{"id":84455,"date":"2026-06-23T10:00:00","date_gmt":"2026-06-23T08:00:00","guid":{"rendered":"https:\/\/fhi.nl\/nieuws\/meer-uit-complexe-samples-halen-sample-prep-als-sleutel-tot-diepere-proteomics-data\/"},"modified":"2026-06-23T10:15:30","modified_gmt":"2026-06-23T08:15:30","slug":"meer-uit-complexe-samples-halen-sample-prep-als-sleutel-tot-diepere-proteomics-data","status":"publish","type":"news","link":"https:\/\/fhi.nl\/en\/news\/meer-uit-complexe-samples-halen-sample-prep-als-sleutel-tot-diepere-proteomics-data\/","title":{"rendered":"Getting more out of complex samples: sample prep as the key to deeper proteomics data!"},"content":{"rendered":"<header id=\"header\" class=\"header header--high header--branch\">\n\n\t\t\t\t\t\t\t\t\t\t<div class=\"header__background header__background--high\">\n\t\t\t\t\t<img decoding=\"async\" class=\"header__background-image\" src=\"https:\/\/fhi.nl\/app\/uploads\/2026\/06\/Nanotrap_Protein_Enrichment_Affinity-Kit\u2013Discovery_Stacked.avif\" alt=\"\">\n\t\t\t\t<\/div>\n\t\t\t\t\t\t\n\t\n\t<div class=\"container\">\n\t\t<div class=\"header__content\">\n\t\t\t<div class=\"header__first\">\n\n\t\t\t\t\n\t\t\t\t\n\t\t\t\t\n\t\t\t\t<h1 class=\"header__title\" >\n\t\t\t\t\tGetting more out of complex samples: sample prep as the key to deeper proteomics data!\t\t\t\t<\/h1>\n\n\t\t\t\t<div class=\"header__dots-line\">\n\t\t\t\t\t<svg width=\"431\" height=\"9\" viewbox=\"0 0 431 9\" fill=\"none\" xmlns=\"http:\/\/www.w3.org\/2000\/svg\"><path d=\"M430.799 4.192a1.136 1.136 0 1 1-2.272-.001 1.136 1.136 0 0 1 2.272 0Zm-27.272 0a1.135 1.135 0 1 1-2.27 0 1.135 1.135 0 0 1 2.27 0Zm-27.27 0a1.136 1.136 0 1 1-2.272-.001 1.136 1.136 0 0 1 2.272 0Zm-27.272 0a1.39 1.39 0 1 1-2.78 0 1.39 1.39 0 0 1 2.78 0Zm-27.78 0a1.645 1.645 0 1 1-3.29 0 1.645 1.645 0 0 1 3.29 0Zm-28.29 0a1.9 1.9 0 1 1-3.799 0 1.9 1.9 0 0 1 3.799 0Zm-28.799 0a2.154 2.154 0 1 1-4.308 0 2.154 2.154 0 0 1 4.308 0Zm-29.308 0a2.41 2.41 0 1 1-4.819 0 2.41 2.41 0 0 1 4.819 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class=\"header__branch-logo\" alt=\"\" \/>\t\t\t\t\t\t\t\t\t\t\t\t\t<\/div>\n\t\t\t\t\t\n\t\t\t\t\t\n\t\t\t\t\t\n\t\t\t\t<\/div>\n\t\t\t\n\t\t<\/div>\n\t<\/div>\n<\/header>\n\n\t<div class=\"header__meta\">\n\t<div class=\"container\">\n\t\t<div class=\"header__meta__category\">\n\n\t\t\t\t\t\t\t<div class=\"header__meta__detail\">\n\t\t\t\t\t<div>Branch<\/div>\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t<a href=\"https:\/\/fhi.nl\/en\/kennishub\/?_branches_kennishub=laboratorium-technologie\" class=\"header__meta__detail--branch\">\n\t\t\t\t\t\t\t\tLaboratory Technology\t\t\t\t\t\t\t<\/a>\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t<\/div>\n\t\t\t\n\t\t\t\t\t\t\t<div class=\"header__meta__detail\">\n\t\t\t\t\t<div>Subject<\/div>\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t<a href=\"https:\/\/fhi.nl\/en\/kennishub\/?_onderwerp_kennishub=laboratorium-technologie\" class=\"header__meta__detail--categorie\">\n\t\t\t\t\t\t\t\tLaboratory Technology\t\t\t\t\t\t\t<\/a>\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t<a href=\"https:\/\/fhi.nl\/en\/kennishub\/?_onderwerp_kennishub=life-science-biotechnologie\" class=\"header__meta__detail--categorie\">\n\t\t\t\t\t\t\t\tLife Science\/Biotechnology\t\t\t\t\t\t\t<\/a>\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t<\/div>\n\t\t\t\n\t\t<\/div>\n\t<\/div>\n<\/div>\n\n<div class=\"text bg--white\">\n\t<div class=\"container\">\n\t\t\t\t\t\t\t\t\t\t\t<div class=\"text__content text__content--1-col\">\n\t\t\t<h2 class=\"aligncenter\"><strong>Getting more out of complex samples: sample prep as the key to deeper proteomics data!<\/strong><\/h2>\r\n<p>Inside<strong> proteomics<\/strong> Attention is often focused on the performance of the mass spectrometer, the LC method, or the data analysis. However, reliable and reproducible LC-MS\/MS data begins much earlier in the workflow: with the sample. <a href=\"https:\/\/www.mswil.com\/sample-prep\/\" target=\"_blank\" rel=\"noopener\">preparation<\/a>.<\/p>\r\n<p>Especially with complex biological samples, such as <a href=\"https:\/\/www.mswil.com\/sample-prep\/equalizer-plasma-proteomics-standards\/\">plasma<\/a>, In cerebrospinal fluid or urine, it can be difficult to clearly visualize low-abundance proteins and peptides. High-abundance components can overshadow relevant signals, leaving part of the biological information difficult to detect. For labs working on biomarker discovery, clinical proteomics, or other applications where deeper insight into the proteome is important, sample preparation can therefore be a decisive step.<\/p>\r\n<h2><strong>From product selection to workflow selection<\/strong><\/h2>\r\n<p>The question is not always which product is the most advanced, but which workflow suits the sample type, research objectives, and existing LC-MS\/MS setup. Many laboratories want to improve their current workflow without immediately switching to a completely new or closed platform. This is precisely where the value of flexible sample preparation solutions lies.<\/p>\r\n<p><a href=\"http:\/\/www.mswil.com\">MS Will<\/a> supports European laboratories with specialized products for sample preparation, separation, and ionization. The focus is not on a single fixed route, but on practical choices that align with existing workflows.<\/p>\r\n<h2><strong>Nanotrap Protein Enrichment Affinity Kits<\/strong><\/h2>\r\n<p>An example within the sample prep portfolio is the <a href=\"https:\/\/www.mswil.com\/sample-prep\/nanotrap-protein-enrichment-affinity-kit\/\" target=\"_blank\" rel=\"noopener\">Nanotrap\u00ae Protein Enrichment Affinity Kit<\/a>. These kits use magnetic, affinity-capture hydrogel particles to capture and concentrate low-abundance proteins and peptides from complex samples. This allows these components to be better detected in LC-MS\/MS analyses.<\/p>\r\n<p>According to the available product information, the Nanotrap kits have been developed for a simplified and rapid workflow, with a processing time of approximately 45 minutes from raw sample to enriched proteins. The technology is aimed at better quantitative precision, with median CVs below 5%, and deeper access to the proteome, with 3\u20135 times more protein IDs than other methods.<\/p>\r\n<p>The kits are available for various sample types, including plasma, cerebrospinal fluid, and urine. Additionally, different Nanotrap particles can be combined to optimize proteome coverage for a specific application.<\/p>\r\n<h2><strong>Practical relevance for proteomics labs<\/strong><\/h2>\r\n<p>For laboratories working with plasma or serum proteomics, biomarker discovery, or low-abundance proteins, enrichment can be an interesting route to extract more information from existing samples. Moreover, the improvement does not always have to lie in a completely new infrastructure. Sometimes, a targeted adjustment in the sample preparation step can already help to better utilize the existing LC-MS\/MS workflow.<\/p>\r\n<p>This aligns with a broader development within proteomics: labs are seeking greater depth, reproducibility, and flexibility, while simultaneously wanting to maintain control over their own workflow. An independent approach, allowing multiple sample preparation routes to be examined side-by-side, helps researchers make a choice that is both substantively and practically justifiable.<\/p>\r\n<h2><strong>Support with the correct workflow route<\/strong><\/h2>\r\n<p>MS Wil is happy to help determine which sample preparation route suits a specific application. This may involve protein enrichment, reproducible digestion, high-throughput sample preparation, phosphopeptide enrichment, or other steps within the LC-MS\/MS workflow.<\/p>\r\n<p>Not every lab needs the same solution. The right choice depends on the sample type, the desired depth, the number of samples, the existing instrumentation, and the degree to which a workflow needs to be scalable.<\/p>\r\n<p>With products such as the Nanotrap Protein Enrichment Affinity Kit, MS Wil offers a practical route for labs that want to get more out of complex samples, without being immediately tied to a single complete platform.<\/p>\r\n<p>More information about the <a href=\"https:\/\/www.mswil.com\/sample-prep\/nanotrap-protein-enrichment-affinity-kit\/\" target=\"_blank\" rel=\"noopener\">Nanotrap Protein Enrichment Affinity Kit<\/a> is available via <a href=\"http:\/\/www.mswil.com\" target=\"_blank\" rel=\"noopener\">MS Will<\/a>.<\/p>\t\t<\/div>\n\t<\/div>\n<\/div>\r\n\t<div class=\"articles bg--offwhite automatic\">\r\n\t\t<div class=\"container\">\r\n\t\t\t<div class=\"articles__header\">\r\n\t\t\t\t\t\t\t\t\t<div class='heading-wrapper'><svg width=\"13\" height=\"13\" viewbox=\"0 0 13 13\" fill=\"none\" xmlns=\"http:\/\/www.w3.org\/2000\/svg\"><circle cx=\"6.394\" cy=\"6.5\" r=\"6.394\" fill=\"#000\"\/><\/svg><h2>Related articles<\/h2><\/div>\t\t\t\t\t\t\t\t\t\t\t\t\t<a href=\"\/en\/profiel\/ms-wil-b-v\/\" class=\"button button--outline\">view profile<\/a>\r\n\t\t\t\t\t\t\t<\/div>\r\n\t\t\t\t\t\t<div class=\"post-grid post-grid--no-padding\">\r\n\t\t\t\t\n<a class=\"single-item single-item__articles\" href=\"https:\/\/fhi.nl\/en\/news\/hoe-zet-je-data-om-naar-bruikbare-inzichten\/\" data-id=\"36561\">\n\t<div class=\"single-item__articles-icon\">\n\t\t<svg width=\"97\" height=\"97\" viewbox=\"0 0 97 97\" fill=\"none\" xmlns=\"http:\/\/www.w3.org\/2000\/svg\">\n<path fill-rule=\"evenodd\" clip-rule=\"evenodd\" d=\"M48.479 93.121C48.8894 93.121 49.2985 93.1155 49.7062 93.1045L56.5858 61.0383C57.9656 54.6068 58.2239 47.9697 57.3623 41.4552C53.7316 46.9378 51.0888 53.0219 49.5612 59.4321L41.6563 92.6029C43.8807 92.9441 46.1592 93.121 48.479 93.121ZM60.0079 61.7725L53.3384 92.8596C75.7077 90.4381 93.121 71.4921 93.121 48.479C93.121 23.8239 73.1341 3.837 48.479 3.837C23.8239 3.837 3.837 23.8239 3.837 48.479C3.837 69.6027 18.5085 87.2998 38.2172 91.9359L46.1565 58.6207C48.0007 50.8821 51.3749 43.5907 56.0803 37.1762C57.3439 35.4536 60.0204 36.1162 60.3918 38.177C61.7986 45.982 61.6719 54.0164 60.0079 61.7725ZM48.479 96.958C75.2532 96.958 96.958 75.2532 96.958 48.479C96.958 21.7048 75.2532 0 48.479 0C21.7048 0 0 21.7048 0 48.479C0 75.2532 21.7048 96.958 48.479 96.958ZM50.8409 13.4297C50.8409 12.3702 49.982 11.5112 48.9224 11.5112C47.8629 11.5112 47.0039 12.3702 47.0039 13.4297V24.9407C47.0039 26.0003 47.8629 26.8592 48.9224 26.8592C49.982 26.8592 50.8409 26.0003 50.8409 24.9407V13.4297ZM67.5169 16.0418C68.4345 16.5716 68.7489 17.7449 68.2192 18.6625L62.8261 28.0036C62.2963 28.9212 61.123 29.2356 60.2054 28.7058C59.2878 28.176 58.9734 27.0027 59.5031 26.0851L64.8962 16.744C65.426 15.8264 66.5993 15.512 67.5169 16.0418ZM29.7398 18.6625C29.21 17.7449 29.5244 16.5716 30.442 16.0418C31.3596 15.512 32.533 15.8264 33.0628 16.744L38.4558 26.0851C38.9856 27.0027 38.6712 28.176 37.7536 28.7058C36.836 29.2356 35.6627 28.9212 35.1329 28.0036L29.7398 18.6625ZM81.7542 29.5272C82.284 30.4448 81.9696 31.6181 81.0519 32.1479L71.7109 37.541C70.7933 38.0708 69.6199 37.7564 69.0901 36.8388C68.5604 35.9212 68.8748 34.7478 69.7924 34.2181L79.1335 28.825C80.0511 28.2952 81.2244 28.6096 81.7542 29.5272ZM16.9071 32.148C15.9895 31.6182 15.6751 30.4449 16.2048 29.5273C16.7346 28.6097 17.908 28.2953 18.8256 28.8251L28.1667 34.2182C29.0843 34.7479 29.3987 35.9213 28.8689 36.8389C28.3391 37.7565 27.1658 38.0709 26.2481 37.5411L16.9071 32.148ZM88.252 47.003C88.252 48.0625 87.393 48.9215 86.3335 48.9215H75.7817C74.7221 48.9215 73.8632 48.0625 73.8632 47.003C73.8632 45.9434 74.7221 45.0845 75.7817 45.0845H86.3335C87.393 45.0845 88.252 45.9434 88.252 47.003ZM11.5102 48.9217C10.4507 48.9217 9.59173 48.0628 9.59173 47.0032C9.59173 45.9437 10.4507 45.0847 11.5102 45.0847H22.062C23.1216 45.0847 23.9805 45.9437 23.9805 47.0032C23.9805 48.0628 23.1216 48.9217 22.062 48.9217H11.5102Z\" fill=\"#08A4BD\"\/>\n<\/svg>\n\t<\/div>\n\t<div class=\"single-item__articles-title\"><div class='heading-wrapper'><h3>How do you convert data into useful insights?<\/h3><\/div><\/div>\n\t<div class=\"single-item__articles-terms\">\n\t\t\n\t\t\n\t\t\t\t\t\t<span class=\"button button--outline single-item__articles-term--branche single-item__articles-term\">Industrial automation<\/span>\n\t\t\t\t\t\t\t\t<span class=\"button button--outline single-item__articles-term--branche single-item__articles-term\">Industrial Ethernet<\/span>\n\t\t\t\t\t\t\t\t<span class=\"button button--outline single-item__articles-term--branche single-item__articles-term\">Data acquisition systems\/ Historians<\/span>\n\t\t\t\t\t\t\t\t<span class=\"button button--outline single-item__articles-term--branche single-item__articles-term\">Automation services<\/span>\n\t\t\t\t\t<\/div>\n\t<div class=\"single-item__articles-author-date-wrapper\">\n\t\t\t\t\t<div class=\"single-item__articles-author\">\n\t\t\t\tIXON BV\t\t\t<\/div>\n\t\t\t\t\t\t\t<div class=\"single-item__articles-date\">\n\t\t\t\tMarch 10, 2022\t\t\t<\/div>\n\t\t\t<\/div>\n<\/a>\n\n<a class=\"single-item single-item__articles\" href=\"https:\/\/fhi.nl\/en\/news\/ventilatie-op-scholen\/\" data-id=\"41158\">\n\t<div class=\"single-item__articles-icon\">\n\t\t<svg width=\"108\" height=\"97\" viewbox=\"0 0 108 97\" fill=\"none\" xmlns=\"http:\/\/www.w3.org\/2000\/svg\">\n<path fill-rule=\"evenodd\" clip-rule=\"evenodd\" d=\"M71.1889 19.4317C71.1889 18.8156 70.905 18.2339 70.4193 17.8549C69.9336 17.4759 69.3003 17.3419 68.7027 17.4917L39.0059 24.935C38.1162 25.1581 37.4922 25.9577 37.4922 26.875V37.3931H29.2051C28.1005 37.3931 27.2051 38.2885 27.2051 39.3931V75.9996H31.2051V41.3931H47.895V75.9996H51.895V39.3931C51.895 38.2885 50.9996 37.3931 49.895 37.3931H41.4922V28.4356L67.1889 21.9948V75.9998H71.1889V34.3866H77.4785V75.9995H81.4785V32.3866C81.4785 31.282 80.5831 30.3866 79.4785 30.3866H71.1889V19.4317ZM37.5508 51.9436C37.5508 53.0482 38.4462 53.9436 39.5508 53.9436C40.6554 53.9436 41.5508 53.0482 41.5508 51.9436V48.7891C41.5508 47.6845 40.6554 46.7891 39.5508 46.7891C38.4462 46.7891 37.5508 47.6845 37.5508 48.7891V51.9436ZM39.5508 67.01C38.4462 67.01 37.5508 66.1146 37.5508 65.01V61.8555C37.5508 60.7509 38.4462 59.8555 39.5508 59.8555C40.6554 59.8555 41.5508 60.7509 41.5508 61.8555V65.01C41.5508 66.1146 40.6554 67.01 39.5508 67.01ZM56.7871 51.9436C56.7871 53.0482 57.6825 53.9436 58.7871 53.9436C59.8917 53.9436 60.7871 53.0482 60.7871 51.9436V48.7891C60.7871 47.6845 59.8917 46.7891 58.7871 46.7891C57.6825 46.7891 56.7871 47.6845 56.7871 48.7891V51.9436ZM58.7871 35.5164C57.6825 35.5164 56.7871 34.6209 56.7871 33.5164V30.3618C56.7871 29.2572 57.6825 28.3618 58.7871 28.3618C59.8917 28.3618 60.7871 29.2572 60.7871 30.3618V33.5164C60.7871 34.6209 59.8917 35.5164 58.7871 35.5164ZM56.7871 65.01C56.7871 66.1146 57.6825 67.01 58.7871 67.01C59.8917 67.01 60.7871 66.1146 60.7871 65.01V61.8555C60.7871 60.7509 59.8917 59.8555 58.7871 59.8555C57.6825 59.8555 56.7871 60.7509 56.7871 61.8555V65.01Z\" fill=\"#B14AED\"\/>\n<path fill-rule=\"evenodd\" clip-rule=\"evenodd\" d=\"M96.9545 4H11C7.13401 4 4 7.13401 4 11V66.7273C4 70.5933 7.134 73.7273 11 73.7273H96.9545C100.821 73.7273 103.955 70.5933 103.955 66.7273V11C103.955 7.134 100.821 4 96.9545 4ZM11 0C4.92487 0 0 4.92486 0 11V66.7273C0 72.8024 4.92486 77.7273 11 77.7273H96.9545C103.03 77.7273 107.955 72.8024 107.955 66.7273V11C107.955 4.92487 103.03 0 96.9545 0H11Z\" fill=\"#B14AED\"\/>\n<path fill-rule=\"evenodd\" clip-rule=\"evenodd\" d=\"M38 74H69V97H38V74ZM42 78V93H65V78H42Z\" fill=\"#B14AED\"\/>\n<path fill-rule=\"evenodd\" clip-rule=\"evenodd\" d=\"M23.9082 95C23.9082 93.8954 24.8036 93 25.9082 93L82.0446 93C83.1491 93 84.0446 93.8954 84.0446 95C84.0446 96.1046 83.1491 97 82.0446 97L25.9082 97C24.8036 97 23.9082 96.1046 23.9082 95Z\" fill=\"#B14AED\"\/>\n<\/svg>\n\t<\/div>\n\t<div class=\"single-item__articles-title\"><div class='heading-wrapper'><h3>Ventilation in schools<\/h3><\/div><\/div>\n\t<div class=\"single-item__articles-terms\">\n\t\t\n\t\t\n\t\t\t\t\t\t<span class=\"button button--outline single-item__articles-term--branche single-item__articles-term\">Building Automation<\/span>\n\t\t\t\t\t\t\t\t<span class=\"button button--outline single-item__articles-term--branche single-item__articles-term\">Digital Building of the Future<\/span>\n\t\t\t\t\t<\/div>\n\t<div class=\"single-item__articles-author-date-wrapper\">\n\t\t\t\t\t<div class=\"single-item__articles-author\">\n\t\t\t\tFHI, Federation of Technology Industries\t\t\t<\/div>\n\t\t\t\t\t\t\t<div class=\"single-item__articles-date\">\n\t\t\t\tSeptember 11, 2020\t\t\t<\/div>\n\t\t\t<\/div>\n<\/a>\n\t\t\t<\/div>\r\n\t\t<\/div>\r\n\t<\/div>","protected":false},"excerpt":{"rendered":"<p>This article describes why sample preparation is a decisive step for reliable and deeper LC-MS\/MS proteomics data. Using the Nanotrap\u00ae Protein Enrichment Affinity Kit, MS Wil demonstrates how low-abundance proteins and peptides can be enriched from complex samples, such as plasma, cerebrospinal fluid, and urine. The emphasis is on workflow selection, reproducibility, and practical support for laboratories looking to get more out of their existing LC-MS\/MS setup.<\/p>","protected":false},"featured_media":84456,"template":"","branches":[14],"events":[],"secretariat":[],"categories":[59,32],"themes_tax":[524,517],"content_types":[514],"class_list":["post-84455","news","type-news","status-publish","has-post-thumbnail","hentry"],"acf":[],"yoast_head":"<!-- This site is optimized with the Yoast SEO plugin v26.8 - https:\/\/yoast.com\/product\/yoast-seo-wordpress\/ -->\n<title>Sample prep voor diepere proteomics-data | MS Wil<\/title>\n<meta name=\"description\" content=\"Ontdek hoe sample prep met de Nanotrap Protein Enrichment Affinity Kit kan helpen om lage-abundantie eiwitten uit complexe samples beter zichtbaar te maken in LC-MS\/MS proteomics workflows.\" \/>\n<meta name=\"robots\" content=\"index, follow, max-snippet:-1, max-image-preview:large, max-video-preview:-1\" \/>\n<link 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