SCIEX, a global leader in life science analytical technologies, and a subsidiary of Danaher Corporation (NYSE:DHR), has launched Zeno SWATH DIA, a significant step forward in biomarker discovery and translation workflows.
The Zeno SWATH DIA digital record of a sample that can help researchers discover more potential biomarkers in clinical trials and in the discovery of new therapies. Through the combination of the sensitivity and robustness of the ZenoTOF 7600 system, researchers can now routinely quantify up x 2 the number of cell and plasma proteins that were previously undetectable. However, the ability to quantify large numbers of lesser abundant proteins that were previously undetectable allows further processing.
This performance can be achieved at large scale. Run-times can be reduced to as little as five minutes with minimal compromise in proteome coverage for the first time. This means the possibility for clinical diagnosis of disease is massively increased.
[With Zeno SWATH DIA], we may do things with greater sensitivity, according to Markus Ralser, Director of the Institute of Biochemistry, Charite, Universitatsmedizin Berlin, and group leader at The Francis Crick Institute, London. If we can do things faster it means we can measure larger sample sets. In clinical trials this means that we may measure samples from more people at different times.
Large quantities of proteins can be quantified from sample loadings that were nearly twice as low as previous SWATH approaches. Reduced sample costs in large scale trials, and reduced sample consumption from expensive in vivo simulations during drug development. Desperation opportunities for large population-scale sample collecting methods such as patient-centric microsampling devices
Markus Ralser said that the higher sensitivity helps to reduce costs. In a 100,000 sample, project costs is very important. With Zeno SWATH DIA we can now do experiments that we couldnt previously do due to the cost alone.
The difference between a research curiosity and translational data, according to Joe Fox, is that this approach will profoundly transform biomarkers into breakthrough technologies. This technique will enable biomarkers to become more widespread, and it has the potential to uncover information to develop advanced pharmaceutical technologies.
