From this sequence database, theoretical precursor isotopic envelopes were generated using the IsoSpecR package. Based on the average molecular weight of each tRNA sequence, theoretical average m/z values were generated for various potential charge states of each tRNA sequence. Those predicted charge states that fell within one of the 2 m/z windows defined above were then searched in the corresponding UVPD spectra. From the expected charge determined from the theoretical average m/z, theoretical isotopic distributions of EPD charge-reduced precursor ions were generated for up to three charge losses -corresponding to detachment of up to 3 electronsand assigned in the UVPD spectrum. For example, a 23.9 kDa tRNA in the 27-charge state, [M-27H] Once precursor candidates were identified in this way, theoretical fragment ions were generated for the corresponding precursor charge state and searched the CAD spectra acquired for the same specific 2 m/z window in which the precursor was identified. All fragment ions were identified using Nucleo-SAFARI as described previously, utilizing a 10-ppm m/z tolerance and a 20% intensity tolerance. Fragment ions were validated based on their predicted isotopic abundances and observed centroided intensity values of each isotope. Scores were assigned for the characterization of each tRNA based on the identified fragments using a method analogous to the P-score implemented in many top-down proteomics workflows, with some minor differences. First, only sequential fragments from either terminus -e.g. a continuous series of a, b, c, or d ions or a continuous series of w, x, y, or z ions -were counted as tag sequences. Second, given the identification of fragments in the m/z domain, the likelihood of identifying a random residue at unit resolution was calculated based on the mean charge state of the tag sequence rather than only corresponding to the mean mass of each residue. Details on the scoring process in Nucleo-SAFARI are described elsewhere. P-scores were utilized to assess which of the tRNAs were identified most confidently and with the best characterization. A schematic of the full workflow is shown in Figure . A total of 376 searches were executed across the 25 isolation windows, a number derived from performing searches on 122 tRNA precursors, each for two CAD conditions (25 NCE and 30 NCE), and the fact that 35 precursors were identified in more than one isolation window and thus subjected to additional searches.