Clinical proteomics最新文献

{"title":"New mechanisms and biomarkers of lymph node metastasis in cervical cancer: reflections from plasma proteomics.","authors":"Sai Han, Xiaoli Liu, Shuang Ju, Wendi Mu, Gulijinaiti Abulikemu, Qianwei Zhen, Jiaqi Yang, Jingjing Zhang, Yi Li, Hongli Liu, Qian Chen, Baoxia Cui, Shuxia Wu, Youzhong Zhang","doi":"10.1186/s12014-023-09427-8","DOIUrl":"10.1186/s12014-023-09427-8","url":null,"abstract":"<p><strong>Objective: </strong>Lymph node metastasis (LNM) and lymphatic vasculature space infiltration (LVSI) in cervical cancer patients indicate a poor prognosis, but satisfactory methods for diagnosing these phenotypes are lacking. This study aimed to find new effective plasma biomarkers of LNM and LVSI as well as possible mechanisms underlying LNM and LVSI through data-independent acquisition (DIA) proteome sequencing.</p><p><strong>Methods: </strong>A total of 20 cervical cancer plasma samples, including 7 LNM-/LVSI-(NC), 4 LNM-/LVSI + (LVSI) and 9 LNM + /LVSI + (LNM) samples from a cohort, were subjected to DIA to identify differentially expressed proteins (DEPs) for LVSI and LNM. Subsequently, Gene Ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) analyses were performed for DEP functional annotation. Protein-protein interaction (PPI) and weighted gene coexpression network analysis (WGCNA) were used to detect new effective plasma biomarkers and possible mechanisms.</p><p><strong>Results: </strong>A total of 79 DEPs were identified in the cohort. GO and KEGG analyses showed that DEPs were mainly enriched in the complement and coagulation pathway, lipid and atherosclerosis pathway, HIF-1 signal transduction pathway and phagosome and autophagy. WGCNA showed that the enrichment of the green module differed greatly between groups. Six interesting core DEPs (SPARC, HPX, VCAM1, TFRC, ERN1 and APMAP) were confirmed to be potential plasma diagnostic markers for LVSI and LNM in cervical cancer patients.</p><p><strong>Conclusion: </strong>Proteomic signatures developed in this study reflected the potential plasma diagnostic markers and new possible pathogenesis mechanisms in the LVSI and LNM of cervical cancer.</p>","PeriodicalId":10468,"journal":{"name":"Clinical proteomics","volume":"20 1","pages":"35"},"PeriodicalIF":3.8,"publicationDate":"2023-09-09","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10492398/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"10211039","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Correction to: Proteomic analysis of human synovial fluid reveals potential diagnostic biomarkers for ankylosing spondylitis.","authors":"Ji-Hyun Lee, Jae Hun Jung, Jeesoo Kim, Won-Ki Baek, Jinseol Rhee, Tae-Hwan Kim, Sang-Hyon Kim, Kwang Pyo Kim, Chang-Nam Son, Jong-Seo Kim","doi":"10.1186/s12014-023-09423-y","DOIUrl":"10.1186/s12014-023-09423-y","url":null,"abstract":"","PeriodicalId":10468,"journal":{"name":"Clinical proteomics","volume":"20 1","pages":"34"},"PeriodicalIF":3.8,"publicationDate":"2023-09-02","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10474741/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"10160142","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Cerebrospinal fluid camk2a levels at baseline predict long-term progression in multiple sclerosis.","authors":"Dorsa Sohaei, Simon Thebault, Lisa M Avery, Ihor Batruch, Brian Lam, Wei Xu, Rubah S Saadeh, Isobel A Scarisbrick, Eleftherios P Diamandis, Ioannis Prassas, Mark S Freedman","doi":"10.1186/s12014-023-09418-9","DOIUrl":"10.1186/s12014-023-09418-9","url":null,"abstract":"<p><strong>Background: </strong>Multiple sclerosis (MS) remains a highly unpredictable disease. Many hope that fluid biomarkers may contribute to better stratification of disease, aiding the personalisation of treatment decisions, ultimately improving patient outcomes.</p><p><strong>Objective: </strong>The objective of this study was to evaluate the predictive value of CSF brain-specific proteins from early in the disease course of MS on long term clinical outcomes.</p><p><strong>Methods: </strong>In this study, 34 MS patients had their CSF collected and stored within 5 years of disease onset and were then followed clinically for at least 15 years. CSF concentrations of 64 brain-specific proteins were analyzed in the 34 patient CSF, as well as 19 age and sex-matched controls, using a targeted liquid-chromatography tandem mass spectrometry approach.</p><p><strong>Results: </strong>We identified six CSF brain-specific proteins that significantly differentiated MS from controls (p < 0.05) and nine proteins that could predict disease course over the next decade. CAMK2A emerged as a biomarker candidate that could discriminate between MS and controls and could predict long-term disease progression.</p><p><strong>Conclusion: </strong>Targeted approaches to identify and quantify biomarkers associated with MS in the CSF may inform on long term MS outcomes. CAMK2A may be one of several candidates, warranting further exploration.</p>","PeriodicalId":10468,"journal":{"name":"Clinical proteomics","volume":"20 1","pages":"33"},"PeriodicalIF":3.8,"publicationDate":"2023-08-29","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10466840/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"10127760","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Mass spectrometry-based proteomics as an emerging tool in clinical laboratories.","authors":"Alemayehu Godana Birhanu","doi":"10.1186/s12014-023-09424-x","DOIUrl":"10.1186/s12014-023-09424-x","url":null,"abstract":"<p><p>Mass spectrometry (MS)-based proteomics have been increasingly implemented in various disciplines of laboratory medicine to identify and quantify biomolecules in a variety of biological specimens. MS-based proteomics is continuously expanding and widely applied in biomarker discovery for early detection, prognosis and markers for treatment response prediction and monitoring. Furthermore, making these advanced tests more accessible and affordable will have the greatest healthcare benefit.This review article highlights the new paradigms MS-based clinical proteomics has created in microbiology laboratories, cancer research and diagnosis of metabolic disorders. The technique is preferred over conventional methods in disease detection and therapy monitoring for its combined advantages in multiplexing capacity, remarkable analytical specificity and sensitivity and low turnaround time.Despite the achievements in the development and adoption of a number of MS-based clinical proteomics practices, more are expected to undergo transition from bench to bedside in the near future. The review provides insights from early trials and recent progresses (mainly covering literature from the NCBI database) in the application of proteomics in clinical laboratories.</p>","PeriodicalId":10468,"journal":{"name":"Clinical proteomics","volume":"20 1","pages":"32"},"PeriodicalIF":3.8,"publicationDate":"2023-08-26","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10464495/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"10122029","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Proteomic analysis of 92 circulating proteins and their effects in cardiometabolic diseases.","authors":"Corinne Carland, Grace Png, Anders Malarstig, Pik Fang Kho, Stefan Gustafsson, Karl Michaelsson, Lars Lind, Emmanouil Tsafantakis, Maria Karaleftheri, George Dedoussis, Anna Ramisch, Erin Macdonald-Dunlop, Lucija Klaric, Peter K Joshi, Yan Chen, Hanna M Björck, Per Eriksson, Julia Carrasco-Zanini, Eleanor Wheeler, Karsten Suhre, Arthur Gilly, Eleftheria Zeggini, Ana Viñuela, Emmanouil T Dermitzakis, James F Wilson, Claudia Langenberg, Gaurav Thareja, Anna Halama, Frank Schmidt, Daniela Zanetti, Themistocles Assimes","doi":"10.1186/s12014-023-09421-0","DOIUrl":"10.1186/s12014-023-09421-0","url":null,"abstract":"<p><strong>Background: </strong>Human plasma contains a wide variety of circulating proteins. These proteins can be important clinical biomarkers in disease and also possible drug targets. Large scale genomics studies of circulating proteins can identify genetic variants that lead to relative protein abundance.</p><p><strong>Methods: </strong>We conducted a meta-analysis on genome-wide association studies of autosomal chromosomes in 22,997 individuals of primarily European ancestry across 12 cohorts to identify protein quantitative trait loci (pQTL) for 92 cardiometabolic associated plasma proteins.</p><p><strong>Results: </strong>We identified 503 (337 cis and 166 trans) conditionally independent pQTLs, including several novel variants not reported in the literature. We conducted a sex-stratified analysis and found that 118 (23.5%) of pQTLs demonstrated heterogeneity between sexes. The direction of effect was preserved but there were differences in effect size and significance. Additionally, we annotate trans-pQTLs with nearest genes and report plausible biological relationships. Using Mendelian randomization, we identified causal associations for 18 proteins across 19 phenotypes, of which 10 have additional genetic colocalization evidence. We highlight proteins associated with a constellation of cardiometabolic traits including angiopoietin-related protein 7 (ANGPTL7) and Semaphorin 3F (SEMA3F).</p><p><strong>Conclusion: </strong>Through large-scale analysis of protein quantitative trait loci, we provide a comprehensive overview of common variants associated with plasma proteins. We highlight possible biological relationships which may serve as a basis for further investigation into possible causal roles in cardiometabolic diseases.</p>","PeriodicalId":10468,"journal":{"name":"Clinical proteomics","volume":"20 1","pages":"31"},"PeriodicalIF":3.8,"publicationDate":"2023-08-07","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10405520/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"9959645","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Identification of SARS-CoV-2 biomarkers in saliva by transcriptomic and proteomics analysis.","authors":"Lina M Marin, George S Katselis, Paulos Chumala, Stephen Sanche, Lucas Julseth, Erika Penz, Robert Skomro, Walter L Siqueira","doi":"10.1186/s12014-023-09417-w","DOIUrl":"10.1186/s12014-023-09417-w","url":null,"abstract":"<p><p>The detection of SARS-CoV-2 biomarkers by real time PCR (rRT-PCR) has shown that the sensitivity of the test is negatively affected by low viral loads and the severity of the disease. This limitation can be overcome by the use of more sensitive approaches such as mass spectrometry (MS), which has not been explored for the detection of SARS-CoV-2 proteins in saliva. Thus, this study aimed at assessing the translational applicability of mass spectrometry-based proteomics approaches to identify viral proteins in saliva from people diagnosed with COVID-19 within fourteen days after the initial diagnosis, and to compare its performance with rRT-PCR. After ethics approval, saliva samples were self-collected by 42 COVID-19 positive and 16 healthy individuals. Samples from people positive for COVID-19 were collected on average on the sixth day (± 4 days) after initial diagnosis. Viable viral particles in saliva were heat-inactivated followed by the extraction of total proteins and viral RNA. Proteins were digested and then subjected to tandem MS analysis (LC-QTOF-MS/MS) using a data-dependent MS/MS acquisition qualitative shotgun proteomics approach. The acquired spectra were queried against a combined SARS-CoV-2 and human database. The qualitative detection of SARS-CoV-2 specific RNA was done by rRT-PCR. SARS-CoV-2 proteins were identified in all COVID-19 samples (100%), while viral RNA was detected in only 24 out of 42 COVID-19 samples (57.1%). Seven out of 18 SARS-CoV-2 proteins were identified in saliva from COVID-19 positive individuals, from which the most frequent were replicase polyproteins 1ab (100%) and 1a (91.3%), and nucleocapsid (45.2%). Neither viral proteins nor RNA were detected in healthy individuals. Our mass spectrometry approach appears to be more sensitive than rRT-PCR for the detection of SARS-CoV-2 biomarkers in saliva collected from COVID-19 positive individuals up to 14 days after the initial diagnostic test. Based on the novel data presented here, our MS technology can be used as an effective diagnostic test of COVID-19 for initial diagnosis or follow-up of symptomatic cases, especially in patients with reduced viral load.</p>","PeriodicalId":10468,"journal":{"name":"Clinical proteomics","volume":"20 1","pages":"30"},"PeriodicalIF":3.8,"publicationDate":"2023-08-03","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10398966/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"9929210","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Proteomic analysis identifies subgroups of patients with active systemic lupus erythematosus.","authors":"Kevin Y C Su, John A Reynolds, Rachel Reed, Rachael Da Silva, Janet Kelsall, Ivona Baricevic-Jones, David Lee, Anthony D Whetton, Nophar Geifman, Neil McHugh, Ian N Bruce","doi":"10.1186/s12014-023-09420-1","DOIUrl":"10.1186/s12014-023-09420-1","url":null,"abstract":"<p><strong>Objective: </strong>Systemic lupus erythematosus (SLE) is a clinically and biologically heterogenous autoimmune disease. We aimed to investigate the plasma proteome of patients with active SLE to identify novel subgroups, or endotypes, of patients.</p><p><strong>Method: </strong>Plasma was collected from patients with active SLE who were enrolled in the British Isles Lupus Assessment Group Biologics Registry (BILAG-BR). The plasma proteome was analysed using a data-independent acquisition method, Sequential Window Acquisition of All theoretical mass spectra mass spectrometry (SWATH-MS). Unsupervised, data-driven clustering algorithms were used to delineate groups of patients with a shared proteomic profile.</p><p><strong>Results: </strong>In 223 patients, six clusters were identified based on quantification of 581 proteins. Between the clusters, there were significant differences in age (p = 0.012) and ethnicity (p = 0.003). There was increased musculoskeletal disease activity in cluster 1 (C1), 19/27 (70.4%) (p = 0.002) and renal activity in cluster 6 (C6) 15/24 (62.5%) (p = 0.051). Anti-SSa/Ro was the only autoantibody that significantly differed between clusters (p = 0.017). C1 was associated with p21-activated kinases (PAK) and Phospholipase C (PLC) signalling. Within C1 there were two sub-clusters (C1A and C1B) defined by 49 proteins related to cytoskeletal protein binding. C2 and C6 demonstrated opposite Rho family GTPase and Rho GDI signalling. Three proteins (MZB1, SND1 and AGL) identified in C6 increased the classification of active renal disease although this did not reach statistical significance (p = 0.0617).</p><p><strong>Conclusions: </strong>Unsupervised proteomic analysis identifies clusters of patients with active SLE, that are associated with clinical and serological features, which may facilitate biomarker discovery. The observed proteomic heterogeneity further supports the need for a personalised approach to treatment in SLE.</p>","PeriodicalId":10468,"journal":{"name":"Clinical proteomics","volume":"20 1","pages":"29"},"PeriodicalIF":2.8,"publicationDate":"2023-07-29","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10385905/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"9910948","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Comparative proteomics analysis in different stages of urothelial bladder cancer for identification of potential biomarkers: highlighted role for antioxidant activity.","authors":"Samira Tabaei,&nbsp;Mohammad Reza Haghshenas,&nbsp;Ali Ariafar,&nbsp;Kambiz Gilany,&nbsp;Allan Stensballe,&nbsp;Shirin Farjadian,&nbsp;Abbas Ghaderi","doi":"10.1186/s12014-023-09419-8","DOIUrl":"https://doi.org/10.1186/s12014-023-09419-8","url":null,"abstract":"<p><strong>Background: </strong>Non-muscle-invasive bladder cancer (NMIBC) has a high recurrence rate and muscle-invasive bladder cancer (MIBC) has unfavorable outcomes in urothelial bladder cancer (UBC) patients. Complex UBC-related protein biomarkers for outcome prediction may provide a more efficient management approach with an improved clinical outcome. The aim of this study is to recognize tumor-associated proteins, which are differentially expressed in different stages of UBC patients compared non-cancerous tissues.</p><p><strong>Methods: </strong>The proteome of tissue samples of 42 UBC patients (NMIBC n = 25 and MIBC n = 17) was subjected to two-dimensional electrophoresis (2-DE) combined with Liquid chromatography-mass spectrometry (LC-MS) system to identify differentially expressed proteins. The intensity of protein spots was quantified and compared with Prodigy SameSpots software. Functional, pathway, and interaction analyses of identified proteins were performed using geneontology (GO), PANTHER, Reactome, Gene MANIA, and STRING databases.</p><p><strong>Results: </strong>Twelve proteins identified by LC-MS showed differential expression (over 1.5-fold, p < 0.05) by LC-MS, including 9 up-regulated in NMIBC and 3 up-regulated in MIBC patients. Proteins involved in the detoxification of reactive oxygen species and cellular responses to oxidative stress showed the most significant changes in UBC patients. Additionally, the most potential functions related to these detected proteins were associated with peroxidase, oxidoreductase, and antioxidant activity.</p><p><strong>Conclusion: </strong>We identified several alterations in protein expression involved in canonical pathways which were correlated with the clinical outcomes suggested might be useful as promising biomarkers for early detection, monitoring, and prognosis of UBC.</p>","PeriodicalId":10468,"journal":{"name":"Clinical proteomics","volume":"20 1","pages":"28"},"PeriodicalIF":3.8,"publicationDate":"2023-07-27","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10373361/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"10263198","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Associations of circulating proteins with lipoprotein profiles: proteomic analyses from the OmniHeart randomized trial and the Atherosclerosis Risk in Communities (ARIC) Study.","authors":"Hyunju Kim, Alice H Lichtenstein, Peter Ganz, Edgar R Miller, Josef Coresh, Lawrence J Appel, Casey M Rebholz","doi":"10.1186/s12014-023-09416-x","DOIUrl":"10.1186/s12014-023-09416-x","url":null,"abstract":"<p><strong>Background: </strong>Within healthy dietary patterns, manipulation of the proportion of macronutrient can reduce CVD risk. However, the biological pathways underlying healthy diet-disease associations are poorly understood. Using an untargeted, large-scale proteomic profiling, we aimed to (1) identify proteins mediating the association between healthy dietary patterns varying in the proportion of macronutrient and lipoproteins, and (2) validate the associations between diet-related proteins and lipoproteins in the Atherosclerosis Risk in Communities (ARIC) Study.</p><p><strong>Methods: </strong>In 140 adults from the OmniHeart trial, a randomized, cross-over, controlled feeding study with 3 intervention periods (carbohydrate-rich; protein-rich; unsaturated fat-rich dietary patterns), 4,958 proteins were quantified at the end of each diet intervention period using an aptamer assay (SomaLogic). We assessed differences in log₂-transformed proteins in 3 between-diet comparisons using paired t-tests, examined the associations between diet-related proteins and lipoproteins using linear regression, and identified proteins mediating these associations using a causal mediation analysis. Levels of diet-related proteins and lipoprotein associations were validated in the ARIC study (n = 11,201) using multivariable linear regression models, adjusting for important confounders.</p><p><strong>Results: </strong>Three between-diet comparisons identified 497 significantly different proteins (protein-rich vs. carbohydrate-rich = 18; unsaturated fat-rich vs. carbohydrate-rich = 335; protein-rich vs. unsaturated fat-rich dietary patterns = 398). Of these, 9 proteins [apolipoprotein M, afamin, collagen alpha-3(VI) chain, chitinase-3-like protein 1, inhibin beta A chain, palmitoleoyl-protein carboxylesterase NOTUM, cathelicidin antimicrobial peptide, guanylate-binding protein 2, COP9 signalosome complex subunit 7b] were positively associated with lipoproteins [high-density lipoprotein (HDL)-cholesterol (C) = 2; triglyceride = 5; non-HDL-C = 3; total cholesterol to HDL-C ratio = 1]. Another protein, sodium-coupled monocarboxylate transporter 1, was inversely associated with HDL-C and positively associated with total cholesterol to HDL-C ratio. The proportion of the association between diet and lipoproteins mediated by these 10 proteins ranged from 21 to 98%. All of the associations between diet-related proteins and lipoproteins were significant in the ARIC study, except for afamin.</p><p><strong>Conclusions: </strong>We identified proteins that mediate the association between healthy dietary patterns varying in macronutrients and lipoproteins in a randomized feeding study and an observational study.</p><p><strong>Trial registration: </strong>NCT00051350 at clinicaltrials.gov.</p>","PeriodicalId":10468,"journal":{"name":"Clinical proteomics","volume":"20 1","pages":"27"},"PeriodicalIF":3.8,"publicationDate":"2023-07-03","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10316599/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"9748437","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Development of a rapid and specific MALDI-TOF mass spectrometric assay for SARS-CoV-2 detection.","authors":"Lydia Kollhoff,&nbsp;Marc Kipping,&nbsp;Manfred Rauh,&nbsp;Uta Ceglarek,&nbsp;Günes Barka,&nbsp;Frederik Barka,&nbsp;Andrea Sinz","doi":"10.1186/s12014-023-09415-y","DOIUrl":"https://doi.org/10.1186/s12014-023-09415-y","url":null,"abstract":"<p><p>We have developed a rapid and highly specific assay for detecting and monitoring SARS-CoV-2 infections by matrix-assisted laser desorption/ionization time-of-flight mass spectrometry (MALDI-TOF-MS). As MALDI-TOF mass spectrometers are available in a clinical setting, our assay has the potential to serve as alternative to the commonly used reverse transcriptase quantitative polymerase chain reaction (RT-qPCR). Sample preparation prior to MALDI-TOF-MS involves the tryptic digestion of SARS-CoV-2 proteins, followed by an enrichment of virus-specific peptides from SARS-CoV-2 nucleoprotein via magnetic antibody beads. Our MALDI-TOF-MS method allows the detection of SARS-CoV-2 nucleoprotein in sample collection medium as low as 8 amol/µl. MALDI-TOF mass spectra are obtained in just a few seconds, which makes our MS-based assay suitable for a high-throughput screening of SARS-CoV-2 in healthcare facilities in addition to PCR. Due to the specific detection of virus peptides, different SARS-CoV-2 variants are readily distinguished from each other. Specifically, we show that our MALDI-TOF-MS assay discriminates SARS-CoV-2 strain B.1.617.2 \"delta variant\" from all other variants in patients' samples, making our method highly valuable to monitor the emergence of new virus variants.</p>","PeriodicalId":10468,"journal":{"name":"Clinical proteomics","volume":"20 1","pages":"26"},"PeriodicalIF":3.8,"publicationDate":"2023-07-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10314570/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"9741922","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}