Optimization of Data-Dependent Acquisition Parameters in Mass Spectrometry for Oligonucleotide Identification

Accurate characterization of RNAs and their chemical modifications is critical for understanding RNA biology and post-transcriptional regulation. Mass spectrometry using data-dependent acquisition (DDA) is a crucial tool for identifying oligonucleotides (OGN) in epitranscriptomics. In this study, the key DDA parameters on an Orbitrap Fusion Lumos mass spectrometer were optimized, and an iterative mass exclusion MS/MS acquisition method was developed to enhance the OGN identification. Optimal performance was achieved with full MS resolving power of 120,000 and an MS/MS resolving power of 15,000, top 15 MS/MS scans, and 30% normalized HCD collision energy. Applying these settings to analyze RNase T1 digested E. coli rRNA resulted in the identification of an average of 358 unique OGNs and 58% rRNA sequence coverage. Our findings highlight the importance of tailored DDA parameter optimization and establish a robust workflow for confident OGN identification in MS-based epitranscriptomics.