Manolo D. Plasencia

Profiling of Human Serum Glycans Associated with Liver Cancer and Cirrhosis by IMS-MS

Aberrant glycosylation of human glycoproteins is related to various physiological states, including the onset of diseases such as cancer. Consequently, the search for glycans that could be markers of diseases or targets of therapeutic drugs has been intensive. Here, we describe a high-throughput ion mobility spectrometry/mass spectrometry analysis of N-linked glycans from human serum. Distributions of glycans are assigned according to their m/z values, while ion mobility distributions provide information about glycan conformational and isomeric composition. Statistical analysis of data from 22 apparently healthy control patients and 39 individuals with known diseases (20 with cirrhosis of the liver and 19 with liver cancer) shows that ion mobility distributions for individual m/z ions appear to be sufficient to distinguish patients with liver cancer or cirrhosis. Measurements of glycan conformational and isomeric distributions by IMS-MS may provide insight that is valuable for detecting and characterizing disease states.

Resolving and assigning N-linked glycan structural isomers from ovalbumin by IMS-MS.

Ion mobility-mass spectrometry (IMS-MS) and molecular modeling techniques have been used to characterize ovalbumin N-linked glycans. Some glycans from this glycoprotein exist as multiple isomeric forms. The gas-phase separation makes it possible to resolve some isomers before MS analysis. Comparisons of experimental cross sections for selected glycan isomers with values that are calculated for iterative structures generated by molecular modeling techniques allow the assignment of sharp features to specific isomers. We focus here on an example glycan set, each having a m/z value of 1046.52 with formula [H5N4+2Na]2+, where H corresponds to a hexose, and N to a N-acetylglucosamine. This glycan appears to exist as three different isomeric forms that are assignable based on comparisons of measured and calculated cross sections. We estimate the relative ratios of the abundances of the three isomers to be in the range of ∼1.0:1.35:0.85 to ∼1.0:1.5:0.80. In total, IMS-MS analysis of ovalbumin N-linked glycans provides evidence for 19 different glycan structures corresponding to high-mannose and hybrid type carbohydrates with a total of 42 distinct features related to isomers and/or conformers.

On the dynamics of fragment isomerization in collision-induced dissociation of peptides.

The structures of peptide collision-induced dissociation (CID) product ions are investigated using ion mobility/mass spectrometry techniques combined with theoretical methods. The cross-section results are consistent with a mixture of linear and cyclic structures for both b4 and a4 fragment ions. Direct evidence for cyclic structures is essential in rationalizing the appearance of fragments with scrambled (i.e., permutated) primary structures, as the cycle may not open up where it was initially formed. It is demonstrated here that cyclic and linear a4 structures can interconvert freely as a result of collisional activation, implying that isomerization takes place prior to dissociation.

Developing liquid chromatography ion mobility mass spectometry techniques.

When a packet of ions in a buffer gas is exposed to a weak electric field, the ions will separate according to differences in their mobilities through the gas. This separation forms the basis of the analytical method known as ion mobility spectroscopy and is highly efficient, in that it can be carried out in a very short time frame (micro- to milliseconds). Recently, efforts have been made to couple the approach with liquid-phase separations and mass spectrometry in order to create a high-throughput and high-coverage approach for analyzing complex mixtures. This article reviews recent work to develop this approach for proteomics analyses. The instrumentation is described briefly. Several multidimensional data sets obtained upon analyzing complex mixtures are shown in order to illustrate the approach as well as provide a view of the limitations and required future work.

Delineating Diseases by IMS-MS Profiling of Serum N-linked Glycans

Altered branching and aberrant expression of N-linked glycans is known to be associated with disease states such as cancer. However, the complexity of determining such variations hinders the development of specific glycomic approaches for assessing disease states. Here, we examine a combination of ion mobility spectrometry (IMS) and mass spectrometry (MS) measurements, with principal component analysis (PCA) for characterizing serum N-linked glycans from 81 individuals: 28 with cirrhosis of the liver, 25 with liver cancer, and 28 apparently healthy. Supervised PCA of combined ion-mobility profiles for several, to as many as 10 different mass-to-charge ratios for glycan ions, improves the delineation of diseased states. This extends an earlier study [J. Proteome Res.2008, 7, 1109-1117] of isomers associated with a single glycan (S(1)H(5)N(4)) in which PCA analysis of the IMS profiles appeared to differentiate the liver cancer group from the other samples. Although performed on a limited number of test subjects, the combination of IMS-MS for different combinations of ions and multivariate PCA analysis shows promise for characterizing disease states.

Artificial neural networks for the prediction of peptide drift time in ion mobility mass spectrometry

BackgroundThere is an increasing usage of ion mobility-mass spectrometry (IMMS) in proteomics. IMMS combines the features of ion mobility spectrometry (IMS) and mass spectrometry (MS). It separates and detects peptide ions on a millisecond time-scale. IMS separates peptide ions based on drift time that is determined by the collision cross-section of each peptide ion in a given experiment condition. A peptide ions collision cross-section is related to the ion size and shape resulted from the peptide amino acid sequence and their modifications. This inherent relation between the drift time of peptide ion and peptide sequence indicates that the drift time of peptide ions can be used to infer peptide sequence and therefore, for peptide identification.ResultsThis paper describes an artificial neural networks (ANNs) regression model for the prediction of peptide ion drift time in IMMS. Each peptide in this work was represented using three descriptors (i.e., molecular weight, sequence length and a two-dimensional sequence index). An ANN predictor consisting of four input nodes, three hidden nodes and one output node was constructed for peptide ion drift time prediction. For the model training and testing, a 10-fold cross-validation strategy was employed for three datasets each containing different charge states. Dataset one contains 212 singly-charged peptide ions, dataset two has 306 doubly-charged peptide ions, and dataset three has 77 triply-charged peptide ions. Our proposed method achieved 94.4%, 93.6% and 74.2% prediction accuracy for singly-, doubly- and triply-charged peptide ions, respectively.ConclusionsAn ANN-based method has been developed for predicting the drift time of peptide ions in IMMS. The results achieved here demonstrate the effectiveness and efficiency of the prediction model. This work can enhance the confidence of protein identification by combining with current database search approaches for protein identification.

Prediction of peptide drift time in ion mobility-mass spectrometry

Background Understanding the proteome, the structure and function of each protein, and the interactions among proteins will give clues to search useful targets and biomarkers for pharmaceutical design. Peptide drift time prediction in IMMS will improve the confidence of peptide identification by limiting the peptide search space during MS/MS database searching and therefore reducing false discovery rate (FDR) of protein identification. A peptide drift time prediction method was proposed here using an artificial neural networks (ANN) regression model. We test our proposed model on three peptide datasets with different charge state assignment (see Table 1). The results can be found in Figure 1, where a higher prediction performance was achieved, over 0.9 for CI and C2, as well as 0.75 for C3.

Oscillations of chiral preference in proline clusters.

Ion mobility/mass spectrometry techniques are used to study the chiral preferences of small proline clusters (containing 2 to 23 proline monomers) produced by electrospray ionization. By varying the composition of the electrospray solution from enantiomerically pure (100% L or 100% D) to racemic (50:50 L:D), it is possible to delineate which cluster sizes prefer homochiral (resolved) or heterochiral (antiresolved) compositions. The results show a remarkable oscillation in chiral preference. Singly protonated clusters, [xPro+H](+) (where x corresponds to the number of prolines), favor homochiral assemblies (for x = 4, 6, 11 and 12); heterochiral structures are preferred (although the preferences are not as strong) for x = 5 and 7. Larger, doubly protonated clusters [xPro+2H](2+) favor homochiral assemblies for x = 18, 19, and 23 and heterochiral structures for x = 14, 16, 17, 20, 21, and 22. Some of the variations that are observed can be rationalized through simple structures that would lead to especially stable geometries. It is suggested that some antiresolved clusters, such as [22Pro+2H](2+), may be comprised of resolved D- and L-proline domains.

Prediction of Drift Time in Ion Mobility-Mass Spectrometry Based on Peptide Molecular Weight

A computational model is introduced for predicting peptide drift time in ion mobility-mass spectrometry (IMMS). Each peptide was represented using a numeric descriptor: molecular weight. A simple linear regression predictor was constructed for peptides drift time prediction. Three datasets with different charge state assignments were used for the model training and testing. The dataset one contains 212 singly charged peptides, dataset two has 306 doubly charged peptides, and dataset three contains 77 triply charged peptides. Our proposed method achieved a prediction accuracy of 86.3%, 72.6%, and 59.7% for the dataset one, two and three, respectively. Peptide drift time prediction in IMMS will improve the confidence of peptide identifications by limiting the peptide search space during MS/MS database searching and therefore, reducing false discovery rate (FDR) of protein identification.