Clinical proteomics最新文献

{"title":"Proteomics study of primary and recurrent adamantinomatous craniopharyngiomas","authors":"Haidong Deng, Ting Lei, Siqi Liu, Wenzhe Hao, Mengqing Hu, Xin Xiang, Ling Ye, Dongting Chen, Yan Li, Fangjun Liu","doi":"10.1186/s12014-024-09479-4","DOIUrl":"https://doi.org/10.1186/s12014-024-09479-4","url":null,"abstract":"Adamantinomatous craniopharyngiomas (ACPs) are rare benign epithelial tumours with high recurrence and poor prognosis. Biological differences between recurrent and primary ACPs that may be associated with disease recurrence and treatment have yet to be evaluated at the proteomic level. In this study, we aimed to determine the proteomic profiles of paired recurrent and primary ACP, gain biological insight into ACP recurrence, and identify potential targets for ACP treatment. Patients with ACP (n = 15) or Rathke’s cleft cyst (RCC; n = 7) who underwent surgery at Sanbo Brain Hospital, Capital Medical University, Beijing, China and received pathological confirmation of ACP or RCC were enrolled in this study. We conducted a proteomic analysis to investigate the characteristics of primary ACP, paired recurrent ACP, and RCC. Western blotting was used to validate our proteomic results and assess the expression of key tumour-associated proteins in recurrent and primary ACPs. Flow cytometry was performed to evaluate the exhaustion of tumour-infiltrating lymphocytes (TILs) in primary and recurrent ACP tissue samples. Immunohistochemical staining for CD3 and PD-L1 was conducted to determine differences in T-cell infiltration and the expression of immunosuppressive molecules between paired primary and recurrent ACP samples. The bioinformatics analysis showed that proteins differentially expressed between recurrent and primary ACPs were significantly associated with extracellular matrix organisation and interleukin signalling. Cathepsin K, which was upregulated in recurrent ACP compared with that in primary ACP, may play a role in ACP recurrence. High infiltration of T cells and exhaustion of TILs were revealed by the flow cytometry analysis of ACP. This study provides a preliminary description of the proteomic differences between primary ACP, recurrent ACP, and RCC. Our findings serve as a resource for craniopharyngioma researchers and may ultimately expand existing knowledge of recurrent ACP and benefit clinical practice.","PeriodicalId":10468,"journal":{"name":"Clinical proteomics","volume":"55 1","pages":""},"PeriodicalIF":3.8,"publicationDate":"2024-04-09","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"140603276","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Plasma proteomic characterization of colorectal cancer patients with FOLFOX chemotherapy by integrated proteomics technology","authors":"Xi Wang, Keren Zhang, Wan He, Luobin Zhang, Biwei Gao, Ruijun Tian, Ruilian Xu","doi":"10.1186/s12014-024-09454-z","DOIUrl":"https://doi.org/10.1186/s12014-024-09454-z","url":null,"abstract":"Colorectal Cancer (CRC) is a prevalent form of cancer, and the effectiveness of the main postoperative chemotherapy treatment, FOLFOX, varies among patients. In this study, we aimed to identify potential biomarkers for predicting the prognosis of CRC patients treated with FOLFOX through plasma proteomic characterization. Using a fully integrated sample preparation technology SISPROT-based proteomics workflow, we achieved deep proteome coverage and trained a machine learning model from a discovery cohort of 90 CRC patients to differentiate FOLFOX-sensitive and FOLFOX-resistant patients. The model was then validated by targeted proteomics on an independent test cohort of 26 patients. We achieved deep proteome coverage of 831 protein groups in total and 536 protein groups in average for non-depleted plasma from CRC patients by using a Orbitrap Exploris 240 with moderate sensitivity. Our results revealed distinct molecular changes in FOLFOX-sensitive and FOLFOX-resistant patients. We confidently identified known prognostic biomarkers for colorectal cancer, such as S100A4, LGALS1, and FABP5. The classifier based on the biomarker panel demonstrated a promised AUC value of 0.908 with 93% accuracy. Additionally, we established a protein panel to predict FOLFOX effectiveness, and several proteins within the panel were validated using targeted proteomic methods. Our study sheds light on the pathways affected in CRC patients treated with FOLFOX chemotherapy and identifies potential biomarkers that could be valuable for prognosis prediction. Our findings showed the potential of mass spectrometry-based proteomics and machine learning as an unbiased and systematic approach for discovering biomarkers in CRC.","PeriodicalId":10468,"journal":{"name":"Clinical proteomics","volume":"3 1","pages":""},"PeriodicalIF":3.8,"publicationDate":"2024-04-05","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"140595949","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"The relationship between serum astroglial and neuronal markers and AQP4 and MOG autoantibodies","authors":"Miyo K. Chatanaka, Lisa M. Avery, Maria D. Pasic, Shanthan Sithravadivel, Dalia Rotstein, Catherine Demos, Rachel Cohen, Taron Gorham, Mingyue Wang, Martin Stengelin, Anu Mathew, George Sigal, Jacob Wohlstadter, Ioannis Prassas, Eleftherios P. Diamandis","doi":"10.1186/s12014-024-09466-9","DOIUrl":"https://doi.org/10.1186/s12014-024-09466-9","url":null,"abstract":"Certain demyelinating disorders, such as neuromyelitis optica spectrum disorder (NMOSD) and myelin oligodendrocyte glycoprotein antibody-associated disease (MOGAD) exhibit serum autoantibodies against aquaporin-4 (αAQP4) and myelin oligodendrocyte glycoprotein (αMOG). The variability of the autoantibody presentation warrants further research into subtyping each case. To elucidate the relationship between astroglial and neuronal protein concentrations in the peripheral circulation with occurrence of these autoantibodies, 86 serum samples were analyzed using immunoassays. The protein concentration of glial fibrillary acidic protein (GFAP), neurofilament light chain (NFL) and tau protein was measured in 3 groups of subcategories of suspected NMOSD: αAQP4 positive (n = 20), αMOG positive (n = 32) and αMOG/αAQP4 seronegative (n = 34). Kruskal-Wallis analysis, univariate predictor analysis, and multivariate logistic regression with ROC curves were performed. GFAP and NFL concentrations were significantly elevated in the αAQP4 positive group (p = 0.003; p = 0.042, respectively), and tau was elevated in the αMOG/αAQP4 seronegative group (p < 0.001). A logistic regression model to classify serostatus was able to separate αAQP4 seropositivity using GFAP + tau, and αMOG seropositivity using tau. The areas under the ROC curves (AUCs) were 0.77 and 0.72, respectively. Finally, a combined seropositivity versus negative status logistic regression model was generated, with AUC = 0.80. The 3 markers can univariately and multivariately classify with moderate accuracy the samples with seropositivity and seronegativity for αAQP4 and αMOG.","PeriodicalId":10468,"journal":{"name":"Clinical proteomics","volume":"54 1","pages":""},"PeriodicalIF":3.8,"publicationDate":"2024-04-05","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"140595945","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Simultaneous targeted and discovery-driven clinical proteotyping using hybrid-PRM/DIA","authors":"Sandra Goetze, Audrey van Drogen, Jonas B. Albinus, Kyle L. Fort, Tejas Gandhi, Damiano Robbiani, Véronique Laforte, Lukas Reiter, Mitchell P. Levesque, Yue Xuan, Bernd Wollscheid","doi":"10.1186/s12014-024-09478-5","DOIUrl":"https://doi.org/10.1186/s12014-024-09478-5","url":null,"abstract":"Clinical samples are irreplaceable, and their transformation into searchable and reusable digital biobanks is critical for conducting statistically empowered retrospective and integrative research studies. Currently, mainly data-independent acquisition strategies are employed to digitize clinical sample cohorts comprehensively. However, the sensitivity of DIA is limited, which is why selected marker candidates are often additionally measured targeted by parallel reaction monitoring. Here, we applied the recently co-developed hybrid-PRM/DIA technology as a new intelligent data acquisition strategy that allows for the comprehensive digitization of rare clinical samples at the proteotype level. Hybrid-PRM/DIA enables enhanced measurement sensitivity for a specific set of analytes of current clinical interest by the intelligent triggering of multiplexed parallel reaction monitoring (MSxPRM) in combination with the discovery-driven digitization of the clinical biospecimen using DIA. Heavy-labeled reference peptides were utilized as triggers for MSxPRM and monitoring of endogenous peptides. We first evaluated hybrid-PRM/DIA in a clinical context on a pool of 185 selected proteotypic peptides for tumor-associated antigens derived from 64 annotated human protein groups. We demonstrated improved reproducibility and sensitivity for the detection of endogenous peptides, even at lower concentrations near the detection limit. Up to 179 MSxPRM scans were shown not to affect the overall DIA performance. Next, we applied hybrid-PRM/DIA for the integrated digitization of biobanked melanoma samples using a set of 30 AQUA peptides against 28 biomarker candidates with relevance in molecular tumor board evaluations of melanoma patients. Within the DIA-detected approximately 6500 protein groups, the selected marker candidates such as UFO, CDK4, NF1, and PMEL could be monitored consistently and quantitatively using MSxPRM scans, providing additional confidence for supporting future clinical decision-making. Combining PRM and DIA measurements provides a new strategy for the sensitive and reproducible detection of protein markers from patients currently being discussed in molecular tumor boards in combination with the opportunity to discover new biomarker candidates.","PeriodicalId":10468,"journal":{"name":"Clinical proteomics","volume":"62 1","pages":""},"PeriodicalIF":3.8,"publicationDate":"2024-04-02","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"140603271","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Correction: Proteomic signature associated with chronic kidney disease (CKD) progression identified by data-independent acquisition mass spectrometry","authors":"Carlos R. Ramírez Medina, Ibrahim Ali, Ivona Baricevic-Jones, Aghogho Odudu, Moin A. Saleem, Anthony D. Whetton, Philip A. Kalra, Nophar Geifman","doi":"10.1186/s12014-024-09471-y","DOIUrl":"https://doi.org/10.1186/s12014-024-09471-y","url":null,"abstract":"&lt;p&gt;&lt;b&gt;Correction to: Clinical Proteomics (2023) 20:19&lt;/b&gt;&lt;/p&gt;&lt;p&gt;&lt;b&gt;https://doi.org/10.1186/s12014-023-09405-0&lt;/b&gt;&lt;/p&gt;&lt;p&gt;In this article the affiliation details for authors Ivona Baricevic-Jones and Philip A Kalra were incorrectly given as “School of Veterinary Medicine, Faculty of Health and Medical Sciences, University of Surrey, Guildford GU2 7XH, UK” but should have been “Salford Royal Hospital, Northern Care Alliance Foundation Trust, Salford, UK”.&lt;/p&gt;&lt;p&gt;The original article has been corrected.&lt;/p&gt;&lt;ol data-track-component=\"outbound reference\"&gt;&lt;li data-counter=\"1.\"&gt;&lt;p&gt;Ram?rez Medina C.R., Ali I., Baricevic-Jones I, et al. Proteomic signature associated with chronic kidney disease (CKD) progression identified by data-independent acquisition mass spectrometry. Clin Proteom. 2023;20:19. https://doi.org/10.1186/s12014-023-09405-0&lt;/p&gt;&lt;/li&gt;&lt;/ol&gt;&lt;p&gt;Download references&lt;svg aria-hidden=\"true\" focusable=\"false\" height=\"16\" role=\"img\" width=\"16\"&gt;&lt;use xlink:href=\"#icon-eds-i-download-medium\" xmlns:xlink=\"http://www.w3.org/1999/xlink\"&gt;&lt;/use&gt;&lt;/svg&gt;&lt;/p&gt;&lt;span&gt;Author notes&lt;/span&gt;&lt;ol&gt;&lt;li&gt;&lt;p&gt;Philip A. Kalra and Nophar Geifman have equal senior authorship.&lt;/p&gt;&lt;/li&gt;&lt;/ol&gt;&lt;h3&gt;Authors and Affiliations&lt;/h3&gt;&lt;ol&gt;&lt;li&gt;&lt;p&gt;Stoller Biomarker Discovery Centre, Faculty of Biology, Medicine and Health, The University of Manchester, Manchester, UK&lt;/p&gt;&lt;p&gt;Carlos R. Ramírez Medina, Ivona Baricevic-Jones &amp; Anthony D. Whetton&lt;/p&gt;&lt;/li&gt;&lt;li&gt;&lt;p&gt;Salford Royal Hospital, Northern Care Alliance NHS Foundation Trust, Salford, UK&lt;/p&gt;&lt;p&gt;Ibrahim Ali, Ivona Baricevic-Jones &amp; Philip A. Kalra&lt;/p&gt;&lt;/li&gt;&lt;li&gt;&lt;p&gt;Division of Cardiovascular Sciences, The University of Manchester, Manchester, UK&lt;/p&gt;&lt;p&gt;Aghogho Odudu&lt;/p&gt;&lt;/li&gt;&lt;li&gt;&lt;p&gt;Bristol Renal and Children’s Renal Unit, Bristol Medical School, University of Bristol, Bristol, UK&lt;/p&gt;&lt;p&gt;Moin A. Saleem&lt;/p&gt;&lt;/li&gt;&lt;li&gt;&lt;p&gt;School of Veterinary Medicine, Faculty of Health and Medical Sciences, University of Surrey, Guildford, GU2 7XH, UK&lt;/p&gt;&lt;p&gt;Anthony D. Whetton &amp; Nophar Geifman&lt;/p&gt;&lt;/li&gt;&lt;li&gt;&lt;p&gt;School of Health Sciences, Faculty of Health and Medical Sciences, University of Surrey, Guildford, GU2 7XH, UK&lt;/p&gt;&lt;p&gt;Nophar Geifman&lt;/p&gt;&lt;/li&gt;&lt;/ol&gt;&lt;span&gt;Authors&lt;/span&gt;&lt;ol&gt;&lt;li&gt;&lt;span&gt;Carlos R. Ramírez Medina&lt;/span&gt;View author publications&lt;p&gt;You can also search for this author in &lt;span&gt;PubMed&lt;span&gt; &lt;/span&gt;Google Scholar&lt;/span&gt;&lt;/p&gt;&lt;/li&gt;&lt;li&gt;&lt;span&gt;Ibrahim Ali&lt;/span&gt;View author publications&lt;p&gt;You can also search for this author in &lt;span&gt;PubMed&lt;span&gt; &lt;/span&gt;Google Scholar&lt;/span&gt;&lt;/p&gt;&lt;/li&gt;&lt;li&gt;&lt;span&gt;Ivona Baricevic-Jones&lt;/span&gt;View author publications&lt;p&gt;You can also search for this author in &lt;span&gt;PubMed&lt;span&gt; &lt;/span&gt;Google Scholar&lt;/span&gt;&lt;/p&gt;&lt;/li&gt;&lt;li&gt;&lt;span&gt;Aghogho Odudu&lt;/span&gt;View author publications&lt;p&gt;You can also search for this author in &lt;span&gt;PubMed&lt;span&gt; &lt;/span&gt;Google Scholar&lt;/span&gt;&lt;/p&gt;&lt;/li&gt;&lt;li&gt;&lt;span&gt;Moin A. Saleem&lt;/span&gt;View author publications&lt;p&gt;You can also search for this author in &lt;span&gt;PubMed&lt;span&gt; &lt;/span&gt;Google Scholar&lt;/span&gt;&lt;/p&gt;&lt;/li&gt;&lt;li&gt;&lt;span&gt;Anthony D. Whetton&lt;/sp","PeriodicalId":10468,"journal":{"name":"Clinical proteomics","volume":"44 1","pages":""},"PeriodicalIF":3.8,"publicationDate":"2024-03-28","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"140313660","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Quantitative proteomic analysis of HER2 protein expression in PDAC tumors","authors":"Jamie Randall, Allison L. Hunt, Aratara Nutcharoen, Laura Johnston, Safae Chouraichi, Hongkun Wang, Arthur Winer, Raymond Wadlow, Jasmine Huynh, Justin Davis, Brian Corgiat, Nicholas W. Bateman, John F. Deeken, Emanuel F. Petricoin, Thomas P. Conrads, Timothy L. Cannon","doi":"10.1186/s12014-024-09476-7","DOIUrl":"https://doi.org/10.1186/s12014-024-09476-7","url":null,"abstract":"Metastatic pancreatic adenocarcinoma (PDAC) is the third leading cause of cancer-related death in the United States, with a 5-year survival rate of only 11%, necessitating identification of novel treatment paradigms. Tumor tissue specimens from patients with PDAC, breast cancer, and other solid tumor malignancies were collected and tumor cells were enriched using laser microdissection (LMD). Reverse phase protein array (RPPA) analysis was performed on enriched tumor cell lysates to quantify a 32-protein/phosphoprotein biomarker panel comprising known anticancer drug targets and/or cancer-related total and phosphorylated proteins, including HER2Total, HER2Y1248, and HER3Y1289. RPPA analysis revealed significant levels of HER2Total in PDAC patients at abundances comparable to HER2-positive (IHC 3+) and HER2-low (IHC 1+ /2+ , FISH−) breast cancer tissues, for which HER2 screening is routinely performed. These data support a critical unmet need for routine clinical evaluation of HER2 expression in PDAC patients and examination of the utility of HER2-directed antibody–drug conjugates in these patients.","PeriodicalId":10468,"journal":{"name":"Clinical proteomics","volume":"87 1","pages":""},"PeriodicalIF":3.8,"publicationDate":"2024-03-20","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"140166454","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Human tear film protein sampling using soft contact lenses.","authors":"Robert K Roden, Nathan Zuniga, Joshua C Wright, David H Parkinson, Fangfang Jiang, Leena M Patil, Rebecca S Burlett, Alyssa A Nitz, Joshua J Rogers, Jarett T Pittman, Kenneth L Virgin, P Christine Ackroyd, Samuel H Payne, John C Price, Kenneth A Christensen","doi":"10.1186/s12014-024-09475-8","DOIUrl":"10.1186/s12014-024-09475-8","url":null,"abstract":"<p><strong>Background: </strong>Human tear protein biomarkers are useful for detecting ocular and systemic diseases. Unfortunately, existing tear film sampling methods (Schirmer strip; SS and microcapillary tube; MCT) have significant drawbacks, such as pain, risk of injury, sampling difficulty, and proteomic disparities between methods. Here, we present an alternative tear protein sampling method using soft contact lenses (SCLs).</p><p><strong>Results: </strong>We optimized the SCL protein sampling in vitro and performed in vivo studies in 6 subjects. Using Etafilcon A SCLs and 4M guanidine-HCl for protein removal, we sampled an average of 60 ± 31 µg of protein per eye. We also performed objective and subjective assessments of all sampling methods. Signs of irritation post-sampling were observed with SS but not with MCT and SCLs. Proteomic analysis by mass spectrometry (MS) revealed that all sampling methods resulted in the detection of abundant tear proteins. However, smaller subsets of unique and shared proteins were identified, particularly for SS and MCT. Additionally, there was no significant intrasubject variation between MCT and SCL sampling.</p><p><strong>Conclusions: </strong>These experiments demonstrate that SCLs are an accessible tear-sampling method with the potential to surpass current methods in sampling basal tears.</p>","PeriodicalId":10468,"journal":{"name":"Clinical proteomics","volume":"21 1","pages":"23"},"PeriodicalIF":3.8,"publicationDate":"2024-03-13","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10936081/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"140118971","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":"Neat plasma proteomics: getting the best out of the worst.","authors":"Ines Metatla, Kevin Roger, Cerina Chhuon, Sara Ceccacci, Manuel Chapelle, Pierre-Olivier Schmit, Vadim Demichev, Ida Chiara Guerrera","doi":"10.1186/s12014-024-09477-6","DOIUrl":"10.1186/s12014-024-09477-6","url":null,"abstract":"<p><p>Plasma proteomics holds immense potential for clinical research and biomarker discovery, serving as a non-invasive \"liquid biopsy\" for tissue sampling. Mass spectrometry (MS)-based proteomics, thanks to improvement in speed and robustness, emerges as an ideal technology for exploring the plasma proteome for its unbiased and highly specific protein identification and quantification. Despite its potential, plasma proteomics is still a challenge due to the vast dynamic range of protein abundance, hindering the detection of less abundant proteins. Different approaches can help overcome this challenge. Conventional depletion methods face limitations in cost, throughput, accuracy, and off-target depletion. Nanoparticle-based enrichment shows promise in compressing dynamic range, but cost remains a constraint. Enrichment strategies for extracellular vesicles (EVs) can enhance plasma proteome coverage dramatically, but current methods are still too laborious for large series. Neat plasma remains popular for its cost-effectiveness, time efficiency, and low volume requirement. We used a test set of 33 plasma samples for all evaluations. Samples were digested using S-Trap and analyzed on Evosep One and nanoElute coupled to a timsTOF Pro using different elution gradients and ion mobility ranges. Data were mainly analyzed using library-free searches using DIA-NN. This study explores ways to improve proteome coverage in neat plasma both in MS data acquisition and MS data analysis. We demonstrate the value of sampling smaller hydrophilic peptides, increasing chromatographic separation, and using library-free searches. Additionally, we introduce the EV boost approach, that leverages on the extracellular vesicle fraction to enhance protein identification in neat plasma samples. Globally, our optimized analysis workflow allows the quantification of over 1000 proteins in neat plasma with a 24SPD throughput. We believe that these considerations can be of help independently of the LC-MS platform used.</p>","PeriodicalId":10468,"journal":{"name":"Clinical proteomics","volume":"21 1","pages":"22"},"PeriodicalIF":3.8,"publicationDate":"2024-03-12","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10935919/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"140109554","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":"Proteomics of prostate cancer serum and plasma using low and high throughput approaches","authors":"Ghaith M. Hamza, Rekha Raghunathan, Stephanie Ashenden, Bairu Zhang, Eric Miele, Andrew F. Jarnuczak","doi":"10.1186/s12014-024-09461-0","DOIUrl":"https://doi.org/10.1186/s12014-024-09461-0","url":null,"abstract":"Despite progress, MS-based proteomics in biofluids, especially blood, faces challenges such as dynamic range and throughput limitations in biomarker and disease studies. In this work, we used cutting-edge proteomics technologies to construct label-based and label-free workflows, capable of quantifying approximately 2,000 proteins in biofluids. With 70µL of blood and a single depletion strategy, we conducted an analysis of a homogenous cohort (n = 32), comparing medium-grade prostate cancer patients (Gleason score: 7(3 + 4); TNM stage: T2cN0M0, stage IIB) to healthy donors. The results revealed dozens of differentially expressed proteins in both plasma and serum. We identified the upregulation of Prostate Specific Antigen (PSA), a well-known biomarker for prostate cancer, in the serum of cancer cohort. Further bioinformatics analysis highlighted noteworthy proteins which appear to be differentially secreted into the bloodstream, making them good candidates for further exploration.","PeriodicalId":10468,"journal":{"name":"Clinical proteomics","volume":"23 1","pages":""},"PeriodicalIF":3.8,"publicationDate":"2024-03-12","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"140106666","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Correction to: Using phosphoproteomics data to understand cellular signaling: a comprehensive guide to bioinformatics resources","authors":"Sara R. Savage, Bing Zhang","doi":"10.1186/s12014-024-09473-w","DOIUrl":"https://doi.org/10.1186/s12014-024-09473-w","url":null,"abstract":"&lt;p&gt;Correction to: Clinical Proteomics (2023) 17:27&lt;/p&gt;&lt;p&gt;https://doi.org/10.1186/s12014-020-09290-x&lt;/p&gt;&lt;p&gt;In the main text, under the section heading “Knowledge bases of kinases and phosphatases“, 6th paragraph, the 3rd sentence that reads as “DEPOD used data from HuPho as a starting point and therefore contains much of the same information [19]” should have read as “DEPOD also includes pathways, substrates, and links to orthologs in addition to interacting partners and upstream kinases [19]”. The original article has been corrected.&lt;/p&gt;&lt;ul data-track-component=\"outbound reference\"&gt;&lt;li&gt;&lt;p&gt;Savage, S.R., Zhang, B. Using phosphoproteomics data to understand cellular signaling: a comprehensive guide to bioinformatics resources. Clin Proteom. 2020;17:27. https://doi.org/10.1186/s12014-020-09290-x.&lt;/p&gt;&lt;/li&gt;&lt;/ul&gt;&lt;p&gt;Download references&lt;svg aria-hidden=\"true\" focusable=\"false\" height=\"16\" role=\"img\" width=\"16\"&gt;&lt;use xlink:href=\"#icon-eds-i-download-medium\" xmlns:xlink=\"http://www.w3.org/1999/xlink\"&gt;&lt;/use&gt;&lt;/svg&gt;&lt;/p&gt;&lt;h3&gt;Authors and Affiliations&lt;/h3&gt;&lt;ol&gt;&lt;li&gt;&lt;p&gt;Department of Biomedical Informatics, Vanderbilt University, Nashville, TN, USA&lt;/p&gt;&lt;p&gt;Sara R. Savage&lt;/p&gt;&lt;/li&gt;&lt;li&gt;&lt;p&gt;Lester and Sue Smith Breast Center, Baylor College of Medicine, Houston, TX, USA&lt;/p&gt;&lt;p&gt;Sara R. Savage &amp; Bing Zhang&lt;/p&gt;&lt;/li&gt;&lt;li&gt;&lt;p&gt;Department of Molecular and Human Genetics, Baylor College of Medicine, Houston, TX, USA&lt;/p&gt;&lt;p&gt;Bing Zhang&lt;/p&gt;&lt;/li&gt;&lt;/ol&gt;&lt;span&gt;Authors&lt;/span&gt;&lt;ol&gt;&lt;li&gt;&lt;span&gt;Sara R. Savage&lt;/span&gt;View author publications&lt;p&gt;You can also search for this author in &lt;span&gt;PubMed&lt;span&gt; &lt;/span&gt;Google Scholar&lt;/span&gt;&lt;/p&gt;&lt;/li&gt;&lt;li&gt;&lt;span&gt;Bing Zhang&lt;/span&gt;View author publications&lt;p&gt;You can also search for this author in &lt;span&gt;PubMed&lt;span&gt; &lt;/span&gt;Google Scholar&lt;/span&gt;&lt;/p&gt;&lt;/li&gt;&lt;/ol&gt;&lt;h3&gt;Corresponding author&lt;/h3&gt;&lt;p&gt;Correspondence to Bing Zhang.&lt;/p&gt;&lt;h3&gt;Publisher’s Note&lt;/h3&gt;&lt;p&gt;Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.&lt;/p&gt;&lt;p&gt;The online version of the original article can be found at https://doi.org/10.1186/s12014-020-09290-x&lt;/p&gt;&lt;p&gt;&lt;b&gt;Open Access&lt;/b&gt; This article is licensed under a Creative Commons Attribution 4.0 International License, which permits use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons licence, and indicate if changes were made. The images or other third party material in this article are included in the article’s Creative Commons licence, unless indicated otherwise in a credit line to the material. If material is not included in the article’s Creative Commons licence and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder. To view a copy of this licence, visit http://creativecommons.org/licenses/by/4.0/. The Creative Commons Public Domain Dedication waiver","PeriodicalId":10468,"journal":{"name":"Clinical proteomics","volume":"1 1","pages":""},"PeriodicalIF":3.8,"publicationDate":"2024-03-07","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"140055498","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}