Exploring Ion Suppression in Mass Spectrometry Imaging of a Heterogeneous Tissue

In this study we have explored several aspects of regional analyte suppression in mass spectrometry imaging (MSI) of a heterogeneous sample, transverse cryosections of mouse brain. Olanzapine was homogeneously coated across the section prior to desorption electrospray ionization (DESI) and matrix-assisted laser desorption ionization (MALDI) mass spectrometry imaging. We employed the concept of a tissue extinction coefficient (TEC) to assess suppression of an analyte on tissue relative to its intensity in an off tissue region. We expanded the use of TEC, by first segmenting anatomical regions using graph-cuts clustering and calculating a TEC for each cluster. The single ion image of the olanzapine [M + H]⁺ ion was seen to vary considerably across the image, with anatomical features such as the white matter and hippocampus visible. While trends in regional ion suppression were conserved across MSI modalities, significant changes in the magnitude of relative regional suppression effects between techniques were seen. Notably the intensity of olanzapine was less suppressed in DESI than for MALDI. In MALDI MSI, significant differences in the concentration dependence of regional TECs were seen, with the TEC of white matter clusters exhibiting a notably stronger correlation with concentration than for clusters associated with gray matter regions. We further employed cluster-specific TECs as regional normalization factors. In comparison to published pixel-by-pixel normalization methods, regional TEC normalization exhibited superior reduction ion suppression artifacts. We also considered the usefulness of a segmentation-based approach to compare spectral information obtained from complementary modalities

NECTAR: A New Algorithm for Characterizing and Correcting Noise in QToF-Mass Spectrometry Imaging Data

A typical mass spectrometry imaging experiment yields a very high number of detected peaks, many of which are noise and thus unwanted. To select only peaks of interest, data preprocessing tasks are applied to raw data. A statistical study to characterize three types of noise in MSI QToF data (random, chemical, and background noise) is presented through NECTAR, a new NoisE CorrecTion AlgoRithm. Random noise is confirmed to be dominant at lower m/z values (∼50–400 Da) while systematic chemical noise dominates at higher m/z values (>400 Da). A statistical approach is presented to demonstrate that chemical noise can be corrected to reduce its presence by a factor of ∼3. Reducing this effect helps to determine a more reliable baseline in the spectrum and therefore a more reliable noise level. Peaks are classified according to their spatial S/N on the single ion images, and background noise is thus removed from the list of peaks of interest. This new algorithm was applied to MALDI and DESI QToF data generated from the analysis of a mouse pancreatic tissue section to demonstrate its applicability and ability to filter out these types of noise in a relevant data set. PCA and t-SNE multivariate analysis reviews of the top 4000 peaks and the final 744 and 299 denoised peak list for MALDI and DESI, respectively, suggests an effective removal of uninformative peaks and proper selection of relevant peaks

Testing for Multivariate Normality in Mass Spectrometry Imaging Data: A Robust Statistical Approach for Clustering Evaluation and the Generation of Synthetic Mass Spectrometry Imaging Data Sets

Spatial clustering is a powerful tool in mass spectrometry imaging (MSI) and has been demonstrated to be capable of differentiating tumor types, visualizing intratumor heterogeneity, and segmenting anatomical structures. Several clustering methods have been applied to mass spectrometry imaging data, but a principled comparison and evaluation of different clustering techniques presents a significant challenge. We propose that testing whether the data has a multivariate normal distribution within clusters can be used to evaluate the performance when using algorithms that assume normality in the data, such as <i>k</i>-means clustering. In cases where clustering has been performed using the cosine distance, conversion of the data to polar coordinates prior to normality testing should be performed to ensure normality is tested in the correct coordinate system. In addition to these evaluations of internal consistency, we demonstrate that the multivariate normal distribution can then be used as a basis for statistical modeling of MSI data. This allows the generation of synthetic MSI data sets with known ground truth, providing a means of external clustering evaluation. To demonstrate this, reference data from seven anatomical regions of an MSI image of a coronal section of mouse brain were modeled. From this, a set of synthetic data based on this model was generated. Results of <i>r</i>² fitting of the chi-squared quantile–quantile plots on the seven anatomical regions confirmed that the data acquired from each spatial region was found to be closer to normally distributed in polar space than in Euclidean. Finally, principal component analysis was applied to a single data set that included synthetic and real data. No significant differences were found between the two data types, indicating the suitability of these methods for generating realistic synthetic data

Two-Phase and Graph-Based Clustering Methods for Accurate and Efficient Segmentation of Large Mass Spectrometry Images

Clustering is widely used in MSI to segment anatomical features and differentiate tissue types, but existing approaches are both CPU and memory-intensive, limiting their application to small, single data sets. We propose a new approach that uses a graph-based algorithm with a two-phase sampling method that overcomes this limitation. We demonstrate the algorithm on a range of sample types and show that it can segment anatomical features that are not identified using commonly employed algorithms in MSI, and we validate our results on synthetic MSI data. We show that the algorithm is robust to fluctuations in data quality by successfully clustering data with a designed-in variance using data acquired with varying laser fluence. Finally, we show that this method is capable of generating accurate segmentations of large MSI data sets acquired on the newest generation of MSI instruments and evaluate these results by comparison with histopathology