The evolving field of imaging mass spectrometry and its impact on future biological research

Spatial metabolomics in tissue injury and regeneration

Tissue homeostasis is intricately linked to cellular metabolism and metabolite exchange within the tissue microenvironment. The orchestration of adaptive cellular responses during injury and repair depends critically upon metabolic adaptation. This adaptation, in turn, shapes cell fate decisions required for the restoration of tissue homeostasis. Understanding the nuances of metabolic processes within the tissue context and comprehending the intricate communication between cells is therefore imperative for unraveling the complexity of tissue homeostasis and the processes of injury and repair. In this review, we focus on mass spectrometry imaging as an advanced platform with the potential to provide such comprehensive insights into the metabolic instruction governing tissue function. Recent advances in this technology allow to decipher the intricate metabolic networks that determine cellular behavior in the context of tissue resilience, injury, and repair. These insights not only advance our fundamental understanding of tissue biology but also hold implications for therapeutic interventions by targeting metabolic pathways critical for maintaining tissue homeostasis.

Mass Spectrometry Imaging Techniques: Non-Ambient and Ambient Ionization Approaches

Molecular information can be acquired from sample surfaces in real time using a revolutionary molecular imaging technique called mass spectrometry imaging (MSI). The technique can concurrently provide high spatial resolution information on the spatial distribution and relative proportion of many different compounds. Thus, many scientists have been drawn to the innovative capabilities of the MSI approach, leading to significant focus in various fields during the past few decades. This review describes the sampling protocol, working principle and applications of a few non-ambient and ambient ionization mass spectrometry imaging techniques. The non-ambient techniques include secondary ionization mass spectrometry and matrix-assisted laser desorption ionization, while the ambient techniques include desorption electrospray ionization, laser ablation electrospray ionization, probe electro-spray ionization, desorption atmospheric pressure photo-ionization and femtosecond laser desorption ionization. The review additionally addresses the advantages and disadvantages of ambient and non-ambient MSI techniques in relation to their suitability, particularly for biological samples used in tissue diagnostics. Last but not least, suggestions and conclusions are made regarding the challenges and future prospects of MSI.

Mass Spectral Imaging to Map Plant-Microbe Interactions

Plant-microbe interactions are of rising interest in plant sustainability, biomass production, plant biology, and systems biology. These interactions have been a challenge to detect until recent advancements in mass spectrometry imaging. Plants and microbes interact in four main regions within the plant, the rhizosphere, endosphere, phyllosphere, and spermosphere. This mini review covers the challenges within investigations of plant and microbe interactions. We highlight the importance of sample preparation and comparisons among time-of-flight secondary ion mass spectroscopy (ToF-SIMS), matrix-assisted laser desorption/ionization (MALDI), laser desorption ionization (LDI/LDPI), and desorption electrospray ionization (DESI) techniques used for the analysis of these interactions. Using mass spectral imaging (MSI) to study plants and microbes offers advantages in understanding microbe and host interactions at the molecular level with single-cell and community communication information. More research utilizing MSI has emerged in the past several years. We first introduce the principles of major MSI techniques that have been employed in the research of microorganisms. An overview of proper sample preparation methods is offered as a prerequisite for successful MSI analysis. Traditionally, dried or cryogenically prepared, frozen samples have been used; however, they do not provide a true representation of the bacterial biofilms compared to living cell analysis and chemical imaging. New developments such as microfluidic devices that can be used under a vacuum are highly desirable for the application of MSI techniques, such as ToF-SIMS, because they have a subcellular spatial resolution to map and image plant and microbe interactions, including the potential to elucidate metabolic pathways and cell-to-cell interactions. Promising results due to recent MSI advancements in the past five years are selected and highlighted. The latest developments utilizing machine learning are captured as an important outlook for maximal output using MSI to study microorganisms.

Mass spectrometry imaging as an emerging tool for studying metabolism in human brain organoids

Human brain organoids are emerging models to study human brain development and pathology as they recapitulate the development and characteristics of major neural cell types, and enable manipulation through an in vitro system. Over the past decade, with the advent of spatial technologies, mass spectrometry imaging (MSI) has become a prominent tool for metabolic microscopy, providing label-free, non-targeted molecular and spatial distribution information of the metabolites within tissue, including lipids. This technology has never been used for studies of brain organoids and here, we set out to develop a standardized protocol for preparation and mass spectrometry imaging of human brain organoids. We present an optimized and validated sample preparation protocol, including sample fixation, optimal embedding solution, homogenous deposition of matrices, data acquisition and processing to maximize the molecular information derived from mass spectrometry imaging. We focus on lipids in organoids, as they play critical roles during cellular and brain development. Using high spatial and mass resolution in positive- and negative-ion modes, we detected 260 lipids in the organoids. Seven of them were uniquely localized within the neurogenic niches or rosettes as confirmed by histology, suggesting their importance for neuroprogenitor proliferation. We observed a particularly striking distribution of ceramide-phosphoethanolamine CerPE 36:1; O2 which was restricted within rosettes and of phosphatidyl-ethanolamine PE 38:3, which was distributed throughout the organoid tissue but not in rosettes. This suggests that ceramide in this particular lipid species might be important for neuroprogenitor biology, while its removal may be important for terminal differentiation of their progeny. Overall, our study establishes the first optimized experimental pipeline and data processing strategy for mass spectrometry imaging of human brain organoids, allowing direct comparison of lipid signal intensities and distributions in these tissues. Further, our data shed new light on the complex processes that govern brain development by identifying specific lipid signatures that may play a role in cell fate trajectories. Mass spectrometry imaging thus has great potential in advancing our understanding of early brain development as well as disease modeling and drug discovery.

Spatial metabolomics identifies lipid profiles of human carotid atherosclerosis

BACKGROUND AND AIMS

Carotid atherosclerosis is an important cause of ischemic stroke. Lipids play a key role in the progression of atherosclerosis. To date, the spatial lipid profile of carotid atherosclerotic plaques related to histology has not been systematically investigated.

METHODS

Carotid atherosclerosis samples from 12 patients were obtained and classified into four classical pathological stages (preatheroma, atheroma, fibroatheroma and complicated lesion) by histological staining. Desorption electrospray ionization-mass spectrometry imaging (DESI-MSI) was used to investigate the lipid profile of carotid atherosclerosis, and correlated it with histological information. Bioinformatics technology was used to process MSI data among different pathological stages of atherosclerosis lesions.

RESULTS

A total of 55 lipids (26 throughout cross-section regions [TCSRs], 13 in lipid-rich regions [LRRs], and 16 in collagen-rich regions [CRRs]) were initially identified in carotid plaque from one patient. Subsequently, 32 of 55 lipids (12 in TCSRs, eight in LRRs, and 12 in CRRs) were further screened in 11 patients. Pathway enrichment analysis showed that multiple metabolic pathways, such as fat digestion and absorption, cholesterol metabolism, lipid and atherosclerosis, were enriched in TCSRs; sphingolipid signaling pathway, necroptosis pathway were enriched in LRRs; and glycerophospholipid metabolism, ether lipid metabolism pathway were mainly enriched in CRRs.

CONCLUSIONS

This study comprehensively showed the spatial lipid metabolism footprint in human carotid atherosclerotic plaques. The lipid profiles and related metabolism pathways in three regions of plaque with disease progression were different markedly, suggesting that the different metabolic mechanisms in these regions of carotid plaque may be critical in atherosclerosis progression.

Advances in mass spectrometry imaging for toxicological analysis and safety evaluation of pharmaceuticals

Safety issues caused by pharmaceuticals have frequently occurred worldwide, posing a tremendous threat to human health. As an essential part of drug development, the toxicological analysis and safety evaluation is of great significance. In addition, the risk of pharmaceuticals accumulation in the environment and the monitoring of the toxicity from natural medicines have also received ongoing concerns. Due to a lack of spatial distribution information provided by common analytical methods, analyses that provide spatial dimensions could serve as complementary safety evaluation methods for better prediction and evaluation of drug toxicity. With advances in technical solutions and software algorithms, mass spectrometry imaging (MSI) has received increasing attention as a popular analytical tool that enables the simultaneous implementation of qualitative, quantitative, and localization without complex sample pretreatment and labeling steps. In recent years, MSI has become more attractive, powerful, and sensitive and has been applied in several scientific fields that can meet the safety assessment requirements. This review aims to cover a detailed summary of the various MSI technologies utilized in the biomedical and pharmaceutical area, including technical principles, advantages, current status, and future trends. Representative applications and developments in the safety-related issues of different pharmaceuticals and natural medicines are also described to provide a reference for pharmaceutical research, improve rational clinical medicine use, and ensure public safety.

Lipid Analysis of Fracture Hematoma With MALDI-MSI: Specific Lipids are Associated to Bone Fracture Healing Over Time

Background: Fracture healing is a complex process, involving cell-cell interactions, various cytokines, and growth factors. Although fracture treatment improved over the last decades, a substantial part of all fractures shows delayed or absent healing. The fracture hematoma (fxh) is known to have a relevant role in this process, while the exact mechanisms by which it influences fracture healing are poorly understood. To improve strategies in fracture treatment, regulatory pathways in fracture healing need to be investigated. Lipids are important molecules in cellular signaling, inflammation, and metabolism, as well as key structural components of the cell. Analysis of the lipid spectrum in fxh may therefore reflect important events during the early healing phase. This study aims to develop a protocol for the determination of lipid signals over time, and the identification of lipids that contribute to these signals, with matrix-assisted laser desorption/ionization mass spectrometry imaging (MALDI-MSI) in fxh in healthy fracture healing.

Methods: Twelve fxh samples (6 porcine; 6 human) were surgically removed, snap frozen, sectioned, washed, and analyzed using MALDI-MSI in positive and negative ion mode at different time points after fracture (porcine: 72 h; human samples: range 1–19 days). A tissue preparation protocol for lipid analysis in fxh has been developed with both porcine and human fxh. Data were analyzed through principal component- and linear discriminant analyses.

Results: A protocol for the preparation of fxh sections was developed and optimized. Although hematoma is a heterogeneous tissue, the intra-variability within fxh was smaller than the inter-variability between fxh. Distinctive m/z values were detected that contributed to the separation of three different fxh age groups: early (1–3 days), middle (6–10 days), and late (12–19 days). Identification of the distinctive m/z values provided a panel of specific lipids that showed a time dependent expression within fxh.

Conclusion: This study shows that MALDI-MSI is a suitable analytical tool for lipid analysis in fxh and that lipid patterns within fxh are time-dependent. These lipid patterns within fxh may serve as a future diagnostic tool. These findings warrant further research into fxh analysis using MALDI-MSI and its possible clinical implications in fracture treatment.

Applications of multivariate analysis and unsupervised machine learning to ToF-SIMS images of organic, bioorganic, and biological systems

Time-of-flight secondary ion mass spectrometry (ToF-SIMS) imaging offers a powerful, label-free method for exploring organic, bioorganic, and biological systems. The technique is capable of very high spatial resolution, while also producing an enormous amount of information about the chemical and molecular composition of a surface. However, this information is inherently complex, making interpretation and analysis of the vast amount of data produced by a single ToF-SIMS experiment a considerable challenge. Much research over the past few decades has focused on the application and development of multivariate analysis (MVA) and machine learning (ML) techniques that find meaningful patterns and relationships in these datasets. Here, we review the unsupervised algorithms—that is, algorithms that do not require ground truth labels—that have been applied to ToF-SIMS images, as well as other algorithms and approaches that have been used in the broader family of mass spectrometry imaging (MSI) techniques. We first give a nontechnical overview of several commonly used classes of unsupervised algorithms, such as matrix factorization, clustering, and nonlinear dimensionality reduction. We then review the application of unsupervised algorithms to various organic, bioorganic, and biological systems including cells and tissues, organic films, residues and coatings, and spatially structured systems such as polymer microarrays. We then cover several novel algorithms employed for other MSI techniques that have received little attention from ToF-SIMS imaging researchers. We conclude with a brief outline of potential future directions for the application of MVA and ML algorithms to ToF-SIMS images.

MALDI imaging mass spectrometry: an emerging tool in neurology

Neurological disease and disorders remain a large public health threat. Thus, research to improve early detection and/or develop more effective treatment approaches are necessary. Although there are many common techniques and imaging modalities utilized to study these diseases, existing approaches often require a label which can be costly and time consuming. Matrix-assisted laser desorption ionization (MALDI) imaging mass spectrometry (IMS) is a label-free, innovative and emerging technique that produces 2D ion density maps representing the distribution of an analyte(s) across a tissue section in relation to tissue histopathology. One main advantage of MALDI IMS over other imaging modalities is its ability to determine the spatial distribution of hundreds of analytes within a single imaging run, without the need for a label or any a priori knowledge. Within the field of neurology and disease there have been several impactful studies in which MALDI IMS has been utilized to better understand the cellular pathology of the disease and or severity. Furthermore, MALDI IMS has made it possible to map specific classes of analytes to regions of the brain that otherwise may have been lost using more traditional methods. This review will highlight key studies that demonstrate the potential of this technology to elucidate previously unknown phenomenon in neurological disease.

ColocML: machine learning quantifies co-localization between mass spectrometry images

Motivation

Imaging mass spectrometry (imaging MS) is a prominent technique for capturing distributions of molecules in tissue sections. Various computational methods for imaging MS rely on quantifying spatial correlations between ion images, referred to as co-localization. However, no comprehensive evaluation of co-localization measures has ever been performed; this leads to arbitrary choices and hinders method development.

Results

We present ColocML, a machine learning approach addressing this gap. With the help of 42 imaging MS experts from nine laboratories, we created a gold standard of 2210 pairs of ion images ranked by their co-localization. We evaluated existing co-localization measures and developed novel measures using term frequency–inverse document frequency and deep neural networks. The semi-supervised deep learning Pi model and the cosine score applied after median thresholding performed the best (Spearman 0.797 and 0.794 with expert rankings, respectively). We illustrate these measures by inferring co-localization properties of 10 273 molecules from 3685 public METASPACE datasets.

Availability and implementation

https://github.com/metaspace2020/coloc.

Supplementary information

Supplementary data are available at Bioinformatics online.

Spatial Metabolomics and Imaging Mass Spectrometry in the Age of Artificial Intelligence

Spatial metabolomics is an emerging field of omics research that has enabled localizing metabolites, lipids, and drugs in tissue sections, a feat considered impossible just two decades ago. Spatial metabolomics and its enabling technology-imaging mass spectrometry-generate big hyper-spectral imaging data that have motivated the development of tailored computational methods at the intersection of computational metabolomics and image analysis. Experimental and computational developments have recently opened doors to applications of spatial metabolomics in life sciences and biomedicine. At the same time, these advances have coincided with a rapid evolution in machine learning, deep learning, and artificial intelligence, which are transforming our everyday life and promise to revolutionize biology and healthcare. Here, we introduce spatial metabolomics through the eyes of a computational scientist, review the outstanding challenges, provide a look into the future, and discuss opportunities granted by the ongoing convergence of human and artificial intelligence.

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.

Unsupervised segmentation of mass spectrometric ion images characterizes morphology of tissues

MOTIVATION

Mass spectrometry imaging (MSI) characterizes the spatial distribution of ions in complex biological samples such as tissues. Since many tissues have complex morphology, treatments and conditions often affect the spatial distribution of the ions in morphology-specific ways. Evaluating the selectivity and the specificity of ion localization and regulation across morphology types is biologically important. However, MSI lacks algorithms for segmenting images at both single-ion and spatial resolution.

RESULTS

This article contributes spatial-Dirichlet Gaussian mixture model (DGMM), an algorithm and a workflow for the analyses of MSI experiments, that detects components of single-ion images with homogeneous spatial composition. The approach extends DGMMs to account for the spatial structure of MSI. Evaluations on simulated and experimental datasets with diverse MSI workflows demonstrated that spatial-DGMM accurately segments ion images, and can distinguish ions with homogeneous and heterogeneous spatial distribution. We also demonstrated that the extracted spatial information is useful for downstream analyses, such as detecting morphology-specific ions, finding groups of ions with similar spatial patterns, and detecting changes in chemical composition of tissues between conditions.

AVAILABILITY AND IMPLEMENTATION

The data and code are available at https://github.com/Vitek-Lab/IonSpattern.

SUPPLEMENTARY INFORMATION

Supplementary data are available at Bioinformatics online.

Imaging Mass Spectrometry: A New Tool to Assess Molecular Underpinnings of Neurodegeneration

Neurodegenerative diseases are prevalent and devastating. While extensive research has been done over the past decades, we are still far from comprehensively understanding what causes neurodegeneration and how we can prevent it or reverse it. Recently, systems biology approaches have led to a holistic examination of the interactions between genome, metabolome, and the environment, in order to shed new light on neurodegenerative pathogenesis. One of the new technologies that has emerged to facilitate such studies is imaging mass spectrometry (IMS). With its ability to map a wide range of small molecules with high spatial resolution, coupled with the ability to quantify them at once, without the need for a priori labeling, IMS has taken center stage in current research efforts in elucidating the role of the metabolome in driving neurodegeneration. IMS has already proven to be effective in investigating the lipidome and the proteome of various neurodegenerative diseases, such as Alzheimer's, Parkinson's, Huntington's, multiple sclerosis, and amyotrophic lateral sclerosis. Here, we review the IMS platform for capturing biological snapshots of the metabolic state to shed more light on the molecular mechanisms of the diseased brain.

Droplet probe: coupling chromatography to the in situ evaluation of the chemistry of nature

Covering: up to 2019The chemistry of nature can be beautiful, inspiring, beneficial and poisonous, depending on perspective. Since the isolation of the first secondary metabolites roughly two centuries ago, much of the chemical research on natural products has been both reductionist and static. Typically, compounds were isolated and characterized from the extract of an entire organism from a single time point. While there could be subtexts to that approach, the general premise has been to determine the chemistry with very little in the way of tools to differentiate spatial and/or temporal changes in secondary metabolite profiles. However, the past decade has seen exponential advances in our ability to observe, measure, and visualize the chemistry of nature in situ. Many of those techniques have been reviewed in this journal, and most are tapping into the power of mass spectrometry to analyze a plethora of sample types. In nearly all of the other techniques used to study chemistry in situ, the element of chromatography has been eliminated, instead using various ionization sources to coax ions of the secondary metabolites directly into the mass spectrometer as a mixture. Much of that science has been driven by the great advances in ambient ionization techniques used with a suite of mass spectrometry platforms, including the alphabet soup from DESI to LAESI to MALDI. This review discusses the one in situ analysis technique that incorporates chromatography, being the droplet-liquid microjunction-surface sampling probe, which is more easily termed "droplet probe". In addition to comparing and contrasting the droplet probe with other techniques, we provide perspective on why scientists, particularly those steeped in natural products chemistry training, may want to include chromatography in in situ analyses. Moreover, we provide justification for droplet sampling, especially for samples with delicate and/or non-uniform topographies. Furthermore, while the droplet probe has been used the most in the analysis of fungal cultures, we digest a variety of other applications, ranging from cyanobacteria, to plant parts, and even delicate documents, such as herbarium specimens.

Biocontrol of Cereal Crop Diseases Using Streptomycetes

A growing world population and an increasing demand for greater food production requires that crop losses caused by pests and diseases are dramatically reduced. Concurrently, sustainability targets mean that alternatives to chemical pesticides are becoming increasingly desirable. Bacteria in the plant root microbiome can protect their plant host against pests and pathogenic infection. In particular, Streptomyces species are well-known to produce a range of secondary metabolites that can inhibit the growth of phytopathogens. Streptomyces are abundant in soils and are also enriched in the root microbiomes of many different plant species, including those grown as economically and nutritionally valuable cereal crops. In this review we discuss the potential of Streptomyces to protect against some of the most damaging cereal crop diseases, particularly those caused by fungal pathogens. We also explore factors that may improve the efficacy of these strains as biocontrol agents in situ, as well as the possibility of exploiting plant mechanisms, such as root exudation, that enable the recruitment of microbial species from the soil to the root microbiome. We argue that a greater understanding of these mechanisms may enable the development of protective plant root microbiomes with a greater abundance of beneficial bacteria, such as Streptomyces species.

Mass Spectrometry for Proteomics and Recent Developments in ESI, MALDI and other Ionization Methodologies

Background:

Mass spectrometry is a tool used in analytical chemistry to identify components in a chemical compound and it is of tremendous importance in the field of biology for high throughput analysis of biomolecules, among which protein is of great interest.

Objective:

Advancement in proteomics based on mass spectrometry has led the way to quantify multiple protein complexes, and proteins interactions with DNA/RNA or other chemical compounds which is a breakthrough in the field of bioinformatics.

Methods:

Many new technologies have been introduced in electrospray ionization (ESI) and Matrixassisted Laser Desorption/Ionization (MALDI) techniques which have enhanced sensitivity, resolution and many other key features for the characterization of proteins.

Results:

The advent of ambient mass spectrometry and its different versions like Desorption Electrospray Ionization (DESI), DART and ELDI has brought a huge revolution in proteomics research. Different imaging techniques are also introduced in MS to map proteins and other significant biomolecules. These drastic developments have paved the way to analyze large proteins of >200kDa easily.

Conclusion:

Here, we discuss the recent advancement in mass spectrometry, which is of great importance and it could lead us to further deep analysis of the molecules from different perspectives and further advancement in these techniques will enable us to find better ways for prediction of molecules and their behavioral properties.

Data-Driven Rescoring of Metabolite Annotations Significantly Improves Sensitivity

When analyzing mass spectrometry imaging data sets, assigning a molecule to each of the thousands of generated images is a very complex task. Recent efforts have taken lessons from (tandem) mass spectrometry proteomics and applied them to imaging mass spectrometry metabolomics, with good results. Our goal is to go a step further in this direction and apply a well established, data-driven method to improve the results obtained from an annotation engine. By using a data-driven rescoring strategy, we are able to consistently improve the sensitivity of the annotation engine while maintaining control of statistics like estimated rate of false discoveries. All the code necessary to run a search and extract the additional features can be found at https://github.com/anasilviacs/sm-engine and to rescore the results from a search in https://github.com/anasilviacs/rescore-metabolites .

Analytical Chemistry in the Regulatory Science of Medical Devices

In the United States, regulatory science is the science of developing new tools, standards, and approaches to assess the safety, efficacy, quality, and performance of all Food and Drug Administration-regulated products. Good regulatory science facilitates consumer access to innovative medical devices that are safe and effective throughout the Total Product Life Cycle (TPLC). Because the need to measure things is fundamental to the regulatory science of medical devices, analytical chemistry plays an important role, contributing to medical device technology in two ways: It can be an integral part of an innovative medical device (e.g., diagnostic devices), and it can be used to support medical device development throughout the TPLC. In this review, we focus on analytical chemistry as a tool for the regulatory science of medical devices. We highlight recent progress in companion diagnostics, medical devices on chips for preclinical testing, mass spectrometry for postmarket monitoring, and detection/characterization of bacterial biofilm to prevent infections.

Characterization of microbial mixtures by mass spectrometry

MS applications in microbiology have increased significantly in the past 10 years, due in part to the proliferation of regulator-approved commercial MALDI MS platforms for rapid identification of clinical infections. In parallel, with the expansion of MS technologies in the "omics" fields, novel MS-based research efforts to characterize organismal as well as environmental microbiomes have emerged. Successful characterization of microorganisms found in complex mixtures of other organisms remains a major challenge for researchers and clinicians alike. Here, we review recent MS advances toward addressing that challenge. These include sample preparation methods and protocols, and established, for example, MALDI, as well as newer, for example, atmospheric pressure ionization (API) techniques. MALDI mass spectra of intact cells contain predominantly information on the highly expressed house-keeping proteins used as biomarkers. The API methods are applicable for small biomolecule analysis, for example, phospholipids and lipopeptides, and facilitate species differentiation. MS hardware and techniques, for example, tandem MS, including diverse ion source/mass analyzer combinations are discussed. Relevant examples for microbial mixture characterization utilizing these combinations are provided. Chemometrics and bioinformatics methods and algorithms, including those applied to large scale MS data acquisition in microbial metaproteomics and MS imaging of biofilms, are highlighted. Select MS applications for polymicrobial culture analysis in environmental and clinical microbiology are reviewed as well.

Top-Down Atmospheric Ionization Mass Spectrometry Microscopy Combined With Proteogenomics

Mass spectrometry-based protein analysis has become an important methodology for proteogenomic mapping by providing evidence for the existence of proteins predicted at the genomic level. However, screening and identification of proteins directly on tissue samples, where histological information is preserved, remain challenging. Here we demonstrate that the ambient ionization source, nanospray desorption electrospray ionization (nanoDESI), interfaced with light microscopy allows for protein profiling directly on animal tissues at the microscopic scale. Peptide fragments for mass spectrometry analysis were obtained directly on ganglia of the medicinal leech (Hirudo medicinalis) without in-gel digestion. We found that a hypothetical protein, which is predicted by the leech genome, is highly expressed on the specialized neural cells that are uniquely found in adult sex segmental ganglia. Via this top-down analysis, a post-translational modification (PTM) of tyrosine sulfation to this neuropeptide was resolved. This three-in-one platform, including mass spectrometry, microscopy, and genome mining, provides an effective way for mappings of proteomes under the lens of a light microscope.

Screening and identification of novel biologically active natural compounds

With the advent of very rapid and cheap genome analyses and the linkage of these plus microbial metabolomics to potential compound structures came the realization that there was an immense sea of novel agents to be mined and tested. In addition, it is now recognized that there is significant microbial involvement in many natural products isolated from “nominally non-microbial sources”.

This short review covers the current screening methods that have evolved and one might even be tempted to say “devolved” in light of the realization that target-based screens had problems when the products entered clinical testing, with off-target effects being the major ones. Modern systems include, but are not limited to, screening in cell lines utilizing very modern techniques (a high content screen) that are designed to show interactions within cells when treated with an “agent”. The underlying principle(s) used in such systems dated back to unpublished attempts in the very early 1980s by the pharmaceutical industry to show toxic interactions within animal cells by using automated light microscopy. Though somewhat successful, the technology was not adequate for any significant commercialization. Somewhat later, mammalian cell lines that were “genetically modified” to alter signal transduction cascades, either up or down, and frequently linked to luciferase readouts, were then employed in a 96-well format. In the case of microbes, specific resistance parameters were induced in isogenic cell lines from approximately the mid-1970s. In the latter two cases, comparisons against parent and sibling cell lines were used in order that a rapid determination of potential natural product “hits” could be made. Obviously, all of these assay systems could also be, and were, used for synthetic molecules.

These methods and their results have led to a change in what the term “screening for bioactivity” means. In practice, versions of phenotypic screening are returning, but in a dramatically different scientific environment from the 1970s, as I hope to demonstrate in the short article that follows.

Differentiating tumor heterogeneity in formalin-fixed paraffin-embedded (FFPE) prostate adenocarcinoma tissues using principal component analysis of matrix-assisted laser desorption/ionization imaging mass spectral data

RATIONALE

Many patients with adenocarcinoma of the prostate present with advanced and metastatic cancer at the time of diagnosis. There is an urgent need to detect biomarkers that will improve the diagnosis and prognosis of this disease. Matrix-assisted laser desorption/ionization imaging mass spectrometry (MALDI-IMS) is playing a key role in cancer research and it can be useful to unravel the molecular profile of prostate cancer biopsies.

METHODS

MALDI imaging data sets are highly complex and their interpretation requires the use of multivariate statistical methods. In this study, MALDI-IMS technology, sequential principal component analysis (PCA) and two-dimensional (2-D) peak distribution tests were employed to investigate tumor heterogeneity in formalin-fixed paraffin-embedded (FFPE) prostate cancer biopsies.

RESULTS

Multivariate statistics revealed a number of mass ion peaks obtained from different tumor regions that were distinguishable from the adjacent normal regions within a given specimen. These ion peaks have been used to generate ion images and visualize the difference between tumor and normal regions. Mass peaks at m/z 3370, 3441, 3447 and 3707 exhibited stronger ion signals in the tumor regions.

CONCLUSIONS

This study reports statistically significant mass ion peaks unique to tumor regions in adenocarcinoma of the prostate and adds to the clinical utility of MALDI-IMS for analysis of FFPE tissue at a molecular level that supersedes all other standard histopathologic techniques for diagnostic purposes used in the current clinical practice. Copyright © 2016 John Wiley & Sons, Ltd.

FDR-controlled metabolite annotation for high-resolution imaging mass spectrometry

High-mass-resolution imaging mass spectrometry promises to localize hundreds of metabolites in tissues, cell cultures, and agar plates with cellular resolution, but it is hampered by the lack of bioinformatics tools for automated metabolite identification. We report pySM, a framework for false discovery rate (FDR)-controlled metabolite annotation at the level of the molecular sum formula, for high-mass-resolution imaging mass spectrometry (https://github.com/alexandrovteam/pySM). We introduce a metabolite-signal match score and a target-decoy FDR estimate for spatial metabolomics.

Reducing Spatial Heterogeneity of MALDI Samples with Marangoni Flows During Sample Preparation

This work demonstrates a method to prepare homogeneous distributions of analytes to improve data reproducibility in matrix-assisted laser desorption/ionization (MALDI) mass spectrometry (MS). Natural-air drying processes normally result in unwanted heterogeneous spatial distributions of analytes in MALDI crystals and make quantitative analysis difficult. This study demonstrates that inducing Marangoni flows within drying droplets can significantly reduce the heterogeneity problem. The Marangoni flows are accelerated by changing substrate temperatures to create temperature gradients across droplets. Such hydrodynamic flows are analyzed semi-empirically. Using imaging mass spectrometry, changes of heterogeneity of molecules with the change of substrate temperature during drying processes are demonstrated. The observed heterogeneities of the biomolecules reduce as predicted Marangoni velocities increase. In comparison to conventional methods, drying droplets on a 5 °C substrate while keeping the surroundings at ambient conditions typically reduces the heterogeneity of biomolecular ions by 65%-80%. The observation suggests that decreasing substrate temperature during droplet drying processes is a simple and effective means to reduce analyte heterogeneity for quantitative applications. Graphical Abstract ᅟ.

Probabilistic Segmentation of Mass Spectrometry (MS) Images Helps Select Important Ions and Characterize Confidence in the Resulting Segments

Mass spectrometry imaging is a powerful tool for investigating the spatial distribution of chemical compounds in a biological sample such as tissue. Two common goals of these experiments are unsupervised segmentation of images into newly discovered homogeneous segments and supervised classification of images into predefined classes. In both cases, the important secondary goals are to characterize the uncertainty associated with the segmentation and with the classification and to characterize the spectral features that define each segment or class. Recent analysis methods have focused on the spatial structure of the data to improve results. However, they either do not address these secondary goals or do this with separate post hoc procedures.

We introduce spatial shrunken centroids, a statistical model-based framework for both supervised classification and unsupervised segmentation. It takes as input sets of previously detected, aligned, quantified, and normalized spectral features and expresses both spatial and multivariate nature of the data using probabilistic modeling. It selects informative subsets of spectral features that define each unsupervised segment or supervised class and quantifies and visualizes the uncertainty in spatial segmentations and in tissue classification. In the unsupervised setting, it also guides the choice of an appropriate number of segments. We demonstrate the usefulness of this framework in a supervised human renal cell carcinoma experimental dataset and several unsupervised experimental datasets, including a pig fetus cross-section, three rodent brains, and a controlled image with known ground truth. This framework is available for use within the open-source R package Cardinal as part of a full pipeline for the processing, visualization, and statistical analysis of mass spectrometry imaging experiments.

Using collective expert judgements to evaluate quality measures of mass spectrometry images

Motivation: Imaging mass spectrometry (IMS) is a maturating technique of molecular imaging. Confidence in the reproducible quality of IMS data is essential for its integration into routine use. However, the predominant method for assessing quality is visual examination, a time consuming, unstandardized and non-scalable approach. So far, the problem of assessing the quality has only been marginally addressed and existing measures do not account for the spatial information of IMS data. Importantly, no approach exists for unbiased evaluation of potential quality measures.

Results: We propose a novel approach for evaluating potential measures by creating a gold-standard set using collective expert judgements upon which we evaluated image-based measures. To produce a gold standard, we engaged 80 IMS experts, each to rate the relative quality between 52 pairs of ion images from MALDI-TOF IMS datasets of rat brain coronal sections. Experts’ optional feedback on their expertise, the task and the survey showed that (i) they had diverse backgrounds and sufficient expertise, (ii) the task was properly understood, and (iii) the survey was comprehensible. A moderate inter-rater agreement was achieved with Krippendorff’s alpha of 0.5. A gold-standard set of 634 pairs of images with accompanying ratings was constructed and showed a high agreement of 0.85. Eight families of potential measures with a range of parameters and statistical descriptors, giving 143 in total, were evaluated. Both signal-to-noise and spatial chaos-based measures performed highly with a correlation of 0.7 to 0.9 with the gold standard ratings. Moreover, we showed that a composite measure with the linear coefficients (trained on the gold standard with regularized least squares optimization and lasso) showed a strong linear correlation of 0.94 and an accuracy of 0.98 in predicting which image in a pair was of higher quality.

Availability and implementation: The anonymized data collected from the survey and the Matlab source code for data processing can be found at: https://github.com/alexandrovteam/IMS_quality.

Contact:theodore.alexandrov@embl.de

Homogeneous matrix deposition on dried agar for MALDI imaging mass spectrometry of microbial cultures

Matrix deposition on agar-based microbial colonies for MALDI imaging mass spectrometry is often complicated by the complex media on which microbes are grown. This Application Note demonstrates how consecutive short spray pulses of a matrix solution can form an evenly closed matrix layer on dried agar. Compared with sieving dry matrix onto wet agar, this method supports analyte cocrystallization, which results in significantly more signals, higher signal-to-noise ratios, and improved ionization efficiency. The even matrix layer improves spot-to-spot precision of measured m/z values when using TOF mass spectrometers. With this technique, we established reproducible imaging mass spectrometry of myxobacterial cultures on nutrient-rich cultivation media, which was not possible with the sieving technique. Graphical Abstract ᅟ.

Mass spectrometry tools and workflows for revealing microbial chemistry

Since the time Van Leeuwenhoek was able to observe microbes through a microscope, an innovation that led to the birth of the field of microbiology, we have aimed to understand how microorganisms function, interact and communicate. The exciting progress in the development of analytical technologies and workflows has demonstrated that mass spectrometry is a very powerful technique for the interrogation of microbiology at the molecular level. In this review, we aim to highlight the available and emerging tools in mass spectrometry for microbial analysis by overviewing the methods and workflow advances for taxonomic identification, microbial interaction, dereplication and drug discovery. We emphasize their potential for future development and point out unsolved problems and future directions that would aid in the analysis of the chemistry produced by microbes.

Benchmark datasets for 3D MALDI- and DESI-imaging mass spectrometry

Background

Three-dimensional (3D) imaging mass spectrometry (MS) is an analytical chemistry technique for the 3D molecular analysis of a tissue specimen, entire organ, or microbial colonies on an agar plate. 3D-imaging MS has unique advantages over existing 3D imaging techniques, offers novel perspectives for understanding the spatial organization of biological processes, and has growing potential to be introduced into routine use in both biology and medicine. Owing to the sheer quantity of data generated, the visualization, analysis, and interpretation of 3D imaging MS data remain a significant challenge. Bioinformatics research in this field is hampered by the lack of publicly available benchmark datasets needed to evaluate and compare algorithms.

Findings

High-quality 3D imaging MS datasets from different biological systems at several labs were acquired, supplied with overview images and scripts demonstrating how to read them, and deposited into MetaboLights, an open repository for metabolomics data. 3D imaging MS data were collected from five samples using two types of 3D imaging MS. 3D matrix-assisted laser desorption/ionization imaging (MALDI) MS data were collected from murine pancreas, murine kidney, human oral squamous cell carcinoma, and interacting microbial colonies cultured in Petri dishes. 3D desorption electrospray ionization (DESI) imaging MS data were collected from a human colorectal adenocarcinoma.

Conclusions

With the aim to stimulate computational research in the field of computational 3D imaging MS, selected high-quality 3D imaging MS datasets are provided that could be used by algorithm developers as benchmark datasets.

Electronic supplementary material

The online version of this article (doi:10.1186/s13742-015-0059-4) contains supplementary material, which is available to authorized users.

Cardinal: an R package for statistical analysis of mass spectrometry-based imaging experiments

Cardinal is an R package for statistical analysis of mass spectrometry-based imaging (MSI) experiments of biological samples such as tissues. Cardinal supports both Matrix-Assisted Laser Desorption/Ionization (MALDI) and Desorption Electrospray Ionization-based MSI workflows, and experiments with multiple tissues and complex designs. The main analytical functionalities include (1) image segmentation, which partitions a tissue into regions of homogeneous chemical composition, selects the number of segments and the subset of informative ions, and characterizes the associated uncertainty and (2) image classification, which assigns locations on the tissue to pre-defined classes, selects the subset of informative ions, and estimates the resulting classification error by (cross-) validation. The statistical methods are based on mixture modeling and regularization.

Contact: o.vitek@neu.edu

Availability and implementation: The code, the documentation, and examples are available open-source at www.cardinalmsi.org under the Artistic-2.0 license. The package is available at www.bioconductor.org.

Mass spectrometry coupled to imaging techniques: the better the view the greater the challenge

These are definitively exciting times for membrane lipid researchers. Once considered just as the cell membrane building blocks, the important role these lipids play is steadily being acknowledged. The improvement occurred in mass spectrometry techniques (MS) allows the establishment of the precise lipid composition of biological extracts. However, to fully understand the biological function of each individual lipid species, we need to know its spatial distribution and dynamics. In the past 10 years, the field has experienced a profound revolution thanks to the development of MS-based techniques allowing lipid imaging (MSI). Images reveal and verify what many lipid researchers had already shown by different means, but none as convincing as an image: each cell type presents a specific lipid composition, which is highly sensitive to its physiological and pathological state. While these techniques will help to place membrane lipids in the position they deserve, they also open the black box containing all the unknown regulatory mechanisms accounting for such tailored lipid composition. Thus, these results urges to different disciplines to redefine their paradigm of study by including the complexity revealed by the MSI techniques.

Automated cell-by-cell tissue imaging and single-cell analysis for targeted morphologies by laser ablation electrospray ionization mass spectrometry

Mass spectrometry imaging (MSI) is an emerging technology for the mapping of molecular distributions in tissues. In most of the existing studies, imaging is performed by sampling on a predefined rectangular grid that does not reflect the natural cellular pattern of the tissue. Delivering laser pulses by a sharpened optical fiber in laser ablation electrospray ionization (LAESI) mass spectrometry (MS) has enabled the direct analysis of single cells and subcellular compartments. Cell-by-cell imaging had been demonstrated using LAESI-MS, where individual cells were manually selected to serve as natural pixels for tissue imaging. Here we describe a protocol for a novel cell-by-cell LAESI imaging approach that automates cell recognition and addressing for systematic ablation of individual cells. Cell types with particular morphologies can also be selected for analysis. First, the cells are recognized as objects in a microscope image. The coordinates of their centroids are used by a stage-control program to sequentially position the cells under the optical fiber tip for laser ablation. This approach increases the image acquisition efficiency and stability, and enables the investigation of extended or selected tissue areas. In the LAESI process, the ablation events result in mass spectra that represent the metabolite levels in the ablated cells. Peak intensities of selected ions are used to represent the metabolite distributions in the tissue with single-cell resolution.

Three-dimensional imaging of lipids and metabolites in tissues by nanospray desorption electrospray ionization mass spectrometry

Three-dimensional (3D) imaging of tissue sections is a new frontier in mass spectrometry imaging (MSI). Here, we report on fast 3D imaging of lipids and metabolites associated with mouse uterine decidual cells and embryo at the implantation site on day 6 of pregnancy. 2D imaging of 16-20 serial tissue sections deposited on the same glass slide was performed using nanospray desorption electrospray ionization (nano-DESI)-an ambient ionization technique that enables sensitive localized analysis of analytes on surfaces without special sample pretreatment. In this proof-of-principle study, nano-DESI was coupled to a high-resolution Q-Exactive instrument operated at high repetition rate of >5 Hz with moderate mass resolution of 35,000 (m/Δm at m/z 200), which enabled acquisition of the entire 3D image with a spatial resolution of ∼150 μm in less than 4.5 h. The results demonstrate localization of acetylcholine in the primary decidual zone (PDZ) of the implantation site throughout the depth of the tissue examined, indicating an important role of this signaling molecule in decidualization. Choline and phosphocholine-metabolites associated with cell growth-are enhanced in the PDZ and abundant in other cellular regions of the implantation site. Very different 3D distributions were obtained for fatty acids (FA), oleic acid and linoleic acid (FA 18:1 and FA 18:2), differing only by one double bond. Localization of FA 18:2 in the PDZ indicates its important role in decidualization while FA 18:1 is distributed more evenly throughout the tissue. In contrast, several lysophosphatidylcholines (LPC) observed in this study show donut-like distributions with localization around the PDZ. Complementary distributions with minimal overlap were observed for LPC 18:0 and FA 18:2 while the 3D image of the potential precursor phosphatidylcholine 36:2 (PC 36:2) showed a significant overlap with both LPC 18:0 and FA 18:2.

In situ label-free visualization of orally dosed strictinin within mouse kidney by MALDI-MS imaging

Matrix-assisted laser desorption/ionization-mass spectrometry imaging (MALDI-MSI) is a powerful technique for visualizing the distribution of a wide range of biomolecules within tissue sections. However, methodology for visualizing a bioactive ellagitannin has not yet been established. This paper presents a novel in situ label-free MALDI-MSI technique for visualizing the distribution of strictinin, a bioactive ellagitannin found in green tea, within mammalian kidney after oral dosing. Among nine representative matrix candidates, 1,5-diaminonaphthalene (1,5-DAN), harmane, and ferulic acid showed higher sensitivity to strictinin spotted onto a MALDI sample plate. Of these, 1,5-DAN enables visualization of a two-dimensional image of strictinin directly spotted on mouse kidney sections with the highest sensitivity. Furthermore, 1,5-DAN-based MALDI-MSI could detect the unique distribution of orally dosed strictinin within kidney sections. This in situ label-free imaging technique will contribute to the localization analysis of strictinin and its biological mechanisms.

A comprehensive high-resolution mass spectrometry approach for characterization of metabolites by combination of ambient ionization, chromatography and imaging methods

RATIONALE

An ideal method for bioanalytical applications would deliver spatially resolved quantitative information in real time and without sample preparation. In reality these requirements can typically not be met by a single analytical technique. Therefore, we combine different mass spectrometry approaches: chromatographic separation, ambient ionization and imaging techniques, in order to obtain comprehensive information about metabolites in complex biological samples.

METHODS

Samples were analyzed by laser desorption followed by electrospray ionization (LD-ESI) as an ambient ionization technique, by matrix-assisted laser desorption/ionization (MALDI) mass spectrometry imaging for spatial distribution analysis and by high-performance liquid chromatography/electrospray ionization mass spectrometry (HPLC/ESI-MS) for quantitation and validation of compound identification. All MS data were acquired with high mass resolution and accurate mass (using orbital trapping and ion cyclotron resonance mass spectrometers). Grape berries were analyzed and evaluated in detail, whereas wheat seeds and mouse brain tissue were analyzed in proof-of-concept experiments.

RESULTS

In situ measurements by LD-ESI without any sample preparation allowed for fast screening of plant metabolites on the grape surface. MALDI imaging of grape cross sections at 20 µm pixel size revealed the detailed distribution of metabolites which were in accordance with their biological function. HPLC/ESI-MS was used to quantify 13 anthocyanin species as well as to separate and identify isomeric compounds. A total of 41 metabolites (amino acids, carbohydrates, anthocyanins) were identified with all three approaches. Mass accuracy for all MS measurements was better than 2 ppm (root mean square error).

CONCLUSIONS

The combined approach provides fast screening capabilities, spatial distribution information and the possibility to quantify metabolites. Accurate mass measurements proved to be critical in order to reliably combine data from different MS techniques. Initial results on the mycotoxin deoxynivalenol (DON) in wheat seed and phospholipids in mouse brain as a model for mammalian tissue indicate a broad applicability of the presented workflow.

Visualizing life with ambient mass spectrometry

Since the development of desorption electrospray ionization (DESI), many other ionization methods for ambient and atmospheric pressure mass spectrometry have been developed. Ambient ionization mass spectrometry has now been used for a wide variety of biological applications, including plant science, microbiology, neuroscience, and cancer pathology. Multimodal integration of atmospheric ionization sources with the other biotechnologies, as well as high performance computational methods for mass spectrometry data processing is one of the major emerging area's for ambient mass spectrometry. In this opinion article, we will highlight some of the most influential technological advances of ambient mass spectrometry in recent years and their applications to the life sciences.

Improved MALDI-TOF microbial mass spectrometry imaging by application of a dispersed solid matrix

The key step in high quality microbial matrix-assisted laser desorption/ionization mass spectrometry imaging (microbial MALDI MSI) is the fabrication of a homogeneous matrix coating showing a fine-grained morphology. This application note addresses a novel method to apply solid MALDI matrices onto microbial cultures grown on thin agar media. A suspension of a mixture of 2,5-DHB and α-CHCA is sprayed onto the agar sample surface to form highly homogeneous matrix coatings. As a result, the signal intensities of metabolites secreted by the fungus Aspergillus fumigatus were found to be clearly enhanced.

Macroscopic and microscopic spatially-resolved analysis of food contaminants and constituents using laser-ablation electrospray ionization mass spectrometry imaging

Laser-ablation electrospray ionization (LAESI) mass spectrometry imaging (MSI) does not require very flat surfaces, high-precision sample preparation, or the addition of matrix. Because of these features, LAESI-MSI may be the method of choice for spatially-resolved food analysis. In this work, LAESI time-of-flight MSI was investigated for macroscopic and microscopic imaging of pesticides, mycotoxins, and plant metabolites on rose leaves, orange and lemon fruit, ergot bodies, cherry tomatoes, and maize kernels. Accurate mass ion-map data were acquired at sampling locations with an x–y center-to-center distance of 0.2–1.0 mm and were superimposed onto co-registered optical images. The spatially-resolved ion maps of pesticides on rose leaves suggest co-application of registered and banned pesticides. Ion maps of the fungicide imazalil reveal that this compound is only localized on the peel of citrus fruit. However, according to three-dimensional LAESI-MSI the penetration depth of imazalil into the peel has significant local variation. Ion maps of different plant alkaloids on ergot bodies from rye reveal co-localization in accordance with expectations. The feasibility of using untargeted MSI for food analysis was revealed by ion maps of plant metabolites in cherry tomatoes and maize-kernel slices. For tomatoes, traveling-wave ion mobility (TWIM) was used to discriminate between different lycoperoside glycoalkaloid isomers; for maize quadrupole time-of-flight tandem mass spectrometry (MS–MS) was successfully used to elucidate the structure of a localized unknown. It is envisaged that LAESI-MSI will contribute to future research in food science, agriforensics, and plant metabolomics.

Figure

Mechanisms of synergy in polymicrobial infections

Communities of microbes can live almost anywhere and contain many different species. Interactions between members of these communities often determine the state of the habitat in which they live. When these habitats include sites on the human body, these interactions can affect health and disease. Polymicrobial synergy can occur during infection, in which the combined effect of two or more microbes on disease is worse than seen with any of the individuals alone. Powerful genomic methods are increasingly used to study microbial communities, including metagenomics to reveal the members and genetic content of a community and metatranscriptomics to describe the activities of community members. Recent efforts focused toward a mechanistic understanding of these interactions have led to a better appreciation of the precise bases of polymicrobial synergy in communities containing bacteria, eukaryotic microbes, and/or viruses. These studies have benefited from advances in the development of in vivo models of polymicrobial infection and modern techniques to profile the spatial and chemical bases of intermicrobial communication. This review describes the breadth of mechanisms microbes use to interact in ways that impact pathogenesis and techniques to study polymicrobial communities.

Whole-body tissue distribution study of drugs in neonate mice using desorption electrospray ionization mass spectrometry imaging

RATIONALE

Although Desorption Electrospray Ionization (DESI) Mass Spectrometry Imaging (MSI) is uniquely suited for whole-body (WB) tissue distribution study of drugs, success in this area has been difficult. Here, we present WB tissue distribution studies using DESI-MSI and a new histological tissue-friendly solvent system.

METHODS

Neonate pups were dosed subcutaneously (SC) with clozapine, compound 1, compound 2, or compound 3. Following euthanization by hypothermia, neonates underwent a transcardiac perfusion (saline) to remove blood. After cryosectioning, DESI-MSI was conducted for the WB tissue slides, followed sequentially by histological staining.

RESULTS

Whole-body tissue imaging showed that clozapine and its N-oxide metabolite were distributed in significant amounts in the brain, spinal cord, liver, heart (ventricle), and lungs. Compound 1 was distributed mainly in the liver and muscle, and its mono-oxygenated metabolite was detected by DESI-MSI exclusively in the liver. Compound 2 was distributed mainly in the muscle and fatty tissue. Compound 3 was distributed mainly in fatty tissue and its metabolites were also mainly detected in the same tissue.

CONCLUSIONS

The results demonstrate the successful application of DESI-MSI in whole-body tissue distribution studies of drugs and metabolites in combination with sequential histology staining for anatomy. The results also identified lipophilicity as the driving force in the tissue distribution of the three Amgen compounds.

Ion sources for mass spectrometric identification and imaging of molecular species

Covering: 2013 The ability to transfer molecular species to the gas phase and ionize them is central to the study of natural products and other molecular species by mass spectrometry (MS). MS-based strategies in natural products have focused on a few established ion sources, such as electron impact and electrospray ionization. However, a variety of other ion sources are either currently in use to evaluate natural products or show significant future promise. This review discusses these various ion sources in the context of other articles in this special issue, but is also applicable to other fields of analysis, including materials science. Ion sources are grouped based on the current understanding of their predominant ion formation mechanisms. This broad overview groups ion sources into the following categories: electron ionization and single photon ionization; chemical ionization-like and plasma-based; electrospray ionization; and, laser desorption-based. Laser desorption-based methods are emphasized with specific examples given for laser desorption postionization sources and their use in the analysis of intact microbial biofilms. Brief consideration is given to the choice of ion source for various sample types and analyses, including MS imaging.

Interactions in the microbiome: communities of organisms and communities of genes

A central challenge in microbial community ecology is the delineation of appropriate units of biodiversity, which can be taxonomic, phylogenetic, or functional in nature. The term 'community' is applied ambiguously; in some cases, the term refers simply to a set of observed entities, while in other cases, it requires that these entities interact with one another. Microorganisms can rapidly gain and lose genes, potentially decoupling community roles from taxonomic and phylogenetic groupings. Trait-based approaches offer a useful alternative, but many traits can be defined based on gene functions, metabolic modules, and genomic properties, and the optimal set of traits to choose is often not obvious. An analysis that considers taxon assignment and traits in concert may be ideal, with the strengths of each approach offsetting the weaknesses of the other. Individual genes also merit consideration as entities in an ecological analysis, with characteristics such as diversity, turnover, and interactions modeled using genes rather than organisms as entities. We identify some promising avenues of research that are likely to yield a deeper understanding of microbial communities that shift from observation-based questions of 'Who is there?' and 'What are they doing?' to the mechanistically driven question of 'How will they respond?'