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Imbert A, Rompais M, Selloum M, et al. ProMetIS, deep phenotyping of mouse models by combined proteomics and metabolomics analysis. Sci Data. 2021;8(1):311doi: 10.1038/s41597-021-01095-3.
Imbert, A., Rompais, M., Selloum, M., Castelli, F., Mouton-Barbosa, E., Brandolini-Bunlon, M., Chu-Van, E., Joly, C., Hirschler, A., Roger, P., Burger, T., Leblanc, S., Sorg, T., Ouzia, S., Vandenbrouck, Y., Médigue, C., Junot, C., Ferro, M., Pujos-Guillot, E., de Peredo, A. G., Fenaille, F., Carapito, C., Herault, Y., & Thévenot, E. A. (2021). ProMetIS, deep phenotyping of mouse models by combined proteomics and metabolomics analysis. Scientific data, 8(1), 311. https://doi.org/10.1038/s41597-021-01095-3
Imbert, Alyssa, et al. "ProMetIS, deep phenotyping of mouse models by combined proteomics and metabolomics analysis." Scientific data vol. 8,1 (2021): 311. doi: https://doi.org/10.1038/s41597-021-01095-3
Imbert A, Rompais M, Selloum M, Castelli F, Mouton-Barbosa E, Brandolini-Bunlon M, Chu-Van E, Joly C, Hirschler A, Roger P, Burger T, Leblanc S, Sorg T, Ouzia S, Vandenbrouck Y, Médigue C, Junot C, Ferro M, Pujos-Guillot E, de Peredo AG, Fenaille F, Carapito C, Herault Y, Thévenot EA. ProMetIS, deep phenotyping of mouse models by combined proteomics and metabolomics analysis. Sci Data. 2021 Dec 03;8(1):311. doi: 10.1038/s41597-021-01095-3. PMID: 34862403.
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Sci Data. 2021 Dec 03;8(1):311. doi: 10.1038/s41597-021-01095-3.

ProMetIS, deep phenotyping of mouse models by combined proteomics and metabolomics analysis.

Scientific data

Alyssa Imbert, Magali Rompais, Mohammed Selloum, Florence Castelli, Emmanuelle Mouton-Barbosa, Marion Brandolini-Bunlon, Emeline Chu-Van, Charlotte Joly, Aurélie Hirschler, Pierrick Roger, Thomas Burger, Sophie Leblanc, Tania Sorg, Sadia Ouzia, Yves Vandenbrouck, Claudine Médigue, Christophe Junot, Myriam Ferro, Estelle Pujos-Guillot, Anne Gonzalez de Peredo, François Fenaille, Christine Carapito, Yann Herault, Etienne A Thévenot

Affiliations

  1. CEA, LIST, Laboratoire Sciences des Données et de la Décision, IFB, MetaboHUB, Gif-sur-Yvette, France. [email protected].
  2. IFB-core, UMS3601, Genoscope, Evry, France. [email protected].
  3. Laboratoire de Spectrométrie de Masse BioOrganique, Université de Strasbourg, CNRS, IPHC UMR 7178, ProFI, Strasbourg, France.
  4. Université de Strasbourg, CNRS, INSERM, Institut Clinique de la Souris, Phenomin-ICS, Illkirch, France.
  5. Université Paris Saclay, CEA, INRAE, Département Médicaments et Technologies pour la Santé (MTS), MetaboHUB, Gif-sur-Yvette, France.
  6. Institut de Pharmacologie et Biologie Structurale (IPBS), Université de Toulouse, CNRS, UPS, ProFI, Toulouse, France.
  7. Université Clermont Auvergne, INRAE, UNH, Plateforme d'Exploration du Métabolisme, MetaboHUB, Clermont-Ferrand, France.
  8. CEA, LIST, Laboratoire Intelligence Artificielle et Apprentissage Automatique, MetaboHUB, Gif-sur-Yvette, France.
  9. Université Grenoble Alpes, INSERM, CEA, UMR BioSanté U1292, FR2048, ProFI, Grenoble, France.
  10. IFB-core, UMS3601, Genoscope, Evry, France.
  11. Laboratoire d'Analyses Bioinformatique en Génomique et Métabolisme (LABGeM), CNRS & CEA/DRF/IFJ, UMR8030, Evry, France.
  12. Université de Strasbourg, CNRS, INSERM, Institut de Génétique Biologie Moléculaire et Cellulaire, IGBMC, Illkirch, France.
  13. Université Paris Saclay, CEA, INRAE, Département Médicaments et Technologies pour la Santé (MTS), MetaboHUB, Gif-sur-Yvette, France. [email protected].

PMID: 34862403 DOI: 10.1038/s41597-021-01095-3

Abstract

Genes are pleiotropic and getting a better knowledge of their function requires a comprehensive characterization of their mutants. Here, we generated multi-level data combining phenomic, proteomic and metabolomic acquisitions from plasma and liver tissues of two C57BL/6 N mouse models lacking the Lat (linker for activation of T cells) and the Mx2 (MX dynamin-like GTPase 2) genes, respectively. Our dataset consists of 9 assays (1 preclinical, 2 proteomics and 6 metabolomics) generated with a fully non-targeted and standardized approach. The data and processing code are publicly available in the ProMetIS R package to ensure accessibility, interoperability, and reusability. The dataset thus provides unique molecular information about the physiological role of the Lat and Mx2 genes. Furthermore, the protocols described herein can be easily extended to a larger number of individuals and tissues. Finally, this resource will be of great interest to develop new bioinformatic and biostatistic methods for multi-omics data integration.

© 2021. The Author(s).

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