Published January 17, 2023
University at Buffalo School of Pharmacy and Pharmaceutical Sciences (UB SPPS) research teams led by Jun Qu, PhD, professor, Department of Pharmaceutical Sciences, published the protocol of their IonStar techniques and was recognized by Nature Protocols, an open access journal affiliated with Nature, the premier international science and technology journal, for their high impact work in proteomics investigation.
In clinical and pharmaceutical research, reliable quantification of large sample cohorts is essential for meaningful investigations. Liquid chromatography–mass spectrometry (LC–MS)-based quantitative proteomics is a promising strategy for unbiased investigations of disease physiopathology and pharmaceutical effects. Analyzing a large number of samples is often necessary to ensure sufficient statistical power and to prevent false discovery.
However, achieving reliable and robust proteomics quantification in large sample cohorts is challenging due to the technical difficulties in reproducible measurement of proteins with numerous biological samples. This is because when large numbers of samples are analyzed, isotope labeling approaches may suffer from substantial batch effects, and with label-free methods, it becomes evident that low-abundance proteins are not reliably measured owing to insufficient reproducibility for quantification.
To address these challenges, the research team developed IonStar, an integrated quantitative proteomics pipeline to enable in-depth and high-quality proteomic profiling of large sample cohorts, especially for low-abundance proteins. The unique capacity of IonStar is that it effectively takes advantage of the high sensitivity and selectivity attainable by ultrahigh-resolution (UHR)-MS1 acquisition, which is now widely available on ultrahigh-field Orbitrap instruments. This holds important benefits, including high sensitivity, selectivity, accuracy and precision for quantification of low-abundance proteins, as well as low missing data and low false-positive rates.
“This strategy substantially minimizes interfering signals, which is the primary source for low-quality quantitative data especially for low-abundance proteins, and thus achieves highly selective protein quantification,” says Qu. “Additionally, the ultra-high-resolution MS1 signals, when procured using IonStar, are stable and intensive, permitting sensitive, reproducible and accurate quantification across a large cohort.”
Qu and his research team are pleased to be able to offer this pipeline platform to the global research community. “In quantitative proteomics, it is vitally important that highly robust and reproducible experimental procedures are stringently followed to ensure top-class quantitative performance,” says Qu. “After many years of research, IonStar now provides optimal sample preparation and LC-MS analysis procedures, as well as needed data processing approaches for accurate, reproducible, and robust protein quantification in large sample cohorts. Individually, the sample preparation, LC-MS analysis, and data processing method have all shown superior performance when compared to peer techniques. The protocol has been adopted in a large body of publications leading to insightful findings in clinical and pharmaceutical investigations and wide acceptance amongst pharmaceutical scientists across the industry. We heartily recommend this protocol to the research community, especially to those who desire high-quality proteomics quantification in clinical and pharmaceutical studies.”
IonStar has demonstrated the ability to achieve highly reproducible and robust proteomics quantification in large sample cohorts, enabling confident and comprehensive discovery of biological leads that can facilitate clinical and pharmaceutical studies.