Perspectives and opinions from scientific leaders on the evolution of data-independent acquisition for quantitative proteomics and novel biological applications

Abstract

The methodology of data-independent acquisition (DIA) within mass spectrometry (MS) was developed into a method of choice for quantitative proteomics, to capture the depth and dynamics of biological systems, and to perform large-scale protein quantification. DIA provides deep quantitative proteome coverage with high sensitivity, high quantitative accuracy, and excellent acquisition-to-acquisition reproducibility. DIA workflows benefited from the latest advancements in MS instrumentation, acquisition/isolation schemes, and computational algorithms, which have further improved data quality and sample throughput. This powerful DIA-MS scan type selects all precursor ions contained in pre-determined isolation windows, and systematically fragments all precursor ions from each window by tandem mass spectrometry, subsequently covering the entire precursor ion m/z range. Comprehensive proteolytic peptide identification and label-free quantification are achieved post-acquisition using spectral library-based or library-free approaches. To celebrate the > 10 years of success of this quantitative DIA workflow, we interviewed some of the scientific leaders who have provided crucial improvements to DIA, to the quantification accuracy and proteome depth achieved, and who have explored DIA applications across a wide range of biology. We discuss acquisition strategies that improve specificity using different isolation schemes, and that reduce complexity by combining DIA with sophisticated chromatography or ion mobility separation. Significant leaps forward were achieved by evolving data processing strategies, such as library-free processing, and machine learning to interrogate data more deeply. Finally, we highlight some of the diverse biological applications that use DIA-MS methods, including large-scale quantitative proteomics, post-translational modification studies, single-cell analysis, food science, forensics, and small molecule analysis.