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Host cell protein quantification workflow using optimized standards combined with data-independent acquisition mass spectrometry

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Journal of Pharmaceutical Analysis 2023年第5期13卷 494-502页

作者： Steve Hessmann Cyrille Chery Anne-Sophie Sikora Annick Gervais Christine Carapito BioOrganic Mass Spectrometry Laboratory(LSMBO) IPHC UMR 7178University of StrasbourgCNRSProFI-FR2048Strasbourg67000France Department of Analytical Development Sciences for Biologicals UCB Pharma S.A.Braine l'Alleud1420Belgium

Monitoring of host cell proteins(HCPs)during the manufacturing of monoclonal antibodies(mAb)has become a critical requirement to provide effective and safe drug products.Enzyme-linked immunosorbent assays are still the gold standard methods for the quantification of protein impurities.However,this technique has several limitations and does,among others,not enable the precise identification of proteins.In this context,mass spectrometry(MS)became an alternative and orthogonal method that delivers qualitative and quantitative information on all identified HCPs.However,in order to be routinely implemented in biopharmaceutical companies,liquid chromatography-MS based methods still need to be standardized to provide highest sensitivity and robust and accurate quantification.Here,we present a promising MS-based analytical workflow coupling the use of an innovative quantification standard,the HCP Profiler solution,with a spectral library-based data-independent acquisition(DIA)method and strict data validation criteria.The performances of the HCP Profiler solution were compared to more conventional standard protein spikes and the DIA approach was benchmarked against a classical datadependent acquisition on a series of samples produced at various stages of the manufacturing process.While we also explored spectral library-free DIA interpretation,the spectral library-based approach still showed highest accuracy and reproducibility(coefficients of variation<10%)with a sensitivity down to the sub-ng/mg mAb level.Thus,this workflow is today mature to be used as a robust and straightforward method to support mAb manufacturing process developments and drug products quality control.

关键词： Host cell proteins Absolute quantification standards data-independent acquisition

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Detection and quantitation of host cell proteins in monoclonal antibody drug products using automated sample preparation and data-independent acquisition LC-MS/MS

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Journal of Pharmaceutical Analysis 2021年第6期11卷 726-731页

作者： Lisa Strasser Giorgio Oliviero Craig Jakes Izabela Zaborowska Patrick Floris Meire Ribeiro da Silva Florian Füssl Sara Carillo Jonathan Bones Characterization and Comparability Laboratory NIBRT-National Institute for Bioprocessing Research and TrainingDublinA94 X099Ireland School of Chemical and Bioprocess Engineering University College DublinDublinD04 V1W8Ireland

Ensuring the removal of host cell proteins(HCPs) during downstream processing of recombinant proteins such as monoclonal antibodies(m Abs) remains a challenge.Since residual HCPs might affect product stability or safety,constant monitoring is required to demonstrate their removal to be below the regulatory accepted level of 100 ng/mg.The current standard analytical approach for this procedure is based on ELISA;however,this approach only measures the overall HCP content.Therefore,the use of orthogonal methods,such as liquid chromatography-mass spectrometry(LC-MS),has been established,as it facilitates the quantitation of total HCPs as well as the identification and quantitation of the individual HCPs present.In the present study,a workflow for HCP detection and quantitation using an automated magnetic bead-based sample preparation,in combination with a data-independent acquisition(DIA) LC-MS analysis,was established.Employing the same instrumental setup commonly used for peptide mapping analysis of m Abs allows for its quick and easy implementation into pre-existing workflows,avoiding the need for dedicated instrumentation or personnel.Thereby,quantitation of HCPs over a broad dynamic range was enabled to allow monitoring of problematic HCPs or to track changes upon altered bioprocessing conditions.

关键词： data-independent acquisition Host cell proteins Critical quality attributes Liquid chromatography-mass spectrometry Monoclonal antibody Chinese hamster ovary cells

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Proteomics-based identification of proteins in tumor-derived exosomes as candidate biomarkers for colorectal cancer

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World Journal of Gastrointestinal Oncology 2023年第7期15卷 1227-1240页

作者： Ge-Yu-Jia Zhou Dong-Yan Zhao Teng-Fei Yin Qian-Qian Wang Yuan-Chen Zhou Shu-Kun Yao Department of Gastroenterology China-Japan Friendship Hospital(Institute of Clinical Medical Sciences)Beijing 100029China Graduate School Chinese Academy of Medical Sciences&Peking Union Medical CollegeBeijing 100730China Graduate School Peking University China-Japan Friendship School of Clinical MedicineBeijing 100029China Department of Gastroenterology China-Japan Friendship HospitalBeijing 100029China

BACKGROUND Colorectal cancer(CRC)is the second leading cause of cancer-related death,with high morbidity worldwide.There is an urgent need to find reliable diagnostic biomarkers of CRC and explore the underlying molecular mechanisms.Exosomes are involved in intercellular communication and participate in multiple pathological processes,serving as an important part of the tumor microenvironment.AIM To investigate the proteomic characteristics of CRC tumor-derived exosomes and to identify candidate exosomal protein markers for CRC.METHODS In this study,10 patients over 50 years old who were diagnosed with moderately differentiated adenocarcinoma were recruited.We paired CRC tissues and adjacent normal intestinal tissues(>5 cm)to form the experimental and control groups.Purified exosomes were extracted separately from each tissue sample.data-independent acquisition mass spectrometry was implemented in 8 matched samples of exosomes to explore the proteomic expression profiles,and differentially expressed proteins(DEPs)were screened by bioinformatics analysis.Promising exosomal proteins were verified using parallel reaction monitoring(PRM)analysis in 10 matched exosome samples.RESULTS A total of 1393 proteins were identified in the CRC tissue group,1304 proteins were identified in the adjacent tissue group,and 283 proteins were significantly differentially expressed between them.Enrichment analysis revealed that DEPs were involved in multiple biological processes related to cytoskeleton construction,cell movement and migration,immune response,tumor growth and telomere metabolism,as well as ECM-receptor interaction,focal adhesion and mTOR signaling pathways.Six differentially expressed exosomal proteins(NHP2,OLFM4,TOP1,SAMP,TAGL and TRIM28)were validated by PRM analysis and evaluated by receiver operating characteristic curve(ROC)analysis.The area under the ROC curve was 0.93,0.96,0.97,0.78,0.75,and 0.88(P<0.05)for NHP2,OLFM4,TOP1,SAMP,TAGL,and TRIM28,respectively,indicating their good ability to distinguish CRC tissues

关键词： Exosomes Colorectal cancer data-independent acquisition Parallel reaction monitoring Biomarker

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Caenorhabditis elegans deep lipidome profiling by using integrative mass spectrometry acquisitions reveals significantly altered lipid networks

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Journal of Pharmaceutical Analysis 2022年第5期12卷 743-754页

作者： Nguyen Hoang Anh Young Cheol Yoon Young Jin Min Nguyen Phuoc Long Cheol Woon Jung Sun Jo Kim Suk Won Kim Eun Goo Lee Daijie Wang Xiao Wang Sung Won Kwon College of Pharmacy Seoul National UniversitySeoul08826Republic of Korea School of Pharmaceutical Sciences Shandong Analysis and Test CenterQilu University of Technology(Shandong Academy of Sciences)Jinan250014China Biological Engineering Technology Innovation Center of Shandong Province Heze Branch of Qilu University of Technology(Shandong Academy of Sciences)HezeShandong274000China

Lipidomics coverage improvement is essential for functional lipid and pathway construction.A powerful approach to discovering organism lipidome is to combine various data acquisitions,such as full scan mass spectrometry(full MS),data-dependent acquisition(DDA),and data-independent acquisition(DIA).Caenorhabditis elegans(C.elegans)is a useful model for discovering toxic-induced metabolism,highthroughput drug screening,and a variety of human disease pathways.To determine the lipidome of C.elegans and investigate lipid disruption from the molecular level to the system biology level,we used integrative data acquisition.The methyl-tert-butyl ether method was used to extract L4 stage C.elegans after exposure to triclosan(TCS),perfluorooctanoic acid,and nanopolystyrene(nPS).Full MS,DDA,and DIA integrations were performed to comprehensively profile the C.elegans lipidome by Q-Exactive Plus MS.All annotated lipids were then analyzed using lipid ontology and pathway analysis.We annotated up to 940 lipids from 20 lipid classes involved in various functions and pathways.The biological investigations revealed that when C.elegans were exposed to nPS,lipid droplets were disrupted,whereas plasma membrane-functionalized lipids were likely to be changed in the TCS treatment group.The nPS treatment caused a significant disruption in lipid storage.Triacylglycerol,glycerophospholipid,and ether class lipids were those primarily hindered by toxicants.Finally,toxicant exposure frequently involved numerous lipid-related pathways,including the phosphoinositide 3-kinase/protein kinase B pathway.In conclusion,an integrative data acquisition strategy was used to characterize the C.elegans lipidome,providing valuable biological insights into hypothesis generation and validation.

关键词： Caenorhabditis elegans Lipidomics data-dependent acquisition data-independent acquisition

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DPHL:A DIA Pan-human Protein Mass Spectrometry Library for Robust Biomarker Discovery

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Genomics, Proteomics & Bioinformatics 2020年第2期18卷 104-119页

作者： Tiansheng Zhu Yi Zhu Yue Xuan Huanhuan Gao Xue Cai Sander R.Piersma Thang V.Pham Tim Schelfhorst Richard R.G.D.Haas Irene V.Bijnsdorp Rui Sun Liang Yue Guan Ruan Qiushi Zhang Mo Hu Yue Zhou Winan J.Van Houdt Tessa Y.S.Le Large Jacqueline Cloos Anna Wojtuszkiewicz Danijela Koppers-Lalic Franziska Bottger Chantal Scheepbouwer Ruud H.Brakenhoff Geert J.L.H.van Leenders Jan N.M.Ijzermans John W.M.Martens Renske D.M.Steenbergen Nicole C.Grieken Sathiyamoorthy Selvarajan Sangeeta Mantoo Sze S.Lee Serene J.Y.Yeow Syed M.F.Alkaff Nan Xiang Yaoting Sun Xiao Yi Shaozheng Dai Wei Liu Tian Lu Zhicheng Wu Xiao Liang Man Wang Yingkuan Shao Xi Zheng Kailun Xu Qin Yang Yifan Meng Cong Lu Jiang Zhu Jin'e Zheng Bo Wang Sai Lou Yibei Dai Chao Xu Chenhuan Yu Huazhong Ying Tony K.Lim Jianmin Wu Xiaofei Gao Zhongzhi Luan Xiaodong Teng Peng Wu Shi'ang Huang Zhihua Tao Narayanan G.Iyer Shuigeng Zhou Wenguang Shao Henry Lam Ding Ma Jiafu Ji Oi L.Kon Shu Zheng Ruedi Aebersold Connie R.Jimenez Tiannan Guo Zhejiang Provincial Laboratory of Life Sciences and Biomedicine Westlake UniversityHangzhou 310024China Key Laboratory of Structural Biology of Zhejiang Province School of Life SciencesWestlake UniversityHangzhou 310024China Institute of Basic Medical Sciences Westlake Institute for Advanced StudyHangzhou 310024China School of Computer Science Shanghai Key Laboratory of Intelligent Information ProcessingFudan UniversityShanghai 200433China Thermo Fisher Scientific(BREMEN)GmbH Bremen 28195Germany OncoProteomics Laboratory Department of Medical OncologyVU University Medical CenterVU UniversityAmsterdam 1011The Netherlands Amsterdam UMC Vrije Universiteit AmsterdamUrologyCancer Center AmsterdamAmsterdam 1011The Netherlands Thermo Fisher Scientific Shanghai 201206China The Netherlands Cancer Institute Surgical OncologyAmsterdam 1011The Netherlands Amsterdam UMC Vrije Universiteit AmsterdamSurgeryCancer Center AmsterdamAmsterdam 1011The Netherlands Amsterdam UMC Vrije Universiteit AmsterdamPediatric Oncology/HematologyCancer Center AmsterdamAmsterdam 1011The Netherlands Amsterdam UMC Vrije Universiteit AmsterdamHematologyCancer Center AmsterdamAmsterdam 1011The Netherlands Amsterdam UMC Vrije Universiteit AmsterdamMedical OncologyCancer Center AmsterdamAmsterdam 1011The Netherlands Amsterdam UMC Vrije Universiteit AmsterdamNeurosurgeryCancer Center AmsterdamAmsterdam 1011The Netherlands Amsterdam UMC Vrije Universiteit AmsterdamPathologyCancer Center AmsterdamAmsterdam 1011The Netherlands Amsterdam UMC Vrije Universiteit AmsterdamOtolaryngology/Head and Neck SurgeryCancer Center AmsterdamAmsterdam 1011The Netherlands Erasmus MC University Medical Center PathologyRotterdam 1016LVThe Netherlands Erasmus MC University Medical Center SurgeryRotterdam 1016LVThe Netherlands Erasmus MC University Medical Center Medical OncologyRotterdam 1016LVThe Netherlands Department of Anatomical Pathology Singapore General HospitalSingapore 169608Singapore Division of Medical Scie

To address the increasing need for detecting and validating protein biomarkers in clinical specimens,mass spectrometry(MS)-based targeted proteomic techniques,including the selected reaction monitoring(SRM),parallel reaction monitoring(PRM),and massively parallel dataindependent acquisition(DIA),have been developed.For optimal performance,they require the fragment ion spectra of targeted peptides as prior knowledge.In this report,we describe a MS pipeline and spectral resource to support targeted proteomics studies for human tissue samples.To build the spectral resource,we integrated common open-source MS computational tools to assemble a freely accessible computational workflow based on Docker.We then applied the workflow to generate DPHL,a comprehensive DIA pan-human library,from 1096 data-dependent acquisition(DDA)MS raw files for 16 types of cancer samples.This extensive spectral resource was then applied to a proteomic study of 17 prostate cancer(PCa)patients.Thereafter,PRM validation was applied to a larger study of 57 PCa patients and the differential expression of three proteins in prostate tumor was validated.As a second application,the DPHL spectral resource was applied to a study consisting of plasma samples from 19 diffuse large B cell lymphoma(DLBCL)patients and 18 healthy control subjects.Differentially expressed proteins between DLBCL patients and healthy control subjects were detected by DIA-MS and confirmed by PRM.These data demonstrate that the DPHL supports DIA and PRM MS pipelines for robust protein biomarker discovery.DPHL is freely accessible at https://***/page/project.html?id=IPX0001400000.

关键词： data-independent acquisition Parallel reaction monitoring Spectral library Prostate cancer Diffuse large B cell lymphoma

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