Interestingly, use of cyclic 1-pass and multi-pass cIM routines resulted in ion populations containing both common and unique peptides. Consequently, 1-pass and multi-pass DynamX cluster data output were merged to generate comparative cyclic 1-pass/multi-pass peptide maps (Figure and Figure ). When compared to data acquired on the Synapt G2-Si instrument, the combined cyclic 1pass/multi-pass peptide output exhibited peptide identification increases of 222, 80, 85, and 31% for SMO, XylE, MsbA and SecYEG, respectively (Figure ), Thus, whilst cyclic 1-pass performed better than multi-pass as an individual cIM routine, use of 1-pass and multi-pass in combination can further improve overall performance in bottom-up LC-MS applications. The increased peptide identification afforded by combined 1-pass/multi-pass cIM analyses further increased sequence coverage and/or redundancy for all assessed targets (Figure and Table ), leading to 96 and 89% sequence coverages for XylE and SMO, respectively. Owing to the identical CID procedure performed when using 1-pass and multi-pass cIM, differences in fragment generation is unlikely to account for the unique peptide populations observed. Closer inspection of 1-pass and multi-pass cIM threshold parameter values revealed that, while peptide identification was highly comparable and reproducible for common peptides across 1-pass and multipass cIM LC-MS replicates, the unique peptide populations showed markedly decreased reproducibility under the opposing cIM routine (Figure ), resulting in many falling below the set threshold (3 out of 4 replicates). Thus, a lack of robust and reproducible peptide identification across replicate appears to be a significant factor contributing to the unique peptide populations observed. In addition, reduced precursor-product matching was also observed for unique peptides under the opposing cIM routine (Figure ). While this alone does not directly push the majority of peptides below the set threshold, it has a knock-on effect in reducing average products per amino acid and consecutively matched products (Figure ). Thus, less efficient precursor-product matching also appears to be a contributing factor. One possible explanation is that successful detection of intact peptide and product ions, and their subsequent drift time alignment, can be interfered by co-eluting ions with identical/similar drift time. Thus, by utilising alternative cIM routines, interference can be reduced via improved separation of the peptides of interest in the IM dimension. Nevertheless, by utilising both 1-pass and multi-pass cIM in combination, these unique peptide populations can be integrated to generate larger reference peptide databases for use in subsequent HDX-MS experiments.