A multicentric study to evaluate the use of relative retention times in targeted proteomics

Vialás, Vital; Colomé-Calls, Núria; Abian, Joaquín; Aloria, Kerman; Alvarez-Llamas, Gloria; Antúnez, Oreto; Arizmendi, Jesús M.; Azkargorta, Mikel; Barceló-Batllori, Sílvia; Barderas, María G.; Blanco, Francisco J.; Casal, J. Ignacio; Casas, Vanessa; Torre, Carolina De La; Chicano-Gálvez, Eduardo; Elortza, Félix; Espadas, Guadalupe; Estanyol, Josep Marı́a; Fernández‐Irigoyen, Joaquín; Fernández-Puente, Patricia; Fidalgo, María José; Fuentes, Manuel; Gay, Marina; Gil, Concha; Hainard, Alexandre; Hernáez, María Luisa; Ibarrola, Nieves; Kopylov, Arthur T.; Lario, Antonio; López, Juan Antonio; López-Lucendo, María; Marcilla, Miguel; Marina-Ramírez, Anabel; Markó-Varga, György; Martín, Lucas; Mora, María I.; Morato-López, Esperanza; Muñoz, Javier; Odena, María Antonia; Oliveira, Eliandre de; Orera, Irene; Ortea, Ignacio; Pasquarello, Carla; Ray, K.P.; Rezeli, Melinda; Ruppen, Isabel; Sabidó, Eduard; Pino, Manuel M. Sánchez del; Sancho, Jaime; Santamaría, Enrique; Vázquez, Jesús; Vilaseca, Marta; Vivanco, Fernando; Walters, Joseph; Zgoda, Victor G.; Corrales, Fernando J.; Canals, Françesc; Paradela, Alberto

doi:10.1016/j.jprot.2016.10.014

Cited by 9 publications

(7 citation statements)

References 30 publications

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“…The use of the same protein sets can be used at later stages for the purpose of calibrating the equipment in multi-site studies. An interesting example is offered in [43], where multiple peptides were analyzed in a multi-centric study. Although this study does not aim to identify prostate cancer biomarkers prostate cancer biomarkers, it is, until now, the best documented example of multi-platform validation, contributing to the improvement of targeted proteomics analyses.…”

Section: Proteomics Discovery Platforms Used In Prostate Cancer Biomamentioning

confidence: 99%

Prostate cancer proteomics: Current trends and future perspectives for biomarker discovery

et al. 2017

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The clinical and fundamental research in prostate cancer - the most common urological cancer in men - is currently entering the proteomic and genomic era. The focus has switched from one single marker (PSA) to panels of biomarkers (including proteins involved in ribosomal function and heat shock proteins). Novel genetic markers (such as Transmembrane protease serine 2 (TMPRSS2)-ERG fusion gene mRNA) or prostate cancer gene 3 (PCA3) had already entered the clinical practice, raising the question whether subsequent protein changes impact the evolution of the disease and the response to treatment. Proteomic technologies such as MALDI-MS, SELDI-MS, i-TRAQ allow a qualitative/quantitative analysis of the proteome variations, in both serum and tumor tissue. A new trend in prostate cancer research is proteomic analysis of prostasomes (prostate-specific exosomes), for the discovery of new biomarkers. This paper provides an update of novel clinical tests used in research and clinical diagnostic, as well as of potential tissue or fluid biomarkers provided by extensive proteomic research data.

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Section: Proteomics Discovery Platforms Used In Prostate Cancer Biomamentioning

confidence: 99%

Prostate cancer proteomics: Current trends and future perspectives for biomarker discovery

et al. 2017

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“…In particular, scientists active in “conventional” proteomics research have tried to address the very same issues over the last decade. Inter-laboratory and software comparison studies have been performed for different experimental strategies, including data-dependent acquisition 11 , selected reaction monitoring 12, 13, 14, 15 , and most recently, data-independent acquisition 16, 17 . In addition, regular comparative studies have been organized by the Association of Biomolecular Resource Facilities (ABRF; https://abrf.org/research-group/proteomics-research-group-prg and https://abrf.org/research-group/proteomics-standards-research-group-sprg).…”

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confidence: 99%

Cross-linking/Mass Spectrometry: A Community-Wide, Comparative Study Towards Establishing Best Practice Guidelines

Iacobucci¹,

Piotrowski²,

Aebersold³

et al. 2018

Preprint

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The number of publications in the field of chemical cross-linking combined with mass spectrometry (XL-MS) to derive constraints for protein three-dimensional structure modeling and to probe protein-protein interactions has largely increased during the last years. As the technique is now becoming routine for in vitro and in vivo applications in proteomics and structural biology there is a pressing need to define protocols as well as data analysis and reporting formats that are generally accepted in the field and that have shown to lead to high-quality results. This first, community-based harmonization study on XL-MS is based on the results of 32 groups participating worldwide. The aim of this paper is to summarize the status quo of XL-MS and to compare and evaluate existing cross-linking strategies. From the results obtained, common protocols will be established. Our study serves as basis for establishing best practice guidelines in the field for conducting cross-linking experiments, performing data analysis, and reporting formats with the ultimate goal of assisting scientists to generate accurate and reproducible XL-MS results.

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“…And, Vialas at al. showed that use of iRT prediction enhances reproducibility across different laboratories [31]. In this study, we tested our workflow with iRT prediction by directly targeting 500 peptides from E .…”

Section: Resultsmentioning

confidence: 99%

A rapid methods development workflow for high-throughput quantitative proteomic applications

Chen

Thompson

et al. 2019

PLoS ONE

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Recent improvements in the speed and sensitivity of liquid chromatography-mass spectrometry systems have driven significant progress toward system-wide characterization of the proteome of many species. These efforts create large proteomic datasets that provide insight into biological processes and identify diagnostic proteins whose abundance changes significantly under different experimental conditions. Yet, these system-wide experiments are typically the starting point for hypothesis-driven, follow-up experiments to elucidate the extent of the phenomenon or the utility of the diagnostic marker, wherein many samples must be analyzed. Transitioning from a few discovery experiments to quantitative analyses on hundreds of samples requires significant resources both to develop sensitive and specific methods as well as analyze them in a high-throughput manner. To aid these efforts, we developed a workflow using data acquired from discovery proteomic experiments, retention time prediction, and standard-flow chromatography to rapidly develop targeted proteomic assays. We demonstrated this workflow by developing MRM assays to quantify proteins of multiple metabolic pathways from multiple microbes under different experimental conditions. With this workflow, one can also target peptides in scheduled/dynamic acquisition methods from a shotgun proteomic dataset downloaded from online repositories, validate with appropriate control samples or standard peptides, and begin analyzing hundreds of samples in only a few minutes.

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A multicentric study to evaluate the use of relative retention times in targeted proteomics

Cited by 9 publications

References 30 publications

Prostate cancer proteomics: Current trends and future perspectives for biomarker discovery

Prostate cancer proteomics: Current trends and future perspectives for biomarker discovery

Cross-linking/Mass Spectrometry: A Community-Wide, Comparative Study Towards Establishing Best Practice Guidelines

A rapid methods development workflow for high-throughput quantitative proteomic applications

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