Despite its extended mobility separation, multi-pass cIM provided decreased precursor-product matching relative to cIM 1-pass (Figure ). Owing to the identical CID procedure performed when using 1-pass and multi-pass cIM, differences in fragment generation are unlikely to account for this difference. To assess whether high quality peptide spectra were present in cIM multi-pass raw data but omitted during PLGS processing, the peptide database generated during cIM 1-pass peptide mapping was used to search for peptide ions in the SecE cIM multi-pass raw data (Figure ). Despite only 2 peptides being identified during PLGS processing, high quality spectra for an additional 24 peptides were observed across all SecE cIM multi-pass LC-MS replicates. Moreover, many of these peptides were of high intensity (i.e., >1e5), suggesting that decreased ion intensity is not the only factor reducing peptide identification in cIM multi-pass experiments. Further comparison of the SecE PLGS output revealed that, whilst high quality peptide ions were reproducibly detected across SecE cIM 1-pass replicates, this reduced when using multi-pass (Figure ) despite their manually observed presence across LC-MS replicate. Consequently, peptide mapping results could not be improved by dropping DynamX threshold parameters (i.e., replication file threshold) without introducing large quantities of false positive identifications. These observations, in combination with the observed decrease in precursor-product matching, led to the hypothesis that Waters' proprietary PLGS software provided suboptimal identification and/or alignment of target precursor and product ions in cIM multi-pass data, as opposed to instrumental issues at the LC-MS level. We set out to address these issues by developing a novel processing strategy with improved performance in handling cIM multi-pass data.