Clinical veterinary proteomics: Techniques and approaches to decipher the animal plasma proteome. (December 2017) - Ghodasara, P.; Sadowski, P.; Satake, N.; Kopp, S.; Mills, P.C.
Highlights: The use of mass spectrometry (MS)-based proteomics is still in its infancy in veterinary medicine. MS-based proteomics are superior to routine protein analyses in detecting lower abundance proteins. MS-based techniques are powerful tools to create quantitative protein profiles from plasma and tissues from various species. Sequential window acquisition of all theoretical fragment ion mass spectra (SWATH-MS) should enhance proteomics studies. Abstract: Over the last two decades, technological advancements in the field of proteomics have advanced our understanding of the complex biological systems of living organisms. Techniques based on mass spectrometry (MS) have emerged as powerful tools to contextualise existing genomic information and to create quantitative protein profiles from plasma, tissues or cell lines of various species. Proteomic approaches have been used increasingly in veterinary science to investigate biological processes responsible for growth, reproduction and pathological events. However, the adoption of proteomic approaches by veterinary investigators lags behind that of researchers in the human medical field. Furthermore, in contrast to human proteomics studies, interpretation of veterinary proteomic data is difficult due to the limited protein databases available for many animal species. This review article examines the current use of advanced proteomics techniques for evaluation of animal health and welfare and covers the current status of clinical veterinary proteomics research, including successful protein identification and data interpretation studies. It includes a description of an emerging tool, sequential window acquisition of all theoretical fragment ion mass spectra (SWATH-MS), available on selected mass spectrometry instruments. This newly developed data acquisition technique combines advantages of discovery and targeted proteomics approaches, and thus has the potential to advance the veterinary proteomics field by enhancing identification and reproducibility of proteomics data.