Dataset Information

Mass Spectrometry-Based De Novo Sequencing of Monoclonal Antibodies Using Multiple Proteases and a Dual Fragmentation Scheme.

ABSTRACT: Antibody sequence information is crucial to understanding the structural basis for antigen binding and enables the use of antibodies as therapeutics and research tools. Here, we demonstrate a method for direct de novo sequencing of monoclonal IgG from the purified antibody products. The method uses a panel of multiple complementary proteases to generate suitable peptides for de novo sequencing by liquid chromatography-tandem mass spectrometry (LC-MS/MS) in a bottom-up fashion. Furthermore, we apply a dual fragmentation scheme, using both stepped high-energy collision dissociation (stepped HCD) and electron-transfer high-energy collision dissociation (EThcD), on all peptide precursors. The method achieves full sequence coverage of the monoclonal antibody herceptin, with an accuracy of 99% in the variable regions. We applied the method to sequence the widely used anti-FLAG-M2 mouse monoclonal antibody, which we successfully validated by remodeling a high-resolution crystal structure of the Fab and demonstrating binding to a FLAG-tagged target protein in Western blot analysis. The method thus offers robust and reliable sequences of monoclonal antibodies.

SUBMITTER: Peng W

PROVIDER: S-EPMC8256418 | biostudies-literature | 2021 Jul

REPOSITORIES: biostudies-literature

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Publications

Mass Spectrometry-Based De Novo Sequencing of Monoclonal Antibodies Using Multiple Proteases and a Dual Fragmentation Scheme.

Peng Weiwei W Pronker Matti F MF Snijder Joost J

Journal of proteome research 20210614 7

Antibody sequence information is crucial to understanding the structural basis for antigen binding and enables the use of antibodies as therapeutics and research tools. Here, we demonstrate a method for direct de novo sequencing of monoclonal IgG from the purified antibody products. The method uses a panel of multiple complementary proteases to generate suitable peptides for de novo sequencing by liquid chromatography-tandem mass spectrometry (LC-MS/MS) in a bottom-up fashion. Furt ...[more]

PMID: 34121409

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Project description:BackgroundImaging mass spectrometry enables in situ label-free detection of thousands of metabolites from intact tissue samples. However, automated steps for multi-omics analyses and interpretation of histological images have not yet been implemented in mass spectrometry data analysis workflows. The characterization of molecular properties within cellular and histological features is done via time-consuming, non-objective, and irreproducible definitions of regions of interest, which are often accompanied by a loss of spatial resolution due to mass spectra averaging.MethodsWe developed a new imaging pipeline called Spatial Correlation Image Analysis (SPACiAL), which is a computational multimodal workflow designed to combine molecular imaging data with multiplex immunohistochemistry (IHC). SPACiAL allows comprehensive and spatially resolved in situ correlation analyses on a cellular resolution. To demonstrate the method, matrix-assisted laser desorption-ionization (MALDI) Fourier-transform ion cyclotron resonance (FTICR) imaging mass spectrometry of metabolites and multiplex IHC staining were performed on the very same tissue section of mouse pancreatic islets and on human gastric cancer tissue specimens. The SPACiAL pipeline was used to perform an automatic, semantic-based, functional tissue annotation of histological and cellular features to identify metabolic profiles. Spatial correlation networks were generated to analyze metabolic heterogeneity associated with cellular features.ResultsTo demonstrate the new method, the SPACiAL pipeline was used to identify metabolic signatures of alpha and beta cells within islets of Langerhans, which are cell types that are not distinguishable via morphology alone. The semantic-based, functional tissue annotation allows an unprecedented analysis of metabolic heterogeneity via the generation of spatial correlation networks. Additionally, we demonstrated intra- and intertumoral metabolic heterogeneity within HER2/neu-positive and -negative gastric tumor cells.ConclusionsWe developed the SPACiAL workflow to provide IHC-guided in situ metabolomics on intact tissue sections. Diminishing the workload by automated recognition of histological and functional features, the pipeline allows comprehensive analyses of metabolic heterogeneity. The multimodality of immunohistochemical staining and extensive molecular information from imaging mass spectrometry has the advantage of increasing both the efficiency and precision for spatially resolved analyses of specific cell types. The SPACiAL method is a stepping stone for the objective analysis of high-throughput, multi-omics data from clinical research and practice that is required for diagnostics, biomarker discovery, or therapy response prediction.

Project description:Out of all categories, monoclonal antibody (mAb) therapeutics attract the most interest due to their strong therapeutic potency and specificity. Six of the 10 top-selling drugs are antibody-based therapeutics that will lose patent protection soon. The European Medicines Agency has pioneered the regulatory framework for approval of biosimilar products and approved the first biosimilar antibodies by the end of 2013. As highly complex glycoproteins with a wide range of micro-variants, mAbs require extensive characterization through multiple analytical methods for structure assessment rendering manufacturing control and biosimilarity studies particularly product and time-consuming. Here, capillary zone electrophoresis coupled to mass spectrometry by a sheathless interface (CESI-MS) was used to characterize marketed reference mAbs and their respective biosimilar candidate simultaneously over different facets of their primary structure. CESI-MS/MS data were compared between approved mAbs and their biosimilar candidates to prove/disconfirm biosimilarity regarding recent regulation directives. Using only a single sample injection of 200 fmol, CESI-MS/MS data enabled 100% amino acids (AA) sequence characterization, which allows a difference of even one AA between 2 samples to be distinguished precisely. Simultaneously glycoforms were characterized regarding their structures and position through fragmentation spectra and glycoforms semiquantitative analysis was established, showing the capacity of the developed methodology to detect up to 16 different glycans. Other posttranslational modifications hotspots were characterized while their relative occurrence levels were estimated and compared to biosimilars. These results proved the value of using CESI-MS because the separation selectivity and ionization efficiency provided by the system allowed substantial improvement in the characterization workflow robustness and accuracy. Biosimilarity assessment could be performed routinely with a single injection of each candidate enabling improvements in the biosimilar development pipeline.

Dataset Information

Mass Spectrometry-Based De Novo Sequencing of Monoclonal Antibodies Using Multiple Proteases and a Dual Fragmentation Scheme.

Publications

Mass Spectrometry-Based <i>De Novo</i> Sequencing of Monoclonal Antibodies Using Multiple Proteases and a Dual Fragmentation Scheme.

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