Benchmarking ZenoSWATH-DIA and Rapid-Gradient Workflows for Plasma Proteomics

Abstract

Abstract Proteomics is the large-scale study of proteins. Mass spectrometry (MS) is one of the most informative techniques for analyzing proteins. Proteomics-based MS examines biological changes in diseases. It also identifies potential biomarker candidates in early disease stages, enabling the diagnosis, prognosis, and evaluation of therapeutic efficacy. Developing efficient and high-throughput proteomics techniques enables comprehensive exploration of the human plasma proteome. In this study, we compared the performance of various MS instruments and software tools for analyzing data-independent acquisition plasma proteomics data. We employed a “mixed species” approach, spiking three different amounts of Escherichia coliproteins into a constant amount of human plasma proteins to evaluate various LC–MS run length and data analysis workflows (DIA-NN and Spectronaut) for their ability to detect differential abundance. Assessing the power of the method to detect differences provides a more informative evaluation of workflow performance than relying solely on CVs. Our research shows that the ZenoTOF 7600 MS workflow, combined with DIA-NN in library mode, was most effective at detecting true-positive changes in protein abundance in our spiked samples. DIA-NN in library mode using the 21 min gradient method outperformed Spectronaut in detecting true-positive change. These results hold promise for detecting differentially abundant proteins in plasma.