Skip to main page content
U.S. flag

An official website of the United States government

Dot gov

The .gov means it’s official.
Federal government websites often end in .gov or .mil. Before sharing sensitive information, make sure you’re on a federal government site.

Https

The site is secure.
The https:// ensures that you are connecting to the official website and that any information you provide is encrypted and transmitted securely.

Access keys NCBI Homepage MyNCBI Homepage Main Content Main Navigation
Review
. 2021 Aug 26:12:723510.
doi: 10.3389/fphys.2021.723510. eCollection 2021.

Towards Building a Quantitative Proteomics Toolbox in Precision Medicine: A Mini-Review

Affiliations
Review

Towards Building a Quantitative Proteomics Toolbox in Precision Medicine: A Mini-Review

Alejandro Correa Rojo et al. Front Physiol. .

Abstract

Precision medicine as a framework for disease diagnosis, treatment, and prevention at the molecular level has entered clinical practice. From the start, genetics has been an indispensable tool to understand and stratify the biology of chronic and complex diseases in precision medicine. However, with the advances in biomedical and omics technologies, quantitative proteomics is emerging as a powerful technology complementing genetics. Quantitative proteomics provide insight about the dynamic behaviour of proteins as they represent intermediate phenotypes. They provide direct biological insights into physiological patterns, while genetics accounting for baseline characteristics. Additionally, it opens a wide range of applications in clinical diagnostics, treatment stratification, and drug discovery. In this mini-review, we discuss the current status of quantitative proteomics in precision medicine including the available technologies and common methods to analyze quantitative proteomics data. Furthermore, we highlight the current challenges to put quantitative proteomics into clinical settings and provide a perspective to integrate proteomics data with genomics data for future applications in precision medicine.

Keywords: bioinformatics; biomarker discovery; clinical diagnostics; precision medicine; protein quantitative trait loci; quantitative proteomics; targeted techniques.

PubMed Disclaimer

Conflict of interest statement

The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest.

Figures

Figure 1
Figure 1
General workflow for quantitative proteomics. The figure describes the different types of targeted technologies, and the common methodologies to analyse quantitative proteomics data. These analyses potentially provide clinical applications in biomarker and drug discovery and patient stratification. Image created with BioRender.

References

    1. Adhikari S., Nice E. C., Deutsch E. W., Lane L., Omenn G. S., Pennington S. R., et al. . (2020). A high-stringency blueprint of the human proteome. Nat. Commun. 11:5301. 10.1038/s41467-020-19045-9 - DOI - PMC - PubMed
    1. Aggarwal S., Yadav A. K. (2016). False discovery rate estimation in proteomics. Methods Mol. Biol. 1362, 119–128. 10.1007/978-1-4939-3106-4_7, PMID: - DOI - PubMed
    1. Assarsson E., Lundberg M., Holmquist G., Björkesten J., Bucht Thorsen S., Ekman D., et al. . (2014). Homogenous 96-Plex PEA immunoassay exhibiting high sensitivity, specificity, and excellent scalability. PLoS One 9:e95192. 10.1371/journal.pone.0095192, PMID: - DOI - PMC - PubMed
    1. Benson M. D., Yang Q., Ngo D., Zhu Y., Shen D., Farrell L. A., et al. . (2018). Genetic architecture of the cardiovascular risk proteome. Circulation 137, 1158–1172. 10.1161/CIRCULATIONAHA.117.029536, PMID: - DOI - PMC - PubMed
    1. Bolstad B. M., Irizarry R. A., Astrand M., Speed T. P. (2003). A comparison of normalization methods for high density oligonucleotide array data based on variance and bias. Bioinformatics 19, 185–193. 10.1093/bioinformatics/19.2.185, PMID: - DOI - PubMed