laser desorption/ionization time-of-flight (MALDI-TOF) was evaluated for the screening of stable recombinant Chinese hamster ovary (CHO) cell lines, an important mammalian cell line in bioprocessing. With rapid and simple cell pretreatments, viabilities of cells could be rapidly distinguished on the different fingerprints of mass spectra. Detectable m/z values on cell surfaces and their relative intensities were processed by two biostatistical methods, principle components analysis (PCA) and partial least squares (PLS), with promising results. Discrimination among cell lines with different expressed recombinant proteins or different productivities could be achieved. The ICM method has the advantage of providing multiple parameters simultaneously and possesses the potential to become a powerful method for routine monitoring of bioprocesses.