A new protein analysis tool developed at the University at Buffalo could vastly increase the speed and precision of disease and drug analysis. The new tool, called IonStar, would provide high accuracy in quantifying the proteins.
Compared to industry standard MaxQuant, IonStar improved the measurement consistency of proteins in low abundance and lowered the amount of missing data in results from 17% to 0.1%, a level that has never been achieved with large samples. The new tool could increase the quality and accuracy of medical diagnosis and quicken the pace of pharmaceutical development.
"IonStar will change the face of clinical and pharmaceutical research and industry, where large investigations are often critical," says Jun Qu, Ph.D., the lead investigator, and professor in the UB School of Pharmacy and Pharmaceutical Sciences.
Playing Spot the Difference
The abundance of proteins in the body that corresponds with disease or pharmaceutical reactions can provide researchers with vital clues for accurately diagnosing a condition, and for developing potential therapies and evaluating drug effects.
Protein analysis tools are used to quantify and compare the abundance of proteins in groups of healthy individuals with those who are ill or treated with a drug. Changes in protein abundances, when analyzed together, often reveal novel biomarkers.
The challenge for researchers is that current tools are not efficient at analyzing large numbers of samples. One type of method, the labeling-method, uses chemical tags to label proteins. The issue: The software can only analyze up to 10 samples at a time, making it difficult for researchers to conduct typical pharmaceutical and clinical studies, says Qu.
IonStar increases accuracy and precision and lowers missing data by improving on sample preparation methods, alignment and feature detection designs for mass spectrometry analysis.
Proving the Concept in Traumatic Brain Injury
Researchers used IonStar to quantify proteins in rats with traumatic brain injury, a debilitating condition that accounts for 2.2 million emergency room visits annually in the United States. Using 100 tissue samples, IonStar identified 7,000 proteins, including 1,000 that differed in abundance, without missing data.
IonStar also measured low-abundance proteins with higher accuracy and precision than other prevalent analysis tools. This capability is critical, says Qu, because proteins that appear in smaller amounts play a more influential role in the body.
Qu has used IonStar and similar techniques to analyze protein variation in cancer, diabetes, cardiovascular disease, neurodegeneration and retinal degeneration as well. Future work on IonStar will focus on expanding the number of samples the tool can analyze.