[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"detail-sidebar-cat-0-en-105":3,"doc-seo-446514-105":59,"doc-detail-446514-en":130},{"code":4,"msg":5,"data":6},0,"success",[7,13,18,23,28,33,38,43,48,51,55],{"id":8,"doc_module":4,"doc_module_name":9,"category_name":10,"show_sort_weight":11,"slug":12},1,"Document","Story & Novel",90,"story-novel",{"id":14,"doc_module":4,"doc_module_name":9,"category_name":15,"show_sort_weight":16,"slug":17},2,"Literature",80,"literature",{"id":19,"doc_module":4,"doc_module_name":9,"category_name":20,"show_sort_weight":21,"slug":22},4,"Exam",70,"exam",{"id":24,"doc_module":4,"doc_module_name":9,"category_name":25,"show_sort_weight":26,"slug":27},5,"Comic",60,"comic",{"id":29,"doc_module":4,"doc_module_name":9,"category_name":30,"show_sort_weight":31,"slug":32},6,"Technology",50,"technology",{"id":34,"doc_module":4,"doc_module_name":9,"category_name":35,"show_sort_weight":36,"slug":37},7,"Healthcare",40,"healthcare",{"id":39,"doc_module":4,"doc_module_name":9,"category_name":40,"show_sort_weight":41,"slug":42},8,"Research & Report",30,"research-report",{"id":44,"doc_module":4,"doc_module_name":9,"category_name":45,"show_sort_weight":46,"slug":47},9,"Religion & Spirituality",20,"religion-spirituality",{"id":46,"doc_module":4,"doc_module_name":9,"category_name":49,"show_sort_weight":46,"slug":50},"World Cup","world-cup",{"id":52,"doc_module":4,"doc_module_name":9,"category_name":53,"show_sort_weight":52,"slug":54},10,"Lifestyle","lifestyle",{"id":56,"doc_module":4,"doc_module_name":9,"category_name":57,"show_sort_weight":24,"slug":58},19,"General","general",{"code":4,"msg":60,"data":61},"ok",{"site_id":62,"language":63,"slug":64,"title":65,"keywords":66,"description":67,"schema_data":68,"social_meta":123,"head_meta":125,"extra_data":127,"updated_unix":129},105,"en","blood-matrices-and-sample-preparation-influence-blood-marker-discovery","Blood Matrices and Sample Preparation Influence Blood Marker Discovery","","While plasma and serum are widely used in high-throughput proteomics, the impact of different blood matrix types remains underexplored. Routine diagnostics often use serum or Li-heparin plasma, whereas proteomics commonly emphasizes EDTA plasma. This study systematically compared LC-MS/MS DIA proteomic profiles of pooled blood samples using serum (with/without separation gel) and EDTA, citrate, or Li-heparin plasma.",{"@graph":69,"@context":122},[70,84,105],{"@type":71,"itemListElement":72},"BreadcrumbList",[73,77,79,82],{"item":74,"name":75,"@type":76,"position":8},"https://docshare.wps.com","Home","ListItem",{"item":78,"name":9,"@type":76,"position":14},"https://docshare.wps.com/document/",{"item":80,"name":35,"@type":76,"position":81},"https://docshare.wps.com/document/healthcare/",3,{"item":83,"name":65,"@type":76,"position":19},"https://docshare.wps.com/document/blood-matrices-and-sample-preparation-influence-blood-marker-discovery/446514/",{"url":83,"name":65,"@type":85,"image":86,"author":91,"headline":65,"publisher":94,"fileFormat":97,"inLanguage":63,"description":67,"dateModified":98,"datePublished":99,"encodingFormat":97,"isAccessibleForFree":100,"interactionStatistic":101},"DigitalDocument",{"url":87,"@type":88,"width":89,"height":90},"https://docshare.wps.com/thumbnails/blood-matrices-and-sample-preparation-influence-blood-marker-discovery/446514.png","ImageObject",300,407,{"name":92,"@type":93},"McQueen","Person",{"url":74,"name":95,"@type":96},"DocShare","Organization","application/pdf","2026-10-31","2026-09-29",true,{"@type":102,"interactionType":103,"userInteractionCount":39},"InteractionCounter",{"@type":104},"ViewAction",{"@type":106,"mainEntity":107},"FAQPage",[108,114,118],{"name":109,"@type":110,"acceptedAnswer":111},"Which blood matrix types were compared in the study?","Question",{"text":112,"@type":113},"The study compared serum (with or without separation gel) with plasma anticoagulated using EDTA, citrate, or Li-heparin.","Answer",{"name":115,"@type":110,"acceptedAnswer":116},"How did sample preparation and matrix choice affect protein identifications?",{"text":117,"@type":113},"Protein identification performance varied across matrix types, with EDTA plasma and serum outperforming citrate plasma. Preparation methods such as SAX beads, ENRICH-iST, and perCA produced the highest identification numbers but also increased variability.",{"name":119,"@type":110,"acceptedAnswer":120},"What mass spectrometry setups were used for the proteomics measurements?",{"text":121,"@type":113},"Measurements were performed on a Q Exactive HF-X and a timsTOF HT using data-independent acquisition (DIA) mode.","https://schema.org",{"og:url":83,"og:type":124,"og:title":65,"og:site_name":95,"og:description":67},"article",{"robots":126,"canonical":83},"index,follow",{"doc_id":128,"site_id":62},446514,1790823481,{"code":4,"msg":5,"data":131},{"doc_id":128,"user_id":132,"nickname":92,"user_avatar":133,"doc_module":4,"category_id":34,"category_name":35,"doc_title":65,"doc_description":67,"doc_content":134,"file_id":135,"file_url":136,"file_type":137,"file_size":138,"view_count":39,"is_deleted":4,"is_public":8,"is_downloadable":8,"audit_status":8,"page_count":139,"language":140,"language_code":63,"site_id":62,"html_lang":63,"table_of_contents":141,"faqs":142,"seo_title":143,"seo_description":67,"update_tm":144,"read_time":145,"preview_page_count":4},5909890329169,"https://ap-avatar.wpscdn.com/davatar_9964176cb1d06d4a9deccf72a44ae3dc","This article is licensed under CC-BY 4.0   \n[pubs.acs.org/jpr](pubs.acs.org/jpr)  Article   \nBlood Matrices and Sample Preparation Influence Blood Marker Discovery  \nThomas F. Gronauer, Juliane Merl-Pham, Christine von Toerne, Katharina Habler, Daniel Teupser, and Stefanie M. Hauck*  \n Cite This: J. Proteome Res. 2026, 25, 405−417  \nRead Online  \n\n|  |  |  |  |  |  |\n| --- | --- | --- | --- | --- | --- |\n| ACCESS   | Metrics & More |  |  Article Recommendations |  | *sı Supporting Information |\n\nABSTRACT: While plasma and serum are widely used in highthroughput proteomics, the impact of different blood matrix types remains underexplored. Routine diagnostics most commonly use serum or Li-heparin plasma, while the proteomics community primarily focuses on advancing analytical depth in EDTA plasma. Here, we systematically investigated the LC−MS/MS proteomic profiles of pooled blood samples from three healthy, voluntary probands including serum (with/without separation gel) and plasma anticoagulated with EDTA, citrate, or Li-heparin. Sample preparation was conducted with the commercially available iSTand ENRICH-iST kits, strong-anion exchange (SAX) beads, the TFA-based approach SPEED, and perchloric acid (perCA) precipitation. Mass-spectrometric measurements were performed  \non a Q Exactive HF-X and a timsTOF HT in data-independent acquisition mode (DIA). Protein identifications varied considerably across matrix types with EDTA plasma and serum outperforming citrate plasma. Sample preparation methods with SAX beads, ENRICH-iST, and perCA yielded the highest identification numbers but also showed increased variability. Across all samples, 181 protein groups overlapped for timsTOF HT data. Subsets of protein groups were specific for the matrix and preparation. This study shows a systematic approach to determining suitable sample preparation and matrix parameters for the robust identification of individual body fluid marker proteins by mass spectrometry.  \nKEYWORDS: plasma, serum, iST, ENRICH-iST, perCA, SPEED, Mag-Net, mpwR, proteomics, biomarkers  \n■ INTRODUCTION  \nIn medical research, blood is the primary body fluid that is investigated to decipher molecular processes in human diseases. While blood samples are easily accessible and available with minimally invasive procedures, mass spectrometry-based proteomics approaches for biomarker discovery in blood and other body fluids face various challenges. The high dynamic range of plasma proteins impairs the analytical depthsand coverage.1 The susceptibility to minor changes during clinical sample collection and preparation such as variation incentrifugation steps,2 collection tubes,3,4 sample temperature,5 and handling critically affects sample composition. Various studies have elaborated on similarities and differences in proteomic profiles of blood matrices such as EDTA plasma, heparin plasma, and serum.4−6 Other studies showed influences of blood collection tube components on clinical assay outcomes.3 A recent study showed that commercially available plasma should also be treated with caution, as the protein content can vary greatly in some cases and there is usually no detailed information on the preparation.7 Therefore, over the years, guidelines have been proposed to ensure the  \nappropriate usage, handling, and storage of these valuable research samples.8−11  \nToday, most of the assays in clinical laboratory medicine are based on serum or Li-heparinized plasma. At the same time, the proteomics community has composed guidelines that should ensure reproducibility and validity in LC−MS-based proteomic biomarker discovery.12 These recommendations primarily comprise the use of EDTA-anticoagulated blood,8, 11 since other coagulants may hamper protein identification and quantification. 11 This complicates the comparability of clinical assay outcomes with LC−MS-based biomarker identification and might additionally slow the implementation of mass spectrometry in the daily clinical routi","cbCaik6RPu2xBNJn","https://ap.wps.com/l/cbCaik6RPu2xBNJn","pdf",6325719,13,"English","# Abstract\n# Introduction\n## Blood matrices and biomarker discovery challenges\n## Clinical use of serum and Li-heparin plasma\n## Proteomics guidelines and reproducibility concerns\n## Need for improved protein identification","[{\"question\":\"Which blood matrix types were compared in the study?\",\"answer\":\"The study compared serum (with or without separation gel) with plasma anticoagulated using EDTA, citrate, or Li-heparin.\"},{\"question\":\"How did sample preparation and matrix choice affect protein identifications?\",\"answer\":\"Protein identification performance varied across matrix types, with EDTA plasma and serum outperforming citrate plasma. Preparation methods such as SAX beads, ENRICH-iST, and perCA produced the highest identification numbers but also increased variability.\"},{\"question\":\"What mass spectrometry setups were used for the proteomics measurements?\",\"answer\":\"Measurements were performed on a Q Exactive HF-X and a timsTOF HT using data-independent acquisition (DIA) mode.\"}]","Blood Matrices and Sample Preparation Influence Blood Marker Discovery | PDF",1790715726,33]