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Figure 3: QqQ SRM traces of singly sialylated biantennary structures, with experimental and calculated retention values (in minutes) as well as the standard deviation listed for each structure.
this work, then a t-stat great than 2 or less than -2 is an indication that the coefficient is significant with >95% confidence. The calculated t-stat values are well outside the -2/2 range, indicating the coefficients are very likely to be statistically significant with a high level of confidence. The p-value is used to test the null hypothesis, and a p-value lower than 0.05 indicates that the null hypothesis may be rejected. To state this in a different way, if a coefficient has a sufficiently low p-value, changes in the predictor value are associated with changes in the response variable. If the p-value is larger, then changes in the predictor value are not associated with changes in the response variable, therefore that value should be removed and another fit attempted. All calculated p-values were well below the threshold necessary to reject the null hypothesis; therefore, all coefficients are meaningful contributions to the prediction model. The upper and lower 95% confidence level thresholds represent the range in which there is a 95% probability the coefficient lies, which means there is only a 2.5% chance that it would be above the upper value or below the lower value, and the narrower this range is the better. All upper and lower ranges were <1, again showcasing the precision of the predictor.
Once the coefficients are calculated, retention values in glucose units for
N-linked glycans can be calculated using the retention model equation:
R = ∑ Nx (Nx
Mx +b
is the number of a particular monosaccharide x present in the glycan of interest, Mx
is the coefficient of
monosaccharide x, b is the intercept of the retention model). Retention is calculated in glucose units, which can be correlated to minutes by way of the dextran ladder standard, so this model may easily be used with other LC-MS systems.
Using the retention model equation, prediction values for the N-linked glycan standards were calculated and compared to experimental values. A graph of calculated versus experimental glucose units was constructed (Figure 2), and the linear trend line of this graph displayed a high correlation coefficient (R2 = 0.9941). Standard deviation in minutes for calculated versus experimental time was calculated by converting the glucose unit values to minutes by way of the dextran ladder reference. These calculations revealed a standard deviation of ≤ ±2 minutes. The high level of correlation and linear relationship between the calculated and experimental values exhibits the effectiveness of the retention prediction model.
The ability to predict retention for glycans assists analysis in two major ways. First,
it provides the retention information for glycans that have not been observed previously. This information allows for the scheduling of selected reaction monitoring (SRM) transitions for these glycans, enhancing detection and confirming identifications during the experiment. Second, predicting retention can assist in identifying glycans subsequent to LCMS analysis. Searching software databases often results in either erroneous or incomplete identification of the observed glycans, therefore a retention model can be used to select an identity out of several possibilities or define specific details of an identified glycan. An example of this is shown in Figure 3, where the four isomers of two closely related sialic acid species are identified. Both are singly sialylated biantennary N-linked glycans, with one trace representing the α2,3- and α2,6-linked NeuAc isomers and the other representing the α2,3- and α2,6-linked NeuGc isomers. While the m/z values can distinguish which pair contains NeuAc and which pair contains NeuGc, it cannot distinguish between linkage isomers as there is no m/z difference between them. Using the retention model, it is a simple matter to assign specific linkage details to the two signals of either moiety. The versatility of the model, having been designed in such a way as to be of use with variations to experimental parameters, makes it a valuable tool for analytical procedures, both prior and subsequent to LC-MS experiments.
The use of glucose units instead of minutes for the retention model allows for its use on different instruments or with experimental parameter modifications while still maintaining prediction accuracy. As instruments can experience retention shifts over time, and since different instruments intrinsically have variables such as void volume, adaptability for retention calculations is essential. A simple experiment with a dextran ladder standard can easily provide the necessary information for either predicting retention times in a scheduled experiment or for aiding in analyte identification in a completed experiment. Another key aspect of the model as it has been constructed which enhances its versatility is that additional coefficients can easily be incorporated, which will expand its application.
Acknowlegements
Support for this work comes from NIH grant GM0 93747 to B.B.
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