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

Randomized Approximation Methods for the Efficient Compression and Analysis of Hyperspectral Data

Hyperspectral imaging techniques such as matrix-assisted laser desorption ionization (MALDI) mass spectrometry imaging produce large, information-rich datasets that are frequently too large to be analyzed as a whole. In addition, the “curse of dimensionality” adds fundamental limits to what can be done with such data, regardless of the resources available. We propose and evaluate random matrix-based methods for the analysis of such data, in this case, a MALDI mass spectrometry image from a section of rat brain. By constructing a randomized orthornormal basis for the data, we are able to achieve reductions in dimensionality and data size of over 100 times. Furthermore, this compression is reversible to within noise limits. This allows more-conventional multivariate analysis techniques such as principal component analysis (PCA) and clustering methods to be directly applied to the compressed data such that the results can easily be back-projected and interpreted in the original measurement space. PCA on the compressed data is shown to be nearly identical to the same analysis on the original data but the run time was reduced from over an hour to 8 seconds. We also demonstrate the generality of the method to other data sets, namely, a hyperspectral optical image of leaves, and a Raman spectroscopy image of an artificial ligament. In order to allow for the full evaluation of these methods on a wide range of data, we have made all software and sample data freely available

Importance of Sample Form and Surface Temperature for Analysis by Ambient Plasma Mass Spectrometry (PADI)

Many different types of samples have been analyzed in the literature using plasma-based ambient mass spectrometry sources; however, comprehensive studies of the important parameters for analysis are only just beginning. Here, we investigate the effect of the sample form and surface temperature on the signal intensities in plasma-assisted desorption ionization (PADI). The form of the sample is very important, with powders of all volatilities effectively analyzed. However, for the analysis of thin films at room temperature and using a low plasma power, a vapor pressure of greater than 10^–4 Pa is required to achieve a sufficiently good quality spectrum. Using thermal desorption, we are able to increase the signal intensity of less volatile materials with vapor pressures less than 10^–4 Pa, in thin film form, by between 4 and 7 orders of magnitude. This is achieved by increasing the temperature of the sample up to a maximum of 200 °C. Thermal desorption can also increase the signal intensity for the analysis of powders

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

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

MALDI Imaging of Liquid Extraction Surface Analysis Sampled Tissue

Combined mass spectrometry imaging methods in which two different techniques are executed on the same sample have recently been reported for a number of sample types. Such an approach can be used to examine the sampling effects of the first technique with a second, higher resolution method and also combines the advantages of each technique for a more complete analysis. In this work matrix-assisted laser desorption ionization mass spectrometry imaging (MALDI MSI) was used to study the effects of liquid extraction surface analysis (LESA) sampling on mouse brain tissue. Complementary multivariate analysis techniques including principal component analysis, non-negative matrix factorization, and <i>t</i>-distributed stochastic neighbor embedding were applied to MALDI MS images acquired from tissue which had been sampled by LESA to gain a better understanding of localized tissue washing in LESA sampling. It was found that MALDI MS images could be used to visualize regions sampled by LESA. The variability in sampling area, spatial precision, and delocalization of analytes in tissue induced by LESA were assessed using both single-ion images and images provided by multivariate analysis

LESA FAIMS Mass Spectrometry for the Spatial Profiling of Proteins from Tissue

We have shown previously that coupling of high field asymmetric waveform ion mobility spectrometry (FAIMS), also known as differential ion mobility, with liquid extraction surface analysis (LESA) mass spectrometry of tissue results in significant improvements in the resulting protein mass spectra. Here, we demonstrate LESA FAIMS mass spectrometry imaging of proteins in sections of mouse brain and liver tissue. The results are compared with LESA mass spectrometry images obtained in the absence of FAIMS. The results show that the number of different protein species detected can be significantly increased by incorporating FAIMS into the workflow. A total of 34 proteins were detected by LESA FAIMS mass spectrometry imaging of mouse brain, of which 26 were unique to FAIMS, compared with 15 proteins (7 unique) detected by LESA mass spectrometry imaging. A number of proteins were identified including α-globin, 6.8 kDa mitochondrial proteolipid, macrophage migration inhibitory factor, ubiquitin, β-thymosin 4, and calmodulin. A total of 40 species were detected by LESA FAIMS mass spectrometry imaging of mouse liver, of which 29 were unique to FAIMS, compared with 24 proteins (13 unique) detected by LESA mass spectrometry imaging. The spatial distributions of proteins identified in both LESA mass spectrometry imaging and LESA FAIMS mass spectrometry imaging were in good agreement indicating that FAIMS is a suitable tool for inclusion in mass spectrometry imaging workflows

Raster-Mode Continuous-Flow Liquid Microjunction Mass Spectrometry Imaging of Proteins in Thin Tissue Sections

Mass spectrometry imaging by use of continuous-flow liquid microjunction sampling at discrete locations (array mode) has previously been demonstrated. In this Letter, we demonstrate continuous-flow liquid microjunction mass spectrometry imaging of proteins from thin tissue sections in raster mode and discuss advantages (a 10-fold reduction in analysis time) and challenges (suitable solvent systems, data interpretation) of the approach. Visualization of data is nontrivial, requiring correlation of solvent-flow, mass spectral data acquisition rate, data quality, and liquid microjunction sampling area. The latter is particularly important for determining optimum pixel size. The minimum achievable pixel size is related to the scan time of the instrument used. Here we show a minimum achievable pixel size of 50 μm (<i>x</i>-dimension) when using an Orbitrap Elite; however a pixel size of 600 μm is recommended in order to minimize the effects of oversampling on image accuracy

Memory Efficient Principal Component Analysis for the Dimensionality Reduction of Large Mass Spectrometry Imaging Data Sets

A memory efficient algorithm for the computation of principal component analysis (PCA) of large mass spectrometry imaging data sets is presented. Mass spectrometry imaging (MSI) enables two- and three-dimensional overviews of hundreds of unlabeled molecular species in complex samples such as intact tissue. PCA, in combination with data binning or other reduction algorithms, has been widely used in the unsupervised processing of MSI data and as a dimentionality reduction method prior to clustering and spatial segmentation. Standard implementations of PCA require the data to be stored in random access memory. This imposes an upper limit on the amount of data that can be processed, necessitating a compromise between the number of pixels and the number of peaks to include. With increasing interest in multivariate analysis of large 3D multislice data sets and ongoing improvements in instrumentation, the ability to retain all pixels and many more peaks is increasingly important. We present a new method which has no limitation on the number of pixels and allows an increased number of peaks to be retained. The new technique was validated against the MATLAB (The MathWorks Inc., Natick, Massachusetts) implementation of PCA (<i>princomp</i>) and then used to reduce, without discarding peaks or pixels, multiple serial sections acquired from a single mouse brain which was too large to be analyzed with <i>princomp</i>. Then, <i>k</i>-means clustering was performed on the reduced data set. We further demonstrate with simulated data of 83 slices, comprising 20 535 pixels per slice and equaling 44 GB of data, that the new method can be used in combination with existing tools to process an entire organ. MATLAB code implementing the memory efficient PCA algorithm is provided

Hemoglobin Variant Analysis via Direct Surface Sampling of Dried Blood Spots Coupled with High-Resolution Mass Spectrometry

Hemoglobinopathies are the most common inherited disorders. Newborn blood screening for clinically significant hemoglobin variants, including sickle (HbS), HbC, and HbD, has been adopted in many countries as it is widely acknowledged that early detection improves the outcome. We present a method for determination of Hb variants by direct surface sampling of dried blood spots by use of an Advion Triversa Nanomate automated electrospray system coupled to a high-resolution mass spectrometer. The method involves no sample preparation. It is possible to unambiguously identify homozygous and heterozygous HbS, HbC, and HbD variants in <10 min without the need for additional confirmation. The method allows for repeated analysis of a single blood spot over a prolonged time period and is tolerant of blood spot storage conditions