Mass Spectrometry Imaging Datasets Research Articles

MassImager: A software for interactive and in-depth analysis of mass spectrometry imaging data

Mass spectrometry imaging (MSI) has become a powerful tool to probe molecule events in biological tissue. However, it is a widely held viewpoint that one of the biggest challenges is an easy-to-use data processing software for discovering the underlying biological information from complicated and huge MSI dataset. Here, a user-friendly and full-featured MSI software including three subsystems, Solution, Visualization and Intelligence, named MassImager, is developed focusing on interactive visualization, in-situ biomarker discovery and artificial intelligent pathological diagnosis. Simplified data preprocessing and high-throughput MSI data exchange, serialization jointly guarantee the quick reconstruction of ion image and rapid analysis of dozens of gigabytes datasets. It also offers diverse self-defined operations for visual processing, including multiple ion visualization, multiple channel superposition, image normalization, visual resolution enhancement and image filter. Regions-of-interest analysis can be performed precisely through the interactive visualization between the ion images and mass spectra, also the overlaid optical image guide, to directly find out the region-specific biomarkers. Moreover, automatic pattern recognition can be achieved immediately upon the supervised or unsupervised multivariate statistical modeling. Clear discrimination between cancer tissue and adjacent tissue within a MSI dataset can be seen in the generated pattern image, which shows great potential in visually in-situ biomarker discovery and artificial intelligent pathological diagnosis of cancer. All the features are integrated together in MassImager to provide a deep MSI processing solution at the in-situ metabolomics level for biomarker discovery and future clinical pathological diagnosis.

Towards enhanced metabolomic data analysis of mass spectrometry image: Multivariate Curve Resolution and Machine Learning

Large amounts of data are generally produced from mass spectrometry imaging (MSI) experiments in obtaining the molecular and spatial information of biological samples. Traditionally, MS images are constructed using manually selected ions, and it is very challenging to comprehensively analyze MSI results due to their large data sizes and highly complex data structures. To overcome these barriers, it is obligatory to develop advanced data analysis approaches to handle the increasingly large MSI data. In the current study, we focused on the method development of using Multivariate Curve Resolution (MCR) and Machine Learning (ML) approaches. We aimed to effectively extract the essential information present in the large and complex MSI data and enhance the metabolomic data analysis of biological tissues. Multivariate Curve Resolution-Alternating Least Squares (MCR-ALS) algorithm was used to obtain major patterns of spatial distribution and grouped metabolites with the same spatial distribution patterns. In addition, both supervised and unsupervised ML methods were established to analyze the MSI data. In the supervised ML approach, Random Forest method was selected, and the model was trained using the selected datasets based on the distribution pattern obtained from MCR-ALS analyses. In the unsupervised ML approach, both DBSCAN (Density-based Spatial Clustering of Applications with Noise) and CLARA (Clustering Large Applications) were applied to cluster the MSI datasets. It is worth noting that similar patterns of spatial distribution were discovered through MSI data analysis using MCR-ALS, supervised ML, and unsupervised ML. Our protocols of data analysis can be applied to process the data acquired using many other types of MSI techniques, and to extract the overall features present in MSI results that are intractable using traditional data analysis approaches.

Interactive Visual Exploration of 3D Mass Spectrometry Imaging Data Using Hierarchical Stochastic Neighbor Embedding Reveals Spatiomolecular Structures at Full Data Resolution.

Technological advances in mass spectrometry imaging (MSI) have contributed to growing interest in 3D MSI. However, the large size of 3D MSI data sets has made their efficient analysis and visualization and the identification of informative molecular patterns computationally challenging. Hierarchical stochastic neighbor embedding (HSNE), a nonlinear dimensionality reduction technique that aims at finding hierarchical and multiscale representations of large data sets, is a recent development that enables the analysis of millions of data points, with manageable time and memory complexities. We demonstrate that HSNE can be used to analyze large 3D MSI data sets at full mass spectral and spatial resolution. To benchmark the technique as well as demonstrate its broad applicability, we have analyzed a number of publicly available 3D MSI data sets, recorded from various biological systems and spanning different mass-spectrometry ionization techniques. We demonstrate that HSNE is able to rapidly identify regions of interest within these large high-dimensionality data sets as well as aid the identification of molecular ions that characterize these regions of interest; furthermore, through clearly separating measurement artifacts, the HSNE analysis exhibits a degree of robustness to measurement batch effects, spatially correlated noise, and mass spectral misalignment.

3D MALDI Mass Spectrometry Imaging of a Single Cell: Spatial Mapping of Lipids in the Embryonic Development of Zebrafish

The zebrafish (Danio rerio) has been widely used as a model vertebrate system to study lipid metabolism, the roles of lipids in diseases, and lipid dynamics in embryonic development. Here, we applied high-spatial resolution matrix-assisted laser desorption/ionization (MALDI)-mass spectrometry imaging (MSI) to map and visualize the three-dimensional spatial distribution of phospholipid classes, phosphatidylcholine (PC), phosphatidylethanolamines (PE), and phosphatidylinositol (PI), in newly fertilized individual zebrafish embryos. This is the first time MALDI-MSI has been applied for three dimensional chemical imaging of a single cell. PC molecular species are present inside the yolk in addition to the blastodisc, while PE and PI species are mostly absent in the yolk. Two-dimensional MSI was also studied for embryos at different cell stages (1-, 2-, 4-, 8-, and 16-cell stage) to investigate the localization changes of some lipids at various cell developmental stages. Four different normalization approaches were compared to find reliable relative quantification in 2D- and 3D- MALDI MSI data sets.

Automated Morphological and Morphometric Analysis of Mass Spectrometry Imaging Data: Application to Biomarker Discovery.

Mass spectrometry imaging datasets are mostly analyzed in terms of average intensity in regions of interest. However, biological tissues have different morphologies with several sizes, shapes, and structures. The important biological information, contained in this highly heterogeneous cellular organization, could be hidden by analyzing the average intensities. Finding an analytical process of morphology would help to find such information, describe tissue model, and support identification of biomarkers. This study describes an informatics approach for the extraction and identification of mass spectrometry image features and its application to sample analysis and modeling. For the proof of concept, two different tissue types (healthy kidney and CT-26 xenograft tumor tissues) were imaged and analyzed. A mouse kidney model and tumor model were generated using morphometric - number of objects and total surface - information. The morphometric information was used to identify m/z that have a heterogeneous distribution. It seems to be a worthwhile pursuit as clonal heterogeneity in a tumor is of clinical relevance. This study provides a new approach to find biomarker or support tissue classification with more information. Graphical Abstract ᅟ.

OpenMSI Arrayed Analysis Toolkit: Analyzing Spatially Defined Samples Using Mass Spectrometry Imaging.

Mass spectrometry imaging (MSI) has primarily been applied in localizing biomolecules within biological matrices. Although well-suited, the application of MSI for comparing thousands of spatially defined spotted samples has been limited. One reason for this is a lack of suitable and accessible data processing tools for the analysis of large arrayed MSI sample sets. The OpenMSI Arrayed Analysis Toolkit (OMAAT) is a software package that addresses the challenges of analyzing spatially defined samples in MSI data sets. OMAAT is written in Python and is integrated with OpenMSI ( http://openmsi.nersc.gov ), a platform for storing, sharing, and analyzing MSI data. By using a web-based python notebook (Jupyter), OMAAT is accessible to anyone without programming experience yet allows experienced users to leverage all features. OMAAT was evaluated by analyzing an MSI data set of a high-throughput glycoside hydrolase activity screen comprising 384 samples arrayed onto a NIMS surface at a 450 μm spacing, decreasing analysis time >100-fold while maintaining robust spot-finding. The utility of OMAAT was demonstrated for screening metabolic activities of different sized soil particles, including hydrolysis of sugars, revealing a pattern of size dependent activities. These results introduce OMAAT as an effective toolkit for analyzing spatially defined samples in MSI. OMAAT runs on all major operating systems, and the source code can be obtained from the following GitHub repository: https://github.com/biorack/omaat .

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 k-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 r2 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.

Abstract 4231: Analysis of FFPE treated clinical tissue sections obtained from human intraocular malignancy, uveal melanoma by mass spectrometry imaging (MSI)

Abstract Introduction Clinical research into human intraocular disease UM FFPE tissue sections is described. UM remains the most common intraocular malignancy in adults, with poor prognosis for UM within the choroid region and distant sites UM metastasis. Two imaging MS techniques Matrix Assisted Laser Desorption/Ionisation (MALDI) and Desorption Electrospray Ionisation (DESI) MSI were applied. Molecular profiles were obtained and analysed by multivariate statistical approaches, providing insight into the biochemical and biological differences/similarities within the patient sample cohort (n = 15). Methods Enucleations were collected over 6 years and subjected to the standard fixation and paraffin embedding protocols. In preparation for MS, 5 μm sections were produced with removal of the paraffin followed by heat induced antigen retrieval. MALDI MSI tissue sections were prepared by applying a matrix solution onto the tissue sections to help ionization of molecules directly from the tissue. All MSI experiments (MALDI and DESI) were carried out using a SYNAPT mass spectrometer (Waters Corporation, Manchester, UK). Multivariate analyses were performed using MATLAB (MathWorks, Inc., Natick, MA, US) and the Eigenvector PLS_Toolbox. Results Initial MALDI MSI images acquired with a spatial resolution of 100 μm x 100 μm in positive ion mode showed distinct spatial distribution of many molecular species throughout the choroid, cornea, retina, lens and UM tumor regions. Further MALDI MSI experiments using consecutive tissue sections at 50 μm x 50 μm spatial resolution show substantial variation in the spatial distribution of species within the low molecular mass range. DESI MSI experiments were carried out at 200 μm x 200 μm in negative ionization mode. Deprotonated molecular ions were detected from a variety of lipid related species, localized to specific regions within the eye including the tumor region. Multivariate analysis classified UM samples (good vs. poor prognosis). Using unsupervised PCA, an unbiased representation of the data was generated, with clear sample grouping and differentiation observed based upon tumor status. Use of the supervised PLS-DA technique provided even clearer separation between the selected samples, with tumor profiles displaying dominant discriminatory peaks. These peaks could be identified as sphingolipids and Lyso-phosphocholine, a phosphatidylcholine degradation product. Conclusions It was possible to analyze clinical research FFPE tissue sections by MALDI and DESI MSI, illustrating that specific small molecular species remained localized to certain tissue types including the UM tumor. Initial correlation with tumor status was determined from the statistical analysis of the MALDI MSI datasets. Citation Format: Laura M. Cole, Hardeep S. Mudhar, Karen Sisley, Andrew Peck, Mike Batey, Emmanuelle Claude, Malcolm Clench. Analysis of FFPE treated clinical tissue sections obtained from human intraocular malignancy, uveal melanoma by mass spectrometry imaging (MSI). [abstract]. In: Proceedings of the 107th Annual Meeting of the American Association for Cancer Research; 2016 Apr 16-20; New Orleans, LA. Philadelphia (PA): AACR; Cancer Res 2016;76(14 Suppl):Abstract nr 4231.

Spatial Autocorrelation in Mass Spectrometry Imaging.

Mass spectrometry imaging (MSI) is a powerful molecular imaging technique. In microprobe MSI, images are created through a grid-wise interrogation of individual spots by mass spectrometry across a surface. Classical statistical tests for within-sample comparisons fail as close-by measurement spots violate the assumption of independence of these tests, which can lead to an increased false-discovery rate. For spatial data, this effect is referred to as spatial autocorrelation. In this study, we investigated spatial autocorrelation in three different matrix-assisted laser desorption/ionization MSI data sets. These data sets cover different molecular classes (metabolites/drugs, lipids, and proteins) and different spatial resolutions ranging from 20 to 100 μm. Significant spatial autocorrelation was detected in all three data sets and found to increase with decreasing pixel size. To enable statistical testing for differences in mass signal intensities between regions of interest within MSI data sets, we propose the use of Conditional Autoregressive (CAR) models. We show that, by accounting for spatial autocorrelation, discovery rates (i.e., the ratio between the features identified and the total number of features) could be reduced between 21% and 69%. The reliability of this approach was validated by control mass signals based on prior knowledge. In light of the advent of larger MSI data sets based on either an increased spatial resolution or 3D data sets, accounting for effects due to spatial autocorrelation becomes even more indispensable. Here, we propose a generic and easily applicable workflow to enable within-sample statistical comparisons.

Mass Spectrometric Imaging of Gold Nanolayer Coated Latent Fingermarks: Deciphering Overlapping Features by Statistical Analysis

Overlapping latent fingermarks constitute a serious challenge to database related recognition and matching algorithms in biometry, forensic and crime scene investigations. Mass spectrometry imaging (MSI) is a powerful tool for deciphering and analyzing overlapping fingermarks based on the individual chemical information of each deposit. Fingermark MSI in practice still requires a subjective judgment of an MSI expert, such that rapid analysis, automation, standardization, and a quantitative evaluation of the complete detection and separation process of overlapped fingermarks from MSI data sets is the ultimate goal and will be necessary to become an accepted process in criminal investigations and law enforcement. Here we investigated the feasibility and efficiency of different statistical approaches for the separation of overlapped latent fingermarks based on MSI data. Entropy analysis of generated m/z-images was used to evaluate the results obtained from the statistical analysis. Furthermore, we demonstrate and discuss the opportunity to reconstitute and separate overlapping fingermarks by discrete scanning at selected x,y-positions defined from a previous image analysis using a more simple schema based on visible and therefore optical distinguishable overlapped ink-based fingermarks. The overlapped latent fingermarks were developed by rapid gold sputter coating and analyzed by laser based MSI, without (organic) matrix preparation. Latent finger marks can be transferred from the substrate/surface with and conserved on a soft gold sputtered soft membrane at low temperatures.

Large-scale mass spectrometry imaging investigation of consequences of cortical spreading depression in a transgenic mouse model of migraine.

Cortical spreading depression (CSD) is the electrophysiological correlate of migraine aura. Transgenic mice carrying the R192Q missense mutation in the Cacna1a gene, which in patients causes familial hemiplegic migraine type 1 (FHM1), exhibit increased propensity to CSD. Herein, mass spectrometry imaging (MSI) was applied for the first time to an animal cohort of transgenic and wild type mice to study the biomolecular changes following CSD in the brain. Ninety-six coronal brain sections from 32 mice were analyzed by MALDI-MSI. All MSI datasets were registered to the Allen Brain Atlas reference atlas of the mouse brain so that the molecular signatures of distinct brain regions could be compared. A number of metabolites and peptides showed substantial changes in the brain associated with CSD. Among those, different mass spectral features showed significant (t-test, P < 0.05) changes in the cortex, 146 and 377 Da, and in the thalamus, 1820 and 1834 Da, of the CSD-affected hemisphere of FHM1 R192Q mice. Our findings reveal CSD- and genotype-specific molecular changes in the brain of FHM1 transgenic mice that may further our understanding about the role of CSD in migraine pathophysiology. The results also demonstrate the utility of aligning MSI datasets to a common reference atlas for large-scale MSI investigations.Graphical ᅟElectronic supplementary materialThe online version of this article (doi:10.1007/s13361-015-1136-8) contains supplementary material, which is available to authorized users.

Comprehensive analysis of the mouse brain proteome sampled in mass spectrometry imaging.

On-tissue enzymatic digestion is performed in mass spectrometry imaging (MSI) experiments to access larger proteins and to assign protein identities. Most on-tissue digestion MSI studies have focused on method development rather than identifying the molecular features observed. Herein, we report a comprehensive study of the mouse brain proteome sampled by MSI. Using complementary proteases, we were able to identify 5337 peptides in the matrix-assisted laser desorption/ionization (MALDI) matrix, corresponding to 1198 proteins. 630 of these peptides, corresponding to 280 proteins, could be assigned to peaks in MSI data sets. Gene ontology and pathway analyses revealed that many of the proteins are involved in neurodegenerative disorders, such as Alzheimer's, Parkinson's, and Huntington's disease.

A public repository for mass spectrometry imaging data.

Mass spectrometry (MS) imaging is a very active field of research, and has seen impressive progress in recent years [1, 2]. The number of groups that are working on this topic is constantly increasing. However, the field is still very heterogeneous in terms of applied instrumentation and data processing methods. In addition, complex datasets are reduced to a set of two-dimensional “images,” which inevitably results in information loss. This simplified graphical representation also strongly depends on processing options such as color scale, intensity normalization, and spatial interpolation. Consequently, experimental data are presented in very diverse ways, and published results can therefore be difficult to evaluate and compare. With a growing number of published studies, the issue of standardization and quality control of MS imaging data is becoming more important. This is a natural process for any new field that is maturing. The MS-based proteomics community has been facing similar issues in the last decade, and this discipline is therefore discussed as a “role model” herein. Since its inception in 2002, the Proteomics Standards Initiative (http://www.psidev.info) has driven the development of a number of minimum reporting guidelines (called “minimum information about a proteomics experiment” documents) [3] and several standard data formats for the different data types relevant in proteomics. For example, for raw and processed MS data, the data standard is called mzML [4]. In addition, several data repositories were established about 10 years ago to address the demand for storage and availability of MS data in the public domain [5–9]. A big step forward in this area has been the establishment of the ProteomeXchange (PX; http://www.proteomexchange.org/) consortium [10], led by the PRIDE [9] and PeptideAtlas [8] resources. The overall aim of PX is to provide a common framework and infrastructure for the cooperation of proteomics resources by defining and implementing consistent, harmonized, user-friendly data deposition and exchange procedures among the members. Thanks to the guidelines promoted by several scientific journals and funding agencies, and the general perception that sharing data is good scientific practice, the culture in the proteomics community has evolved toward data deposition as part of the publication process. In analogy to these activities in the MS proteomics field, similar mechanisms have been discussed and to some extent already implemented in the MS imaging community in recent years. A common data format for MS imaging—imzML—has been established [11]. This format is being used more and more, and the number of available tools is constantly growing (see http://www.imzml.org for more details). Reporting guidelines have been discussed for several years, and a first suggestion of those is included in this topical collection [12]. Nevertheless, owing to the lack of suitable resources, a missing element so far has been the possibility to make MS imaging datasets available in the public domain. Earlier attempts to develop a data repository were abandoned mainly because of the large size of MS imaging datasets. However, nowadays very large datasets (i.e., file size on the order of a few terabytes) can also be generated in MS-based proteomic and metabolomic studies, and can be submitted to established repositories. From a purely technical point of view, the infrastructure available in existing MS repositories is also suited for MS imaging data. Therefore, the missing step is to define and adopt a submission procedure in order to be compatible with MS-imaging-specific parameters. Here we describe the newly implemented way of submitting MS imaging data to PX via the PRIDE database. We also describe how to retrieve these data and to reproduce the MS images.

Standardized processing of MALDI imaging raw data for enhancement of weak analyte signals in mouse models of gastric cancer and Alzheimer's disease.

Conventional mass spectrometry image preprocessing methods used for denoising, such as the Savitzky-Golay smoothing or discrete wavelet transformation, typically do not only remove noise but also weak signals. Recently, memory-efficient principal component analysis (PCA) in conjunction with random projections (RP) has been proposed for reversible compression and analysis of large mass spectrometry imaging datasets. It considers single-pixel spectra in their local context and consequently offers the prospect of using information from the spectra of adjacent pixels for denoising or signal enhancement. However, little systematic analysis of key RP-PCA parameters has been reported so far, and the utility and validity of this method for context-dependent enhancement of known medically or pharmacologically relevant weak analyte signals in linear-mode matrix-assisted laser desorption/ionization (MALDI) mass spectra has not been explored yet. Here, we investigate MALDI imaging datasets from mouse models of Alzheimer's disease and gastric cancer to systematically assess the importance of selecting the right number of random projections k and of principal components (PCs) L for reconstructing reproducibly denoised images after compression. We provide detailed quantitative data for comparison of RP-PCA-denoising with the Savitzky-Golay and wavelet-based denoising in these mouse models as a resource for the mass spectrometry imaging community. Most importantly, we demonstrate that RP-PCA preprocessing can enhance signals of low-intensity amyloid-β peptide isoforms such as Aβ1-26 even in sparsely distributed Alzheimer's β-amyloid plaques and that it enables enhanced imaging of multiply acetylated histone H4 isoforms in response to pharmacological histone deacetylase inhibition in vivo. We conclude that RP-PCA denoising may be a useful preprocessing step in biomarker discovery workflows.

Software tools of the Computis European project to process mass spectrometry images.

Among the needs usually expressed by teams using mass spectrometry imaging, one that often arises is that for user-friendly software able to manage huge data volumes quickly and to provide efficient assistance for the interpretation of data. To answer this need, the Computis European project developed several complementary software tools to process mass spectrometry imaging data. Data Cube Explorer provides a simple spatial and spectral exploration for matrix-assisted laser desorption/ionisation-time of flight (MALDI-ToF) and time of flight-secondary-ion mass spectrometry (ToF-SIMS) data. SpectViewer offers visualisation functions, assistance to the interpretation of data, classification functionalities, peak list extraction to interrogate biological database and image overlay, and it can process data issued from MALDI-ToF, ToF-SIMS and desorption electrospray ionisation (DESI) equipment. EasyReg2D is able to register two images, in American Standard Code for Information Interchange (ASCII) format, issued from different technologies. The collaboration between the teams was hampered by the multiplicity of equipment and data formats, so the project also developed a common data format (imzML) to facilitate the exchange of experimental data and their interpretation by the different software tools. The BioMap platform for visualisation and exploration of MALDI-ToF and DESI images was adapted to parse imzML files, enabling its access to all project partners and, more globally, to a larger community of users. Considering the huge advantages brought by the imzML standard format, a specific editor (vBrowser) for imzML files and converters from proprietary formats to imzML were developed to enable the use of the imzML format by a broad scientific community. This initiative paves the way toward the development of a large panel of software tools able to process mass spectrometry imaging datasets in the future.

Automatic Registration of Mass Spectrometry Imaging Data Sets to the Allen Brain Atlas

Mass spectrometry imaging holds great potential for understanding the molecular basis of neurological disease. Several key studies have demonstrated its ability to uncover disease-related biomolecular changes in rodent models of disease, even if highly localized or invisible to established histological methods. The high analytical reproducibility necessary for the biomedical application of mass spectrometry imaging means it is widely developed in mass spectrometry laboratories. However, many lack the expertise to correctly annotate the complex anatomy of brain tissue, or have the capacity to analyze the number of animals required in preclinical studies, especially considering the significant variability in sizes of brain regions. To address this issue, we have developed a pipeline to automatically map mass spectrometry imaging data sets of mouse brains to the Allen Brain Reference Atlas, which contains publically available data combining gene expression with brain anatomical locations. Our pipeline enables facile and rapid interanimal comparisons by first testing if each animal's tissue section was sampled at a similar location and enabling the extraction of the biomolecular signatures from specific brain regions.

Methods for full resolution data exploration and visualization for large 2D and 3D mass spectrometry imaging datasets

Mass spectrometry imaging (MSI) produces such large amounts of high-resolution data that fast visualization of full data sets in both high spatial and spectral resolution is often problematic. Instrument specific software tools are available, but often struggle with the size and the complexity of the MSI data sets. We describe new methods to improve the handling of these large MSI data sets by means of innovative data structures and visualization strategies developed specifically for MSI. Two new software instruments implement these new methods for rapid data exploration and visualization of both 2D and 3D data sets in full spatial and spectral resolution, and can handle existing MSI data formats, including imzML and BioMap.

Multiorder Correction Algorithms to Remove Image Distortions from Mass Spectrometry Imaging Data Sets

Time-of-flight secondary ion mass spectrometry imaging is a rapidly evolving technology. Its main application is the study of the distribution of small molecules on biological tissues. The sequential image acquisition process remains susceptible to measurement distortions that can render imaging data less analytically useful. Most of these artifacts show a repetitive nature from tile to tile. Here we statistically describe these distortions and derive two different algorithms to correct them. Both a generalized linear model approach and the linear discriminant analysis approach are able to increase image quality for negative and positive ion mode data sets. Additionally, performing simulation studies with repetitive and nonrepetitive tiling error we show that both algorithms are only removing repetitive distortions. It is further shown that the spectral component of the data set is not altered by the use of these correction methods. Both algorithms presented in this work greatly increase the image quality and improve the analytical usefulness of distorted images dramatically.

OpenMSI: A High-Performance Web-Based Platform for Mass Spectrometry Imaging

Mass spectrometry imaging (MSI) enables researchers to directly probe endogenous molecules directly within the architecture of the biological matrix. Unfortunately, efficient access, management, and analysis of the data generated by MSI approaches remain major challenges to this rapidly developing field. Despite the availability of numerous dedicated file formats and software packages, it is a widely held viewpoint that the biggest challenge is simply opening, sharing, and analyzing a file without loss of information. Here we present OpenMSI, a software framework and platform that addresses these challenges via an advanced, high-performance, extensible file format and Web API for remote data access (http://openmsi.nersc.gov). The OpenMSI file format supports storage of raw MSI data, metadata, and derived analyses in a single, self-describing format based on HDF5 and is supported by a large range of analysis software (e.g., Matlab and R) and programming languages (e.g., C++, Fortran, and Python). Careful optimization of the storage layout of MSI data sets using chunking, compression, and data replication accelerates common, selective data access operations while minimizing data storage requirements and are critical enablers of rapid data I/O. The OpenMSI file format has shown to provide >2000-fold improvement for image access operations, enabling spectrum and image retrieval in less than 0.3 s across the Internet even for 50 GB MSI data sets. To make remote high-performance compute resources accessible for analysis and to facilitate data sharing and collaboration, we describe an easy-to-use yet powerful Web API, enabling fast and convenient access to MSI data, metadata, and derived analysis results stored remotely to facilitate high-performance data analysis and enable implementation of Web based data sharing, visualization, and analysis.

Imaging mass spectrometry distinguished the cancer and stromal regions of oral squamous cell carcinoma by visualizing phosphatidylcholine (16:0/16:1) and phosphatidylcholine (18:1/20:4)

Most oral cancers are oral squamous cell carcinoma (OSCC). The anatomical features of OSCC have been histochemically evaluated with hematoxylin and eosin. However, the border between the cancer and stromal regions is unclear and large portions of the cancer and stromal regions are resected in surgery. To reduce the resected area and maintain oral function, a new method of diagnosis is needed. In this study, we tried to clearly distinguish the border on the basis of biomolecule distributions visualized by imaging mass spectrometry (IMS). In the IMS dataset, eleven signals were significantly different in intensity (p < 0.01) between the cancer and stromal regions. Two signals at m/z 770.5 and m/z 846.6 were distributed in each region, and a clear border was revealed. Tandem mass spectrometric (MS/MS) analysis identified these signals as phosphatidylcholine (PC) (16:0/16:1) at m/z 770.5 in the cancer region and PC (18:1/20:4) at m/z 846.6 in the stromal region. Moreover, the distribution of PC species containing arachidonic acid in the stromal region suggests that lymphocytes accumulated in response to the inflammation caused by cancer invasion. In conclusion, the cancer and stromal regions of OSCCs were clearly distinguished by use of these PC species and IMS analysis, and this molecular identification can provide important information to elucidate the mechanism of cancer invasion.