Lausanne, Switzerland – January 19th, 2016. NIHS researchers have taken a major step forward in unlocking the full potential of proteomics.
Three recently published articles describe advances made by researchers from NIHS to improve the speed and accuracy of proteomic analysis, potentially opening up a range of applications for this relatively novel form of clinical assessment and diagnostics.
Proteomics (the large-scale study of proteins) has been shown to be translatable from descriptive biology to clinical applications in health monitoring and disease diagnosis and treatment. Identifying proteins associated with a particular condition is an important first step in developing (a) new foods to possibly prevent that disease, and (b) new drugs to combat that disease, by modulating the activity of the relevant proteins. In addition, by understanding differences between individuals, researchers hope to be able to use proteomics to develop personalised nutritional solutions to meet their specific needs.
Understanding the relative quantitative protein difference between people may help in developing effective diagnostic techniques and health/disease management. The main obstacle researchers have so far struggled to overcome is the inability to first prepare and then analyse protein samples in sufficient quantities with the speed and accuracy required to fully exploit the potential offered by mass spectrometry-based proteomics. Until recently, no sufficiently versatile, automated process of sample preparation has been available to allow researchers to analyse the type of large sample cohorts required for reliable, reproducible biomarker discovery in clinical research and application.
NIHS researchers have now developed a highly automated proteomic biomarker discovery workflow which has so far been used to analyse human blood plasma and cerebrospinal fluid (that is, the fluid surrounding the brain and present in the spinal cord) samples in numbers and with a throughput and accuracy not previously achieved by other laboratories. One of those studies, involving more than 1,000 human plasma samples, demonstrated that analysing a large number of samples for biomarker discovery with mass spectrometry is feasible, providing robust and consistent biological results.
“Analysing cohorts comprising hundreds to thousands of subjects and samples using our new platform can ultimately help reduce the number of false-positive candidates and increase the translation rate of true biomarker candidates”, explains Loïc Dayon, who leads the Proteomics team at NIHS. “This in turn will facilitate, accelerate, and improve clinical proteomic discovery in biological samples”.
“From diabetes and obesity to brain injury and Alzheimer’s disease, we are only beginning to exploit the true potential of proteomic research in establishing the causes of and potential treatments for various human diseases and conditions. Our integrated, automated method of sample preparation and mass spectrometric analysis provides a valuable platform for faster, more accurate biomarker discovery, providing a robust ‘reference pipeline’ to find biomarkers for health/disease diagnosis, prognosis, and monitoring.”
NIHS is a biomedical research institute, part of Nestlé’s global R&D network, dedicated to fundamental research aimed at understanding health and disease and developing science-based, targeted nutritional solutions for the maintenance of health. To achieve its aim, NIHS employs state-of-the-art technologies and biological models to characterise health and disease with a holistic and integrated approach. The ultimate goal of the Institute is to develop knowledge that can empower people to better maintain their health through nutritional approaches, especially in relation to their molecular profile and lifestyle status.
Dayon, L., Núñez Galindo, A., Corthésy, J., Cominetti, O. and Kussmann, M. (2014). Comprehensive and scalable highly automated MS-based proteomic workﬂow for clinical biomarker discovery in human plasma. Journal of Proteome Research, 13, 3837−3845.
Núñez Galindo, A., Kussmann, M. and Dayon, L. (2015). Proteomics of cerebrospinal fluid: Throughput and robustness using a scalable automated analysis pipeline for biomarker discovery. Analytical Chemistry, 87, 10755-10761.
Cominetti, O., Núñez Galindo, A., Corthésy, J., Oller Moreno, S., Irincheeva, I., Valsesia, A., Astrup, A., Saris, W.H.M., Hager, J., Kussmann, M. and Dayon, L. (2016). Proteomic biomarker discovery in 1000 human plasma samples with mass spectrometry. Journal of Proteome Research, DOI: 10.1021/acs.jproteome.5b00901.
For enquiries, please contact:
Laura Camurri, Communications, NIHS