Extended similarity methods for efficient data mining in imaging mass spectrometry.

Imaging mass spectrometry is a label-free imaging modality that allows for the spatial mapping of many compounds directly in tissues. In an imaging mass spectrometry experiment, a raster of the tissue surface produces a mass spectrum at each sampled x, y position, resulting in thousands of individual mass spectra, each comprising a pixel in the resulting ion images. However, efficient analysis of imaging mass spectrometry datasets can be challenging due to the hyperspectral characteristics of the data. Each spectrum contains several thousand unique compounds at discrete m/z values that result in unique ion images, which demands robust and efficient algorithms for searching, statistical analysis, and visualization. Some traditional post-processing techniques are fundamentally ill-equipped to dissect these types of data. For example, while principal component analysis (PCA) has long served as a useful tool for mining imaging mass spectrometry datasets to identify correlated analytes and biological regions of interest, the interpretation of the PCA scores and loadings can be non-trivial. The loadings often contain negative peaks in the PCA-derived pseudo-spectra, which are difficult to ascribe to underlying tissue biology. Herein, we have utilized extended similarity indices to streamline the interpretation of imaging mass spectrometry data. This novel workflow uses PCA as a pixel-selection method to parse out the most and least correlated pixels, which are then compared using the extended similarity indices. The extended similarity indices complement PCA by removing all non-physical artifacts and streamlining the interpretation of large volumes of imaging mass spectrometry spectra simultaneously. The linear complexity, O(N), of these indices suggests that large imaging mass spectrometry datasets can be analyzed in a 1 : 1 scale of time and space with respect to the size of the input data. The extended similarity indices algorithmic workflow is exemplified here by identifying discrete biological regions of mouse brain tissue.

Formation of multiple ion types during MALDI imaging mass spectrometry analysis of Mitragyna speciosa alkaloids in dosed rat brain tissue.

Mitragyna speciosa, more commonly known as kratom, has emerged as an alternative to treat chronic pain and addiction. However, the alkaloid components of kratom, which are the major contributors to kratom's pharmaceutical properties, have not yet been fully investigated. In this study, matrix-assisted laser desorption/ionization (MALDI) imaging mass spectrometry was used to map the biodistribution of three alkaloids (corynantheidine, mitragynine, and speciogynine) in rat brain tissues. The alkaloids produced three main ion types during MALDI analysis: [M + H]+, [M - H]+, and [M - 3H]+. Contrary to previous reports suggesting that the [M - H]+ and [M - 3H]+ ion types form during laser ablation, these ion types can also be produced during the MALDI matrix application process. Several strategies are proposed to accurately map the biodistribution of the alkaloids. Due to differences in the relative abundances of the ions in different biological regions of the tissue, differences in ionization efficiencies of the ions, and potential overlap of the [M - H]+ and [M - 3H]+ ion types with endogenous metabolites of the same empirical formula, a matrix that mainly produces the [M + H]+ ion type is optimal for accurate mapping of the alkaloids. Alternatively, the most abundant ion type can be mapped or the intensities of all ion types can be summed together to generate a composite image. The accuracy of each of these approaches is explored and validated.

Extended Similarity Methods for Efficient Data Mining in Imaging Mass Spectrometry.

Imaging mass spectrometry is a label-free imaging modality that allows for the spatial mapping of many compounds directly in tissues. In an imaging mass spectrometry experiment, a raster of the tissue surface produces a mass spectrum at each sampled x, y position, resulting in thousands of individual mass spectra, each comprising a pixel in the resulting ion images. However, efficient analysis of imaging mass spectrometry datasets can be challenging due to the hyperspectral characteristics of the data. Each spectrum contains several thousand unique compounds at discrete m/z values that result in unique ion images, which demands robust and efficient algorithms for searching, statistical analysis, and visualization. Some traditional post-processing techniques are fundamentally ill-equipped to dissect these types of data. For example, while principal component analysis (PCA) has long served as a useful tool for mining imaging mass spectrometry datasets to identify correlated analytes and biological regions of interest, the interpretation of the PCA scores and loadings can be non-trivial. The loadings often containing negative peaks in the PCA-derived pseudo-spectra, which are difficult to ascribe to underlying tissue biology. Herein, we have utilized extended similarity indices to streamline the interpretation of imaging mass spectrometry data. This novel workflow uses PCA as a pixel-selection method to parse out the most and least correlated pixels, which are then compared using the extended similarity indices. The extended similarity indices complement PCA by removing all non-physical artifacts and streamlining the interpretation of large volumes of IMS spectra simultaneously. The linear complexity, O(N), of these indices suggests that large imaging mass spectrometry datasets can be analyzed in a 1:1 scale of time and space with respect to the size of the input data. The extended similarity indices algorithmic workflow is exemplified here by identifying discrete biological regions of mouse brain tissue.

Selective Schiff base formation via gas-phase ion/ion reactions to enable differentiation of isobaric lipids in imaging mass spectrometry.

The separation and identification of lipids in complex mixtures are critical to deciphering their cellular functions. Failure to resolve isobaric compounds (e.g., via high mass resolution or tandem mass spectrometry) can result in incorrect identifications in mass spectrometry experiments. In imaging mass spectrometry, unresolved peaks can also result in composite images of multiple compounds, giving inaccurate depictions of molecular distributions. Gas-phase ion/ion reactions can be used to selectively react with specific chemical functional groups on a target analyte, thereby extracting it from a complex mixture and shifting its m/z value to an unobstructed region of the mass range. Herein, we use selective Schiff base formation via a novel charge inversion ion/ion reaction to purify phosphatidylserines from other isobaric (i.e., same nominal mass) lipids and reveal their singular distributions in imaging mass spectrometry. The selective Schiff base formation between singly deprotonated phosphatidylserine (PS) lipid anions and doubly charged N,N,N',N'-tetramethyl-N,N'-bis(6-oxohexyl)hexane-1,6-diaminium (TMODA) cations is performed using a modified commercial dual source hybrid Fourier transform ion cyclotron resonance (FTICR) mass spectrometer. This process is demonstrated using the isobaric lipids [PS 40:6 - H]- (m/z 834.528) and [SHexCer d38:1 - H]- (m/z 834.576), which produces [PS 40:6 + TMODA - H - H2O]+ (m/z 1186.879), and [SHexCer d38:1 + TMODA - H]+ (m/z 1204.938) product ions following the gas-phase charge inversion reaction. These product ions differ by roughly 18 Da in mass and are easily separated by low mass resolution analysis, while the isobaric precursor ions require roughly 45,000 mass resolving power (full-width at half maximum) to separate. Imaging mass spectrometry using targeted gas-phase ion/ion reactions shows distinct spatial distributions for the separated lipid product ions relative to the composite images of the unseparated precursor ions.