Molecular & Cellular Proteomics最新文献

{"title":"Comprehensive Proteomics Metadata and Integrative Web Portals Facilitate Sharing and Integration of LINCS Multiomics Data.","authors":"Dušica Vidović, Behrouz Shamsaei, Stephan C Schürer, Phillip Kogan, Szymon Chojnacki, Michal Kouril, Mario Medvedovic, Wen Niu, Evren U Azeloglu, Marc R Birtwistle, Yibang Chen, Tong Chen, Jens Hansen, Bin Hu, Ravi Iyengar, Gomathi Jayaraman, Hong Li, Tong Liu, Eric A Sobie, Yuguang Xiong, Matthew J Berberich, Gary Bradshaw, Mirra Chung, Robert A Everley, Ben Gaudio, Marc Hafner, Marian Kalocsay, Caitlin E Mills, Maulik K Nariya, Peter K Sorger, Kartik Subramanian, Chiara Victor, Maria Banuelos, Victoria Dardov, Ronald Holewinski, Danica-Mae Manalo, Berhan Mandefro, Andrea D Matlock, Loren Ornelas, Dhruv Sareen, Clive N Svendsen, Vineet Vaibhav, Jennifer E Van Eyk, Vidya Venkatraman, Steve Finkbiener, Ernest Fraenkel, Jeffrey Rothstein, Leslie Thompson, Jacob Asiedu, Steven A Carr, Karen E Christianson, Desiree Davison, Deborah O Dele-Oni, Katherine C DeRuff, Shawn B Egri, Alvaro Sebastian Vaca Jacome, Jacob D Jaffe, Daniel Lam, Lev Litichevskiy, Xiaodong Lu, James Mullahoo, Adam Officer, Malvina Papanastasiou, Ryan Peckner, Caidin Toder, Joel Blanchard, Michael Bula, Tak Ko, Li-Huei Tsai, Jennie Z Young, Vagisha Sharma, Ajay Pillai, Jarek Meller, Michael J MacCoss","doi":"10.1016/j.mcpro.2025.100947","DOIUrl":"10.1016/j.mcpro.2025.100947","url":null,"abstract":"<p><p>The Library of Integrated Network-based Cellular Signatures (LINCS), an NIH Common Fund program, has cataloged and analyzed cellular function and molecular activity profiles in response to >80,000 perturbing agents that are potentially disruptive to cells. Because of the importance of proteins and their modifications to the response of specific cellular perturbations, four of the six LINCS centers have included significant proteomics efforts in the characterization of the resulting phenotype. This manuscript aims to describe this effort and the data harmonization and integration of the LINCS proteomics data discussed in recent LINCS papers.</p>","PeriodicalId":18712,"journal":{"name":"Molecular & Cellular Proteomics","volume":" ","pages":"100947"},"PeriodicalIF":5.5,"publicationDate":"2025-07-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC12332945/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143634289","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"生物学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Recent Advances in Mass Spectrometry-Based Studies of Post-Translational Modifications in Alzheimer's Disease.","authors":"Feixuan Wu, Wei Li, Haiyan Lu, Lingjun Li","doi":"10.1016/j.mcpro.2025.101003","DOIUrl":"10.1016/j.mcpro.2025.101003","url":null,"abstract":"<p><p>Alzheimer's disease (AD) is a progressive neurodegenerative disorder characterized by cognitive decline. There are more than 10 million new cases of AD each year worldwide, implying one new case every 3.2 s. Post-translational modifications (PTMs) such as phosphorylation, glycosylation, and citrullination have emerged as key modulators of protein function in AD, influencing protein aggregation, clearance, and toxicity. Mass spectrometry (MS) has become an indispensable tool for detecting and quantifying these PTMs, offering valuable insights into their role in AD pathogenesis. This review explores recent advancements in MS-based studies of PTMs in AD, with emphasis on MS techniques, such as data-dependent acquisition (DDA) and data-independent acquisition (DIA), as well as enrichment methods used to characterize PTMs. The applications of these MS-based approaches to the study of various PTMs are highlighted, which have significantly accelerated the biomarker discovery process, providing new avenues for early diagnosis and therapeutic targeting. Advances in biological understanding and analytical techniques, while addressing the challenges and future directions, will be discussed.</p>","PeriodicalId":18712,"journal":{"name":"Molecular & Cellular Proteomics","volume":" ","pages":"101003"},"PeriodicalIF":5.5,"publicationDate":"2025-07-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC12270687/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144192024","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"生物学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"From Discovery to Delivery: A Rapid and Targeted Proteomics Workflow for Monitoring Chinese Hamster Ovary Biomanufacturing.","authors":"Charles Eldrid, Ellie Hawke, Kathleen M Cain, Kate Meeson, Joanne Watson, Reynard Spiess, Luke Johnston, William Smith, Matthew Russell, Robyn Hoare, John Raven, Jean-Marc Schwartz, Magnus Rattray, Leon Pybus, Alan Dickson, Andrew Pitt, Perdita Barran","doi":"10.1016/j.mcpro.2025.101011","DOIUrl":"10.1016/j.mcpro.2025.101011","url":null,"abstract":"<p><p>Chinese hamster ovary (CHO) cells are the industrial workhorse for manufacturing biopharmaceuticals, including monoclonal antibodies. CHO cell line development requires a more data-driven approach for the accelerated identification of hyperproductive cell lines. Traditional methods, which rely on time-consuming hierarchical screening, often fail to elucidate the underlying cellular mechanisms driving optimal bioreactor performance. Big data analytics, coupled with advancements in \"omics\" technologies, are revolutionizing the study of industrial cell lines. Translating this knowledge into practical methods widely utilized in industrial biomanufacturing remains a significant challenge. This study leverages discovery proteomics to characterize dynamic changes within the CHO cell proteome during a 14-day fed-batch bioreactor cultivation. Utilizing a global untargeted proteomics workflow on both a ZenoTOF 7600 and a Cyclic IMS QToF, we identify 3358 proteins and present a comprehensive data set that describes the molecular changes that occur within a well-characterized host chassis. By mapping relative abundances to key cellular processes, eight protein targets were selected as potential biomarkers. The abundance of these proteins through the production run is quantified using a 15-min targeted triple quadrupole (MRM) assay, which provides a molecular-level QC for cell viability. This discovery to target workflow has the potential to assist engineering of new chassis and provide simple readouts of successful bioreactor batches.</p>","PeriodicalId":18712,"journal":{"name":"Molecular & Cellular Proteomics","volume":" ","pages":"101011"},"PeriodicalIF":5.5,"publicationDate":"2025-07-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC12274832/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144248702","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"生物学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"High-Throughput Single-Cell Proteomics of In Vivo Cells.","authors":"Shiri Karagach, Joachim Smollich, Ofir Atrakchi, Vishnu Mohan, Tamar Geiger","doi":"10.1016/j.mcpro.2025.101018","DOIUrl":"10.1016/j.mcpro.2025.101018","url":null,"abstract":"<p><p>Single-cell mass spectrometry-based proteomics (SCP) can resolve cellular heterogeneity in complex biological systems and provide a system-level view of the proteome of each cell. Major advancements in SCP methodologies have been introduced in recent years, providing highly sensitive sample preparation methods and mass spectrometric technologies. However, most studies present limited throughput and mainly focus on the analysis of cultured cells. To enhance the depth, accuracy, and throughput of SCP for tumor analysis, we developed an automated, high-throughput pipeline that enables the analysis of 1536 single cells in a single experiment. This approach integrates low-volume sample preparation, automated sample purification, and LC-MS analysis with the Slice-PASEF method. Integration of these methodologies into a streamlined pipeline led to a robust and reproducible identification of more than 3000 proteins per cell. We applied this pipeline to analyze tumor macrophages in a murine lung metastasis model. We identified over 1700 proteins per cell, including key macrophage markers and more than 500 differentially expressed proteins between tumor and control macrophages. PCA analysis successfully separated these populations, revealing the utility of SCP in capturing biologically relevant signals in the tumor microenvironment. Our results demonstrate a robust and scalable pipeline poised to advance single-cell proteomics in cancer research.</p>","PeriodicalId":18712,"journal":{"name":"Molecular & Cellular Proteomics","volume":" ","pages":"101018"},"PeriodicalIF":5.5,"publicationDate":"2025-07-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC12301781/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144369073","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"生物学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"A Method for Comparing Proteins Measured in Serum and Plasma by Olink Proximity Extension Assay.","authors":"Rawan Shraim, Caroline Diorio, Scott W Canna, Erin Macdonald-Dunlop, Hamid Bassiri, Zachary Martinez, Anders Mälarstig, Afrouz Abbaspour, David T Teachey, Robert B Lindell, Edward M Behrens","doi":"10.1016/j.mcpro.2025.101000","DOIUrl":"10.1016/j.mcpro.2025.101000","url":null,"abstract":"<p><p>Accurate measurement of secreted proteins in serum and plasma is essential for understanding mechanisms and developing reliable biomarkers. Recent technological advancements, such as proximity extension assay (PEA), have enabled high-throughput multiplex protein analyses from small sample volumes in either serum or plasma. Despite the increasing use of PEA-based proteomics and the generation of extensive datasets, integrated data from these two mediums remains challenging due to inherent differences in sample processing. To address this issue, we developed and validated protein-specific transformation factors using linear modeling to normalize protein measurements between serum and plasma proteins quantified using Olink. Our analysis surveyed 1463 proteins across matched serum and plasma samples, identifying 686 transformation factors. The transformation factors were further validated using independent datasets generated from patients with different disease phenotypes and ages, and 551 of the models and transformation factors were reproducible. These transformation factors provide a valuable resource for normalizing PEA-based proteomic data across serum and plasma, ultimately enhancing the capacity for collaborative analyses and facilitating comprehensive insights across diverse disease phenotypes.</p>","PeriodicalId":18712,"journal":{"name":"Molecular & Cellular Proteomics","volume":" ","pages":"101000"},"PeriodicalIF":5.5,"publicationDate":"2025-07-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC12418474/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144182517","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"生物学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Corrigendum to \"Comprehensive Proteomics Metadata and Integrative Web Portals Facilitate Sharing and Integration of LINCS Multiomics Data\".","authors":"Dušica Vidović, Behrouz Shamsaei, Stephan C Schürer, Phillip Kogan, Szymon Chojnacki, Michal Kouril, Mario Medvedovic, Wen Niu, Evren U Azeloglu, Marc R Birtwistle, Yibang Chen, Tong Chen, Jens Hansen, Bin Hu, Ravi Iyengar, Gomathi Jayaraman, Hong Li, Tong Liu, Eric A Sobie, Yuguang Xiong, Matthew J Berberich, Gary Bradshaw, Mirra Chung, Robert A Everley, Ben Gaudio, Marc Hafner, Marian Kalocsay, Caitlin E Mills, Maulik K Nariya, Peter K Sorger, Kartik Subramanian, Chiara Victor, Maria Banuelos, Victoria Dardov, Ronald Holewinski, Danica-Mae Manalo, Berhan Mandefro, Andrea D Matlock, Loren Ornelas, Dhruv Sareen, Clive N Svendsen, Vineet Vaibhav, Jennifer E Van Eyk, Vidya Venkatraman, Steve Finkbiener, Ernest Fraenkel, Jeffrey Rothstein, Leslie Thompson, Jacob Asiedu, Steven A Carr, Karen E Christianson, Desiree Davison, Deborah O Dele-Oni, Katherine C DeRuff, Shawn B Egri, Alvaro Sebastian Vaca Jacome, Jacob D Jaffe, Daniel Lam, Lev Litichevskiy, Xiaodong Lu, James Mullahoo, Adam Officer, Malvina Papanastasiou, Ryan Peckner, Caidin Toder, Joel Blanchard, Michael Bula, Tak Ko, Li-Huei Tsai, Jennie Z Young, Vagisha Sharma, Jarek Meller, Michael J MacCoss","doi":"10.1016/j.mcpro.2025.100995","DOIUrl":"10.1016/j.mcpro.2025.100995","url":null,"abstract":"","PeriodicalId":18712,"journal":{"name":"Molecular & Cellular Proteomics","volume":"24 7","pages":"100995"},"PeriodicalIF":5.5,"publicationDate":"2025-07-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC12365493/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144812133","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"生物学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"PTMFusionNet: A Deep Learning Approach for Predicting Disease Related Post-translational Modification and Classifying Disease Subtypes.","authors":"Jie Ni, Yifan Zhou, Bin Li, Xinting Zhang, Yuanyuan Deng, Jie Sun, Donghui Yan, Shengqi Jing, Shan Lu, Zhuoying Xie, Xin Zhang, Yun Liu","doi":"10.1016/j.mcpro.2025.101009","DOIUrl":"10.1016/j.mcpro.2025.101009","url":null,"abstract":"<p><p>With the advancement of technologies such as mass spectrometry, it has become possible to simultaneously perform large-scale detection of protein intensity and corresponding post-translational modification (PTM) information, thereby facilitating clinical diagnosis and treatment. However, existing PTM information is insufficient to fully integrate with protein expression data. We propose a deep learning method called PTMFusionNet, which predicts potential disease-related PTMs and integrates them with protein expression data to classify disease subtypes. PTMFusionNet includes two Graph Convolutional Network (GCN) models: the Layer-Attention Graph Convolutional Network (LAGCN) and the Feature Weighting Graph Convolutional Network (FWGCN). LAGCN is used to predict PTM potentiality scores, while FWGCN integrates these scores with protein expression data for disease subtype classification. Experimental results across three datasets (KIPAN, COADREAD, and THCA) demonstrate that PTMFusionNet outperforms benchmark algorithms in accuracy, F1 score, and AUC, highlighting its robustness in identifying critical PTM biomarkers and advancing disease subtyping.</p>","PeriodicalId":18712,"journal":{"name":"Molecular & Cellular Proteomics","volume":" ","pages":"101009"},"PeriodicalIF":5.5,"publicationDate":"2025-07-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC12365514/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144225897","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"生物学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"P4PP: A Universal Shotgun Proteomics Data Analysis Pipeline for Virus Identification.","authors":"Armand Paauw, Evgeni Levin, Ingrid A I Voskamp-Visser, Ilka M F Marissen, Vincent Ramisse, Marine Eschlimann, Jiří Dresler, Petr Pajer, Christoph Stingl, Hans C van Leeuwen, Theo M Luider, Luc M Hornstra","doi":"10.1016/j.mcpro.2025.101004","DOIUrl":"10.1016/j.mcpro.2025.101004","url":null,"abstract":"<p><p>Humans can be infected by a wide variety of virus species. We developed a data analysis approach for shotgun proteomic data to detect these viruses. A proteome for pandemic preparedness (P4PP) pipeline, a corresponding database (P4PP v01), and a web application (P4PP) were constructed. The P4PP pipeline enables the identification of 1896 virus species from the 32 virus families, based on multiple identified discriminatory peptides, in which at least one human infectious virus is described. P4PP was evaluated using different datasets of cell-cultivated viruses, generated at different institutes, measured with different instruments, and prepared with different sample preparation methods. In total, 174 mass spectrometry datasets of 160 and 14 protein trypsin digests of virus-infected and noninfected cell lines were analyzed, respectively. Of the 160 samples, 146 were correctly identified at the species level, and an additional four samples were identified at the family level. In the remaining 10 samples, no virus was detected. However, all these 10 samples tested positive in follow-up samples obtained later in time series were negative samples were measured, indicating that the number of peptides derived from the virus was initially too low in the samples obtained at the start of the experiment. Furthermore, results show that influenza A or severe acute respiratory syndrome coronavirus 2 can be subtyped if enough discriminative peptides of the virus are identified. In the noninfected cell lines, no virus was detected except in one sample where the in that experiment studied virus was detected. Shotgun proteomics, in combination with the developed data analysis approach, can identify all types of virus species after cultivation in a cell line. Implementing this agnostic virus proteome analysis capability in viral diagnostic laboratories has the potential to improve their capabilities to cope with unexpected, mutated, or re-emerging viruses.</p>","PeriodicalId":18712,"journal":{"name":"Molecular & Cellular Proteomics","volume":" ","pages":"101004"},"PeriodicalIF":5.5,"publicationDate":"2025-07-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC12418414/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144192023","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"生物学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Cytosolic WPRa4 and Plastoskeletal PMI4 Proteins Mediate Touch Response in a Model Organism Arabidopsis.","authors":"Kebin Wu, Nan Yang, Jia Ren, Shichang Liu, Kai Wang, Shuaijian Dai, Yinglin Lu, Yuxing An, Fuyun Tian, Zhaobing Gao, Zhu Yang, Yage Zhang, Weichuan Yu, Ning Li","doi":"10.1016/j.mcpro.2025.101015","DOIUrl":"10.1016/j.mcpro.2025.101015","url":null,"abstract":"<p><p>Plastoskeletal PMI4 Protein is a Key Regulator of ThigmomorphogenesisTo elucidate the early signaling components involved in thigmomorphogenesis in Arabidopsis thaliana, we combined microscopy and proximity-labeling (PL)-based quantitative biotinylproteomics to characterize the touch-responsive putative cytoskeleton-interacting protein WPRa4 (TREPH1). Our findings revealed that WPRa4 localizes near plastids and interacts with cytosolic Plastid Movement-Impaired (PMI) proteins and a plastidic translocon component, suggesting a cytoskeleton-plastid network in mechanosensing. Bioinformatic analysis of PL and cross-linking mass spectrometry (XL-MS) data identified PMI4 as a key mediator, with pmi4 mutants lacking touch-induced bolting delay, rosette size reduction, and Ca²⁺ oscillations. Transcriptomics further showed that PMI4 regulates touch-responsive and jasmonic acid (JA)-associated genes, such as LOX2. We propose a molecular model where interconnected Cytoskeleton-Plastoskeleton Continuum (CPC) proteins act as early mechanosensors, integrating the touch responses of plant aerial organs with calcium signaling and transcriptional reprogramming in Arabidopsis.</p>","PeriodicalId":18712,"journal":{"name":"Molecular & Cellular Proteomics","volume":" ","pages":"101015"},"PeriodicalIF":5.5,"publicationDate":"2025-07-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC12274932/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144294106","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"生物学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Top-Down and Middle-Down Mass Spectrometry of Antibodies.","authors":"Nina A Khristenko, Konstantin O Nagornov, Camille Garcia, Natalia Gasilova, Megan Gant, Karen Druart, Anton N Kozhinov, Laure Menin, Julia Chamot-Rooke, Yury O Tsybin","doi":"10.1016/j.mcpro.2025.100989","DOIUrl":"10.1016/j.mcpro.2025.100989","url":null,"abstract":"<p><p>Therapeutic antibodies, primarily immunoglobulin G-based monoclonal antibodies, are developed to treat cancer, autoimmune disorders, and infectious diseases. Their large size, structural complexity, and heterogeneity pose significant analytical challenges, requiring advanced characterization techniques. This review traces the 30-year evolution of top-down (TD) and middle-down (MD) mass spectrometry (MS) for antibody analysis, beginning with their initial applications and highlighting key advances and challenges throughout this period. TD MS allows for the analysis of intact antibodies, and MD MS performs analysis of the antibody subunits, even in complex biological samples. Both approaches preserve critical quality attributes such as sequence integrity, post-translational modifications (PTMs), disulfide bonds, and glycosylation patterns. Key milestones in TD and MD MS of antibodies include the use of structure-specific enzymes for subunit generation, the implementation of high-resolution mass spectrometers, and the adoption of non-ergodic ion activation methods such as electron transfer dissociation (ETD), electron capture dissociation (ECD), ultraviolet photodissociation (UVPD), and matrix-assisted laser desorption/ionization in-source decay (MALDI-ISD). The combination of complementary dissociation methods and consecutive ion activation approaches has further enhanced TD/MD MS performance. The current TD MS record of antibody sequencing with terminal product ions is about 60% sequence coverage obtained using the activated ion-ETD approach on a high-resolution MS platform. Current MD MS analyses with about 95% sequence coverage were achieved using combinations of ion activation and dissociation techniques. The review explores TD and MD MS analysis of novel mAb modalities, including antibody-drug conjugates, bispecific antibodies, endogenous antibodies from biofluids, and immunoglobulin A and M-type classes.</p>","PeriodicalId":18712,"journal":{"name":"Molecular & Cellular Proteomics","volume":" ","pages":"100989"},"PeriodicalIF":5.5,"publicationDate":"2025-07-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC12343474/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144078715","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"生物学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}