Imaging mass spectrometry in biomarker discovery and validation

Multiple two-sample testing under arbitrary covariance dependency with an application in imaging mass spectrometry

Large-scale hypothesis testing has become a ubiquitous problem in high-dimensional statistical inference, with broad applications in various scientific disciplines. One relevant application is constituted by imaging mass spectrometry (IMS) association studies, where a large number of tests are performed simultaneously in order to identify molecular masses that are associated with a particular phenotype, for example, a cancer subtype. Mass spectra obtained from matrix-assisted laser desorption/ionization (MALDI) experiments are dependent, when considered as statistical quantities. False discovery proportion (FDP) estimation and  control under arbitrary dependency structure among test statistics is an active topic in modern multiple testing research. In this context, we are concerned with the evaluation of associations between the binary outcome variable (describing the phenotype) and multiple predictors derived from MALDI measurements. We propose an inference procedure in which the correlation matrix of the test statistics is utilized. The approach is based on multiple marginal models. Specifically, we fit a marginal logistic regression model for each predictor individually. Asymptotic joint normality of the stacked vector of the marginal regression coefficients is established under standard regularity assumptions, and their (limiting) correlation matrix is estimated. The proposed method extracts common factors from the resulting empirical correlation matrix. Finally, we estimate the realized FDP of a thresholding procedure for the marginal p-values. We demonstrate a practical application of the proposed workflow to MALDI IMS data in an oncological context.

Emerging Computational Methods in Mass Spectrometry Imaging

Mass spectrometry imaging (MSI) is a powerful analytical technique that generates maps of hundreds of molecules in biological samples with high sensitivity and molecular specificity. Advanced MSI platforms with capability of high-spatial resolution and high-throughput acquisition generate vast amount of data, which necessitates the development of computational tools for MSI data analysis. In addition, computation-driven MSI experiments have recently emerged as enabling technologies for further improving the MSI capabilities with little or no hardware modification. This review provides a critical summary of computational methods and resources developed for MSI data analysis and interpretation along with computational approaches for improving throughput and molecular coverage in MSI experiments. This review is focused on the recently developed artificial intelligence methods and provides an outlook for a future paradigm shift in MSI with transformative computational methods.

New Insights into the Role of PD-1 and Its Ligands in Allergic Disease

Programmed cell death 1 (PD-1) and its ligands PD-L1 and PD-L2 are receptors that act in co-stimulatory and coinhibitory immune responses. Signaling the PD-1/PD-L1 or PD-L2 pathway is essential to regulate the inflammatory responses to infections, autoimmunity, and allergies, and it has been extensively studied in cancer. Allergic diseases include asthma, rhinoconjunctivitis, atopic dermatitis, drug allergy, and anaphylaxis. These overactive immune responses involve IgE-dependent activation and increased CD4+ T helper type 2 (Th2) lymphocytes. Recent studies have shown that PD-L1 and PD-L2 act to regulate T-cell activation and function. However, the main role of PD-1 and its ligands is to balance the immune response; however, the inflammatory process of allergic diseases is poorly understood. These immune checkpoint molecules can function as a brake or a kick-start to regulate the adaptive immune response. These findings suggest that PD-1 and its ligands may be a key factor in studying the exaggerated response in hypersensitivity reactions in allergies. This review summarizes the current understanding of the role of PD-1 and PD-L1 and PD-L2 pathway regulation in allergic diseases and how this immunomodulatory pathway is currently being targeted to develop novel therapeutic immunotherapy.

Automated biomarker candidate discovery in imaging mass spectrometry data through spatially localized Shapley additive explanations

The search for molecular species that are differentially expressed between biological states is an important step towards discovering promising biomarker candidates. In imaging mass spectrometry (IMS), performing this search manually is often impractical due to the large size and high-dimensionality of IMS datasets. Instead, we propose an interpretable machine learning workflow that automatically identifies biomarker candidates by their mass-to-charge ratios, and that quantitatively estimates their relevance to recognizing a given biological class using Shapley additive explanations (SHAP). The task of biomarker candidate discovery is translated into a feature ranking problem: given a classification model that assigns pixels to different biological classes on the basis of their mass spectra, the molecular species that the model uses as features are ranked in descending order of relative predictive importance such that the top-ranking features have a higher likelihood of being useful biomarkers. Besides providing the user with an experiment-wide measure of a molecular species' biomarker potential, our workflow delivers spatially localized explanations of the classification model's decision-making process in the form of a novel representation called SHAP maps. SHAP maps deliver insight into the spatial specificity of biomarker candidates by highlighting in which regions of the tissue sample each feature provides discriminative information and in which regions it does not. SHAP maps also enable one to determine whether the relationship between a biomarker candidate and a biological state of interest is correlative or anticorrelative. Our automated approach to estimating a molecular species' potential for characterizing a user-provided biological class, combined with the untargeted and multiplexed nature of IMS, allows for the rapid screening of thousands of molecular species and the obtention of a broader biomarker candidate shortlist than would be possible through targeted manual assessment. Our biomarker candidate discovery workflow is demonstrated on mouse-pup and rat kidney case studies.

Targeted Expression of TGFBIp Peptides in Mouse and Human Tissue by MALDI-Mass Spectrometry Imaging

Stromal corneal dystrophies are a group of hereditary disorders caused by mutations in the TGFBI gene. The mutant TGFBIp is prone to protein aggregation and the mutant protein gets deposited in the cornea, leading to severe visual impairment. The mutations lead to a corneal specific protein aggregation suggesting the involvement of tissue-specific factors. The exact molecular mechanism of the process of tissue-specific protein aggregation remains to be elucidated. Differential proteolysis of mutant TGFBIp is a critical component of the disease pathology. The differential proteolysis gives rise to shorter peptides that are highly aggregation-prone and initiate the aggregation cascade. Analyzing the proteolytic processing of the different TGFBIp mutant may provide insight to aid in understanding the amyloid aggregation mechanism. We developed a MALDI-MSI methodology to identify expression and spatial localization of TGFBIp peptides in the cornea. Corneal tissue samples were collected from both control and dystrophic patients (with 2 different mutations), embedded in OCT and sectioned. The sections were trypsin digested and subjected to mass spectrometry imaging using a targeted approach to detect TGFBIp. MALDI-MSI identified peptides from TGFBIp that co-localized with the amyloid corneal deposits. In addition to the relative abundance data, the specific location of the peptides across the corneal sections as molecular signatures was also identified. Spatial distribution and intensity of the TGFBIp peptides showed differences between diseased and control models but also between the two LCD phenotypes. The TGFBIp peptide with m/z of 787.474 and m/z of 1179.579 showed increased expression in both LCD mutants compared to the controls. The peptide with m/z of 929.5 showed increased expression in the LCD phenotype with H626R mutation while the peptide with m/z of 1315.802 was abundant in the sample with R124C mutation. This initial report of 2D spatial protein signature and localization of TGFBIp may be expanded to other mutations to understand the proteolytic patterns of TGFBIp in different mutations.

Mass Spectrometry Imaging of Fibroblasts: Promise and Challenge

INTRODUCTION

Fibroblasts maintain tissue and organ homeostasis through output of extracellular matrix that affects nearby cell signaling within the stroma. Altered fibroblast signaling contributes to many disease states and extracellular matrix secreted by fibroblasts has been used to stratify patient by outcome, recurrence, and therapeutic resistance. Recent advances in imaging mass spectrometry allow access to single cell fibroblasts and their ECM niche within clinically relevant tissue samples.

AREAS COVERED

We review biological and technical challenges as well as new solutions to proteomic access of fibroblast expression within the complex tissue microenvironment. Review topics cover conventional proteomic methods for single fibroblast analysis and current approaches to accessing single fibroblast proteomes by imaging mass spectrometry approaches. Strategies to target and evaluate the single cell stroma proteome on the basis of cell signaling are presented.

EXPERT OPINION

The promise of defining proteomic signatures from fibroblasts and their extracellular matrix niches is the discovery of new disease markers and the ability to refine therapeutic treatments. Several imaging mass spectrometry approaches exist to define the fibroblast in the setting of pathological changes from clinically acquired samples. Continued technology advances are needed to access and understand the stromal proteome and apply testing to the clinic.

Removal of optimal cutting temperature (O.C.T.) compound from embedded tissue for MALDI imaging of lipids

Matrix-assisted laser desorption/ionisation mass spectrometry imaging (MALDI-MSI) is a common molecular imaging modality used to characterise the abundance and spatial distribution of lipids in situ. There are several technical challenges predominantly involving sample pre-treatment and preparation which have complicated the analysis of clinical tissues by MALDI-MSI. Firstly, the common embedding of samples in optimal cutting temperature (O.C.T.), which contains high concentrations of polyethylene glycol (PEG) polymers, causes analyte signal suppression during mass spectrometry (MS) by competing for available ions during ionisation. This suppressive effect has constrained the application of MALDI-MSI for the molecular mapping of clinical tissues. Secondly, the complexity of the mass spectra is obtained by the formation of multiple adduct ions. The process of analyte ion formation during MALDI can generate multiple m/z peaks from a single lipid species due to the presence of alkali salts in tissues, resulting in the suppression of protonated adduct formation and the generation of multiple near isobaric ions which produce overlapping spatial distributions. Presented is a method to simultaneously remove O.C.T. and endogenous salts. This approach was applied to lipid imaging in order to prevent analyte suppression, simplify data interpretation, and improve sensitivity by promoting lipid protonation and reducing the formation of alkali adducts.

The distribution and changes of glycoalkaloids in potato tubers under different storage time based on MALDI-TOF mass spectrometry imaging

Glycoalkaloids (GAs) are toxic secondary metabolites in potatoes, which are harmful to human body. The storage time has a great influence on the biosynthesis and distribution of GAs. In present study, an imaging mass microscope (iMScope) was used to investigate the distribution and changes of GAs in potato tubers under different storage time (0, 10, 15, 20, 30, 40 and 60 days). We established a growth model with logistic equation to evaluate the growth trends of four major GAs in sprout, periderm and medulla. The results showed that the growth rate and relative contents of four GAs in sprout and periderm were significantly higher than that in medulla. In addition, four GAs also presented different change trends. For dehydrosolanine and α-solanine, rapid growth period of these two GAs in sprout (about at the day 23, similar to these in medulla) was later than which period in periderm (about at the day 17), while rapid growth of dehydrochaconine and α-chaconine appeared at almost the same time (about at the day 20). Based on the biosynthesis and metabolism of GAs, we have made possible explanations for these results. This study is useful for comprehending the metabolism of GAs in different parts and monitoring food safety in potatoes.

LipostarMSI: Comprehensive, Vendor-Neutral Software for Visualization, Data Analysis, and Automated Molecular Identification in Mass Spectrometry Imaging

Mass Spectrometry Imaging (MSI) is an established and powerful MS technique that enables molecular mapping of tissues and cells finding widespread applications in academic, medical, and pharmaceutical industries. As both the applications and MSI technology have undergone rapid growth and improvement, the challenges associated both with analyzing large datasets and identifying the many detected molecular species have become apparent. The lack of readily available and comprehensive software covering all necessary data analysis steps has further compounded this challenge. To address this issue we developed LipostarMSI, comprehensive and vendor-neutral software for targeted and untargeted MSI data analysis. Through user-friendly implementation of image visualization and co-registration, univariate and multivariate image and spectral analysis, and for the first time, advanced lipid, metabolite, and drug metabolite (MetID) automated identification, LipostarMSI effectively streamlines biochemical interpretation of the data. Here, we introduce LipostarMSI and case studies demonstrating the versatility and many capabilities of the software.

Delineation of disease phenotypes associated with esophageal adenocarcinoma by MALDI-IMS-MS analysis of serum N-linked glycans

N-Linked glycans, extracted from patient sera and healthy control individuals, are analyzed by Matrix-assisted laser desorption ionization (MALDI) in combination with ion mobility spectrometry (IMS), mass spectrometry (MS) and pattern recognition methods. MALDI-IMS-MS data were collected in duplicate for 58 serum samples obtained from individuals diagnosed with Barrett's esophagus (BE, 14 patients), high-grade dysplasia (HGD, 7 patients), esophageal adenocarcinoma (EAC, 20 patients) and disease-free control (NC, 17 individuals). A combined mobility distribution of 9 N-linked glycans is established for 90 MALDI-IMS-MS spectra (training set) and analyzed using a genetic algorithm for feature selection and classification. Two models for phenotype delineation are subsequently developed and as a result, the four phenotypes (BE, HGD, EAC and NC) are unequivocally differentiated. Next, the two models are tested against 26 blind measurements. Interestingly, these models allowed for the correct phenotype prediction of as many as 20 blinds. Although applied to a limited number of blind samples, this methodology appears promising as a means of discovering molecules from serum that may have capabilities as markers of disease.

The Chemistry of Plant-Microbe Interactions in the Rhizosphere and the Potential for Metabolomics to Reveal Signaling Related to Defense Priming and Induced Systemic Resistance

Plant roots communicate with microbes in a sophisticated manner through chemical communication within the rhizosphere, thereby leading to biofilm formation of beneficial microbes and, in the case of plant growth-promoting rhizomicrobes/-bacteria (PGPR), resulting in priming of defense, or induced resistance in the plant host. The knowledge of plant-plant and plant-microbe interactions have been greatly extended over recent years; however, the chemical communication leading to priming is far from being well understood. Furthermore, linkage between below- and above-ground plant physiological processes adds to the complexity. In metabolomics studies, the main aim is to profile and annotate all exo- and endo-metabolites in a biological system that drive and participate in physiological processes. Recent advances in this field has enabled researchers to analyze 100s of compounds in one sample over a short time period. Here, from a metabolomics viewpoint, we review the interactions within the rhizosphere and subsequent above-ground 'signalomics', and emphasize the contributions that mass spectrometric-based metabolomic approaches can bring to the study of plant-beneficial - and priming events.

Integration of Ion Mobility MSE after Fully Automated, Online, High-Resolution Liquid Extraction Surface Analysis Micro-Liquid Chromatography

Direct analysis by mass spectrometry (imaging) has become increasingly deployed in preclinical and clinical research due to its rapid and accurate readouts. However, when it comes to biomarker discovery or histopathological diagnostics, more sensitive and in-depth profiling from localized areas is required. We developed a comprehensive, fully automated online platform for high-resolution liquid extraction surface analysis (HR-LESA) followed by micro–liquid chromatography (LC) separation and a data-independent acquisition strategy for untargeted and low abundant analyte identification directly from tissue sections. Applied to tissue sections of rat pituitary, the platform demonstrated improved spatial resolution, allowing sample areas as small as 400 μm to be studied, a major advantage over conventional LESA. The platform integrates an online buffer exchange and washing step for removal of salts and other endogenous contamination that originates from local tissue extraction. Our carry over–free platform showed high reproducibility, with an interextraction variability below 30%. Another strength of the platform is the additional selectivity provided by a postsampling gas-phase ion mobility separation. This allowed distinguishing coeluted isobaric compounds without requiring additional separation time. Furthermore, we identified untargeted and low-abundance analytes, including neuropeptides deriving from the pro-opiomelanocortin precursor protein and localized a specific area of the pituitary gland (i.e., adenohypophysis) known to secrete neuropeptides and other small metabolites related to development, growth, and metabolism. This platform can thus be applied for the in-depth study of small samples of complex tissues with histologic features of ∼400 μm or more, including potential neuropeptide markers involved in many diseases such as neurodegenerative diseases, obesity, bulimia, and anorexia nervosa.

Mass spectrometry imaging identifies palmitoylcarnitine as an immunological mediator during Salmonella Typhimurium infection

Salmonella Typhimurium causes a self-limiting gastroenteritis that may lead to systemic disease. Bacteria invade the small intestine, crossing the intestinal epithelium from where they are transported to the mesenteric lymph nodes (MLNs) within migrating immune cells. MLNs are an important site at which the innate and adaptive immune responses converge but their architecture and function is severely disrupted during S. Typhimurium infection. To further understand host-pathogen interactions at this site, we used mass spectrometry imaging (MSI) to analyse MLN tissue from a murine model of S. Typhimurium infection. A molecule, identified as palmitoylcarnitine (PalC), was of particular interest due to its high abundance at loci of S. Typhimurium infection and MLN disruption. High levels of PalC localised to sites within the MLNs where B and T cells were absent and where the perimeter of CD169⁺ sub capsular sinus macrophages was disrupted. MLN cells cultured ex vivo and treated with PalC had reduced CD4⁺CD25⁺ T cells and an increased number of B220⁺CD19⁺ B cells. The reduction in CD4⁺CD25⁺ T cells was likely due to apoptosis driven by increased caspase-3/7 activity. These data indicate that PalC significantly alters the host response in the MLNs, acting as a decisive factor in infection outcome.

A sensitive label-free immunosensor for detection α-Fetoprotein in whole blood based on anticoagulating magnetic nanoparticles

Accurate values of tumor markers in blood play an especially important role in the diagnosis of illness. Here, based on the combination of three techniques include anticoagulant technology, nanotechnology and biosensing technology, a sensitive label-free immunosensor with anti-biofouling electrode for detection α-Fetoprotein (AFP) in whole blood was developed by anticoagulating magnetic nanoparticles. The obtained products of Fe₃O₄-ɛ-PL-Hep nanoparticles were characterized by fourier transform infrared (FT-IR) spectra, transmission electron microscopy (TEM), ζ-potential and vibrating sample magnetometry (VSM). Moreover, the blood compatibility of anticoagulating magnetic nanoparticles was characterized by in vitro coagulation tests, hemolysis assay and whole blood adhesion tests. Combining the anticoagulant property of heparin (Hep) and the good magnetism of Fe₃O₄, the Fe₃O₄-ɛ-PL-Hep nanoparticles could improve not only the anti-biofouling property of the electrode surface when they contact with whole blood, but also the stability and reproducibility of the proposed immunosensor. Thus, the prepared anticoagulating magnetic nanoparticles modified immunosensor for the detection of AFP showed excellent electrochemical properties with a wide concentration range from 0.1 to 100ng/mL and a low detection limit of 0.072ng/mL. Furthermore, five blood samples were assayed using the developed immunosensor. The results showed satisfactory accuracy with low relative errors. It indicated that our developed immunoassay was competitive and could be potentially used for the detection of whole blood samples directly.

Progress and Potential of Imaging Mass Spectrometry Applied to Biomarker Discovery

Mapping provides a direct means to assess the impact of protein biomarkers and puts into context their relevance in the type of cancer being examined. To this end, mass spectrometry imaging (MSI) was developed to provide the needed spatial information which is missing in traditional liquid-based mass spectrometric proteomics approaches. Aptly described as a "molecular histology" technique, MSI gives an additional dimension in characterizing tumor biopsies, allowing for mapping of hundreds of molecules in a single analysis. A decade of developments focused on improving and standardizing MSI so that the technique can be translated into the clinical setting. This review describes the progress made in addressing the technological development that allows to bridge local protein detection by MSI to its identification and to illustrate its potential in studying various aspects of cancer biomarker discovery.

Reliable Entity Subtyping in Non-small Cell Lung Cancer by Matrix-assisted Laser Desorption/Ionization Imaging Mass Spectrometry on Formalin-fixed Paraffin-embedded Tissue Specimens

Histopathological subtyping of non-small cell lung cancer (NSCLC) into adenocarcinoma (ADC), and squamous cell carcinoma (SqCC) is of utmost relevance for treatment stratification. However, current immunohistochemistry (IHC) based typing approaches on biopsies are imperfect, therefore novel analytical methods for reliable subtyping are needed. We analyzed formalin-fixed paraffin-embedded tissue cores of NSCLC by Matrix-assisted laser desorption/ionization (MALDI) imaging on tissue microarrays to identify and validate discriminating MALDI imaging profiles for NSCLC subtyping. 110 ADC and 98 SqCC were used to train a Linear Discriminant Analysis (LDA) model. Results were validated on a separate set of 58 ADC and 60 SqCC. Selected differentially expressed proteins were identified by tandem mass spectrometry and validated by IHC. The LDA classification model incorporated 339 m/z values. In the validation cohort, in 117 cases (99.1%) MALDI classification on tissue cores was in accordance with the pathological diagnosis made on resection specimen. Overall, three cases in the combined cohorts were discordant, after reevaluation two were initially misclassified by pathology whereas one was classified incorrectly by MALDI. Identification of differentially expressed peptides detected well-known IHC discriminators (CK5, CK7), but also less well known differentially expressed proteins (CK15, HSP27). In conclusion, MALDI imaging on NSCLC tissue cores as small biopsy equivalents is capable to discriminate lung ADC and SqCC with a very high accuracy. In addition, replacing multislide IHC by an one-slide MALDI approach may also save tissue for subsequent predictive molecular testing. We therefore advocate to pursue routine diagnostic implementation strategies for MALDI imaging in solid tumor typing.

Mass spectrometry-based plant metabolomics: Metabolite responses to abiotic stress

Metabolomics is one omics approach that can be used to acquire comprehensive information on the composition of a metabolite pool to provide a functional screen of the cellular state. Studies of the plant metabolome include analysis of a wide range of chemical species with diverse physical properties, from ionic inorganic compounds to biochemically derived hydrophilic carbohydrates, organic and amino acids, and a range of hydrophobic lipid-related compounds. This complexitiy brings huge challenges to the analytical technologies employed in current plant metabolomics programs, and powerful analytical tools are required for the separation and characterization of this extremely high compound diversity present in biological sample matrices. The use of mass spectrometry (MS)-based analytical platforms to profile stress-responsive metabolites that allow some plants to adapt to adverse environmental conditions is fundamental in current plant biotechnology research programs for the understanding and development of stress-tolerant plants. In this review, we describe recent applications of metabolomics and emphasize its increasing application to study plant responses to environmental (stress-) factors, including drought, salt, low oxygen caused by waterlogging or flooding of the soil, temperature, light and oxidative stress (or a combination of them). Advances in understanding the global changes occurring in plant metabolism under specific abiotic stress conditions are fundamental to enhance plant fitness and increase stress tolerance. © 2015 Wiley Periodicals, Inc. Mass Spec Rev 35:620-649, 2016.

Applications of the Single-probe: Mass Spectrometry Imaging and Single Cell Analysis under Ambient Conditions

Mass spectrometry imaging (MSI) and in-situ single cell mass spectrometry (SCMS) analysis under ambient conditions are two emerging fields with great potential for the detailed mass spectrometry (MS) analysis of biomolecules from biological samples. The single-probe, a miniaturized device with integrated sampling and ionization capabilities, is capable of performing both ambient MSI and in-situ SCMS analysis. For ambient MSI, the single-probe uses surface micro-extraction to continually conduct MS analysis of the sample, and this technique allows the creation of MS images with high spatial resolution (8.5 µm) from biological samples such as mouse brain and kidney sections. Ambient MSI has the advantage that little to no sample preparation is needed before the analysis, which reduces the amount of potential artifacts present in data acquisition and allows a more representative analysis of the sample to be acquired. For in-situ SCMS, the single-probe tip can be directly inserted into live eukaryotic cells such as HeLa cells, due to the small sampling tip size (< 10 µm), and this technique is capable of detecting a wide range of metabolites inside individual cells at near real-time. SCMS enables a greater sensitivity and accuracy of chemical information to be acquired at the single cell level, which could improve our understanding of biological processes at a more fundamental level than previously possible. The single-probe device can be potentially coupled with a variety of mass spectrometers for broad ranges of MSI and SCMS studies.

Clinical proteomics-driven precision medicine for targeted cancer therapy: current overview and future perspectives

Cancer is a common disease that is a leading cause of death worldwide. Currently, early detection and novel therapeutic strategies are urgently needed for more effective management of cancer. Importantly, protein profiling using clinical proteomic strategies, with spectacular sensitivity and precision, offer excellent promise for the identification of potential biomarkers that would direct the development of targeted therapeutic anticancer drugs for precision medicine. In particular, clinical sample sources, including tumor tissues and body fluids (blood, feces, urine and saliva), have been widely investigated using modern high-throughput mass spectrometry-based proteomic approaches combined with bioinformatic analysis, to pursue the possibilities of precision medicine for targeted cancer therapy. Discussed in this review are the current advantages and limitations of clinical proteomics, the available strategies of clinical proteomics for the management of precision medicine, as well as the challenges and future perspectives of clinical proteomics-driven precision medicine for targeted cancer therapy.

Analysis of Antiretrovirals in Single Hair Strands for Evaluation of Drug Adherence with Infrared-Matrix-Assisted Laser Desorption Electrospray Ionization Mass Spectrometry Imaging

Adherence to a drug regimen can be a strong predictor of health outcomes, and validated measures of adherence are necessary at all stages of therapy from drug development to prescription. Many of the existing metrics of drug adherence (e.g., self-report, pill counts, blood monitoring) have limitations, and analysis of hair strands has recently emerged as an objective alternative. Traditional methods of hair analysis based on LC-MS/MS (segmenting strands at ≥1 cm length) are not capable of preserving a temporal record of drug intake at higher resolution than approximately 1 month. Here, we evaluated the detectability of HIV antiretrovirals (ARVs) in hair from a range of drug classes using infrared matrix-assisted laser desorption electrospray ionization (IR-MALDESI) mass spectrometry imaging (MSI) with 100 μm resolution. Infrared laser desorption of hair strands was shown to penetrate into the strand cortex, allowing direct measurement by MSI without analyte extraction. Using optimized desorption conditions, a linear correlation between IR-MALDESI ion abundance and LC-MS/MS response was observed for six common ARVs with estimated limits of detection less than or equal to 1.6 ng/mg hair. The distribution of efavirenz (EFV) was then monitored in a series of hair strands collected from HIV infected, virologically suppressed patients. Because of the role hair melanin plays in accumulation of basic drugs (like most ARVs), an MSI method to quantify the melanin biomarker pyrrole-2,3,5-tricarboxylic acid (PTCA) was evaluated as a means of normalizing drug response between patients to develop broadly applicable adherence criteria.

High-Resolution Ambient MS Imaging of Negative Ions in Positive Ion Mode: Using Dicationic Reagents with the Single-Probe

We have used the Single-probe, a miniaturized sampling device utilizing in-situ surface microextraction for ambient mass spectrometry (MS) analysis, for the high resolution MS imaging (MSI) of negatively charged species in the positive ionization mode. Two dicationic compounds, 1,5-pentanediyl-bis(1-butylpyrrolidinium) difluoride [C5(bpyr)2F2] and 1,3-propanediyl-bis(tripropylphosphonium) difluoride [C3(triprp)2F2], were added into the sampling solvent to form 1+ charged adducts with the negatively charged species extracted from tissues. We were able to detect 526 and 322 negatively charged species this way using [C5(bpyr)2F2] and [C3(triprp)2F2], respectively, including oleic acid, arachidonic acid, and several species of phosphatidic acid, phosphoethanolamine, phosphatidylserine, phosphatidylglycerol, phosphatidylinositol, and others. In conjunction with the identification of the non-adduct cations, we have tentatively identified a total number of 1200 and 828 metabolites from mouse brain sections using [C5(bpyr)2F2] and [C3(triprp)2F2], respectively, through high mass accuracy measurements (mass error <5 ppm); MS/MS analyses were also performed to verify the identity of selected species. In addition to the high mass accuracy measurement, we were able to generate high spatial resolution (~17 μm) MS images of mouse brain sections. Our study demonstrated that utilization of dicationic compounds in the surface microextraction with the Single-probe device can perform high mass and spatial resolution ambient MSI measurements of broader types of compounds in tissues. Other reagents can be potentially used with the Single-probe device for a variety of reactive MSI studies to enable the analysis of species that are previously intractable.

MALDI-MS imaging for the study of tissue pharmacodynamics and toxicodynamics

Pharmacodynamics and toxicodynamics are the study of the biochemical and physiological effects of therapeutic agents and toxicants and their mechanisms of action. MALDI-MS imaging offers great potential for the study of pharmaco/toxicodynamic responses in tissue owing is its ability to study multiple biomarkers simultaneously in a label-free manner. Here, existing examples of such studies examining anticancer drugs and topically applied treatments are described. Examination of the literature shows that the use of MS imaging in pharmaco/toxicodynamic studies is in fact quite low. The reasons for this are discussed and potential developments in the methodology that might lead to its further use are described.

Mass spectrometry imaging tools in oncology

MS imaging allows profiling and imaging of compounds directly from tumor tissue, tissue microarrays and tissue-engineered models of tumors. Methodologies for the quantitative analysis of localized/colocalized ion signals from a single cancer cell would be a major advance. Alternative methods of generating ions to matrix-assisted laser desorption ionization are increasingly employed. Desorption electrospray ionization has been used for the intraoperative diagnosis of human brain tumors and secondary ion MS imaging with cluster primary ion sources has been used for high spatial resolution imaging tumor sections. Extensive validation of the technique for the analysis of disease biomarkers is required, if imaging MS is to have a future role in the clinic.

Influence of Desorption Conditions on Analyte Sensitivity and Internal Energy in Discrete Tissue or Whole Body Imaging by IR-MALDESI

Analyte signal in a laser desorption/postionization scheme such as infrared matrix-assisted laser desorption electrospray ionization (IR-MALDESI) is strongly coupled to the degree of overlap between the desorbed plume of neutral material from a sample and an orthogonal electrospray. In this work, we systematically examine the effect of desorption conditions on IR-MALDESI response to pharmaceutical drugs and endogenous lipids in biological tissue using a design of experiments approach. Optimized desorption conditions have then been used to conduct an untargeted lipidomic analysis of whole body sagittal sections of neonate mouse. IR-MALDESI response to a wide range of lipid classes has been demonstrated, with enhanced lipid coverage received by varying the laser wavelength used for mass spectrometry imaging (MSI). Targeted MS(2) imaging (MS(2)I) of an analyte, cocaine, deposited beneath whole body sections allowed determination of tissue-specific ion response factors, and CID fragments of cocaine were monitored to comment on wavelength-dependent internal energy deposition based on the "survival yield" method.

High Resolution Tissue Imaging Using the Single-probe Mass Spectrometry under Ambient Conditions

Ambient mass spectrometry imaging (MSI) is an emerging field with great potential for the detailed spatial analysis of biological samples with minimal pretreatment. We have developed a miniaturized sampling and ionization device, the Single-probe, which uses in-situ surface micro-extraction to achieve high detection sensitivity and spatial resolution during MSI experiments. The Single-probe was coupled to a Thermo LTQ Orbitrap XL mass spectrometer and was able to create high spatial and high mass resolution MS images at 8 ± 2 and 8.5 μm on flat polycarbonate microscope slides and mouse kidney sections, respectively, which are among the highest resolutions available for ambient MSI techniques. Our proof-of-principle experiments indicate that the Single-probe MSI technique has the potential to obtain ambient MS images with very high spatial resolutions with minimal sample preparation, which opens the possibility for subcellular ambient tissue MSI to be performed in the future.

Imaging phosphorylated peptide distribution in human lens by MALDI MS

Phosphorylation plays vital roles in complex biological processes such as cellular growth, division and signaling transduction. However, due to the low ionization efficiency of phosphorylated peptides, it is still a huge challenge to obtain region-specific phosphorylated peptide distribution by imaging mass spectrometry. To achieve the on-tissue analysis of phosphorylated peptides, we took advantage of a graphene oxide-immobilized enzyme reactor to conduct the in situ digestion, followed by dephosphorylation treatment that removed the phosphate groups and thereby helped to improve the signal intensity of phosphorylated peptides. A visual representation of the phosphoproteome of a human lens was successfully mapped. Results showed that phosphorylated peptides localized mainly in the nucleus region of a healthy lens while the outer cortex is the dominant region for phosphorylated peptides of a cataractous lens.

Mass spectrometry imaging for the proteomic study of clinical tissue

Over the last decade, MALDI-MS imaging has been used by researchers to explore areas of proteomics, lipidomics and metabolomics in samples of clinical origin for both targeted and global biomarker analysis. Numerous technological advancements in MS and clinical tissue MS imaging have been accomplished; hence, in this article we aim to critically discuss whether MS imaging has now in fact become a true champion of the 'Omics Era'. In order to assess the potential for it to be routinely used in the clinical setting, it is pertinent to discuss some of its limitations, and to examine how these have been addressed by researchers. The key limitations of the technique we will discuss in this viewpoint article are as follows: sample throughput; relevance to patients, the availability of validated/standardised techniques; and integration with conventional pathology and other medical imaging techniques. Good progress has been made over the last 5 years in overcoming these limitations that had previously restricted the use of this technology in the clinical setting.

Rapid evaporative ionization mass spectrometry imaging platform for direct mapping from bulk tissue and bacterial growth media

Rapid evaporative ionization mass spectrometry (REIMS) technology allows real time intraoperative tissue classification and the characterization and identification of microorganisms. In order to create spectral libraries for training the classification models, reference data need to be acquired in large quantities as classification accuracy generally improves as a function of number of training samples. In this study, we present an automated high-throughput method for collecting REIMS data from heterogeneous organic tissue. The underlying instrumentation consists of a 2D stage with an additional high-precision z-axis actuator that is equipped with an electrosurgical diathermy-based sampling probe. The approach was validated using samples of human liver with metastases and bacterial strains, cultured on solid medium, belonging to the species P. aeruginosa, B. subtilis, and S. aureus. For both sample types, spatially resolved spectral information was obtained that resulted in clearly distinguishable multivariate clustering between the healthy/cancerous liver tissues and between the bacterial species.

MALDI-imaging mass spectrometry on tissues

Matrix-assisted laser desorption ionization time-of-flight (MALDI-TOF)-profiling and imaging mass spectrometry (MSI) are promising technologies for measuring hundreds of different molecules directly on tissues. For instance, small molecules, drugs and their metabolites, endogenous lipids, carbohydrates and complex peptides/proteins can be measured at the same time. In the most advanced instruments, it is achieved without significant disruption of sample integrity. MSI is a unique approach for assessing the spatial distribution of molecules using graphical multidimensional maps of their constituent analytes, which may for instance be correlated with histopathological alterations in patient tissues. MALDI-TOF-MSI technology has been implemented in hospitals of several countries, where it is routinely used for quick pathogen(s) identification, a task formerly accomplished by laborious and expensive DNA/RNA-based PCR (polymerase chain reaction) screening.In this chapter, we describe how MSI is performed, what is required from the researcher, the instrument vendors and finally what can be achieved with MSI. We restrict our descriptions only to MALDI-MSI although several other MS techniques of ionization can easily be linked to MSI.

Mapping antiretroviral drugs in tissue by IR-MALDESI MSI coupled to the Q Exactive and comparison with LC-MS/MS SRM assay

This work describes the coupling of the IR-MALDESI imaging source with the Q Exactive mass spectrometer. IR-MALDESI MSI was used to elucidate the spatial distribution of several HIV drugs in cervical tissues that had been incubated in either a low or high concentration. Serial sections of those analyzed by IR-MALDESI MSI were homogenized and analyzed by LC-MS/MS to quantify the amount of each drug present in the tissue. By comparing the two techniques, an agreement between the average intensities from the imaging experiment and the absolute quantities for each drug was observed. This correlation between these two techniques serves as a prerequisite to quantitative IR-MALDESI MSI. In addition, a targeted MS(2) imaging experiment was also conducted to demonstrate the capabilities of the Q Exactive and to highlight the added selectivity that can be obtained with SRM or MRM imaging experiments.

Investigation of neutrophilic peptides in periprosthetic tissue by matrix-assisted laser desorption ionisation time-of-flight imaging mass spectrometry

PURPOSE

The accurate diagnosis of periprosthetic joint infection (PJI) relies on clinical investigation, laboratory parameters, radiological methods, sterile joint aspiration for synovial fluid leucocyte count and microbiological analysis and tissue sampling for histopathology. Due to the limits in specificity and sensitivity of these methods, molecular techniques and new biomarkers were introduced into the diagnostic procedure. Histological examination is related to the amount of neutrophils in the periprosthetic tissue in frozen sections and formalin-fixed paraffin embedded material (FFPE). However, the threshold of neutrophils per defined area of tissue among various studies is very inconsistent.

METHODS

We have applied matrix-assisted laser desorption ionisation time-of-flight imaging mass spectrometry (MALDI IMS) to a total of 32 periprosthetic tissue samples of patients with PJI to detect peptides associated with areas of neutrophil infiltration.

RESULTS

Specific peaks associated with a high amount of neutrophils were detected. Of these m/z peaks, four could be assigned to predictive neutrophil molecules. These peptides include annexin A1, calgizzarin (S100A11), calgranulin C (S100A12) and histone H2A. By MALDI IMS, these peptides could be shown to be co-localised with the infiltration of neutrophils in the immediate vicinity of the periprosthetic interface, whereas more distant areas did not show neutrophil invasion or infection-related peptides.

CONCLUSIONS

MALDI IMS is a new method allowing identification of neutrophil peptides in periprosthetic tissues and may be a surrogate for counting neutrophils as an objective parameter for PJI.

Imaging mass spectrometry to discriminate breast from pancreatic cancer metastasis in formalin-fixed paraffin-embedded tissues

Diagnosis of the origin of metastasis is mandatory for adequate therapy. In the past, classification of tumors was based on histology (morphological expression of a complex protein pattern), while supportive immunohistochemical investigation relied only on few "tumor specific" proteins. At present, histopathological diagnosis is based on clinical information, morphology, immunohistochemistry, and may include molecular methods. This process is complex, expensive, requires an experienced pathologist and may be time consuming. Currently, proteomic methods have been introduced in various clinical disciplines. MALDI imaging MS combines detection of numerous proteins with morphological features, and seems to be the ideal tool for objective and fast histopathological tumor classification. To study a special tumor type and to identify predictive patterns that could discriminate metastatic breast from pancreatic carcinoma MALDI imaging MS was applied to multitissue paraffin blocks. A statistical classification model was created using a training set of primary carcinoma biopsies. This model was validated on two testing sets of different breast and pancreatic carcinoma specimens. We could discern breast from pancreatic primary tumors with an overall accuracy of 83.38%, a sensitivity of 85.95% and a specificity of 76.96%. Furthermore, breast and pancreatic liver metastases were tested and classified correctly.

Chemo-informatic strategy for imaging mass spectrometry-based hyperspectral profiling of lipid signatures in colorectal cancer

Mass spectrometry imaging (MSI) provides the opportunity to investigate tumor biology from an entirely novel biochemical perspective and could lead to the identification of a new pool of cancer biomarkers. Effective clinical translation of histology-driven MSI in systems oncology requires precise colocalization of morphological and biochemical features as well as advanced methods for data treatment and interrogation. Currently proposed MSI workflows are subject to several limitations, including nonoptimized raw data preprocessing, imprecise image coregistration, and limited pattern recognition capabilities. Here we outline a comprehensive strategy for histology-driven MSI, using desorption electrospray ionization that covers (i) optimized data preprocessing for improved information recovery; (ii) precise image coregistration; and (iii) efficient extraction of tissue-specific molecular ion signatures for enhanced biochemical distinction of different tissue types. The proposed workflow has been used to investigate region-specific lipid signatures in colorectal cancer tissue. Unique lipid patterns were observed using this approach according to tissue type, and a tissue recognition system using multivariate molecular ion patterns allowed highly accurate (>98%) identification of pixels according to morphology (cancer, healthy mucosa, smooth muscle, and microvasculature). This strategy offers unique insights into tumor microenvironmental biochemistry and should facilitate compilation of a large-scale tissue morphology-specific MSI spectral database with which to pursue next-generation, fully automated histological approaches.

Application of Matrix-Assisted Laser Desorption/Ionization Time-of-Flight Imaging Mass Spectrometry (MALDI-TOF IMS) for Premalignant Gastrointestinal Lesions

Imaging mass spectrometry (IMS) is currently receiving large attention from the mass spectrometric community, although its use is not yet well known in the clinic. As matrix-assisted laser desorption/ionization time-of-flight (MALDI)-IMS can show the biomolecular changes in cells as well as tissues, it can be an ideal tool for biomedical diagnostics as well as the molecular diagnosis of clinical specimens, especially aimed at the prompt detection of premalignant lesions much earlier before overt mass formation, or for obtaining histologic clues from endoscopic biopsy. Besides its use for pathologic diagnosis, MALDI-IMS is also a powerful tool for the detection and localization of drugs, proteins, and lipids in tissue. Measurement of parameters that define and control the implications, challenges, and opportunities associated with the application of IMS to biomedical tissue studies might be feasible through a deep understanding of mass spectrometry. In this focused review series, new insights into the molecular processes relevant to IMS as well as other field applications are introduced.

Testing for presence of known and unknown molecules in imaging mass spectrometry

MOTIVATION

Imaging mass spectrometry has emerged in the past decade as a label-free, spatially resolved and multi-purpose bioanalytical technique for direct analysis of biological samples. However, solving two everyday data analysis problems still requires expert judgment: (i) the detection of unknown molecules and (ii) the testing for presence of known molecules.

RESULTS

We developed a measure of spatial chaos of a molecular image corresponding to a mass-to-charge value, which is a proxy for the molecular presence, and developed methods solving considered problems. The statistical evaluation was performed on a dataset from a rat brain section with test sets of molecular images selected by an expert. The measure of spatial chaos has shown high agreement with expert judges. The method for detection of unknown molecules allowed us to find structured molecular images corresponding to spectral peaks of any low intensity. The test for presence applied to a list of endogenous peptides ranked them according to the proposed measure of their presence in the sample.

AVAILABILITY

The source code and test sets of mass-to-charge images are available at http://www.math.uni-bremen.de/∼theodore.

SUPPLEMENTARY INFORMATION

Supplementary materials are available at Bioinformatics online.

CONTACT

theodore@uni-bremen.de.

MALDI imaging MS reveals candidate lipid markers of polycystic kidney disease

Autosomal recessive polycystic kidney disease (ARPKD) is a severe, monogenetically inherited kidney and liver disease. PCK rats carrying the orthologous mutant gene serve as a model of human disease, and alterations in lipid profiles in PCK rats suggest that defined subsets of lipids may be useful as molecular disease markers. Whereas MALDI protein imaging mass spectrometry (IMS) has become a promising tool for disease classification, widely applicable workflows that link MALDI lipid imaging and identification as well as structural characterization of candidate disease-classifying marker lipids are lacking. Here, we combine selective MALDI imaging of sulfated kidney lipids and Fisher discriminant analysis (FDA) of imaging data sets for identification of candidate markers of progressive disease in PCK rats. Our study highlights strong increases in lower mass lipids as main classifiers of cystic disease. Structure determination by high-resolution mass spectrometry identifies these altered lipids as taurine-conjugated bile acids. These sulfated lipids are selectively elevated in the PCK rat model but not in models of related hepatorenal fibrocystic diseases, suggesting that they be molecular markers of the disease and that a combination of MALDI imaging with high-resolution MS methods and Fisher discriminant data analysis may be applicable for lipid marker discovery.

MALDI mass spectrometry in prostate cancer biomarker discovery

Matrix-assisted laser desorption/ionisation (MALDI) mass spectrometry (MS) is a highly versatile and sensitive analytical technique, which is known for its soft ionisation of biomolecules such as peptides and proteins. Generally, MALDI MS analysis requires little sample preparation, and in some cases like MS profiling it can be automated through the use of robotic liquid-handling systems. For more than a decade now, MALDI MS has been extensively utilised in the search for biomarkers that could aid clinicians in diagnosis, prognosis, and treatment decision making. This review examines the various MALDI-based MS techniques like MS imaging, MS profiling and proteomics in-depth analysis where MALDI MS follows fractionation and separation methods such as gel electrophoresis, and how these have contributed to prostate cancer biomarker research. This article is part of a Special Issue entitled: Biomarkers: A Proteomic Challenge.

Mass spectrometry imaging with high resolution in mass and space

Mass spectrometry (MS) imaging links molecular information and the spatial distribution of analytes within a sample. In contrast to most histochemical techniques, mass spectrometry imaging can differentiate molecular modifications and does not require labeling of targeted compounds. We have recently introduced the first mass spectrometry imaging method that provides highly specific molecular information (high resolution and accuracy in mass) at cellular dimensions (high resolution in space). This method is based on a matrix-assisted laser desorption/ionization (MALDI) imaging source working at atmospheric pressure which is coupled to an orbital trapping mass spectrometer. Here, we present a number of application examples and demonstrate the benefit of ‘mass spectrometry imaging with high resolution in mass and space.’ Phospholipids, peptides and drug compounds were imaged in a number of tissue samples at a spatial resolution of 5–10 μm. Proteins were analyzed after on-tissue tryptic digestion at 50-μm resolution. Additional applications include the analysis of single cells and of human lung carcinoma tissue as well as the first MALDI imaging measurement of tissue at 3 μm pixel size. MS image analysis for all these experiments showed excellent correlation with histological staining evaluation. The high mass resolution (R = 30,000) and mass accuracy (typically 1 ppm) proved to be essential for specific image generation and reliable identification of analytes in tissue samples. The ability to combine the required high-quality mass analysis with spatial resolution in the range of single cells is a unique feature of our method. With that, it has the potential to supplement classical histochemical protocols and to provide new insights about molecular processes on the cellular level.

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.