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

Scientific data

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.
PMID: 34862403
Sci Data. 2021 Dec 03;8(1):311. doi: 10.1038/s41597-021-01095-3.

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...

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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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Phosphoproteomics Identifies PI3K Inhibitor-selective Adaptive Responses in Pancreatic Cancer Cell Therapy and Resistance.

Molecular cancer therapeutics

Cintas C, Douche T, Dantes Z, Mouton-Barbosa E, Bousquet MP, Cayron C, Therville N, Pont F, Ramos-Delgado F, Guyon C, Garmy-Susini B, Cappello P, Burlet-Schiltz O, Hirsch E, Gomez-Brouchet A, Thibault B, Reichert M, Guillermet-Guibert J.
PMID: 34552006
Mol Cancer Ther. 2021 Dec;20(12):2433-2445. doi: 10.1158/1535-7163.MCT-20-0981. Epub 2021 Sep 22.

The PI3K pathway is highly active in human cancers. The four class I isoforms of PI3K are activated by distinct mechanisms leading to a common downstream signaling. Their downstream redundancy is thought to be responsible for treatment failures of...

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Cintas C, Douche T, Dantes Z, et al. Phosphoproteomics Identifies PI3K Inhibitor-selective Adaptive Responses in Pancreatic Cancer Cell Therapy and Resistance. Mol Cancer Ther. 2021;20(12):2433-2445doi: 10.1158/1535-7163.MCT-20-0981.
Cintas, C., Douche, T., Dantes, Z., Mouton-Barbosa, E., Bousquet, M. P., Cayron, C., Therville, N., Pont, F., Ramos-Delgado, F., Guyon, C., Garmy-Susini, B., Cappello, P., Burlet-Schiltz, O., Hirsch, E., Gomez-Brouchet, A., Thibault, B., Reichert, M., & Guillermet-Guibert, J. (2021). Phosphoproteomics Identifies PI3K Inhibitor-selective Adaptive Responses in Pancreatic Cancer Cell Therapy and Resistance. Molecular cancer therapeutics, 20(12), 2433-2445. https://doi.org/10.1158/1535-7163.MCT-20-0981
Cintas, Célia, et al. "Phosphoproteomics Identifies PI3K Inhibitor-selective Adaptive Responses in Pancreatic Cancer Cell Therapy and Resistance." Molecular cancer therapeutics vol. 20,12 (2021): 2433-2445. doi: https://doi.org/10.1158/1535-7163.MCT-20-0981
Cintas C, Douche T, Dantes Z, Mouton-Barbosa E, Bousquet MP, Cayron C, Therville N, Pont F, Ramos-Delgado F, Guyon C, Garmy-Susini B, Cappello P, Burlet-Schiltz O, Hirsch E, Gomez-Brouchet A, Thibault B, Reichert M, Guillermet-Guibert J. Phosphoproteomics Identifies PI3K Inhibitor-selective Adaptive Responses in Pancreatic Cancer Cell Therapy and Resistance. Mol Cancer Ther. 2021 Dec;20(12):2433-2445. doi: 10.1158/1535-7163.MCT-20-0981. Epub 2021 Sep 22. PMID: 34552006.
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Spiked proteomic standard dataset for testing label-free quantitative software and statistical methods.

Data in brief

Ramus C, Hovasse A, Marcellin M, Hesse AM, Mouton-Barbosa E, Bouyssié D, Vaca S, Carapito C, Chaoui K, Bruley C, Garin J, Cianférani S, Ferro M, Dorssaeler AV, Burlet-Schiltz O, Schaeffer C, Couté Y, Gonzalez de Peredo A.
PMID: 26862574
Data Brief. 2015 Dec 17;6:286-94. doi: 10.1016/j.dib.2015.11.063. eCollection 2016 Mar.

This data article describes a controlled, spiked proteomic dataset for which the "ground truth" of variant proteins is known. It is based on the LC-MS analysis of samples composed of a fixed background of yeast lysate and different spiked...

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Ramus C, Hovasse A, Marcellin M, et al. Spiked proteomic standard dataset for testing label-free quantitative software and statistical methods. Data Brief. 2015;6:286-94doi: 10.1016/j.dib.2015.11.063.
Ramus, C., Hovasse, A., Marcellin, M., Hesse, A. M., Mouton-Barbosa, E., Bouyssié, D., Vaca, S., Carapito, C., Chaoui, K., Bruley, C., Garin, J., Cianférani, S., Ferro, M., Dorssaeler, A. V., Burlet-Schiltz, O., Schaeffer, C., Couté, Y., & Gonzalez de Peredo, A. (2016). Spiked proteomic standard dataset for testing label-free quantitative software and statistical methods. Data in brief, 6286-94. https://doi.org/10.1016/j.dib.2015.11.063
Ramus, Claire, et al. "Spiked proteomic standard dataset for testing label-free quantitative software and statistical methods." Data in brief vol. 6 (2016): 286-94. doi: https://doi.org/10.1016/j.dib.2015.11.063
Ramus C, Hovasse A, Marcellin M, Hesse AM, Mouton-Barbosa E, Bouyssié D, Vaca S, Carapito C, Chaoui K, Bruley C, Garin J, Cianférani S, Ferro M, Dorssaeler AV, Burlet-Schiltz O, Schaeffer C, Couté Y, Gonzalez de Peredo A. Spiked proteomic standard dataset for testing label-free quantitative software and statistical methods. Data Brief. 2015 Dec 17;6:286-94. doi: 10.1016/j.dib.2015.11.063. eCollection 2016 Mar. PMID: 26862574; PMCID: PMC4706616.
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Showing 1 to 3 of 3 entries