Proteomics最新文献

{"title":"Editorial Board: Proteomics 21–22'24","authors":"","doi":"10.1002/pmic.202470172","DOIUrl":"https://doi.org/10.1002/pmic.202470172","url":null,"abstract":"","PeriodicalId":224,"journal":{"name":"Proteomics","volume":"24 21-22","pages":""},"PeriodicalIF":3.4,"publicationDate":"2024-11-19","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://onlinelibrary.wiley.com/doi/epdf/10.1002/pmic.202470172","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142685336","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"生物学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Contents: Proteomics 21–22'24","authors":"","doi":"10.1002/pmic.202470173","DOIUrl":"https://doi.org/10.1002/pmic.202470173","url":null,"abstract":"","PeriodicalId":224,"journal":{"name":"Proteomics","volume":"24 21-22","pages":""},"PeriodicalIF":3.4,"publicationDate":"2024-11-19","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://onlinelibrary.wiley.com/doi/epdf/10.1002/pmic.202470173","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142692073","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"生物学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Standard abbreviations","authors":"","doi":"10.1002/pmic.202470174","DOIUrl":"10.1002/pmic.202470174","url":null,"abstract":"","PeriodicalId":224,"journal":{"name":"Proteomics","volume":"24 21-22","pages":""},"PeriodicalIF":3.4,"publicationDate":"2024-11-19","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142674591","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"生物学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Special Issue on “Metaproteomics and meta-omics perspectives to decrypt Microbiome Functionality”","authors":"Lucia Grenga, Magnus Øverlie Arntzen, Jean Armengaud","doi":"10.1002/pmic.202400072","DOIUrl":"10.1002/pmic.202400072","url":null,"abstract":"<p><i>Proteomics</i> is inviting submissions to a special issue dedicated to microbiome research, emphasizing the integration of omics to uncover the functionality of microbiomes. This special issue is tentatively scheduled for publication for mid-2025. It provides an ideal platform for showcasing cutting-edge research on microbiomes, proposing new strategies to make the most of acquired molecular data, and fostering discussions on the future prospects of metaproteomics in the field and the synergies with other omics. The objective of this special issue is to cover the full spectrum of technologies aimed at enhancing our understanding of microbiome and holobionts' function and illustrate their practical applications. We encourage submissions from all areas of microbiome research focusing on functionality. We are open to considering different types of papers, including research articles, review articles, technical briefs, dataset briefs, and viewpoint articles.</p><p>Microorganisms contribute to crucial biological processes within vast and intricate ecosystems like soils and oceans [<span>1</span>]. Typically operating within complex communities known as microbiota, microorganisms employ an ingenious mixture of task specialization, cooperation, and competition as a winning strategy to navigate environmental conditions [<span>2</span>] and ensure the stability of ecosystems [<span>3</span>]. Establishing symbiotic relationships with their hosts if any, they often provide mutual benefits, although in some instances, they may contribute to host diseases. The significance of ecosystem services rendered by microbiota is increasingly recognized, underscoring the growing importance of characterizing these ecosystems. Enhanced understanding holds promise for diverse fields, including medicine, well-being, food industry, agriculture, animal breeding and fish farming, biotechnology, remediation and protection of the environment.</p><p>It's time to face the music and admit that exploring microbial communities will entail an extra layer of challenging hurdles due to their extensive taxonomic diversity, genomic heterogeneity, dynamic nature, and our limited understanding of their components, which primarily focuses on cultivable species [<span>4</span>]. Beyond mere taxonomic catalogue of microorganisms within a microbiota and their enumeration to determine their abundance, it is crucial to discern who the active contributors are and what the ongoing molecular processes are to grasp these biological systems fully. The functionality of microbiomes involves a complex interplay of numerous interconnected variables, ranging from genetic makeup and mRNA transcripts to proteins and their potential post-translational modifications, inherent protein catalytic properties, subcellular localization, and the resultant enzymatic products that can retroact on catalysis levels. Omics technologies have become indispensable in unravelling the intricacies of these molecular pro","PeriodicalId":224,"journal":{"name":"Proteomics","volume":"24 23-24","pages":""},"PeriodicalIF":3.4,"publicationDate":"2024-11-18","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://onlinelibrary.wiley.com/doi/epdf/10.1002/pmic.202400072","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142646460","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"生物学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"In-Depth Proteome Profiling of the Hippocampus of LDLR Knockout Mice Reveals Alternation in Synaptic Signaling Pathway","authors":"Hong-Beom Park,&nbsp;Hyeyoon Kim,&nbsp;Dohyun Han","doi":"10.1002/pmic.202400152","DOIUrl":"10.1002/pmic.202400152","url":null,"abstract":"<div>\u0000 \u0000 <p>The low-density lipoprotein receptor (LDLR) is a major apolipoprotein receptor that regulates cholesterol homeostasis. LDLR deficiency is associated with cognitive impairment by the induction of synaptopathy in the hippocampus. Despite the close relationship between LDLR and neurodegenerative disorders, proteomics research for protein profiling in the LDLR knockout (KO) model remains insufficient. Therefore, understanding LDLR KO-mediated differential protein expression within the hippocampus is crucial for elucidating a role of LDLR in neurodegenerative disorders. In this study, we conducted first-time proteomic profiling of hippocampus tissue from LDLR KO mice using tandem mass tag (TMT)-based MS analysis. LDLR deficiency induces changes in proteins associated with the transport of diverse molecules, and activity of kinase and catalyst within the hippocampus. Additionally, significant alterations in the expression of components in the major synaptic pathways were found. Furthermore, these synaptic effects were verified using a data-independent acquisition (DIA)-based proteomic method. Our data will serve as a valuable resource for further studies to discover the molecular function of LDLR in neurodegenerative disorders.</p>\u0000 </div>","PeriodicalId":224,"journal":{"name":"Proteomics","volume":"25 4","pages":""},"PeriodicalIF":3.4,"publicationDate":"2024-11-16","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142643571","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"生物学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Review and Practical Guide for Getting Started With Single-Cell Proteomics","authors":"Hsien-Jung L. Lin,&nbsp;Kei G. I. Webber,&nbsp;Andikan J. Nwosu,&nbsp;Ryan T. Kelly","doi":"10.1002/pmic.202400021","DOIUrl":"10.1002/pmic.202400021","url":null,"abstract":"<div>\u0000 \u0000 <p>Single-cell proteomics (SCP) has advanced significantly in recent years, with new tools specifically designed for the preparation and analysis of single cells now commercially available to researchers. The field is sufficiently mature to be broadly accessible to any lab capable of isolating single cells and performing bulk-scale proteomic analyses. In this review, we highlight recent work in the SCP field that has significantly lowered the barrier to entry, thus providing a practical guide for those who are newly entering the SCP field. We outline the fundamental principles and report multiple paths to accomplish the key steps of a successful SCP experiment including sample preparation, separation, and mass spectrometry data acquisition and analysis. We recommend that researchers start with a label-free SCP workflow, as achieving high-quality and quantitatively accurate results is more straightforward than label-based multiplexed strategies. By leveraging these accessible means, researchers can confidently perform SCP experiments and make meaningful discoveries at the single-cell level.</p>\u0000 </div>","PeriodicalId":224,"journal":{"name":"Proteomics","volume":"25 1-2","pages":""},"PeriodicalIF":3.4,"publicationDate":"2024-11-16","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142643573","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"生物学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Parallel Analyses by Mass Spectrometry (MS) and Reverse Phase Protein Array (RPPA) Reveal Complementary Proteomic Profiles in Triple-Negative Breast Cancer (TNBC) Patient Tissues and Cell Cultures","authors":"Nan Wang,&nbsp;Yiying Zhu,&nbsp;Lianshui Wang,&nbsp;Wenshuang Dai,&nbsp;Taobo Hu,&nbsp;Zhentao Song,&nbsp;Xia Li,&nbsp;Qi Zhang,&nbsp;Jianfei Ma,&nbsp;Qianghua Xia,&nbsp;Jin Li,&nbsp;Yiqiang Liu,&nbsp;Mengping Long,&nbsp;Zhiyong Ding","doi":"10.1002/pmic.202400107","DOIUrl":"10.1002/pmic.202400107","url":null,"abstract":"<div>\u0000 \u0000 <p>High-plex proteomic technologies have made substantial contributions to mechanism studies and biomarker discovery in complex diseases, particularly cancer. Despite technological advancements, inherent limitations in individual proteomic approaches persist, impeding the achievement of comprehensive quantitative insights into the proteome. In this study, we employed two widely used proteomic technologies, mass spectrometry (MS) and reverse phase protein array (RPPA) to analyze identical samples, aiming to systematically assess the outcomes and performance of the different technologies. Additionally, we sought to establish an integrated workflow by combining these two proteomic approaches to augment the coverage of protein targets for discovery purposes. We used 14 fresh frozen tissue samples from triple-negative breast cancer (TNBC: seven tumors versus seven adjacent non-cancerous tissues) and cell line samples to evaluate both technologies and implement this dual-proteomic strategy. Using a single-step protein denaturation and extraction protocol, protein samples were subjected to reverse-phase liquid chromatography (LC) followed by electrospray ionization (ESI)-mediated MS/MS for proteomic profiling. Concurrently, identical sample aliquots were analyzed by RPPA for profiling of over 300 proteins and phosphoproteins that are in key signaling pathways or druggable targets in cancer. Both proteomic methods demonstrated the expected ability to differentiate samples by groups, revealing distinct proteomic patterns under various experimental conditions, albeit with minimal overlap in identified targets. Mechanism-based analysis uncovered divergent biological processes identified with the two proteomic technologies, capitalizing on their complementary exploratory potential.</p>\u0000 </div>","PeriodicalId":224,"journal":{"name":"Proteomics","volume":"25 4","pages":""},"PeriodicalIF":3.4,"publicationDate":"2024-11-16","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142643572","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"生物学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Omics Studies in CKD: Diagnostic Opportunities and Therapeutic Potential.","authors":"Merita Rroji, Goce Spasovski","doi":"10.1002/pmic.202400151","DOIUrl":"https://doi.org/10.1002/pmic.202400151","url":null,"abstract":"<p><p>Omics technologies have significantly advanced the prediction and therapeutic approaches for chronic kidney disease (CKD) by providing comprehensive molecular insights. This is a review of the current state and future prospects of integrating biomarkers into the clinical practice for CKD, aiming to improve patient outcomes by targeted therapeutic interventions. In fact, the integration of genomic, transcriptomic, proteomic, and metabolomic data has enhanced our understanding of CKD pathogenesis and identified novel biomarkers for an early diagnosis and targeted treatment. Advanced computational methods and artificial intelligence (AI) have further refined multi-omics data analysis, leading to more accurate prediction models for disease progression and therapeutic responses. These developments highlight the potential to improve CKD patient care with a precise and individualized treatment plan .</p>","PeriodicalId":224,"journal":{"name":"Proteomics","volume":" ","pages":"e202400151"},"PeriodicalIF":3.4,"publicationDate":"2024-11-11","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142612513","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"生物学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Transforming peptide hormone prediction: The role of AI in modern proteomics","authors":"Nguyen Quoc Khanh Le","doi":"10.1002/pmic.202400156","DOIUrl":"10.1002/pmic.202400156","url":null,"abstract":"","PeriodicalId":224,"journal":{"name":"Proteomics","volume":"25 3","pages":""},"PeriodicalIF":3.4,"publicationDate":"2024-11-07","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142602744","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"生物学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Proteome integral solubility alteration via label-free DIA approach (PISA-DIA), game changer in drug target deconvolution","authors":"Zheng Ser,&nbsp;Radoslaw M. Sobota","doi":"10.1002/pmic.202400147","DOIUrl":"10.1002/pmic.202400147","url":null,"abstract":"<p>Drug protein-target identification in past decades required screening compound libraries against known proteins to determine drugs binding to specific protein. Protein targets used in drug-target screening were selected predominantly used laborious genetic manipulation assays. In 2013, a team led by Pär Nordlund from Karolinska Institutet (Stockholm, Sweden) developed Cellular Thermal Shift Assay (CETSA), a method which, for the first time, enabled the possibility of drug protein-target identification in the complex cellular proteome. High throughput, quantitative mass spectrometry (MS) proteomics appeared as a compatible analytical method of choice to complement CETSA, aka Thermal Protein Profiling assay (TPP). Since the seminal CETSA-MS/ TPP-MS publications, different protein-target deconvolution strategies emerged including Proteome Integral Solubility Alteration (PISA). The work of Emery–Corbin et al. (Proteomics 2024, 2300644), titled Proteome Integral Solubility Alteration via label-free DIA approach (PISA-DIA), introduces Data–Independent Acquisition (DIA) as a quantification method, opening new avenues in drug target-deconvolution field. Application of DIA for target deconvolution offers attractive alternative to widely used data dependent methodology.</p>","PeriodicalId":224,"journal":{"name":"Proteomics","volume":"25 3","pages":""},"PeriodicalIF":3.4,"publicationDate":"2024-11-07","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142602743","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"生物学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}