Top-Down Proteomics of Large Proteins up to 223 kDa Enabled by Serial Size Exclusion Chromatography Strategy
Mass spectrometry (MS)-based top-down proteomics is a powerful method for the comprehensive analysis of proteoforms that arise from genetic variations and post-translational modifications (PTMs). However, top-down MS analysis of high molecular weight (MW) proteins remains challenging mainly due to t...
Gespeichert in:
| Veröffentlicht in: | Analytical chemistry (Washington) 2017-05, Vol.89 (10), p.5467-5475 |
|---|---|
| Hauptverfasser: | , , , , , , , |
| Format: | Artikel |
| Sprache: | eng |
| Schlagworte: | |
| Online-Zugang: | Volltext |
| Tags: |
Tag hinzufügen
Keine Tags, Fügen Sie den ersten Tag hinzu!
|
| container_end_page | 5475 |
|---|---|
| container_issue | 10 |
| container_start_page | 5467 |
| container_title | Analytical chemistry (Washington) |
| container_volume | 89 |
| creator | Cai, Wenxuan Tucholski, Trisha Chen, Bifan Alpert, Andrew J McIlwain, Sean Kohmoto, Takushi Jin, Song Ge, Ying |
| description | Mass spectrometry (MS)-based top-down proteomics is a powerful method for the comprehensive analysis of proteoforms that arise from genetic variations and post-translational modifications (PTMs). However, top-down MS analysis of high molecular weight (MW) proteins remains challenging mainly due to the exponential decay of signal-to-noise ratio with increasing MW. Size exclusion chromatography (SEC) is a favored method for size-based separation of biomacromolecules but typically suffers from low resolution. Herein, we developed a serial size exclusion chromatography (sSEC) strategy to enable high-resolution size-based fractionation of intact proteins (10–223 kDa) from complex protein mixtures. The sSEC fractions could be further separated by reverse phase chromatography (RPC) coupled online with high-resolution MS. We have shown that two-dimensional (2D) sSEC-RPC allowed for the identification of 4044 more unique proteoforms and a 15-fold increase in the detection of proteins above 60 kDa, compared to one-dimensional (1D) RPC. Notably, effective sSEC-RPC separation of proteins significantly enhanced the detection of high MW proteins up to 223 kDa and also revealed low abundance proteoforms that are post-translationally modified. This sSEC method is MS-friendly, robust, and reproducible and, thus, can be applied to both high-efficiency protein purification and large-scale proteomics analysis of cell or tissue lysate for enhanced proteome coverage, particularly for low abundance and high MW proteoforms. |
| doi_str_mv | 10.1021/acs.analchem.7b00380 |
| format | Article |
| fullrecord | <record><control><sourceid>proquest_pubme</sourceid><recordid>TN_cdi_pubmedcentral_primary_oai_pubmedcentral_nih_gov_5511113</recordid><sourceformat>XML</sourceformat><sourcesystem>PC</sourcesystem><sourcerecordid>1888683077</sourcerecordid><originalsourceid>FETCH-LOGICAL-a543t-c6bbddc0cbfd40f43dc5c7019bf12f07fb5331e8ae7ddb7d6d50e9e1af05c3fe3</originalsourceid><addsrcrecordid>eNp9kUtvEzEURi0EoqHwDxCyxIbNhOvxPJwNEkrDQ4oEUsva8uM6cZkZD_ZMIfx6XCUtjwXeWLo-9_O1DyHPGSwZlOy1MmmpBtWZPfbLVgNwAQ_IgtUlFI0Q5UOygFwsyhbgjDxJ6RqAMWDNY3JWigqaBlYL0l2FsbgI3wf6OYYJQ-9NosHRrYo7PNb8kOg80inQsuT064Wim0HpDi3VB3qJ0auOXvqfSDc_TDcnHwa63sfQqynsohr3GZqimnB3eEoeOdUlfHbaz8mXd5ur9Ydi--n9x_XbbaHqik-FabS21oDRzlbgKm5NbVpgK-1Y6aB1uuacoVDYWqtb29gacIVMOagNd8jPyZtj7jjrHq3BIQ_QyTH6XsWDDMrLv08Gv5e7cCPrmuXFc8CrU0AM32ZMk-x9Mth1asAwJ8mEEI3g0LYZffkPeh3mmL1kagWC54-GKlPVkTIxpBTR3Q_DQN7qlFmnvNMpTzpz24s_H3LfdOcvA3AEbtt_X_y_zF-j87FA</addsrcrecordid><sourcetype>Open Access Repository</sourcetype><iscdi>true</iscdi><recordtype>article</recordtype><pqid>1908340604</pqid></control><display><type>article</type><title>Top-Down Proteomics of Large Proteins up to 223 kDa Enabled by Serial Size Exclusion Chromatography Strategy</title><source>ACS Journals</source><creator>Cai, Wenxuan ; Tucholski, Trisha ; Chen, Bifan ; Alpert, Andrew J ; McIlwain, Sean ; Kohmoto, Takushi ; Jin, Song ; Ge, Ying</creator><creatorcontrib>Cai, Wenxuan ; Tucholski, Trisha ; Chen, Bifan ; Alpert, Andrew J ; McIlwain, Sean ; Kohmoto, Takushi ; Jin, Song ; Ge, Ying</creatorcontrib><description>Mass spectrometry (MS)-based top-down proteomics is a powerful method for the comprehensive analysis of proteoforms that arise from genetic variations and post-translational modifications (PTMs). However, top-down MS analysis of high molecular weight (MW) proteins remains challenging mainly due to the exponential decay of signal-to-noise ratio with increasing MW. Size exclusion chromatography (SEC) is a favored method for size-based separation of biomacromolecules but typically suffers from low resolution. Herein, we developed a serial size exclusion chromatography (sSEC) strategy to enable high-resolution size-based fractionation of intact proteins (10–223 kDa) from complex protein mixtures. The sSEC fractions could be further separated by reverse phase chromatography (RPC) coupled online with high-resolution MS. We have shown that two-dimensional (2D) sSEC-RPC allowed for the identification of 4044 more unique proteoforms and a 15-fold increase in the detection of proteins above 60 kDa, compared to one-dimensional (1D) RPC. Notably, effective sSEC-RPC separation of proteins significantly enhanced the detection of high MW proteins up to 223 kDa and also revealed low abundance proteoforms that are post-translationally modified. This sSEC method is MS-friendly, robust, and reproducible and, thus, can be applied to both high-efficiency protein purification and large-scale proteomics analysis of cell or tissue lysate for enhanced proteome coverage, particularly for low abundance and high MW proteoforms.</description><identifier>ISSN: 0003-2700</identifier><identifier>EISSN: 1520-6882</identifier><identifier>DOI: 10.1021/acs.analchem.7b00380</identifier><identifier>PMID: 28406609</identifier><language>eng</language><publisher>United States: American Chemical Society</publisher><subject>Abundance ; Chemistry ; Chromatography ; Coupling (molecular) ; Decay ; Efficiency ; Fractionation ; Genetic diversity ; High resolution ; Internet ; Mass spectrometry ; Mass spectroscopy ; Molecular weight ; Noise prediction ; Post-translation ; Protein purification ; Proteins ; Proteomics ; Purification ; Robustness ; Separation ; Signal to noise ratio ; Size exclusion chromatography ; Strategy ; Translation</subject><ispartof>Analytical chemistry (Washington), 2017-05, Vol.89 (10), p.5467-5475</ispartof><rights>Copyright © 2017 American Chemical Society</rights><rights>Copyright American Chemical Society May 16, 2017</rights><lds50>peer_reviewed</lds50><oa>free_for_read</oa><woscitedreferencessubscribed>false</woscitedreferencessubscribed><citedby>FETCH-LOGICAL-a543t-c6bbddc0cbfd40f43dc5c7019bf12f07fb5331e8ae7ddb7d6d50e9e1af05c3fe3</citedby><cites>FETCH-LOGICAL-a543t-c6bbddc0cbfd40f43dc5c7019bf12f07fb5331e8ae7ddb7d6d50e9e1af05c3fe3</cites><orcidid>0000-0003-1407-6922 ; 0000-0001-8693-7010 ; 0000-0001-5211-6812</orcidid></display><links><openurl>$$Topenurl_article</openurl><openurlfulltext>$$Topenurlfull_article</openurlfulltext><thumbnail>$$Tsyndetics_thumb_exl</thumbnail><linktopdf>$$Uhttps://pubs.acs.org/doi/pdf/10.1021/acs.analchem.7b00380$$EPDF$$P50$$Gacs$$H</linktopdf><linktohtml>$$Uhttps://pubs.acs.org/doi/10.1021/acs.analchem.7b00380$$EHTML$$P50$$Gacs$$H</linktohtml><link.rule.ids>230,314,780,784,885,2763,27075,27923,27924,56737,56787</link.rule.ids><backlink>$$Uhttps://www.ncbi.nlm.nih.gov/pubmed/28406609$$D View this record in MEDLINE/PubMed$$Hfree_for_read</backlink></links><search><creatorcontrib>Cai, Wenxuan</creatorcontrib><creatorcontrib>Tucholski, Trisha</creatorcontrib><creatorcontrib>Chen, Bifan</creatorcontrib><creatorcontrib>Alpert, Andrew J</creatorcontrib><creatorcontrib>McIlwain, Sean</creatorcontrib><creatorcontrib>Kohmoto, Takushi</creatorcontrib><creatorcontrib>Jin, Song</creatorcontrib><creatorcontrib>Ge, Ying</creatorcontrib><title>Top-Down Proteomics of Large Proteins up to 223 kDa Enabled by Serial Size Exclusion Chromatography Strategy</title><title>Analytical chemistry (Washington)</title><addtitle>Anal. Chem</addtitle><description>Mass spectrometry (MS)-based top-down proteomics is a powerful method for the comprehensive analysis of proteoforms that arise from genetic variations and post-translational modifications (PTMs). However, top-down MS analysis of high molecular weight (MW) proteins remains challenging mainly due to the exponential decay of signal-to-noise ratio with increasing MW. Size exclusion chromatography (SEC) is a favored method for size-based separation of biomacromolecules but typically suffers from low resolution. Herein, we developed a serial size exclusion chromatography (sSEC) strategy to enable high-resolution size-based fractionation of intact proteins (10–223 kDa) from complex protein mixtures. The sSEC fractions could be further separated by reverse phase chromatography (RPC) coupled online with high-resolution MS. We have shown that two-dimensional (2D) sSEC-RPC allowed for the identification of 4044 more unique proteoforms and a 15-fold increase in the detection of proteins above 60 kDa, compared to one-dimensional (1D) RPC. Notably, effective sSEC-RPC separation of proteins significantly enhanced the detection of high MW proteins up to 223 kDa and also revealed low abundance proteoforms that are post-translationally modified. This sSEC method is MS-friendly, robust, and reproducible and, thus, can be applied to both high-efficiency protein purification and large-scale proteomics analysis of cell or tissue lysate for enhanced proteome coverage, particularly for low abundance and high MW proteoforms.</description><subject>Abundance</subject><subject>Chemistry</subject><subject>Chromatography</subject><subject>Coupling (molecular)</subject><subject>Decay</subject><subject>Efficiency</subject><subject>Fractionation</subject><subject>Genetic diversity</subject><subject>High resolution</subject><subject>Internet</subject><subject>Mass spectrometry</subject><subject>Mass spectroscopy</subject><subject>Molecular weight</subject><subject>Noise prediction</subject><subject>Post-translation</subject><subject>Protein purification</subject><subject>Proteins</subject><subject>Proteomics</subject><subject>Purification</subject><subject>Robustness</subject><subject>Separation</subject><subject>Signal to noise ratio</subject><subject>Size exclusion chromatography</subject><subject>Strategy</subject><subject>Translation</subject><issn>0003-2700</issn><issn>1520-6882</issn><fulltext>true</fulltext><rsrctype>article</rsrctype><creationdate>2017</creationdate><recordtype>article</recordtype><recordid>eNp9kUtvEzEURi0EoqHwDxCyxIbNhOvxPJwNEkrDQ4oEUsva8uM6cZkZD_ZMIfx6XCUtjwXeWLo-9_O1DyHPGSwZlOy1MmmpBtWZPfbLVgNwAQ_IgtUlFI0Q5UOygFwsyhbgjDxJ6RqAMWDNY3JWigqaBlYL0l2FsbgI3wf6OYYJQ-9NosHRrYo7PNb8kOg80inQsuT064Wim0HpDi3VB3qJ0auOXvqfSDc_TDcnHwa63sfQqynsohr3GZqimnB3eEoeOdUlfHbaz8mXd5ur9Ydi--n9x_XbbaHqik-FabS21oDRzlbgKm5NbVpgK-1Y6aB1uuacoVDYWqtb29gacIVMOagNd8jPyZtj7jjrHq3BIQ_QyTH6XsWDDMrLv08Gv5e7cCPrmuXFc8CrU0AM32ZMk-x9Mth1asAwJ8mEEI3g0LYZffkPeh3mmL1kagWC54-GKlPVkTIxpBTR3Q_DQN7qlFmnvNMpTzpz24s_H3LfdOcvA3AEbtt_X_y_zF-j87FA</recordid><startdate>20170516</startdate><enddate>20170516</enddate><creator>Cai, Wenxuan</creator><creator>Tucholski, Trisha</creator><creator>Chen, Bifan</creator><creator>Alpert, Andrew J</creator><creator>McIlwain, Sean</creator><creator>Kohmoto, Takushi</creator><creator>Jin, Song</creator><creator>Ge, Ying</creator><general>American Chemical Society</general><scope>NPM</scope><scope>AAYXX</scope><scope>CITATION</scope><scope>7QF</scope><scope>7QO</scope><scope>7QQ</scope><scope>7SC</scope><scope>7SE</scope><scope>7SP</scope><scope>7SR</scope><scope>7TA</scope><scope>7TB</scope><scope>7TM</scope><scope>7U5</scope><scope>7U7</scope><scope>7U9</scope><scope>8BQ</scope><scope>8FD</scope><scope>C1K</scope><scope>F28</scope><scope>FR3</scope><scope>H8D</scope><scope>H8G</scope><scope>H94</scope><scope>JG9</scope><scope>JQ2</scope><scope>KR7</scope><scope>L7M</scope><scope>L~C</scope><scope>L~D</scope><scope>P64</scope><scope>7X8</scope><scope>5PM</scope><orcidid>https://orcid.org/0000-0003-1407-6922</orcidid><orcidid>https://orcid.org/0000-0001-8693-7010</orcidid><orcidid>https://orcid.org/0000-0001-5211-6812</orcidid></search><sort><creationdate>20170516</creationdate><title>Top-Down Proteomics of Large Proteins up to 223 kDa Enabled by Serial Size Exclusion Chromatography Strategy</title><author>Cai, Wenxuan ; Tucholski, Trisha ; Chen, Bifan ; Alpert, Andrew J ; McIlwain, Sean ; Kohmoto, Takushi ; Jin, Song ; Ge, Ying</author></sort><facets><frbrtype>5</frbrtype><frbrgroupid>cdi_FETCH-LOGICAL-a543t-c6bbddc0cbfd40f43dc5c7019bf12f07fb5331e8ae7ddb7d6d50e9e1af05c3fe3</frbrgroupid><rsrctype>articles</rsrctype><prefilter>articles</prefilter><language>eng</language><creationdate>2017</creationdate><topic>Abundance</topic><topic>Chemistry</topic><topic>Chromatography</topic><topic>Coupling (molecular)</topic><topic>Decay</topic><topic>Efficiency</topic><topic>Fractionation</topic><topic>Genetic diversity</topic><topic>High resolution</topic><topic>Internet</topic><topic>Mass spectrometry</topic><topic>Mass spectroscopy</topic><topic>Molecular weight</topic><topic>Noise prediction</topic><topic>Post-translation</topic><topic>Protein purification</topic><topic>Proteins</topic><topic>Proteomics</topic><topic>Purification</topic><topic>Robustness</topic><topic>Separation</topic><topic>Signal to noise ratio</topic><topic>Size exclusion chromatography</topic><topic>Strategy</topic><topic>Translation</topic><toplevel>peer_reviewed</toplevel><toplevel>online_resources</toplevel><creatorcontrib>Cai, Wenxuan</creatorcontrib><creatorcontrib>Tucholski, Trisha</creatorcontrib><creatorcontrib>Chen, Bifan</creatorcontrib><creatorcontrib>Alpert, Andrew J</creatorcontrib><creatorcontrib>McIlwain, Sean</creatorcontrib><creatorcontrib>Kohmoto, Takushi</creatorcontrib><creatorcontrib>Jin, Song</creatorcontrib><creatorcontrib>Ge, Ying</creatorcontrib><collection>PubMed</collection><collection>CrossRef</collection><collection>Aluminium Industry Abstracts</collection><collection>Biotechnology Research Abstracts</collection><collection>Ceramic Abstracts</collection><collection>Computer and Information Systems Abstracts</collection><collection>Corrosion Abstracts</collection><collection>Electronics & Communications Abstracts</collection><collection>Engineered Materials Abstracts</collection><collection>Materials Business File</collection><collection>Mechanical & Transportation Engineering Abstracts</collection><collection>Nucleic Acids Abstracts</collection><collection>Solid State and Superconductivity Abstracts</collection><collection>Toxicology Abstracts</collection><collection>Virology and AIDS Abstracts</collection><collection>METADEX</collection><collection>Technology Research Database</collection><collection>Environmental Sciences and Pollution Management</collection><collection>ANTE: Abstracts in New Technology & Engineering</collection><collection>Engineering Research Database</collection><collection>Aerospace Database</collection><collection>Copper Technical Reference Library</collection><collection>AIDS and Cancer Research Abstracts</collection><collection>Materials Research Database</collection><collection>ProQuest Computer Science Collection</collection><collection>Civil Engineering Abstracts</collection><collection>Advanced Technologies Database with Aerospace</collection><collection>Computer and Information Systems Abstracts Academic</collection><collection>Computer and Information Systems Abstracts Professional</collection><collection>Biotechnology and BioEngineering Abstracts</collection><collection>MEDLINE - Academic</collection><collection>PubMed Central (Full Participant titles)</collection><jtitle>Analytical chemistry (Washington)</jtitle></facets><delivery><delcategory>Remote Search Resource</delcategory><fulltext>fulltext</fulltext></delivery><addata><au>Cai, Wenxuan</au><au>Tucholski, Trisha</au><au>Chen, Bifan</au><au>Alpert, Andrew J</au><au>McIlwain, Sean</au><au>Kohmoto, Takushi</au><au>Jin, Song</au><au>Ge, Ying</au><format>journal</format><genre>article</genre><ristype>JOUR</ristype><atitle>Top-Down Proteomics of Large Proteins up to 223 kDa Enabled by Serial Size Exclusion Chromatography Strategy</atitle><jtitle>Analytical chemistry (Washington)</jtitle><addtitle>Anal. Chem</addtitle><date>2017-05-16</date><risdate>2017</risdate><volume>89</volume><issue>10</issue><spage>5467</spage><epage>5475</epage><pages>5467-5475</pages><issn>0003-2700</issn><eissn>1520-6882</eissn><abstract>Mass spectrometry (MS)-based top-down proteomics is a powerful method for the comprehensive analysis of proteoforms that arise from genetic variations and post-translational modifications (PTMs). However, top-down MS analysis of high molecular weight (MW) proteins remains challenging mainly due to the exponential decay of signal-to-noise ratio with increasing MW. Size exclusion chromatography (SEC) is a favored method for size-based separation of biomacromolecules but typically suffers from low resolution. Herein, we developed a serial size exclusion chromatography (sSEC) strategy to enable high-resolution size-based fractionation of intact proteins (10–223 kDa) from complex protein mixtures. The sSEC fractions could be further separated by reverse phase chromatography (RPC) coupled online with high-resolution MS. We have shown that two-dimensional (2D) sSEC-RPC allowed for the identification of 4044 more unique proteoforms and a 15-fold increase in the detection of proteins above 60 kDa, compared to one-dimensional (1D) RPC. Notably, effective sSEC-RPC separation of proteins significantly enhanced the detection of high MW proteins up to 223 kDa and also revealed low abundance proteoforms that are post-translationally modified. This sSEC method is MS-friendly, robust, and reproducible and, thus, can be applied to both high-efficiency protein purification and large-scale proteomics analysis of cell or tissue lysate for enhanced proteome coverage, particularly for low abundance and high MW proteoforms.</abstract><cop>United States</cop><pub>American Chemical Society</pub><pmid>28406609</pmid><doi>10.1021/acs.analchem.7b00380</doi><tpages>9</tpages><orcidid>https://orcid.org/0000-0003-1407-6922</orcidid><orcidid>https://orcid.org/0000-0001-8693-7010</orcidid><orcidid>https://orcid.org/0000-0001-5211-6812</orcidid><oa>free_for_read</oa></addata></record> |
| fulltext | fulltext |
| identifier | ISSN: 0003-2700 |
| ispartof | Analytical chemistry (Washington), 2017-05, Vol.89 (10), p.5467-5475 |
| issn | 0003-2700 1520-6882 |
| language | eng |
| recordid | cdi_pubmedcentral_primary_oai_pubmedcentral_nih_gov_5511113 |
| source | ACS Journals |
| subjects | Abundance Chemistry Chromatography Coupling (molecular) Decay Efficiency Fractionation Genetic diversity High resolution Internet Mass spectrometry Mass spectroscopy Molecular weight Noise prediction Post-translation Protein purification Proteins Proteomics Purification Robustness Separation Signal to noise ratio Size exclusion chromatography Strategy Translation |
| title | Top-Down Proteomics of Large Proteins up to 223 kDa Enabled by Serial Size Exclusion Chromatography Strategy |
| url | https://sfx.bib-bvb.de/sfx_tum?ctx_ver=Z39.88-2004&ctx_enc=info:ofi/enc:UTF-8&ctx_tim=2025-01-10T15%3A03%3A17IST&url_ver=Z39.88-2004&url_ctx_fmt=infofi/fmt:kev:mtx:ctx&rfr_id=info:sid/primo.exlibrisgroup.com:primo3-Article-proquest_pubme&rft_val_fmt=info:ofi/fmt:kev:mtx:journal&rft.genre=article&rft.atitle=Top-Down%20Proteomics%20of%20Large%20Proteins%20up%20to%20223%20kDa%20Enabled%20by%20Serial%20Size%20Exclusion%20Chromatography%20Strategy&rft.jtitle=Analytical%20chemistry%20(Washington)&rft.au=Cai,%20Wenxuan&rft.date=2017-05-16&rft.volume=89&rft.issue=10&rft.spage=5467&rft.epage=5475&rft.pages=5467-5475&rft.issn=0003-2700&rft.eissn=1520-6882&rft_id=info:doi/10.1021/acs.analchem.7b00380&rft_dat=%3Cproquest_pubme%3E1888683077%3C/proquest_pubme%3E%3Curl%3E%3C/url%3E&disable_directlink=true&sfx.directlink=off&sfx.report_link=0&rft_id=info:oai/&rft_pqid=1908340604&rft_id=info:pmid/28406609&rfr_iscdi=true |