{"id":7657,"date":"2016-02-19T07:01:12","date_gmt":"2016-02-19T12:01:12","guid":{"rendered":"http:\/\/admin.acceleratingscience.com\/?p=7657"},"modified":"2016-04-29T16:52:39","modified_gmt":"2016-04-29T16:52:39","slug":"hyperlopit-for-the-mouse-stem-cell-proteome","status":"publish","type":"post","link":"https:\/\/www.thermofisher.com\/blog\/proteomics\/hyperlopit-for-the-mouse-stem-cell-proteome\/","title":{"rendered":"HyperLOPIT for the Mouse Stem Cell Proteome"},"content":{"rendered":"<p><span class=\"TextRun SCX119305153\"><span class=\"NormalTextRun SCX119305153\"><img loading=\"lazy\" decoding=\"async\" src=\"http:\/\/admin.acceleratingscience.com\/proteomics\/wp-content\/uploads\/sites\/2\/2016\/02\/mouse_embryonic_stem_cells1.jpg\" style=\"float: left;margin: 10px\" alt=\"Mouse embryonic stem cells. Image: Public Domain, https:\/\/commons.wikimedia.org\/w\/index.php?curid=456457\" width=\"350\" height=\"296\" \/>When researchers want to know more about&nbsp;<\/span><\/span><span class=\"TextRun SCX119305153\"><span class=\"NormalTextRun SCX119305153\">pluripotency<\/span><\/span><span class=\"TextRun SCX119305153\"><span class=\"NormalTextRun SCX119305153\">&nbsp;and the push towards stem cell differentiation, they need to know more than just which genes <span class=\"thread\">are firing up<\/span>.&nbsp;<\/span><\/span><span class=\"TextRun SCX119305153\"><span class=\"SpellingError SCX119305153\">Christoforou<\/span><\/span><span class=\"TextRun SCX119305153\"><span class=\"NormalTextRun SCX119305153\">&nbsp;et al. (2016) suggest that they also need to know what proteins are on the go, and furthermore, where they are&nbsp;<\/span><\/span><span class=\"TextRun SCX119305153\"><span class=\"NormalTextRun SCX119305153\">spatially<\/span><\/span><span class=\"TextRun SCX119305153\"><span class=\"NormalTextRun SCX119305153\">&nbsp;<\/span><\/span><span class=\"TextRun SCX119305153\"><span class=\"NormalTextRun SCX119305153\">located<\/span><\/span><span class=\"TextRun SCX119305153\"><span class=\"NormalTextRun SCX119305153\">&nbsp;within the cell.<\/span><\/span><sup><span class=\"TextRun SCX119305153\"><span class=\"NormalTextRun SCX119305153\">1<\/span><\/span><\/sup><span class=\"TextRun SCX119305153\"><span class=\"NormalTextRun SCX119305153\">&nbsp;<\/span><\/span><span class=\"EOP SCX119305153\">&nbsp;<\/span><\/p>\n<p><span class=\"TextRun SCX119305153\"><span class=\"NormalTextRun SCX119305153\">The researchers propose an upgrade of a technique used to determine spatial proteomics:&nbsp;<\/span><\/span><span class=\"TextRun SCX119305153\"><span class=\"SpellingError SCX119305153\">hyperLOPIT<\/span><\/span><span class=\"TextRun SCX119305153\"><span class=\"NormalTextRun SCX119305153\">, which stands for a high-throughput&nbsp;<\/span><\/span><span class=\"TextRun SCX119305153\"><span class=\"NormalTextRun SCX119305153\">mass<\/span><\/span><span class=\"TextRun SCX119305153\"><span class=\"NormalTextRun SCX119305153\">&nbsp;spectrometric analysis of the products of localization of organelle proteins by isotope tagging (LOPIT). Using a combination of density ultracentrifugation, sample multiplexing and a liquid chromatography&ndash;mass spectrometry (LC-MS) proteomics approach, the new workflow can identify proteins and pinpoint location with organelles or sub-organelle fractions.<\/span><\/span><span class=\"EOP SCX119305153\">&nbsp;<\/span><\/p>\n<p><span class=\"TextRun SCX119305153\"><span class=\"NormalTextRun SCX119305153\">The research team used the pluripotent mouse embryonic stem cell line E14TG2A for the experiments. First<\/span><\/span><span class=\"TextRun SCX119305153\"><span class=\"NormalTextRun SCX119305153\">,<\/span><\/span><span class=\"TextRun SCX119305153\"><span class=\"NormalTextRun SCX119305153\">&nbsp;they lysed the cells using a detergent-free system that would preserve organelle structure with minimal disruption to contents. They then subjected the lysate to extensive fractionation, using density gradient ultracentrifugation.&nbsp;<\/span><\/span><span class=\"TextRun SCX119305153\"><span class=\"NormalTextRun SCX119305153\">Since each membrane and organelle group within a cell displays distinct enrichment patterns, t<\/span><\/span><span class=\"TextRun SCX119305153\"><span class=\"NormalTextRun SCX119305153\">his step separated&nbsp;<\/span><\/span><span class=\"TextRun SCX119305153\"><span class=\"NormalTextRun SCX119305153\">them<\/span><\/span><span class=\"TextRun SCX119305153\"><span class=\"NormalTextRun SCX119305153\">&nbsp;into&nbsp;<\/span><\/span><span class=\"TextRun SCX119305153\"><span class=\"NormalTextRun SCX119305153\">fractions that could be prepared individually for subsequent MS evaluation.<\/span><\/span><span class=\"EOP SCX119305153\">&nbsp;<\/span><\/p>\n<p><span class=\"TextRun SCX119305153\"><span class=\"NormalTextRun SCX119305153\">Following fractionation, the researchers chose 10 fractions for MS analysis. First, they digested the fractionated lysates using trypsin and then labeled the proteolytic digests using isobaric <\/span><\/span><a href=\"https:\/\/www.thermofisher.com\/ca\/en\/home\/life-science\/protein-biology\/protein-mass-spectrometry-analysis\/protein-quantitation-mass-spectrometry\/tandem-mass-tag-systems.html\" target=\"_blank\"><span class=\"thread\"><span class=\"TextRun SCX119305153\"><span class=\"thread\">tandem&nbsp;<\/span><\/span><span class=\"TextRun SCX119305153\"><span class=\"NormalTextRun SCX119305153\">mass<\/span><\/span><span class=\"TextRun SCX119305153\"><span class=\"NormalTextRun SCX119305153\">&nbsp;tagging (TMT)<\/span><\/span><\/span><\/a><span class=\"TextRun SCX119305153\"><span class=\"NormalTextRun SCX119305153\"> techniques (<\/span><\/span><span class=\"TextRun SCX119305153\"><span class=\"SpellingError SCX119305153\">Thermo<\/span><\/span><span class=\"TextRun SCX119305153\"><span class=\"NormalTextRun SCX119305153\">&nbsp;Scientific). Using the m<\/span><\/span><span class=\"TextRun SCX119305153\"><span class=\"NormalTextRun SCX119305153\">ost recent<\/span><\/span><span class=\"TextRun SCX119305153\"><span class=\"NormalTextRun SCX119305153\">&nbsp;TMT technology available, this meant that the digests&nbsp;<\/span><\/span><span class=\"TextRun SCX119305153\"><span class=\"NormalTextRun SCX119305153\">could be multiplexed<\/span><\/span><span class=\"TextRun SCX119305153\"><span class=\"NormalTextRun SCX119305153\">&nbsp;to a 10-plex analytical preparation, thus maximizing data forthcoming from the cell studies.<\/span><\/span><span class=\"EOP SCX119305153\">&nbsp;<\/span><\/p>\n<p><span class=\"TextRun SCX119305153\"><span class=\"SpellingError SCX119305153\">Christoforou<\/span><\/span><span class=\"TextRun SCX119305153\"><span class=\"NormalTextRun SCX119305153\">&nbsp;et al. initially focused on optimizing&nbsp;<\/span><\/span><span class=\"TextRun SCX119305153\"><span class=\"NormalTextRun SCX119305153\">the LC-MS analytical workflow, using a&nbsp;<\/span><\/span><a href=\"http:\/\/www.thermoscientific.com\/en\/product\/easy-nlc-1000-liquid-chromatograph.html\" target=\"_blank\"><span class=\"TextRun SCX119305153\"><span class=\"SpellingError SCX119305153\">Proxeon<\/span><\/span><span class=\"TextRun SCX119305153\"><span class=\"NormalTextRun SCX119305153\">&nbsp;Easy-<\/span><\/span><span class=\"TextRun SCX119305153\"><span class=\"SpellingError SCX119305153\">nLC<\/span><\/span><\/a><span class=\"TextRun SCX119305153\"><span class=\"NormalTextRun SCX119305153\"><a href=\"http:\/\/www.thermoscientific.com\/en\/product\/easy-nlc-1000-liquid-chromatograph.html\" target=\"_blank\">&nbsp;1000<\/a> system in combination with an&nbsp;<\/span><\/span><a href=\"http:\/\/www.thermoscientific.com\/en\/product\/orbitrap-fusion-tribrid-mass-spectrometer.html\" target=\"_blank\"><span class=\"TextRun SCX119305153\"><span class=\"SpellingError SCX119305153\">Orbitrap<\/span><\/span><span class=\"TextRun SCX119305153\"><span class=\"NormalTextRun SCX119305153\">&nbsp;Fusion&nbsp;<\/span><\/span><span class=\"TextRun SCX119305153\"><span class=\"SpellingError SCX119305153\">Tribrid<\/span><\/span><\/a><span class=\"TextRun SCX119305153\"><span class=\"NormalTextRun SCX119305153\">&nbsp;mass spectrometer (both&nbsp;<\/span><\/span><span class=\"TextRun SCX119305153\"><span class=\"SpellingError SCX119305153\">Thermo<\/span><\/span><span class=\"TextRun SCX119305153\"><span class=\"NormalTextRun SCX119305153\">&nbsp;Scientific). In order to collect as many peptide identities as possible, the team looked to synchronous precursor selection (SPS) with MS3 instrument operation. They found that using an SPS approach<\/span><\/span><span class=\"TextRun SCX119305153\"><span class=\"NormalTextRun SCX119305153\">&nbsp;<\/span><\/span><span class=\"TextRun SCX119305153\"><span class=\"NormalTextRun SCX119305153\">balance<\/span><\/span><span class=\"TextRun SCX119305153\"><span class=\"NormalTextRun SCX119305153\">d<\/span><\/span><span class=\"TextRun SCX119305153\"><span class=\"NormalTextRun SCX119305153\">&nbsp;the gains in quantitation brought by MS3 operation with the sensitivity required for peptide identification.<\/span><\/span><span class=\"TextRun SCX119305153\"><span class=\"NormalTextRun SCX119305153\">&nbsp;Furthermore, by increasing the frequency notches in the isolation waveforms, they could enhance signals from TMT reporter ions and thus improve organelle resolution in addition to boosting the accuracy of the quantitation data gathered. Although the number of peptide spectral matches&nbsp;<\/span><\/span><span class=\"TextRun SCX119305153\"><span class=\"NormalTextRun SCX119305153\">decreased from 137,912 to 6<\/span><\/span><span class=\"TextRun SCX119305153\"><span class=\"NormalTextRun SCX119305153\">1,090<\/span><\/span><span class=\"TextRun SCX119305153\"><span class=\"NormalTextRun SCX119305153\">&nbsp;with SPS-MS3, the team considered that the approach brought benefits over MS2 data acquisition, since they achieved an acceptable number of quantifiable protein groups (7,114 for MS2 vs. 5,489 for SPS-MS3).<\/span><\/span><span class=\"EOP SCX119305153\">&nbsp;<\/span><\/p>\n<p><span class=\"TextRun SCX119305153\"><span class=\"NormalTextRun SCX119305153\">Once the team optimized the workflow and r<\/span><\/span><span class=\"TextRun SCX119305153\"><span class=\"NormalTextRun SCX119305153\">a<\/span><\/span><span class=\"TextRun SCX119305153\"><span class=\"NormalTextRun SCX119305153\">n the experimental replicates,<\/span><\/span><span class=\"TextRun SCX119305153\"><span class=\"NormalTextRun SCX119305153\">&nbsp;they analyzed the data&nbsp;<\/span><\/span><span class=\"TextRun SCX119305153\"><span class=\"NormalTextRun SCX119305153\">u<\/span><\/span><span class=\"TextRun SCX119305153\"><span class=\"NormalTextRun SCX119305153\">s<\/span><\/span><span class=\"TextRun SCX119305153\"><span class=\"NormalTextRun SCX119305153\">ing&nbsp;<span class=\"thread\"><a href=\"http:\/\/www.thermoscientific.com\/en\/product\/proteome-discoverer-software.html\" target=\"_blank\">Proteome Discover<\/a>&nbsp;software&nbsp;<\/span>v1.4 for analysis (<\/span><\/span><span class=\"TextRun SCX119305153\"><span class=\"SpellingError SCX119305153\">Thermo<\/span><\/span><span class=\"TextRun SCX119305153\"><span class=\"NormalTextRun SCX119305153\">&nbsp;Scientific). They explored&nbsp;<\/span><\/span><span class=\"TextRun SCX119305153\"><span class=\"NormalTextRun SCX119305153\">manual localization of protein identities against a constructed software algorithm for determining spatial identity. From the spectral&nbsp;<\/span><\/span><span class=\"TextRun SCX119305153\"><span class=\"NormalTextRun SCX119305153\">data<\/span><\/span><span class=\"TextRun SCX119305153\"><span class=\"NormalTextRun SCX119305153\">&nbsp;they found that fewer than 5% of proteins were assigned to contradictory locations within the cell. Overall, the workflow and subsequent analysis located 2,855 proteins with 14 organelles and sub-organelles, giving new spatial data for around 350 proteins. This represented more than 50% of the proteins identified during the SPS-MS3 run. Of the remainder,&nbsp;<\/span><\/span><span class=\"TextRun SCX119305153\"><span class=\"SpellingError SCX119305153\">Christoforou<\/span><\/span><span class=\"TextRun SCX119305153\"><span class=\"NormalTextRun SCX119305153\">&nbsp;et al. found that these located to cytoskeletal elements or multiple compartments, or were in transit<\/span><\/span><span class=\"TextRun SCX119305153\"><span class=\"NormalTextRun SCX119305153\">.<\/span><\/span><span class=\"EOP SCX119305153\">&nbsp;<\/span><\/p>\n<p><span class=\"TextRun SCX119305153\"><span class=\"NormalTextRun SCX119305153\">In conclu<\/span><\/span><span class=\"TextRun SCX119305153\"><span class=\"NormalTextRun SCX119305153\">sion<\/span><\/span><span class=\"TextRun SCX119305153\"><span class=\"NormalTextRun SCX119305153\">, the authors believe strongly that the workflow and methodology presented are valuable tools for investigating organelle structure, protein complexes and functional networks within cells. Furthermore, they suggest that&nbsp;<\/span><\/span><span class=\"TextRun SCX119305153\"><span class=\"SpellingError SCX119305153\">hyperLOPIT<\/span><\/span><span class=\"TextRun SCX119305153\"><span class=\"NormalTextRun SCX119305153\">&nbsp;is also useful for determining protein isoform localization and looking at other post-transcriptional events important as regulators of differentiation in pluripotent stem cells.<\/span><\/span><span class=\"EOP SCX119305153\">&nbsp;<\/span><\/p>\n<p><span class=\"TextRun SCX119305153\"><span class=\"NormalTextRun SCX119305153\"><\/span><\/span><span class=\"EOP SCX119305153\">&nbsp;<\/span><\/p>\n<p><span class=\"TextRun SCX119305153\"><span class=\"NormalTextRun SCX119305153\">The software behind&nbsp;<\/span><\/span><span class=\"TextRun SCX119305153\"><span class=\"SpellingError SCX119305153\">hyperLOPIT<\/span><\/span><span class=\"TextRun SCX119305153\"><span class=\"NormalTextRun SCX119305153\">&nbsp;data analysis is available as <a href=\"http:\/\/bioconductor.org\/packages\/devel\/bioc\/html\/pRolocGUI.html\" target=\"_blank\"><span class=\"thread\">an <\/span>Open Source resource<\/a> for visualizing and annotating spatial proteomics<\/span><\/span><span class=\"TextRun SCX119305153\"><span class=\"NormalTextRun SCX119305153\">&nbsp;<\/span><\/span><span class=\"TextRun SCX119305153\"><span class=\"NormalTextRun SCX119305153\">data.<\/span><\/span><span class=\"TextRun SCX119305153\"><span class=\"NormalTextRun SCX119305153\">&nbsp;<\/span><\/span><span class=\"TextRun SCX119305153\"><span class=\"NormalTextRun SCX119305153\">&nbsp;<\/span><\/span><\/p>\n<p><span class=\"TextRun SCX119305153\"><span class=\"NormalTextRun SCX119305153\"><\/span><\/span><span class=\"EOP SCX119305153\">&nbsp;<\/span><\/p>\n<p><strong><span class=\"TextRun SCX119305153\"><span class=\"NormalTextRun SCX119305153\">Reference<\/span><\/span><\/strong><span class=\"EOP SCX119305153\">&nbsp;<\/span><\/p>\n<p><span class=\"TextRun SCX119305153\"><span class=\"NormalTextRun SCX119305153\">1. <span class=\"SpellingError SCX119305153\">Christoforou<\/span><span class=\"NormalTextRun SCX119305153\">, A., et al. (2016) &#8220;<\/span><a href=\"http:\/\/www.ncbi.nlm.nih.gov\/pubmed\/26754106\" target=\"_blank\">A draft map of the mouse pluripotent stem cell&nbsp;spatial proteome<\/a>,<\/span><\/span><span class=\"TextRun SCX119305153\"><span class=\"NormalTextRun SCX119305153\">&#8221;&nbsp;<\/span><\/span><span class=\"TextRun SCX119305153\"><span class=\"NormalTextRun SCX119305153\">Nature Communications, 7(8992),&nbsp;<\/span><\/span><span class=\"TextRun SCX119305153\"><span class=\"NormalTextRun SCX119305153\">doi: 10.1038\/ncomms9992.<\/span><\/span><span class=\"EOP SCX119305153\">&nbsp;<\/span><\/p>\n","protected":false},"excerpt":{"rendered":"<p>When researchers want to know more about&nbsp;pluripotency&nbsp;and the push towards stem cell differentiation, they need to know more than just which genes are firing up.&nbsp;Christoforou&nbsp;et al. (2016) suggest that they also need to know what proteins are on the go, and furthermore, where they are&nbsp;spatially&nbsp;located&nbsp;within the cell.1&nbsp;&nbsp; The researchers propose an upgrade of a technique<\/p>\n","protected":false},"author":21,"featured_media":7733,"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":[824,825,598],"division":[],"class_list":{"0":"post-7657","1":"post","2":"type-post","3":"status-publish","4":"format-standard","5":"has-post-thumbnail","7":"category-methods","8":"tag-hyperlopit","9":"tag-spatial-proteome","10":"tag-stem-cells","11":"entry"},"_selected_authors":"","_selected_reviewers":"","acf":[],"yoast_head":"<!-- This site is optimized with the Yoast SEO Premium plugin v27.8 (Yoast SEO v27.8) - https:\/\/yoast.com\/product\/yoast-seo-premium-wordpress\/ -->\n<title>HyperLOPIT for the Mouse Stemcell Proteome<\/title>\n<meta name=\"description\" content=\"Using HyperLOPIT for spatial proteomics determines a draft mouse stem cell proteome that shows the regulation of differentiation from pluripotency.\" \/>\n<meta name=\"robots\" content=\"index, follow, max-snippet:-1, max-image-preview:large, max-video-preview:-1\" \/>\n<link rel=\"canonical\" href=\"https:\/\/www.thermofisher.com\/blog\/proteomics\/hyperlopit-for-the-mouse-stem-cell-proteome\/\" \/>\n<meta property=\"og:locale\" content=\"en_US\" \/>\n<meta property=\"og:type\" content=\"article\" \/>\n<meta property=\"og:title\" content=\"HyperLOPIT for the Mouse Stem Cell Proteome\" \/>\n<meta property=\"og:description\" content=\"Using HyperLOPIT for spatial proteomics determines a draft mouse stem cell proteome that shows the regulation of differentiation from pluripotency.\" \/>\n<meta property=\"og:url\" content=\"https:\/\/www.thermofisher.com\/blog\/proteomics\/hyperlopit-for-the-mouse-stem-cell-proteome\/\" \/>\n<meta property=\"og:site_name\" content=\"Accelerating Proteomics\" \/>\n<meta property=\"article:publisher\" content=\"https:\/\/www.facebook.com\/thermofisher\" \/>\n<meta property=\"article:published_time\" content=\"2016-02-19T12:01:12+00:00\" \/>\n<meta property=\"article:modified_time\" content=\"2016-04-29T16:52:39+00:00\" \/>\n<meta property=\"og:image\" content=\"https:\/\/admin.acceleratingscience.com\/proteomics\/wp-content\/uploads\/sites\/2\/2016\/02\/mouse_embryonic_stem_cells1.jpg\" \/>\n\t<meta property=\"og:image:width\" content=\"600\" \/>\n\t<meta property=\"og:image:height\" content=\"507\" \/>\n\t<meta property=\"og:image:type\" content=\"image\/jpeg\" \/>\n<meta name=\"author\" content=\"Amanda Maxwell\" \/>\n<meta name=\"twitter:card\" content=\"summary_large_image\" \/>\n<meta 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