GlyQ-IQ: glycomics quintavariate-informed quantification with high-performance computing and GlycoGrid 4D visualization

Abstract

Glycomics quintavariate-informed quantification (GlyQ-IQ) is a biologically guided glycomics analysis tool for identifying N-glycans in liquid chromatography-mass spectrometry (LC-MS) data. Glycomics LC-MS data sets have convoluted extracted ion chromatograms that are challenging to deconvolve with existing software tools. LC deconvolution into constituent pieces is critical in glycomics data sets because chromatographic peaks correspond to different intact glycan structural isomers. The biological targeted analysis approach offers several key advantages to traditional LC-MS data processing. A priori glycan information about the individual target's elemental composition allows for improved sensitivity by utilizing the exact isotope profile information to focus chromatogram generation and LC peak fitting on the isotopic species having the highest intensity. Glycan target annotation utilizes glycan family relationships and in source fragmentation in addition to high specificity feature LC-MS detection to improve the specificity of the analysis. The GlyQ-IQ software was developed in this work and evaluated in the context of profiling the N-glycan compositions from human serum LC-MS data sets. A case study is presented to demonstrate how GlyQ-IQ identifies and removes confounding chromatographic peaks from high mannose glycan isomers from human blood serum. In addition, GlyQ-IQ was used to generate a broad human serum N-glycan profile from a high resolution nanoelectrospray-liquid chromatography-tandem mass spectrometry (nESI-LC-MS/MS) data set. A total of 156 glycan compositions and 640 glycan isomers were detected from a single sample. Over 99% of the GlyQ-IQ glycan-feature assignments passed manual validation and are backed with high-resolution mass spectra.

Figure 1

Glycan compositions detected in LC–MS experiments can be in multiple forms that need to be identified in the data sets (isomers, charge states). The multiple forms need to be cross correlated and searched for consistency. The multipliers used in this example represent 1 glycan target containing 4 charge states, 4 isomers, searching for fragments in both directions (2, greater or smaller by one monosaccharide), 4 monosaccharide differences to search for, and 4 charge states possible for the fragment ions.

Figure 2

Example depicting the penalization of the isotope fit score calculations when an unrelated peak lower in mass (1 Da, 0.33 m/z at 3+ charge) is observed in the mass spectra. (A) Fit score calculated when modeled data is fit to the observed data with 1 Da penalty assigned. The high fit score greater than 0.1 cutoff triages further chromatographic analysis. (B) EICs from the most abundant isotope m/z and the penalty peak m/z are modeled, fit, and correlated. Failed EIC correlations trigger a rescoring of the isotope profile while excluding the penalty peak because it was deemed not part of the targeted distribution. The large score decrease from 1.07 to 0.011 leads to a correct assignment of the target. The mass differences between the 3+ charge state isotopes are shown in blue at the bottom.

Figure 3

Deconvolution of Man₈ EIC into Gaussian peak shapes. Summing the individual deconvoluted features provides the model dotted line that is consistent with the smoothed EIC. Observing multiple isobaric chromatographically separated compositions with different elution times correspond to different chemical isomeric structures.

Figure 4

GlycoGrid 4D of 156 human blood serum glycan compositions annotated with GlyQ-IQ (green) and confirmed with CID or HCD (yellow). Glycans identified as insource fragments are depicted in red. The major Y axis corresponds to the number of hexose and the minor Y corresponds to the number of fucose. The major X corresponds to the number of N-acetylhexosamine and the minor X corresponds to the number of sialic acid.