Wavelet based classification of MALDI-IMS-MS spectra of serum N-Linked glycans from normal controls and patients diagnosed with Barrett's esophagus, high grade dysplasia, and esophageal adenocarcinoma

Profiling of complex biological samples (e.g., serum) using mass spectrometry continues to be an active area of research with a large and growing literature. Pattern recognition techniques can be effective methods for the analysis of complex data sets generated in these types of studies. Currently, we are investigating the discrimination of disease phenotypes associated with esophageal adenocarcinoma by analysis of single N-linked glycans using matrix assisted laser desorption ionization-ion mobility spectrometry-mass spectrometry (MALDI-IMS-MS). The glycans were extracted from sera of healthy (normal) controls (NC) and patients diagnosed with Barrett's Esophagus (BE), high grade dysplasia (HGD), and esophageal adenocarcinoma (EAC). MALDI-IMS-MS spectral images were collected in duplicate for these 58 serum samples: BE (14 individuals), HGD (7 individuals), EAC (20 individuals) and NC (17 individuals). Ion mobility distributions of N-linked glycans that possessed sufficient signal to noise in all 116 spectra were extracted from the images by box selection across a specific drift bin and m/z range corresponding to a single linked N-glycan ion. A composite ion mobility distribution profile was obtained for each image by sequentially splicing together the mobility distributions of each N-linked glycan across an arbitrary drift bin axis. Wavelet preprocessing of the composite ion mobility distribution profiles was performed using the discrete wavelet transform, which was coupled to a genetic algorithm for variable selection to identify a subset of wavelet coefficients within the data set that optimized the separation of the four classes (BE, HGD, EAC, and NC) in a plot of the two largest principal components of the wavelet transformed data. A discriminant developed from the wavelet coefficients identified by the pattern recognition GA correctly classified all ion mobility distribution profiles in the training set (45 individuals and 87 distribution profiles) and 23 of 26 blinds (13 individuals and 26 distribution profiles) in the prediction set. The proposed MALDI-IMS-MS and pattern recognition methodology has the potential to exploit molecules in serum samples that can serve as the basis of a potential method for cancer prescreening.