Deepening into intracellular signaling landscape through integrative spatial proteomics and transcriptomics in a lymphoma model

© 2021 by the authors.

Detalles Bibliográficos
Autores: Landeira-Viñuela, Alicia, Díez, Paula, Juanes-Velasco, Pablo, Lécrevisse, Quentin, Orfao, Alberto, De Las Rivas, Javier, Fuentes, Manuel
Tipo de recurso: artículo
Estado:Versión publicada
Fecha de publicación:2021
País:España
Institución:Consejo Superior de Investigaciones Científicas (CSIC)
Repositorio:DIGITAL.CSIC. Repositorio Institucional del CSIC
OAI Identifier:oai:digital.csic.es:10261/261324
Acceso en línea:http://hdl.handle.net/10261/261324
Access Level:acceso abierto
Palabra clave:Affinity-based proteomics
Human proteome project
LC-MS/MS
Transcriptomics
Size-exclusion-chromatography
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spelling Deepening into intracellular signaling landscape through integrative spatial proteomics and transcriptomics in a lymphoma modelLandeira-Viñuela, AliciaDíez, PaulaJuanes-Velasco, PabloLécrevisse, QuentinOrfao, AlbertoDe Las Rivas, JavierFuentes, ManuelAffinity-based proteomicsHuman proteome projectLC-MS/MSTranscriptomicsSize-exclusion-chromatography© 2021 by the authors.Human Proteome Project (HPP) presents a systematic characterization of the protein landscape under different conditions using several complementary-omic techniques (LC-MS/MS proteomics, affinity proteomics, transcriptomics, etc.). In the present study, using a B-cell lymphoma cell line as a model, comprehensive integration of RNA-Seq transcriptomics, MS/MS, and antibody-based affinity proteomics (combined with size-exclusion chromatography) (SEC-MAP) were performed to uncover correlations that could provide insights into protein dynamics at the intracellular level. Here, 5672 unique proteins were systematically identified by MS/MS analysis and subcellular protein extraction strategies (neXtProt release 2020-21, MS/MS data are available via ProteomeXchange with identifier PXD003939). Moreover, RNA deep sequencing analysis of this lymphoma B-cell line identified 19,518 expressed genes and 5707 protein coding genes (mapped to neXtProt). Among these data sets, 162 relevant proteins (targeted by 206 antibodies) were systematically analyzed by the SEC-MAP approach, providing information about PTMs, isoforms, protein complexes, and subcellular localization. Finally, a bioinformatic pipeline has been designed and developed for orthogonal integration of these high-content proteomics and transcriptomics datasets, which might be useful for comprehensive and global characterization of intracellular protein profiles.We are financial support from the Spanish Health Institute Carlos III (ISCIII) for the grants: FIS PI14/01538, FIS PI17/01930 and CB16/12/00400.Multidisciplinary Digital Publishing InstituteInstituto de Salud Carlos IIIConsejo Superior de Investigaciones Científicas [https://ror.org/02gfc7t72]2022202220212022info:eu-repo/semantics/articlehttp://purl.org/coar/resource_type/c_6501Publisher's versioninfo:eu-repo/semantics/publishedVersionhttp://hdl.handle.net/10261/261324reponame:DIGITAL.CSIC. Repositorio Institucional del CSICinstname:Consejo Superior de Investigaciones Científicas (CSIC)Ingléshttp://dx.doi.org/10.3390/biom11121776Síinfo:eu-repo/semantics/openAccessoai:digital.csic.es:10261/2613242026-05-22T06:33:51Z
dc.title.none.fl_str_mv Deepening into intracellular signaling landscape through integrative spatial proteomics and transcriptomics in a lymphoma model
title Deepening into intracellular signaling landscape through integrative spatial proteomics and transcriptomics in a lymphoma model
spellingShingle Deepening into intracellular signaling landscape through integrative spatial proteomics and transcriptomics in a lymphoma model
Landeira-Viñuela, Alicia
Affinity-based proteomics
Human proteome project
LC-MS/MS
Transcriptomics
Size-exclusion-chromatography
title_short Deepening into intracellular signaling landscape through integrative spatial proteomics and transcriptomics in a lymphoma model
title_full Deepening into intracellular signaling landscape through integrative spatial proteomics and transcriptomics in a lymphoma model
title_fullStr Deepening into intracellular signaling landscape through integrative spatial proteomics and transcriptomics in a lymphoma model
title_full_unstemmed Deepening into intracellular signaling landscape through integrative spatial proteomics and transcriptomics in a lymphoma model
title_sort Deepening into intracellular signaling landscape through integrative spatial proteomics and transcriptomics in a lymphoma model
dc.creator.none.fl_str_mv Landeira-Viñuela, Alicia
Díez, Paula
Juanes-Velasco, Pablo
Lécrevisse, Quentin
Orfao, Alberto
De Las Rivas, Javier
Fuentes, Manuel
author Landeira-Viñuela, Alicia
author_facet Landeira-Viñuela, Alicia
Díez, Paula
Juanes-Velasco, Pablo
Lécrevisse, Quentin
Orfao, Alberto
De Las Rivas, Javier
Fuentes, Manuel
author_role author
author2 Díez, Paula
Juanes-Velasco, Pablo
Lécrevisse, Quentin
Orfao, Alberto
De Las Rivas, Javier
Fuentes, Manuel
author2_role author
author
author
author
author
author
dc.contributor.none.fl_str_mv Instituto de Salud Carlos III
Consejo Superior de Investigaciones Científicas [https://ror.org/02gfc7t72]
dc.subject.none.fl_str_mv Affinity-based proteomics
Human proteome project
LC-MS/MS
Transcriptomics
Size-exclusion-chromatography
topic Affinity-based proteomics
Human proteome project
LC-MS/MS
Transcriptomics
Size-exclusion-chromatography
description © 2021 by the authors.
publishDate 2021
dc.date.none.fl_str_mv 2021
2022
2022
2022
dc.type.none.fl_str_mv info:eu-repo/semantics/article
http://purl.org/coar/resource_type/c_6501
Publisher's version
info:eu-repo/semantics/publishedVersion
format article
status_str publishedVersion
dc.identifier.none.fl_str_mv http://hdl.handle.net/10261/261324
url http://hdl.handle.net/10261/261324
dc.language.none.fl_str_mv Inglés
language_invalid_str_mv Inglés
dc.relation.none.fl_str_mv http://dx.doi.org/10.3390/biom11121776
Sí
dc.rights.none.fl_str_mv info:eu-repo/semantics/openAccess
eu_rights_str_mv openAccess
dc.publisher.none.fl_str_mv Multidisciplinary Digital Publishing Institute
publisher.none.fl_str_mv Multidisciplinary Digital Publishing Institute
dc.source.none.fl_str_mv reponame:DIGITAL.CSIC. Repositorio Institucional del CSIC
instname:Consejo Superior de Investigaciones Científicas (CSIC)
instname_str Consejo Superior de Investigaciones Científicas (CSIC)
reponame_str DIGITAL.CSIC. Repositorio Institucional del CSIC
collection DIGITAL.CSIC. Repositorio Institucional del CSIC
repository.name.fl_str_mv
repository.mail.fl_str_mv
_version_ 1869407160163631104
score 15.811543

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