{"id":5383,"date":"2015-09-07T07:00:49","date_gmt":"2015-09-07T11:00:49","guid":{"rendered":"http:\/\/admin.acceleratingscience.com\/?p=5383"},"modified":"2016-04-29T16:52:54","modified_gmt":"2016-04-29T16:52:54","slug":"processing-a-complex-lipid-data-set-with-lipidsearch-software","status":"publish","type":"post","link":"https:\/\/www.thermofisher.com\/blog\/proteomics\/processing-a-complex-lipid-data-set-with-lipidsearch-software\/","title":{"rendered":"Processing a Complex Lipid Data Set with LipidSearch Software"},"content":{"rendered":"<p><span><img loading=\"lazy\" decoding=\"async\" width=\"250\" height=\"250\" style=\"float: left;margin: 10px\" alt=\"LipidSearch software.\" src=\"http:\/\/admin.acceleratingscience.com\/wp-content\/uploads\/2015\/09\/cq5dam.thumbnail.250.2501.png\" \/>Lipidomics can provide vital information essential&nbsp;for understanding a wide range of disease states, particularly in cancer and diabetes. Because of the complexities of the lipidome, accessing this information using untargeted liquid chromatography and tandem mass spectrometry (LC-MS\/MS) presents a challenge.&nbsp;For the most comprehensive analyses of data, past researchers&nbsp;have been&nbsp;dependent upon complex software and large databases.<\/span><\/p>\n<p>Recently, a team of scientists demonstrated the capacity of<span>&nbsp;<\/span><a href=\"http:\/\/www.thermoscientific.com\/en\/product\/lipidsearch-software.html\" target=\"_blank\">LipidSearch software<\/a>&nbsp;(<span>Thermo Scientific)<\/span> as a strategy to overcome the obstacles associated with manually processing MS data from lipidomics studies.<sup>1<\/sup> In a 60-min liquid chromatography and mass spectrometry (LC-MS) run, the&nbsp;scientists were&nbsp;able to identify and quantify approximately 1,000 isomeric lipid species from human plasma using an experimental C30 ultra-high-performance liquid chromatography (UHPLC) column.<\/p>\n<p>The research team performed LC-MS using three aliquots of human plasma provided by the NIST. For these experiments, the team relied upon a <a href=\"http:\/\/www.dionex.com\/en-us\/products\/liquid-chromatography\/lc-systems\/rslc\/lp-72455.html\" target=\"_blank\">Dionex UltiMate 3000 Rapid Separation LC [RSLC] system<\/a> and a <a href=\"https:\/\/www.thermoscientific.com\/en\/product\/q-exactive-hf-hybrid-quadrupole-orbitrap.html\" target=\"_blank\">Q Exactive HF hybrid quadrupole-Orbitrap mass spectrometer<\/a>&nbsp;<span>(both Thermo Scientific)&nbsp;<\/span>.<\/p>\n<p>The LipidSearch software enhances lipid identification by searching databases of precursor accurate masses and their predicted fragment ions. When hits <span>are discovered<\/span>, the software identifies the&nbsp;lipids by ranking them based on mass tolerance and matching them&nbsp;to the theoretical fragment ions and fraction of total MS2 intensity. Additionally,&nbsp;LipidSearch identifies the&nbsp;lipid species in each LC-dd-MS2 experiment, assessing them at sum composition (MS) and isomer (MS2) levels. The team obtained each lipid&nbsp;identification using a single, high-quality Orbitrap MS2 scan over four orders of concentration dynamic range. They identified potential lipid species&nbsp;using the predicted MS2 fragments for molecular species observed in positive or negative ion mode. <span>Table 1 summarily provides, for each lipid sub-class,&nbsp;the numbers of lipid species.<\/span><\/p>\n<p><strong>Table 1. A summary of the number of lipid species for each lipid sub-class<\/strong><\/p>\n<table style=\"width: 401px;height: 382px\">\n<colgroup>\n<col width=\"100\" \/>\n<col width=\"100\" \/>\n<col width=\"100\" \/>\n<col width=\"100\" \/> <\/colgroup>\n<tbody>\n<tr>\n<td>\n<p><strong>Lipid Class<\/strong><\/p>\n<\/td>\n<td>\n<p><strong>Unfiltered Species&nbsp; &nbsp;<\/strong><\/p>\n<\/td>\n<td>\n<p><strong>Filtered Species<\/strong><\/p>\n<\/td>\n<td>\n<p><strong>Reported Species<\/strong><\/p>\n<\/td>\n<\/tr>\n<tr>\n<td>\n<p><span>Che<\/span><\/p>\n<\/td>\n<td>\n<p><span>22<\/span><\/p>\n<\/td>\n<td>\n<p><span>19<\/span><\/p>\n<\/td>\n<td>\n<p><span>18<\/span><\/p>\n<\/td>\n<\/tr>\n<tr>\n<td>\n<p><span>DG<\/span><\/p>\n<\/td>\n<td>\n<p><span>81<\/span><\/p>\n<\/td>\n<td>\n<p><span>46<\/span><\/p>\n<\/td>\n<td>\n<p><span>45<\/span><\/p>\n<\/td>\n<\/tr>\n<tr>\n<td>\n<p><span>TG<\/span><\/p>\n<\/td>\n<td>\n<p><span>665<\/span><\/p>\n<\/td>\n<td>\n<p><span>468<\/span><\/p>\n<\/td>\n<td>\n<p><span>452<\/span><\/p>\n<\/td>\n<\/tr>\n<tr>\n<td>\n<p><span>PC<\/span><\/p>\n<\/td>\n<td>\n<p><span>508<\/span><\/p>\n<\/td>\n<td>\n<p><span>221<\/span><\/p>\n<\/td>\n<td>\n<p><span>220<\/span><\/p>\n<\/td>\n<\/tr>\n<tr>\n<td>\n<p><span>LPC<\/span><\/p>\n<\/td>\n<td>\n<p><span>106<\/span><\/p>\n<\/td>\n<td>\n<p><span>58<\/span><\/p>\n<\/td>\n<td>\n<p><span>57<\/span><\/p>\n<\/td>\n<\/tr>\n<tr>\n<td>\n<p><span>PE\/LPE<\/span><\/p>\n<\/td>\n<td>\n<p><span>53<\/span><\/p>\n<\/td>\n<td>\n<p><span>33<\/span><\/p>\n<\/td>\n<td>\n<p><span>32<\/span><\/p>\n<\/td>\n<\/tr>\n<tr>\n<td>\n<p><span>PI<\/span><\/p>\n<\/td>\n<td>\n<p><span>34<\/span><\/p>\n<\/td>\n<td>\n<p><span>25<\/span><\/p>\n<\/td>\n<td>\n<p><span>24<\/span><\/p>\n<\/td>\n<\/tr>\n<tr>\n<td>\n<p><span>Cer<\/span><\/p>\n<\/td>\n<td>\n<p><span>33<\/span><\/p>\n<\/td>\n<td>\n<p><span>22<\/span><\/p>\n<\/td>\n<td>\n<p><span>21<\/span><\/p>\n<\/td>\n<\/tr>\n<tr>\n<td>\n<p><span>CerG<\/span><\/p>\n<\/td>\n<td>\n<p><span>20<\/span><\/p>\n<\/td>\n<td>\n<p><span>13<\/span><\/p>\n<\/td>\n<td>\n<p><span>13<\/span><\/p>\n<\/td>\n<\/tr>\n<tr>\n<td>\n<p><span>SM<\/span><\/p>\n<\/td>\n<td>\n<p><span>138<\/span><\/p>\n<\/td>\n<td>\n<p><span>92<\/span><\/p>\n<\/td>\n<td>\n<p><span>91<\/span><\/p>\n<\/td>\n<\/tr>\n<tr>\n<td>\n<p><strong>Total<\/strong><\/p>\n<\/td>\n<td>\n<p><span>1,660<\/span><\/p>\n<\/td>\n<td>\n<p><span>997<\/span><\/p>\n<\/td>\n<td>\n<p><span>973<\/span><\/p>\n<\/td>\n<\/tr>\n<\/tbody>\n<\/table>\n<p>These results demonstrate that the LipidSearch software was able to process high-quality Orbitrap LC-MS2 untargeted lipidomics data. The software made it possible to comprehensively identify and quantify nearly 1,000 lipid species in a single LC-MS run; as another plus, the high mass accuracy in both MS (120K) and MS2 (30K) obtained with the Q Exactive HF instrument produced confident CV values below 15%, demonstrating excellent reproducibility. The scientists&nbsp;propose&nbsp;that&nbsp;this particular combination of software and instrumentation&nbsp;is ideal for analyzing data from lipidomics experiments.<\/p>\n<p>&nbsp;<\/p>\n<p><strong>Reference<\/strong><\/p>\n<p><span><span>1. Kiyonami, R., et al. (2015, May) &ldquo;<a href=\"http:\/\/planetorbitrap.com\/library?t=QTE3MTllODg0OWE1NWYzYw%3D%3D&amp;keywords=A1719#.VblUbPm6eCg\" target=\"_blank\">Processing of a complex lipid dataset for the NIST inter-laboratory comparison exercise for lipidomics measurements in human serum and plasma,<\/a>&rdquo; poster presented at the LIPID MAPS Annual Meeting, La Jolla, CA, May 12&ndash;13.<\/span><\/span><\/p>\n","protected":false},"excerpt":{"rendered":"<p>Lipidomics can provide vital information essential&nbsp;for understanding a wide range of disease states, particularly in cancer and diabetes. Because of the complexities of the lipidome, accessing this information using untargeted liquid chromatography and tandem mass spectrometry (LC-MS\/MS) presents a challenge.&nbsp;For the most comprehensive analyses of data, past researchers&nbsp;have been&nbsp;dependent upon complex software and large databases.<\/p>\n","protected":false},"author":13,"featured_media":5657,"comment_status":"open","ping_status":"open","sticky":false,"template":"","format":"standard","meta":{"_acf_changed":false,"_monsterinsights_skip_tracking":false,"_genesis_hide_title":false,"_genesis_hide_breadcrumbs":false,"_genesis_hide_singular_image":false,"_genesis_hide_footer_widgets":false,"_genesis_custom_body_class":"","_genesis_custom_post_class":"","_genesis_layout":"","_jetpack_newsletter_access":"","_jetpack_dont_email_post_to_subs":false,"_jetpack_newsletter_tier_id":0,"_jetpack_memberships_contains_paywalled_content":false,"_jetpack_memberships_contains_paid_content":false,"footnotes":""},"categories":[12],"tags":[482,483],"division":[],"class_list":{"0":"post-5383","1":"post","2":"type-post","3":"status-publish","4":"format-standard","5":"has-post-thumbnail","7":"category-methods","8":"tag-blood-plasma","9":"tag-lipidomics","10":"entry"},"_selected_authors":"","_selected_reviewers":"","acf":[],"yoast_head":"<!-- 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