Combining bioinformatics and MS-based proteomics: clinical implications

Abstract

Clinical proteomics research aims at i) discovery of protein biomarkers for screening, diagnosis and prognosis of disease, ii) discovery of protein therapeutic targets for improvement of disease prevention, treatment and follow-up, and iii) development of mass spectrometry (MS)-based assays that could be implemented in clinical chemistry, microbiology or hematology laboratories. MS has been increasingly applied in clinical proteomics studies for the identification and quantification of proteins. Bioinformatics plays a key role in the exploitation of MS data in several aspects such as the generation and curation of protein sequence databases, the development of appropriate software for MS data treatment and integration with other omics data and the establishment of adequate standard files for data sharing. In this article, we discuss the main MS approaches and bioinformatics solutions that are currently applied to accomplish the objectives of clinical proteomic research.

Keywords::

• biocuration
• bioinformatics
• biomarker discovery
• clinical chemistry proteomics
• clinical proteomics
• database
• mass spectrometry
• systems biology

Acknowledgements

The authors thank their colleagues of the University of Geneva Pr Bairoch and Pr Hochstrasser for critical reading of the manuscript, and P Antinori-Malaspina for her helpful insight.

Financial & competing interests disclosure

This work was supported by the University of Geneva, the SIB-Swiss Institute of Bioinformatics and Swiss National Science Foundation Grant No 32003B_143809. The authors have no relevant affiliations or financial involvement with any organization or entity with a financial interest in or financial conflict with the subject matter or materials discussed in the manuscript apart from those disclosed. This includes employment, consultancies, honoraria, stock ownership or options, expert testimony, grants or patents received or pending or royalties.

No writing assistance was utilized in the production of this manuscript.

Key issues

• The study of proteins represents a great opportunity for the discovery of novel disease biomarkers and therapeutic targets, as well as for the comprehension of disease phenotypes. Proteomic techniques in general, and mass spectrometry (MS) in particular, are largely used for the study of proteins in different aspects of clinical research.

• MS can be applied at all stages of the protein biomarker discovery pipeline, and in the development of novel therapeutic targets. However, the rate of unsuccessful clinical proteomics projects is still very high, mainly due to poor study design or inadequate sample treatment.

• Sensitivity of MS approaches is still lower than antibody-based assays, and needs to be improved in order to cover the high dynamic range of protein concentration in biological samples.

• The progress of robust and accurate quantitative MS approaches has opened the door for the implementation of MS-based protein assays in clinical laboratories. Regulatory agencies are establishing appropriate test classifications for multiplexed targeted MS-based assays.

• MS-based proteomic assays are already used in clinical laboratories for identification of microbial pathogens (matrix-assisted laser desorption/ionization-TOF MS profiling) and for the quantification of protein biomarkers (selected reaction monitoring-MS assays).

• The field of bioinformatics comprises a wide variety of applications such as software engineering, and data extraction, storage and curation.

• Bioinformatics plays a key role in the exploitation of large amounts of MS data generated in clinical proteomic studies, including the generation and curation of protein sequence databases, the development of appropriate software for MS data treatment and integration with other -omics data and the establishment of adequate standard files for data sharing.

• In the search of protein biomarkers, bioinformatics can be used to interpret the biological knowledge generated from clinical proteomics studies, to extend the number of protein candidates identified by MS, and to generate new protein candidate lists from MS data stored in public repositories.

• Clinical research is significantly benefiting from MS and bioinformatics applications. However, improvements in both areas are still required to apply MS-driven discoveries in the clinic.

Notes