Imaging mass spectrometry data reduction: automated feature identification and extraction

massNet: integrated processing and classification of spatially resolved mass spectrometry data using deep learning for rapid tumor delineation

MOTIVATION

Mass spectrometry imaging (MSI) provides rich biochemical information in a label-free manner and therefore holds promise to substantially impact current practice in disease diagnosis. However, the complex nature of MSI data poses computational challenges in its analysis. The complexity of the data arises from its large size, high-dimensionality and spectral nonlinearity. Preprocessing, including peak picking, has been used to reduce raw data complexity; however, peak picking is sensitive to parameter selection that, perhaps prematurely, shapes the downstream analysis for tissue classification and ensuing biological interpretation.

RESULTS

We propose a deep learning model, massNet, that provides the desired qualities of scalability, nonlinearity and speed in MSI data analysis. This deep learning model was used, without prior preprocessing and peak picking, to classify MSI data from a mouse brain harboring a patient-derived tumor. The massNet architecture established automatically learning of predictive features, and automated methods were incorporated to identify peaks with potential for tumor delineation. The model's performance was assessed using cross-validation, and the results demonstrate higher accuracy and a substantial gain in speed compared to the established classical machine learning method, support vector machine.

AVAILABILITY AND IMPLEMENTATION

https://github.com/wabdelmoula/massNet. The data underlying this article are available in the NIH Common Fund's National Metabolomics Data Repository (NMDR) Metabolomics Workbench under project id (PR001292) with http://dx.doi.org/10.21228/M8Q70T.

SUPPLEMENTARY INFORMATION

Supplementary data are available at Bioinformatics online.

Application of Intraoperative Mass Spectrometry and Data Analytics for Oncological Margin Detection, A Review

OBJECTIVE

A common phase of early-stage oncological treatment is the surgical resection of cancerous tissue. The presence of cancer cells on the resection margin, referred to as positive margin, is correlated with the recurrence of cancer and may require re-operation, negatively impacting many facets of patient outcomes. There exists a significant gap in the surgeons ability to intraoperatively delineate between tissues. Mass spectrometry methods have shown considerable promise as intraoperative tissue profiling tools that can assist with the complete resection of cancer. To do so, the vastness of the information collected through these modalities must be digested, relying on robust and efficient extraction of insights through data analysis pipelines.

METHODS

We review clinical mass spectrometry literature and prioritize intraoperatively applied modalities. We also survey the data analysis methods employed in these studies.

RESULTS

Our review outlines the advantages and shortcomings of mass spectrometry imaging and point-based tissue probing methods. For each modality, we identify statistical, linear transformation and machine learning techniques that demonstrate high performance in classifying cancerous tissues across several organ systems. A limited number of studies presented results captured intraoperatively.

CONCLUSION

Through continued research of data centric techniques, like mass spectrometry, and the development of robust analysis approaches, intraoperative margin assessment is becoming feasible.

SIGNIFICANCE

By establishing the relatively short history of mass spectrometry techniques applied to surgical studies, we hope to inform future applications and aid in the selection of suitable data analysis frameworks for the development of intraoperative margin detection technologies.

RECENT ADVANCES IN INSTRUMENTAL APPROACHES TO TIME-OF-FLIGHT MASS SPECTROMETRY

Time-of-flight mass spectrometry (TOFMS) is one of the simplest and most powerful approaches for mass spectrometry. Realization of the advantages inherent in TOFMS requires innovation in the theory and practice of the technique. Instrumental developments, in turn, create new capabilities that enable applications in chemical measurement. This review focuses on the recent advances in TOFMS instrumentation. New strategies for ion acceleration, multiplexed detection, miniaturized TOFMS instruments, approaches to extend the length of ion flight, and novel ion detection technologies are reviewed. Techniques that change the basic paradigm of TOFMS by measuring m/z based on ion flight distance are considered, as are applications at the frontiers of instrumental performance. © 2020 John Wiley & Sons Ltd. Mass Spec Rev.

rMSIannotation: A peak annotation tool for mass spectrometry imaging based on the analysis of isotopic intensity ratios

Mass spectrometry imaging (MSI) consist of spatially located spectra with thousands of peaks. Only a fraction of these peaks corresponds to unique monoisotopic peaks, as mass spectra include isotopes, adducts and fragments of compounds. Current peak annotation solutions depend on matching MS features to compounds libraries. We present rMSIannotation, a peak annotation algorithm to annotate carbon isotopes and adducts in metabolomics and lipidomics imaging mass spectrometry datasets without using supporting libraries. rMSIannotation measures and evaluates the intensity ratio between carbon isotopic peaks and models their distribution across the m/z axis of the compounds in the Human Metabolome Database. Monoisotopic peak selection is based on the isotopic likelihood score (ILS) made of three components: image morphology correlation, validation of isotopic intensity ratios, and peak centroid mass deviation. rMSIannotation proposes pairs of peaks that can be adducts based on three scores: isotopic pattern coherence, image correlation and mass error. We validated rMSIannotation with three MALDI-MSI datasets which were manually annotated by experts, and compared the annotations obtained with rMSIannotation and with the METASPACE annotation platform. rMSIannotation replicated more than 90% of the manual annotation reported in FT-ICR datasets and expanded the list of annotated compounds with additional monoisotopic peaks and neutral masses. Finally, we evaluated isotopic peak annotation as a data reduction method for MSI by comparing the results of PCA and k-means segmentation before and after removing non-monoisotopic peaks. The results show that monoisotopic peaks retain most of the biologic variance in the dataset.

Mass Spectrometry Imaging as a Tool to Investigate Region Specific Lipid Alterations in Symptomatic Human Carotid Atherosclerotic Plaques

Atherosclerosis is characterized by fatty plaques in large and medium sized arteries. Their rupture can causes thrombi, occlusions of downstream vessels and adverse clinical events. The investigation of atherosclerotic plaques is made difficult by their highly heterogeneous nature. Here we propose a spatially resolved approach based on matrix-assisted laser desorption/ionization (MALDI) mass spectrometry imaging to investigate lipids in specific regions of atherosclerotic plaques. The method was applied to a small dataset including symptomatic and asymptomatic human carotid atherosclerosis plaques. Tissue sections of symptomatic and asymptomatic human carotid atherosclerotic plaques were analyzed by MALDI mass spectrometry imaging (MALDI MSI) of lipids, and adjacent sections analyzed by histology and immunofluorescence. These multimodal datasets were used to compare the lipid profiles of specific histopathological regions within the plaque. The lipid profiles of macrophage-rich regions and intimal vascular smooth muscle cells exhibited the largest changes associated with plaque outcome. Macrophage-rich regions from symptomatic lesions were found to be enriched in sphingomyelins, and intimal vascular smooth muscle cells of symptomatic plaques were enriched in cholesterol and cholesteryl esters. The proposed method enabled the MALDI MSI analysis of specific regions of the atherosclerotic lesion, confirming MALDI MSI as a promising tool for the investigation of histologically heterogeneous atherosclerotic plaques.

Spatially Resolved Mass Spectrometry at the Single Cell: Recent Innovations in Proteomics and Metabolomics

Biological systems are composed of heterogeneous populations of cells that intercommunicate to form a functional living tissue. Biological function varies greatly across populations of cells, as each single cell has a unique transcriptome, proteome, and metabolome that translates to functional differences within single species and across kingdoms. Over the past decade, substantial advancements in our ability to characterize omic profiles on a single cell level have occurred, including in multiple spectroscopic and mass spectrometry (MS)-based techniques. Of these technologies, spatially resolved mass spectrometry approaches, including mass spectrometry imaging (MSI), have shown the most progress for single cell proteomics and metabolomics. For example, reporter-based methods using heavy metal tags have allowed for targeted MS investigation of the proteome at the subcellular level, and development of technologies such as laser ablation electrospray ionization mass spectrometry (LAESI-MS) now mean that dynamic metabolomics can be performed in situ. In this Perspective, we showcase advancements in single cell spatial metabolomics and proteomics over the past decade and highlight important aspects related to high-throughput screening, data analysis, and more which are vital to the success of achieving proteomic and metabolomic profiling at the single cell scale. Finally, using this broad literature summary, we provide a perspective on how the next decade may unfold in the area of single cell MS-based proteomics and metabolomics.

rMSIcleanup: an open-source tool for matrix-related peak annotation in mass spectrometry imaging and its application to silver-assisted laser desorption/ionization

Mass spectrometry imaging (MSI) has become a mature, widespread analytical technique to perform non-targeted spatial metabolomics. However, the compounds used to promote desorption and ionization of the analyte during acquisition cause spectral interferences in the low mass range that hinder downstream data processing in metabolomics applications. Thus, it is advisable to annotate and remove matrix-related peaks to reduce the number of redundant and non-biologically-relevant variables in the dataset. We have developed rMSIcleanup, an open-source R package to annotate and remove signals from the matrix, according to the matrix chemical composition and the spatial distribution of its ions. To validate the annotation method, rMSIcleanup was challenged with several images acquired using silver-assisted laser desorption ionization MSI (AgLDI MSI). The algorithm was able to correctly classify m/z signals related to silver clusters. Visual exploration of the data using Principal Component Analysis (PCA) demonstrated that annotation and removal of matrix-related signals improved spectral data post-processing. The results highlight the need for including matrix-related peak annotation tools such as rMSIcleanup in MSI workflows.

Unsupervised machine learning for exploratory data analysis in imaging mass spectrometry

Imaging mass spectrometry (IMS) is a rapidly advancing molecular imaging modality that can map the spatial distribution of molecules with high chemical specificity. IMS does not require prior tagging of molecular targets and is able to measure a large number of ions concurrently in a single experiment. While this makes it particularly suited for exploratory analysis, the large amount and high-dimensional nature of data generated by IMS techniques make automated computational analysis indispensable. Research into computational methods for IMS data has touched upon different aspects, including spectral preprocessing, data formats, dimensionality reduction, spatial registration, sample classification, differential analysis between IMS experiments, and data-driven fusion methods to extract patterns corroborated by both IMS and other imaging modalities. In this work, we review unsupervised machine learning methods for exploratory analysis of IMS data, with particular focus on (a) factorization, (b) clustering, and (c) manifold learning. To provide a view across the various IMS modalities, we have attempted to include examples from a range of approaches including matrix assisted laser desorption/ionization, desorption electrospray ionization, and secondary ion mass spectrometry-based IMS. This review aims to be an entry point for both (i) analytical chemists and mass spectrometry experts who want to explore computational techniques; and (ii) computer scientists and data mining specialists who want to enter the IMS field. © 2019 The Authors. Mass Spectrometry Reviews published by Wiley Periodicals, Inc. Mass SpecRev 00:1-47, 2019.

Data Processing Pipeline for Lipid Profiling of Carotid Atherosclerotic Plaque with Mass Spectrometry Imaging

Atherosclerosis is a lipid and inflammation-driven disease of the arteries that is characterized by gradual buildup of plaques in the vascular wall. A so-called vulnerable plaque, consisting of a lipid-rich necrotic core contained by a thin fibrous cap, may rupture and trigger thrombus formation, which can lead to ischemia in the heart (heart attack) or in the brain (stroke). In this study, we present a protocol to investigate the lipid composition of advanced human carotid plaques using matrix-assisted laser desorption ionization (MALDI) mass spectrometry imaging (MSI), providing a framework that should enable the discrimination of vulnerable from stable plaques based on lipid composition. We optimized the tissue preparation and imaging methods by systematically analyzing data from three specimens: two human carotid endarterectomy samples (advanced plaque) and one autopsy sample (early stage plaque). We show a robust data reduction method and evaluate the variability of the endarterectomy samples. We found diacylglycerols to be more abundant in a thrombotic area compared to other plaque areas and could distinguish advanced plaque from early stage plaque based on cholesteryl ester composition. We plan to use this systematic approach to analyze a larger dataset of carotid atherosclerotic plaques.

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.

Molecular similarities and differences from human pulmonary fibrosis and corresponding mouse model: MALDI imaging mass spectrometry in comparative medicine

Animal models can reproduce some model-specific aspects of human diseases, but some animal models translate poorly or fail to translate to the corresponding human disease. Here, we develop a strategy to systematically compare human and mouse tissues, and conduct a proof-of-concept experiment to identify molecular similarities and differences using patients with idiopathic pulmonary fibrosis and a bleomycin-induced fibrosis mouse model. Our novel approach employs high-throughput tissue microarrays (TMAs) of humans and mice, high-resolution matrix-assisted laser desorption/ionization-Fourier transform-ion cyclotron resonance-mass spectrometry imaging (MALDI-FT-ICR-MSI) to spatially resolve mass spectra at the level of specific metabolites, and hierarchical clustering and pathway enrichment analysis to identify functionally similar/different molecular patterns and pathways in pathological lesions of humans and mice. We identified a large number of common molecules (n=1366) and fewer exclusive molecules in humans (n=83) and mice (n=54). Among the common molecules, the 'ascorbate and aldarate metabolism' pathway had the highest similarity in human and mouse lesions. This proof-of-concept study demonstrates that our novel strategy employing a reliable and easy-to-perform experimental design accurately identifies pathways and factors that can be directly compared between animal models and human diseases.

High-grade sarcoma diagnosis and prognosis: Biomarker discovery by mass spectrometry imaging

The combination of high heterogeneity, both intratumoral and intertumoral, with their rarity has made diagnosis, prognosis of high-grade sarcomas difficult. There is an urgent need for more objective molecular biomarkers, to differentiate between the many different subtypes, and to also provide new treatment targets. Mass spectrometry imaging (MSI) has amply demonstrated its ability to identify potential new markers for patient diagnosis, survival, metastasis and response to therapy in cancer research. In this study, we investigated the ability of MALDI-MSI of proteins to distinguish between high-grade osteosarcoma (OS), leiomyosarcoma (LMS), myxofibrosarcoma (MFS) and undifferentiated pleomorphic sarcoma (UPS) (Ntotal = 53). We also investigated if there are individual proteins or protein signatures that are statistically associated with patient survival. Twenty diagnostic protein signals were found characteristic for specific tumors (p ≤ 0.05), amongst them acyl-CoA-binding protein (m/z 11 162), macrophage migration inhibitory factor (m/z 12 350), thioredoxin (m/z 11 608) and galectin-1 (m/z 14 633) were assigned. Another nine protein signals were found to be associated with overall survival (p ≤ 0.05), including proteasome activator complex subunit 1 (m/z 9753), indicative for non-OS patients with poor survival; and two histone H4 variants (m/z 11 314 and 11 355), indicative of poor survival for LMS patients.

N-acyl Taurines and Acylcarnitines Cause an Imbalance in Insulin Synthesis and Secretion Provoking β Cell Dysfunction in Type 2 Diabetes

The processes contributing to β cell dysfunction in type 2 diabetes (T2D) are uncertain, largely because it is difficult to access β cells in their intact immediate environment. We examined the pathophysiology of β cells under T2D progression directly in pancreatic tissues. We used MALDI imaging of Langerhans islets (LHIs) within mouse tissues or from human tissues to generate in situ-omics data, which we supported with in vitro experiments. Molecular interaction networks provided information on functional pathways and molecules. We found that stearoylcarnitine accumulated in β cells, leading to arrest of insulin synthesis and energy deficiency via excessive β-oxidation and depletion of TCA cycle and oxidative phosphorylation metabolites. Acetylcarnitine and an accumulation of N-acyl taurines, a group not previously detected in β cells, provoked insulin secretion. Thus, β cell dysfunction results from enhanced insulin secretion combined with an arrest of insulin synthesis.

Mass spectrometry imaging shows major derangements in neurogranin and in purine metabolism in the triple-knockout 3×Tg Alzheimer mouse model

Matrix-assisted laser desorption/ionization (MALDI) mass spectrometry imaging (MSI) can simultaneously measure hundreds of biomolecules directly from tissue. Using different sample preparation strategies, proteins and metabolites have been profiled to study the molecular changes in a 3×Tg mouse model of Alzheimer's disease. In comparison with wild-type (WT) control mice MALDI-MSI revealed Alzheimer's disease-specific protein profiles, highlighting dramatic reductions of a protein with m/z 7560, which was assigned to neurogranin and validated by immunohistochemistry. The analysis also revealed substantial metabolite changes, especially in metabolites related to the purine metabolic pathway, with a shift towards an increase in hypoxanthine/xanthine/uric acid in the 3×Tg AD mice accompanied by a decrease in AMP and adenine. Interestingly these changes were also associated with a decrease in ascorbic acid, consistent with oxidative stress. Furthermore, the metabolite N-arachidonyl taurine was increased in the diseased mouse brain sections, being highly abundant in the hippocampus. Overall, we describe an interesting shift towards pro-inflammatory molecules (uric acid) in the purinergic pathway associated with a decrease in anti-oxidant level (ascorbic acid). Together, these observations fit well with the increased oxidative stress and neuroinflammation commonly observed in AD. This article is part of a Special Issue entitled: MALDI Imaging, edited by Dr. Corinna Henkel and Prof. Peter Hoffmann.

Ultra-High Mass Resolution MALDI Imaging Mass Spectrometry of Proteins and Metabolites in a Mouse Model of Glioblastoma

MALDI mass spectrometry imaging is able to simultaneously determine the spatial distribution of hundreds of molecules directly from tissue sections, without labeling and without prior knowledge. Ultra-high mass resolution measurements based on Fourier-transform mass spectrometry have been utilized to resolve isobaric lipids, metabolites and tryptic peptides. Here we demonstrate the potential of 15T MALDI-FTICR MSI for molecular pathology in a mouse model of high-grade glioma. The high mass accuracy and resolving power of high field FTICR MSI enabled tumor specific proteoforms, and tumor-specific proteins with overlapping and isobaric isotopic distributions to be clearly resolved. The protein ions detected by MALDI MSI were assigned to proteins identified by region-specific microproteomics (0.8 mm2 regions isolated using laser capture microdissection) on the basis of exact mass and isotopic distribution. These label free quantitative experiments also confirmed the protein expression changes observed by MALDI MSI and revealed changes in key metabolic proteins, which were supported by in-situ metabolite MALDI MSI.

Prognostic Metabolite Biomarkers for Soft Tissue Sarcomas Discovered by Mass Spectrometry Imaging

Metabolites can be an important read-out of disease. The identification and validation of biomarkers in the cancer metabolome that can stratify high-risk patients is one of the main current research aspects. Mass spectrometry has become the technique of choice for metabolomics studies, and mass spectrometry imaging (MSI) enables their visualization in patient tissues. In this study, we used MSI to identify prognostic metabolite biomarkers in high grade sarcomas; 33 high grade sarcoma patients, comprising osteosarcoma, leiomyosarcoma, myxofibrosarcoma, and undifferentiated pleomorphic sarcoma were analyzed. Metabolite MSI data were obtained from sections of fresh frozen tissue specimens with matrix-assisted laser/desorption ionization (MALDI) MSI in negative polarity using 9-aminoarcridine as matrix. Subsequent annotation of tumor regions by expert pathologists resulted in tumor-specific metabolite signatures, which were then tested for association with patient survival. Metabolite signals with significant clinical value were further validated and identified by high mass resolution Fourier transform ion cyclotron resonance (FTICR) MSI. Three metabolite signals were found to correlate with overall survival (m/z 180.9436 and 241.0118) and metastasis-free survival (m/z 160.8417). FTICR-MSI identified m/z 241.0118 as inositol cyclic phosphate and m/z 160.8417 as carnitine. Graphical Abstract ᅟ.

An experimental guideline for the analysis of histologically heterogeneous tumors by MALDI-TOF mass spectrometry imaging

Mass spectrometry imaging (MSI) has been widely used for the direct molecular assessment of tissue samples and has demonstrated great potential to complement current histopathological methods in cancer research. It is now well established that tissue preparation is key to a successful MSI experiment; for histologically heterogeneous tumor tissues, other parts of the workflow are equally important to the experiment's success. To demonstrate these facets here we describe a matrix-assisted laser desorption/ionization MSI biomarker discovery investigation of high-grade, complex karyotype sarcomas, which often have histological overlap and moderate response to chemo-/radio-therapy. Multiple aspects of the workflow had to be optimized, ranging from the tissue preparation and data acquisition protocols, to the post-MSI histological staining method, data quality control, histology-defined data selection, data processing and statistical analysis. Only as a result of developing every step of the biomarker discovery workflow was it possible to identify a panel of protein signatures that could distinguish between different subtypes of sarcomas or could predict patient survival outcome. This article is part of a Special Issue entitled: MALDI Imaging, edited by Dr. Corinna Henkel and Prof. Peter Hoffmann.

Scores for standardization of on-tissue digestion of formalin-fixed paraffin-embedded tissue in MALDI-MS imaging

On-slide digestion of formalin-fixed and paraffin-embedded human biopsy tissue followed by mass spectrometry imaging of resulting peptides may have the potential to become an additional analytical modality in future ePathology. Multiple workflows have been described for dewaxing, antigen retrieval, digestion and imaging in the past decade. However, little is known about suitable statistical scores for method comparison and systematic workflow standardization required for development of processes that would be robust enough to be compatible with clinical routine. To define scores for homogeneity of tissue processing and imaging as well as inter-day repeatability for five different processing methods, we used human liver and gastrointestinal stromal tumor tissue, both judged by an expert pathologist to be >98% histologically homogeneous. For mean spectra-based as well as pixel-wise data analysis, we propose the coefficient of determination R², the natural fold-change (natFC) value and the digest efficiency DE% as readily accessible scores. Moreover, we introduce two scores derived from principal component analysis, the variance of the mean absolute deviation, MAD, and the interclass overlap, J_overlap, as computational scores that may help to avoid user bias during future workflow development. This article is part of a Special Issue entitled: MALDI Imaging, edited by Dr. Corinna Henkel and Prof. Peter Hoffmann.

High-mass-resolution MALDI mass spectrometry imaging of metabolites from formalin-fixed paraffin-embedded tissue

Formalin-fixed and paraffin-embedded (FFPE) tissue specimens are the gold standard for histological examination, and they provide valuable molecular information in tissue-based research. Metabolite assessment from archived tissue samples has not been extensively conducted because of a lack of appropriate protocols and concerns about changes in metabolite content or chemical state due to tissue processing. We present a protocol for the in situ analysis of metabolite content from FFPE samples using a high-mass-resolution matrix-assisted laser desorption/ionization fourier-transform ion cyclotron resonance mass spectrometry imaging (MALDI-FT-ICR-MSI) platform. The method involves FFPE tissue sections that undergo deparaffinization and matrix coating by 9-aminoacridine before MALDI-MSI. Using this platform, we previously detected ∼1,500 m/z species in the mass range m/z 50-1,000 in FFPE samples; the overlap compared with fresh frozen samples is 72% of m/z species, indicating that metabolites are largely conserved in FFPE tissue samples. This protocol can be reproducibly performed on FFPE tissues, including small samples such as tissue microarrays and biopsies. The procedure can be completed in a day, depending on the size of the sample measured and raster size used. Advantages of this approach include easy sample handling, reproducibility, high throughput and the ability to demonstrate molecular spatial distributions in situ. The data acquired with this protocol can be used in research and clinical practice.

The Need for Speed in Matrix-Assisted Laser Desorption/Ionization Imaging Mass Spectrometry

Imaging mass spectrometry (IMS) has emerged as a powerful analytical tool enabling the direct molecular mapping of many types of tissue. Specifically, matrix-assisted laser desorption/ ionization (MALDI) represents one of the most broadly applicable IMS technologies. In recent years, advances in solid state laser technology, mass spectrometry instrumentation, computer technology, and experimental methodology have produced IMS systems capable of unprecedented data acquisition speeds (>50 pixels/second). In applications of this technology, throughput is an important consideration when designing an IMS experiment. As IMS becomes more widely adopted, continual improvements in experimental setups will be important to address biologically and clinically relevant time scales.

Pharmacokinetic and pharmacometabolomic study of pirfenidone in normal mouse tissues using high mass resolution MALDI-FTICR-mass spectrometry imaging

Given the importance of pirfenidone as the first worldwide-approved drug for idiopathic pulmonary fibrosis treatment, its pharmacodynamic properties and the metabolic response to pirfenidone treatment have not been fully elucidated. The aim of the present study was to get molecular insights of pirfenidone-related pharmacometabolomic response using MALDI-FTICR-MSI. Quantitative MALDI-FTICR-MSI was carried out for determining the pharmacokinetic properties of pirfenidone and its related metabolites 5-hydroxymethyl pirfenidone and 5-carboxy pirfenidone in lung, liver and kidney. To monitor the effect of pirfenidone administration on endogenous cell metabolism, additional in situ endogenous metabolite imaging was performed in lung tissue sections. While pirfenidone is highly abundant and delocalized across the whole micro-regions of lung, kidney and liver, 5-hydroxymethyl pirfenidone and 5-carboxy pirfenidone demonstrate heterogeneous distribution patterns in lung and kidney. In situ endogenous metabolite imaging study of lung tissue indicates no significant effects of pirfenidone on metabolic pathways. Remarkably, we found 129 discriminative m/z values which represent clear differences between control and treated lungs, the majority of which are currently unknown. PCA analysis and heatmap view can accurately distinguish control and treated groups. This is the first pharmacokinetic study to investigate the tissue distribution of orally administered pirfenidone and its related metabolites simultaneously in organs without labeling. The combination of pharmametabolome with histological features provides detailed mapping of drug effects on metabolism as response of healthy lung tissue to pirfenidone treatment.

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.

MSI.R scripts reveal volatile and semi-volatile features in low-temperature plasma mass spectrometry imaging (LTP-MSI) of chilli (Capsicum annuum)

In cartography, the combination of colour and contour lines is used to express a three-dimensional landscape on a two-dimensional map. We transferred this concept to the analysis of mass spectrometry imaging (MSI) data and developed a collection of R scripts for the efficient evaluation of .imzML archives in a four-step strategy: (1) calculation of the density distribution of mass-to-charge ratio (m/z) signals in the .imzML file and assembling of a pseudo-master spectrum with peak list, (2) automated generation of mass images for a defined scan range and subsequent visual inspection, (3) visualisation of individual ion distributions and export of relevant .mzML spectra and (4) creation of overlay graphics of ion images and photographies. The use of a Hue-Chroma-Luminance (HCL) colour model in MSI graphics takes into account the human perception for colours and supports the correct evaluation of signal intensities. Further, readers with colour blindness are supported. Contour maps promote the visual recognition of patterns in MSI data, which is particularly useful for noisy data sets. We demonstrate the scalability of MSI.R scripts by running them on different systems: on a personal computer, on Amazon Web Services (AWS) instances and on an institutional cluster. By implementing a parallel computing strategy, the execution speed for .imzML data scanning with image generation could be improved by more than an order of magnitude. Applying our MSI.R scripts ( http://www.bioprocess.org/MSI.R ) to low-temperature plasma (LTP)-MSI data shows the localisation of volatile and semi-volatile compounds in the cross-cut of a chilli (Capsicum annuum) fruit. The subsequent identification of compounds by gas and liquid chromatography coupled to mass spectrometry (GC-MS, LC-MS) proves that LTP-MSI enables the direct measurement of volatile organic compound (VOC) distributions from biological tissues.

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.

Application of chemometric algorithms to MALDI mass spectrometry imaging of pharmaceutical tablets

During drug product development, the nature and distribution of the active substance have to be controlled to ensure the correct activity and the safety of the final medication. Matrix assisted laser desorption/ionization mass spectrometry imaging (MALDI-MSI), due to its structural and spatial specificities, provides an excellent way to analyze these two critical parameters in the same acquisition. The aim of this work is to demonstrate that MALDI-MSI, coupled with four well known multivariate statistical analysis algorithms (PCA, ICA, MCR-ALS and NMF), is a powerful technique to extract spatial and spectral information about chemical compounds from known or unknown solid drug product formulations. To test this methodology, an in-house manufactured tablet and a commercialized Coversyl(®) tablet were studied. The statistical analysis was decomposed into three steps: preprocessing, estimation of the number of statistical components (manually or using singular value decomposition), and multivariate statistical analysis. The results obtained showed that while principal component analysis (PCA) was efficient in searching for sources of variation in the matrix, it was not the best technique to estimate an unmixing model of a tablet. Independent component analysis (ICA) was able to extract appropriate contributions of chemical information in homogeneous and heterogeneous datasets. Non-negative matrix factorization (NMF) and multivariate curve resolution-alternating least squares (MCR-ALS) were less accurate in obtaining the right contribution in a homogeneous sample but they were better at distinguishing the semi-quantitative information in a heterogeneous MALDI dataset.

De novo discovery of phenotypic intratumour heterogeneity using imaging mass spectrometry

An essential and so far unresolved factor influencing the evolution of cancer and the clinical management of patients is intratumour clonal and phenotypic heterogeneity. However, the de novo identification of tumour subpopulations is so far both a challenging and an unresolved task. Here we present the first systematic approach for the de novo discovery of clinically detrimental molecular tumour subpopulations. In this proof-of-principle study, spatially resolved, tumour-specific mass spectra were acquired, using matrix-assisted laser desorption/ionization (MALDI) imaging mass spectrometry, from tissues of 63 gastric carcinoma and 32 breast carcinoma patients. The mass spectra, representing the proteomic heterogeneity within tumour areas, were grouped by a corroborated statistical clustering algorithm in order to obtain segmentation maps of molecularly distinct regions. These regions were presumed to represent different phenotypic tumour subpopulations. This was confirmed by linking the presence of these tumour subpopulations to the patients' clinical data. This revealed several of the detected tumour subpopulations to be associated with a different overall survival of the gastric cancer patients (p = 0.025) and the presence of locoregional metastases in patients with breast cancer (p = 0.036). The procedure presented is generic and opens novel options in cancer research, as it reveals microscopically indistinct tumour subpopulations that have an adverse impact on clinical outcome. This enables their further molecular characterization for deeper insights into the biological processes of cancer, which may finally lead to new targeted therapies.

Automatic generic registration of mass spectrometry imaging data to histology using nonlinear stochastic embedding

The combination of mass spectrometry imaging and histology has proven a powerful approach for obtaining molecular signatures from specific cells/tissues of interest, whether to identify biomolecular changes associated with specific histopathological entities or to determine the amount of a drug in specific organs/compartments. Currently there is no software that is able to explicitly register mass spectrometry imaging data spanning different ionization techniques or mass analyzers. Accordingly, the full capabilities of mass spectrometry imaging are at present underexploited. Here we present a fully automated generic approach for registering mass spectrometry imaging data to histology and demonstrate its capabilities for multiple mass analyzers, multiple ionization sources, and multiple tissue types.

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.

MALDI MS imaging as a tool for biomarker discovery: methodological challenges in a clinical setting

MALDI MS imaging (MSI) is an analytical tool capable of providing spatial distribution and relative abundance of biomolecules directly in tissue. After 15 years of intense efforts to improve the acquisition and quality of molecular images, MSI has matured into an asset of the proteomic toolbox. The power of MSI lies in the ability to differentiate tissue regions that are not histologically distinct but are characterized by different MS profiles. Recently, MSI has been gaining momentum in biomedical research and has found applications in disease diagnosis and prognosis, biomarker discovery, and drug therapy. Although the technology holds great promise, MSI is still faced with a set of methodological challenges presented by the clinical setting. There is a growing awareness regarding this topic and efforts are being taken to develop clear and practical standards to overcome these challenges. This review presents an overview of MALDI MSI as a biomarker discovery tool and recent methodological progress in the field.

Imaging mass spectrometry: from tissue sections to cell cultures

Imaging mass spectrometry (IMS) has been a useful tool for investigating protein, peptide, drug and metabolite distributions in human and animal tissue samples for almost 15years. The major advantages of this method include a broad mass range, the ability to detect multiple analytes in a single experiment without the use of labels and the preservation of biologically relevant spatial information. Currently the majority of IMS experiments are based on imaging animal tissue sections or small tumor biopsies. An alternative method currently being developed is the application of IMS to three-dimensional cell and tissue culture systems. With new advances in tissue culture and engineering, these model systems are able to provide increasingly accurate, high-throughput and cost-effective models that recapitulate important characteristics of cell and tissue growth in vivo. This review will describe the most recent advances in IMS technology and the bright future of applying IMS to the field of three-dimensional cell and tissue culture.

Matrix-assisted laser desorption ionization imaging mass spectrometry: in situ molecular mapping

Matrix-assisted laser desorption ionization imaging mass spectrometry (IMS) is a relatively new imaging modality that allows mapping of a wide range of biomolecules within a thin tissue section. The technology uses a laser beam to directly desorb and ionize molecules from discrete locations on the tissue that are subsequently recorded in a mass spectrometer. IMS is distinguished by the ability to directly measure molecules in situ ranging from small metabolites to proteins, reporting hundreds to thousands of expression patterns from a single imaging experiment. This article reviews recent advances in IMS technology, applications, and experimental strategies that allow it to significantly aid in the discovery and understanding of molecular processes in biological and clinical samples.

MSiReader: an open-source interface to view and analyze high resolving power MS imaging files on Matlab platform

During the past decade, the field of mass spectrometry imaging (MSI) has greatly evolved, to a point where it has now been fully integrated by most vendors as an optional or dedicated platform that can be purchased with their instruments. However, the technology is not mature and multiple research groups in both academia and industry are still very actively studying the fundamentals of imaging techniques, adapting the technology to new ionization sources, and developing new applications. As a result, there important varieties of data file formats used to store mass spectrometry imaging data and, concurrent to the development of MSi, collaborative efforts have been undertaken to introduce common imaging data file formats. However, few free software packages to read and analyze files of these different formats are readily available. We introduce here MSiReader, a free open source application to read and analyze high resolution MSI data from the most common MSi data formats. The application is built on the Matlab platform (Mathworks, Natick, MA, USA) and includes a large selection of data analysis tools and features. People who are unfamiliar with the Matlab language will have little difficult navigating the user-friendly interface, and users with Matlab programming experience can adapt and customize MSiReader for their own needs.

Correlation queries for mass spectrometry imaging

Mass spectrometry imaging (MSI) generates large volumetric data sets consisting of mass to charge ratio (m/z), ion current, and x,y coordinate location. These data sets usually serve limited purposes centered on measuring the distribution of a small set of ions with known m/z. Such earmarked queries consider only a fraction of the full mass spectrum captured, and there are few tools to assist the exploration of the remaining volume of unknown data in terms of demonstrating similarity or discordance in tissue compartment distribution patterns. Here we present a novel, interactive approach to extract information from MSI data that relies on precalculated data structures to perform queries of large data sets with a typical laptop. We have devised methods to query the full volume to find new m/z values of potential interest based on similarity to biological structures or to the spatial distribution of known ions. We describe these query methods in detail and provide examples demonstrating the power of the methods to "discover" m/z values of ions that have such potentially interesting correlations. The "discovered" ions may be further correlated with either positional locations or the coincident distribution of other ions using successive queries. Finally, we show it is possible to gain insight to the fragmentation pattern of the parent molecule from such correlations. The ability to discover new ions of interest in the unknown bulk of an MSI data set offers the potential to further our understanding of biological and physiological processes related to health and disease.

Mass spectrometry imaging for biomedical applications

The development of technologies for mass spectrometry imaging is of substantial research interest. Mass spectrometry is potentially capable of providing highly specific information about the distribution of compounds in tissues, with high sensitivity. The in-situ analysis needed for tissue imaging requires MS to be performed under conditions different from the traditional ones, typically with intensive sample preparation and optimized for pharmaceutical applications. In this paper we critically review the current status of MS imaging with different methods of sample ionization and discuss the 3D and quantitative imaging capabilities which need further development, the importance of the multi-modal imaging, and the balance between the pursuit of high-resolution imaging and the practical application of MS imaging in biomedicine.

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 (princomp) 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 princomp. Then, k-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.

Histology-driven data mining of lipid signatures from multiple imaging mass spectrometry analyses: application to human colorectal cancer liver metastasis biopsies

Imaging mass spectrometry (IMS) represents an innovative tool in the cancer research pipeline, which is increasingly being used in clinical and pharmaceutical applications. The unique properties of the technique, especially the amount of data generated, make the handling of data from multiple IMS acquisitions challenging. This work presents a histology-driven IMS approach aiming to identify discriminant lipid signatures from the simultaneous mining of IMS data sets from multiple samples. The feasibility of the developed workflow is evaluated on a set of three human colorectal cancer liver metastasis (CRCLM) tissue sections. Lipid IMS on tissue sections was performed using MALDI-TOF/TOF MS in both negative and positive ionization modes after 1,5-diaminonaphthalene matrix deposition by sublimation. The combination of both positive and negative acquisition results was performed during data mining to simplify the process and interrogate a larger lipidome into a single analysis. To reduce the complexity of the IMS data sets, a sub data set was generated by randomly selecting a fixed number of spectra from a histologically defined region of interest, resulting in a 10-fold data reduction. Principal component analysis confirmed that the molecular selectivity of the regions of interest is maintained after data reduction. Partial least-squares and heat map analyses demonstrated a selective signature of the CRCLM, revealing lipids that are significantly up- and down-regulated in the tumor region. This comprehensive approach is thus of interest for defining disease signatures directly from IMS data sets by the use of combinatory data mining, opening novel routes of investigation for addressing the demands of the clinical setting.

Towards understanding region-specificity of triplet repeat diseases: coupled immunohistology and mass spectrometry imaging

Many trinucleotide repeat disorders exhibit region-specific toxicity within tissues, the basis of which cannot be explained by traditional methods. For example, in Huntington's Disease (HD), the toxic disease-causing protein is ubiquitously expressed. However, only the medium spiny neurons in the striatum are initially targeted for death. Many changes are likely to initiate in these cells at an intracellular and microstructural level long before there is a measureable phenotype, but why some regions of the brain are more susceptible to death is unknown. This chapter describes a method to detect functional changes among brain regions and cell types, and link them directly with region-specific physiology. Due to the neurodegeneration that accompanies many triplet repeat disorders, we focus on the brain, although the methods described in this chapter can be translated to other tissue types. We integrate immunohistology and traditional mass spectrometry with a novel mass spectrometry imaging technique, called nanostructure initiated mass spectrometry (NIMS). When used together, these tools offer unique insights into region-specific physiology of the brain, and a basis for understanding the region-specific toxicity associated with triplet repeat disorders.

Advanced mass calibration and visualization for FT-ICR mass spectrometry imaging

Mass spectrometry imaging by Fourier transform ion cyclotron resonance (FT-ICR) yields hundreds of unique peaks, many of which cannot be resolved by lower performance mass spectrometers. The high mass accuracy and high mass resolving power allow confident identification of small molecules and lipids directly from biological tissue sections. Here, calibration strategies for FT-ICR MS imaging were investigated. Sub-parts-per-million mass accuracy is demonstrated over an entire tissue section. Ion abundance fluctuations are corrected by addition of total and relative ion abundances for a root-mean-square error of 0.158 ppm on 16,764 peaks. A new approach for visualization of FT-ICR MS imaging data at high resolution is presented. The "Mosaic Datacube" provides a flexible means to visualize the entire mass range at a mass spectral bin width of 0.001 Da. The high resolution Mosaic Datacube resolves spectral features not visible at lower bin widths, while retaining the high mass accuracy from the calibration methods discussed.