Method of atmospheric pressure charge stripping for electrospray ionization mass spectrometry and its application for the analysis of large poly(ethylene glycol)s.

We introduce a new atmospheric pressure charge stripping (AP-CS) method for the electrospray ionization mass spectrometry (ESI-MS) analysis of heterogeneous mixtures, utilizing ion/ion proton transfer reactions within an experimental ion source to remove excess charge from sample ions and thereby reduce spectral congestion. The new method enables the extent of charge stripping to be easily controlled, independent of primary ionization, and there are no complications due to adduct formation. Here, we demonstrate AP-CS with a Xevo G2-S Q-TOF from Waters-Micromass using an ion source originally designed for atmospheric pressure-electron capture dissociation (AP-ECD) experiments; repurposing the AP-ECD ion source for AP-CS requires only adding a supplemental reagent (e.g., a perfluorocompound) to scavenge the electrons and generate anions for the charge-stripping reactions. Results from model peptides are first presented to demonstrate the basic method, including differences between the AP-CS and AP-ECD operating modes, and how the extent of charge stripping may be controlled. This is followed by a demonstration of AP-CS for the ESI-MS analysis of several large poly(ethylene glycol)s (PEGs), up to 40 kDa, typical of those used in biopharmaceutical development.

Two-Dimensional Clustering of Spectral Changes for the Interpretation of Raman Hyperspectra.

Two-dimensional correlation spectroscopy (2D-COS) is a technique that permits the examination of synchronous and asynchronous changes present in hyperspectral data. It produces two-dimensional correlation coefficient maps that represent the mutually correlated changes occurring at all Raman wavenumbers during an implemented perturbation. To focus our analysis on clusters of wavenumbers that tend to change together, we apply a k-means clustering to the wavenumber profiles in the perturbation domain decomposition of the two-dimensional correlation coefficient map. These profiles (or trends) reflect peak intensity changes as a function of the perturbation. We then plot the co-occurrences of cluster members two-dimensionally in a manner analogous to a two-dimensional correlation coefficient map. Because wavenumber profiles are clustered based on their similarity, two-dimensional cluster member spectra reveal which Raman peaks change in a similar manner, rather than how much they are correlated. Furthermore, clustering produces a discrete partitioning of the wavenumbers, thus a two-dimensional cluster member spectrum exhibits a discrete presentation of related Raman peaks as opposed to the more continuous representations in a two-dimensional correlation coefficient map. We demonstrate first the basic principles of the technique with the aid of synthetic data. We then apply it to Raman spectra obtained from a polystyrene perchlorate model system followed by Raman spectra from mammalian cells fixed with different percentages of methanol. Both data sets were designed to produce differential changes in sample components. In both cases, all the peaks pertaining to a given component should then change in a similar manner. We observed that component-based profile clustering did occur for polystyrene and perchlorate in the model system and lipids, nucleic acids, and proteins in the mammalian cell example. This confirmed that the method can translate to "real world" samples. We contrast these results with two-dimensional correlation spectroscopy results. To supplement interpretation, we present the cluster-segmented mean spectrum of the hyperspectral data. Overall, this technique is expected to be a valuable adjunct to two-dimensional correlation spectroscopy to further facilitate hyperspectral data interpretation and analysis.