OpenMSI: a high-performance web-based platform for mass spectrometry imaging

Recent developments and applications of ambient mass spectrometry imaging in pharmaceutical research: an overview

The application of ambient mass spectrometry imaging "MSI" is expanding in the areas of fundamental research on drug delivery and multiple phases of the process of identifying and developing drugs. Precise monitoring of a drug's pharmacological workflows, such as intake, distribution, metabolism, and discharge, is made easier by MSI's ability to determine the concentrations of the initiating drug and its metabolites across dosed samples without losing spatial data. Lipids, glycans, and proteins are just a few of the many phenotypes that MSI may be used to concurrently examine. Each of these substances has a particular distribution pattern and biological function throughout the body. MSI offers the perfect analytical tool for examining a drug's pharmacological features, especially in vitro and in vivo effectiveness, security, probable toxic effects, and putative molecular pathways, because of its high responsiveness in chemical and physical environments. The utilization of MSI in the field of pharmacy has further extended from the traditional tissue examination to the early stages of drug discovery and development, including examining the structure-function connection, high-throughput capabilities in vitro examination, and ex vivo research on individual cells or tumor spheroids. Additionally, an enormous array of endogenous substances that may function as tissue diagnostics can be scanned simultaneously, giving the specimen a highly thorough characterization. Ambient MSI techniques are soft enough to allow for easy examination of the native sample to gather data on exterior chemical compositions. This paper provides a scientific and methodological overview of ambient MSI utilization in research on pharmaceuticals.

Desorption electrospray ionization and matrix-assisted laser desorption/ionization as imaging approaches for biological samples analysis

The imaging of biological tissues can offer valuable information about the sample composition, which improves the understanding of analyte distribution in such complex samples. Different approaches using mass spectrometry imaging (MSI), also known as imaging mass spectrometry (IMS), enabled the visualization of the distribution of numerous metabolites, drugs, lipids, and glycans in biological samples. The high sensitivity and multiple analyte evaluation/visualization in a single sample provided by MSI methods lead to various advantages and overcome drawbacks of classical microscopy techniques. In this context, the application of MSI methods, such as desorption electrospray ionization-MSI (DESI-MSI) and matrix-assisted laser desorption/ionization-MSI (MALDI-MSI), has significantly contributed to this field. This review discusses the evaluation of exogenous and endogenous molecules in biological samples using DESI and MALDI imaging. It offers rare technical insights not commonly found in the literature (scanning speed and geometric parameters), making it a comprehensive guide for applying these techniques step-by-step. Furthermore, we provide an in-depth discussion of recent research findings on using these methods to study biological tissues.

Mass spectrometry imaging-based assays for aminotransferase activity reveal a broad substrate spectrum for a previously uncharacterized enzyme

Aminotransferases (ATs) catalyze pyridoxal 5'-phosphate-dependent transamination reactions between amino donor and keto acceptor substrates and play central roles in nitrogen metabolism of all organisms. ATs are involved in the biosynthesis and degradation of both proteinogenic and nonproteinogenic amino acids and also carry out a wide variety of functions in photorespiration, detoxification, and secondary metabolism. Despite the importance of ATs, their functionality is poorly understood as only a small fraction of putative ATs, predicted from DNA sequences, are associated with experimental data. Even for characterized ATs, the full spectrum of substrate specificity, among many potential substrates, has not been explored in most cases. This is largely due to the lack of suitable high-throughput assays that can screen for AT activity and specificity at scale. Here we present a new high-throughput platform for screening AT activity using bioconjugate chemistry and mass spectrometry imaging-based analysis. Detection of AT reaction products is achieved by forming an oxime linkage between the ketone groups of transaminated amino donors and a probe molecule that facilitates mass spectrometry-based analysis using nanostructure-initiator mass spectrometry or MALDI-mass spectrometry. As a proof-of-principle, we applied the newly established method and found that a previously uncharacterized Arabidopsis thaliana tryptophan AT-related protein 1 is a highly promiscuous enzyme that can utilize 13 amino acid donors and three keto acid acceptors. These results demonstrate that this oxime-mass spectrometry imaging AT assay enables high-throughput discovery and comprehensive characterization of AT enzymes, leading to an accurate understanding of the nitrogen metabolic network.

Advancements in Oncoproteomics Technologies: Treading toward Translation into Clinical Practice

Proteomics continues to forge significant strides in the discovery of essential biological processes, uncovering valuable information on the identity, global protein abundance, protein modifications, proteoform levels, and signal transduction pathways. Cancer is a complicated and heterogeneous disease, and the onset and progression involve multiple dysregulated proteoforms and their downstream signaling pathways. These are modulated by various factors such as molecular, genetic, tissue, cellular, ethnic/racial, socioeconomic status, environmental, and demographic differences that vary with time. The knowledge of cancer has improved the treatment and clinical management; however, the survival rates have not increased significantly, and cancer remains a major cause of mortality. Oncoproteomics studies help to develop and validate proteomics technologies for routine application in clinical laboratories for (1) diagnostic and prognostic categorization of cancer, (2) real-time monitoring of treatment, (3) assessing drug efficacy and toxicity, (4) therapeutic modulations based on the changes with prognosis and drug resistance, and (5) personalized medication. Investigation of tumor-specific proteomic profiles in conjunction with healthy controls provides crucial information in mechanistic studies on tumorigenesis, metastasis, and drug resistance. This review provides an overview of proteomics technologies that assist the discovery of novel drug targets, biomarkers for early detection, surveillance, prognosis, drug monitoring, and tailoring therapy to the cancer patient. The information gained from such technologies has drastically improved cancer research. We further provide exemplars from recent oncoproteomics applications in the discovery of biomarkers in various cancers, drug discovery, and clinical treatment. Overall, the future of oncoproteomics holds enormous potential for translating technologies from the bench to the bedside.

When cancer drug resistance meets metabolomics (bulk, single-cell and/or spatial): Progress, potential, and perspective

Resistance to drug treatment is a critical barrier in cancer therapy. There is an unmet need to explore cancer hallmarks that can be targeted to overcome this resistance for therapeutic gain. Over time, metabolic reprogramming has been recognised as one hallmark that can be used to prevent therapeutic resistance. With the advent of metabolomics, targeting metabolic alterations in cancer cells and host patients represents an emerging therapeutic strategy for overcoming cancer drug resistance. Driven by technological and methodological advances in mass spectrometry imaging, spatial metabolomics involves the profiling of all the metabolites (metabolomics) so that the spatial information is captured bona fide within the sample. Spatial metabolomics offers an opportunity to demonstrate the drug-resistant tumor profile with metabolic heterogeneity, and also poses a data-mining challenge to reveal meaningful insights from high-dimensional spatial information. In this review, we discuss the latest progress, with the focus on currently available bulk, single-cell and spatial metabolomics technologies and their successful applications in pre-clinical and translational studies on cancer drug resistance. We provide a summary of metabolic mechanisms underlying cancer drug resistance from different aspects; these include the Warburg effect, altered amino acid/lipid/drug metabolism, generation of drug-resistant cancer stem cells, and immunosuppressive metabolism. Furthermore, we propose solutions describing how to overcome cancer drug resistance; these include early detection during cancer initiation, monitoring of clinical drug response, novel anticancer drug and target metabolism, immunotherapy, and the emergence of spatial metabolomics. We conclude by describing the perspectives on how spatial omics approaches (integrating spatial metabolomics) could be further developed to improve the management of drug resistance in cancer patients.

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.

Image to insight: exploring natural products through mass spectrometry imaging

Covering: 2017 to 2022Mass spectrometry imaging (MSI) has become a mature molecular imaging technique that is well-matched for natural product (NP) discovery. Here we present a brief overview of MSI, followed by a thorough discussion of different MSI applications in NP research. This review will mainly focus on the recent progress of MSI in plants and microorganisms as they are the main producers of NPs. Specifically, the opportunity and potential of combining MSI with other imaging modalities and stable isotope labeling are discussed. Throughout, we focus on both the strengths and weaknesses of MSI, with an eye on future improvements that are necessary for the progression of MSI toward routine NP studies. Finally, we discuss new areas of research, future perspectives, and the overall direction that the field may take in the years to come.

The application of mass spectrometry imaging in traditional Chinese medicine: a review

Mass spectrometry imaging is a frontier technique which connects classical mass spectrometry with ion imaging. Various types of chemicals could be visualized in their native tissues using mass spectrometry imaging. Up to now, the most commonly applied mass spectrometry imaging techniques are matrix assisted laser desorption ionization mass spectrometry imaging, desorption electrospray ionization mass spectrometry imaging and secondary ion mass spectrometry imaging. This review gives an introduction to the principles, development and applications of commonly applied mass spectrometry imaging techniques, and then illustrates the application of mass spectrometry imaging in the investigation of traditional Chinese medicine. Recently, mass spectrometry imaging has been adopted to explore the spatial distribution of endogenous metabolites in traditional Chinese medicine. Data collected from mass spectrometry imaging can be further utilized to search for marker components of traditional Chinese medicine, discover new compounds from traditional herbs, and differentiate between medicinal plants that are similar in botanical features. Moreover, mass spectrometry imaging also plays a role in revealing the pharmacological and toxicological mechanisms of traditional Chinese medicine.

A Critical and Concise Review of Mass Spectrometry Applied to Imaging in Drug Discovery

During the past decade, mass spectrometry imaging (MSI) has become a robust and versatile methodology to support modern pharmaceutical research and development. The technologies provide data on the biodistribution, metabolism, and delivery of drugs in tissues, while also providing molecular maps of endogenous metabolites, lipids, and proteins. This allows researchers to make both pharmacokinetic and pharmacodynamic measurements at cellular resolution in tissue sections or clinical biopsies. Despite drug imaging within samples now playing a vital role within research and development (R&D) in leading pharmaceutical companies, however, the challenges in turning compounds into medicines continue to evolve as rapidly as the technologies used to discover them. The increasing cost of development of new and emerging therapeutic modalities, along with the associated risks of late-stage program attrition, means there is still an unmet need in our ability to address an increasing array of challenging bioanalytical questions within drug discovery. We require new capabilities and strategies of integrated imaging to provide context for fundamental disease-related biological questions that can also offer insights into specific project challenges. Integrated molecular imaging and advanced image analysis have the opportunity to provide a world-class capability that can be deployed on projects in which we cannot answer the question with our battery of established assays. Therefore, here we will provide an updated concise review of the use of MSI for drug discovery; we will also critically consider what is required to embed MSI into a wider evolving R&D landscape and allow long-lasting impact in the industry.

Root morphology and exudate availability are shaped by particle size and chemistry in Brachypodium distachyon

Root morphology and exudation define a plants' sphere of influence in soils. In turn, soil characteristics influence plant growth, morphology, root microbiome, and rhizosphere chemistry. Collectively, all these parameters have significant implications on the major biogeochemical cycles, crop yield, and ecosystem health. However, how plants are shaped by the physiochemistry of soil particles is still not well understood. We explored how particle size and chemistry of growth substrates affect root morphology and exudation of a model grass. We grew Brachypodium distachyon in glass beads with various sizes (0.5, 1, 2, 3 mm), as well as in sand (0.005, 0.25, 4 mm) and in clay (4 mm) particles and in particle-free hydroponic medium. Plant morphology, root weight, and shoot weight were measured. We found that particle size significantly influenced root fresh weight and root length, whereas root number and shoot weight remained constant. Next, plant exudation profiles were analyzed with mass spectrometry imaging and liquid chromatography-mass spectrometry. Mass spectrometry imaging suggested that both, root length and number shape root exudation. Exudate profiles were comparable for plants growing in glass beads or sand with various particles sizes, but distinct for plants growing in clay for in situ exudate collection. Clay particles were found to sorb 20% of compounds exuded by clay-grown plants, and 70% of compounds from a defined exudate medium. The sorbed compounds belonged to a range of chemical classes, among them nucleosides, organic acids, sugars, and amino acids. Some of the sorbed compounds could be desorbed by a rhizobacterium (Pseudomonas fluorescens WCS415), supporting its growth. This study demonstrates the effect of different characteristics of particles on root morphology, plant exudation and availability of nutrients to microorganisms. These findings further support the critical importance of the physiochemical properties of soils when investigating plant morphology, plant chemistry, and plant-microbe interactions.

Unsupervised machine learning for exploratory data analysis in imaging mass spectrometry

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

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.

HDMF: Hierarchical Data Modeling Framework for Modern Science Data Standards

A ubiquitous problem in aggregating data across different experimental and observational data sources is a lack of software infrastructure that enables flexible and extensible standardization of data and metadata. To address this challenge, we developed HDMF, a hierarchical data modeling framework for modern science data standards. With HDMF, we separate the process of data standardization into three main components: (1) data modeling and specification, (2) data I/O and storage, and (3) data interaction and data APIs. To enable standards to support the complex requirements and varying use cases throughout the data life cycle, HDMF provides object mapping infrastructure to insulate and integrate these various components. This approach supports the flexible development of data standards and extensions, optimized storage backends, and data APIs, while allowing the other components of the data standards ecosystem to remain stable. To meet the demands of modern, large-scale science data, HDMF provides advanced data I/O functionality for iterative data write, lazy data load, and parallel I/O. It also supports optimization of data storage via support for chunking, compression, linking, and modular data storage. We demonstrate the application of HDMF in practice to design NWB 2.0 [13], a modern data standard for collaborative science across the neurophysiology community.

Surface chemical defence of the eelgrass Zostera marina against microbial foulers

Plants rely on both mechanical and chemical defence mechanisms to protect their surfaces against microorganisms. The recently completed genome of the eelgrass Zostera marina, a marine angiosperm with fundamental importance for coastal ecosystems, showed that its re-adaptation from land to the sea has led to the loss of essential genes (for chemical communication and defence) and structural features (stomata and thick cuticle) that are typical of terrestrial plants. This study was designed to understand the molecular nature of surface protection and fouling-control strategy of eelgrass against marine epiphytic yeasts. Different surface extraction methods and comparative metabolomics by tandem mass spectrometry (LC-MS/MS) were used for targeted and untargeted identification of the metabolite profiles of the leaf surface and the whole tissue extracts. Desorption electrospray ionization-imaging mass spectrometry (DESI-IMS) coupled with traditional bioassays revealed, for the first time, the unique spatial distribution of the eelgrass surface-associated phenolics and fatty acids, as well as their differential bioactivity against the growth and settlement of epiphytic yeasts. This study provides insights into the complex chemical defence system of the eelgrass leaf surface. It suggests that surface-associated metabolites modulate biotic interactions and provide chemical defence and structural protection to eelgrass in its marine environment.

Software solutions for evaluation and visualization of laser ablation inductively coupled plasma mass spectrometry imaging (LA-ICP-MSI) data: a short overview

Mass spectrometry imaging (MSI) using laser ablation (LA) inductively coupled plasma (ICP) is an innovative and exciting methodology to perform highly sensitive elemental analyses. LA-ICP-MSI of metals, trace elements or isotopes in tissues has been applied to a range of biological samples. Several LA-ICP-MSI studies have shown that metals have a highly compartmentalized distribution in some organs, which might be altered in consequence of genetic diseases, intoxication, or malnutrition. Although metal imaging by LA-ICP-MSI is an established methodology, potential pitfalls in the determination of metal concentrations might result from erroneous calibration, standardization, and normalization. In addition, for simple display of final imaging results, most LA-ICP-MSI users prefer to process their measurements by commercial processing software. Such programs typically visualize the regional metal differences in colorful and vivid imaging maps, but might not represent the actual signal densities correctly. There is a great abundance of such MSI data processing programs available differing in quality, usability, integrated features, workflow, reliability, system requirements, speed of data processing, and price. Some software packages contain a multitude of features which are superfluous for most users. In contrast, often only few data formats are used, in case of commercial programs even only the instrument provider’s own raw data format. Therefore, first time and average users are often confused and helpless in choosing the correct software for processing their data. Here we have briefly summarized software packages, data routines, macros, programming tools, scripts, algorithms, or self-written patches and updates for existing programs presently in use for mining LA-ICP-MSI data.

Cytomine: Toward an Open and Collaborative Software Platform for Digital Pathology Bridged to Molecular Investigations

PURPOSE

Digital histology is being increasingly used in research and clinical applications. In parallel, new tissue imaging methods (e.g., imaging mass spectrometry) are currently regarded as very promising approaches for better molecular diagnosis in pathology. However, these new data sources are still often underexploited because of the lack of collaborative software to share and correlate information for multimodal analysis.

EXPERIMENTAL DESIGN

The open science paradigm is followed to develop new features in the web-based Cytomine software to support next-generation digital pathology bridged to molecular investigations.

RESULTS

New open-source developments allow to explore whole-slide classical histology with Matrix Assisted Laser Desorption Ionisation (MALDI) imaging and to support preprocessing for biomarker discovery using laser microdissection-based microproteomics.

CONCLUSIONS AND CLINICAL RELEVANCE

The updated version of Cytomine is the first open and web-based tool to enable sharing data from classical histology, molecular imaging, and cell counting for proteomics preprocessing. It holds good promise to fulfill imminent needs in molecular histopathology.

Molecular imaging mass spectrometry for probing protein dynamics in neurodegenerative disease pathology

Recent advances in the understanding of basic pathological mechanisms in various neurological diseases depend directly on the development of novel bioanalytical technologies that allow sensitive and specific chemical imaging at high resolution in cells and tissues. Mass spectrometry-based molecular imaging (IMS) has gained increasing popularity in biomedical research for mapping the spatial distribution of molecular species in situ. The technology allows for comprehensive, untargeted delineation of in situ distribution profiles of metabolites, lipids, peptides and proteins. A major advantage of IMS over conventional histochemical techniques is its superior molecular specificity. Imaging mass spectrometry has therefore great potential for probing molecular regulations in CNS-derived tissues and cells for understanding neurodegenerative disease mechanism. The goal of this review is to familiarize the reader with the experimental workflow, instrumental developments and methodological challenges as well as to give a concise overview of the major advances and recent developments and applications of IMS-based protein and peptide profiling with particular focus on neurodegenerative diseases. This article is part of the Special Issue "Proteomics".

Error-Free Data Visualization and Processing through imzML and mzML Validation

Open data formats are key to facilitating data processing, sharing, and integration. The imzML format ( http://imzml.org/ ) has drastically improved these aspects of mass spectrometry imaging data. Efficient processing of data depends on data sets which are consistent and adhere to the specifications; however, this is not always the case. Here we present a validation tool for data stored in both imzML and the HUPO-PSI mass spectrometery counterpart, mzML, to identify any deviations from the published (i)mzML standard which could cause issues for the user when visualizing or processing data. The tool is released in two forms, a graphical user interface (GUI) for ease of use, and a command line version to fit into existing workflows and pipelines. When certain known issues are encountered, such as the presence of negative values for the location of the binary data, the validator resolves the issue automatically upon saving. The GUI version of the validator also allows editing of the metadata included within the (i)mzML files in order to resolve inconsistencies. We also present a means of performing conditional validation on the metadata within (i)mzML files, where user-defined rules are validated against depending on whether specific metadata are present (or not). For example, if the MALDI term is present, then additional rules related to MALDI (such as the requirement of inclusion of laser parameters) can be validated against this. This enables a flexible and more thorough automated validation of (i)mzML data. Such a system is necessary for validating data against more comprehensive sets of metadata such as minimum reporting guidelines or metadata requirements prior to submission and acceptance of data to data repositories. We demonstrate how this tool can be used to validate against the proposed minimum reporting guidelines in MSI as well as institute specific metadata criteria. The validator tool is endorsed for validation of imzML ( http://imzml.org/ ) and mzML ( http://www.psidev.info/mzml ) and is made available through the respective Web sites. The validator is also released as open source under Mozilla Public License 2.0 at https://gitlab.com/imzML/imzMLValidator .

Insulator Nanostructure Desorption Ionization Mass Spectrometry

Surface-assisted laser desorption ionization (SALDI) is an approach for gas-phase ion generation for mass spectrometry using laser excitation on typically conductive or semiconductive nanostructures. Here, we introduce insulator nanostructure desorption ionization mass spectrometry (INDI-MS), a nanostructured polymer substrate for SALDI-MS analysis of small molecules and peptides. INDI-MS surfaces are produced through the self-assembly of a perfluoroalkyl silsesquioxane nanostructures in a single chemical vapor deposition silanization-step. We find that surfaces formed from the perfluorooctyltrichlorosilane monomer assemble semielliptical features with a 10 nm height, diameters between 10 and 50 nm, and have attomole-femtomole sensitivities for selected analytes. Surfaces prepared with silanes that either lack the trichloro or perfluoro groups, lack sensitivity. Further, we demonstrate that hydrophobic INDI regions can be micropatterned onto hydrophilic surfaces to perform on-chip self-desalting in an array format.

Software Tools and Approaches for Compound Identification of LC-MS/MS Data in Metabolomics

The annotation of small molecules remains a major challenge in untargeted mass spectrometry-based metabolomics. We here critically discuss structured elucidation approaches and software that are designed to help during the annotation of unknown compounds. Only by elucidating unknown metabolites first is it possible to biologically interpret complex systems, to map compounds to pathways and to create reliable predictive metabolic models for translational and clinical research. These strategies include the construction and quality of tandem mass spectral databases such as the coalition of MassBank repositories and investigations of MS/MS matching confidence. We present in silico fragmentation tools such as MS-FINDER, CFM-ID, MetFrag, ChemDistiller and CSI:FingerID that can annotate compounds from existing structure databases and that have been used in the CASMI (critical assessment of small molecule identification) contests. Furthermore, the use of retention time models from liquid chromatography and the utility of collision cross-section modelling from ion mobility experiments are covered. Workflows and published examples of successfully annotated unknown compounds are included.

Signal preprocessing, multivariate analysis and software tools for MA(LDI)-TOF mass spectrometry imaging for biological applications

Mass spectrometry imaging (MSI) is a label-free analytical technique capable of molecularly characterizing biological samples, including tissues and cell lines. The constant development of analytical instrumentation and strategies over the previous decade makes MSI a key tool in clinical research. Nevertheless, most MSI studies are limited to targeted analysis or the mere visualization of a few molecular species (proteins, peptides, metabolites, or lipids) in a region of interest without fully exploiting the possibilities inherent in the MSI technique, such as tissue classification and segmentation or the identification of relevant biomarkers from an untargeted approach. MSI data processing is challenging due to several factors. The large volume of mass spectra involved in a MSI experiment makes choosing the correct computational strategies critical. Furthermore, pixel to pixel variation inherent in the technique makes choosing the correct preprocessing steps critical. The primary aim of this review was to provide an overview of the data-processing steps and tools that can be applied to an MSI experiment, from preprocessing the raw data to the more advanced strategies for image visualization and segmentation. This review is particularly aimed at researchers performing MSI experiments and who are interested in incorporating new data-processing features, improving their computational strategy, and/or desire access to data-processing tools currently available. © 2016 Wiley Periodicals, Inc. Mass Spec Rev 37:281-306, 2018.

Ecosystem Fabrication (EcoFAB) Protocols for The Construction of Laboratory Ecosystems Designed to Study Plant-microbe Interactions

Beneficial plant-microbe interactions offer a sustainable biological solution with the potential to boost low-input food and bioenergy production. A better mechanistic understanding of these complex plant-microbe interactions will be crucial to improving plant production as well as performing basic ecological studies investigating plant-soil-microbe interactions. Here, a detailed description for ecosystem fabrication is presented, using widely available 3D printing technologies, to create controlled laboratory habitats (EcoFABs) for mechanistic studies of plant-microbe interactions within specific environmental conditions. Two sizes of EcoFABs are described that are suited for the investigation of microbial interactions with various plant species, including Arabidopsis thaliana, Brachypodium distachyon, and Panicum virgatum. These flow-through devices allow for controlled manipulation and sampling of root microbiomes, root chemistry as well as imaging of root morphology and microbial localization. This protocol includes the details for maintaining sterile conditions inside EcoFABs and mounting independent LED light systems onto EcoFABs. Detailed methods for addition of different forms of media, including soils, sand, and liquid growth media coupled to the characterization of these systems using imaging and metabolomics are described. Together, these systems enable dynamic and detailed investigation of plant and plant-microbial consortia including the manipulation of microbiome composition (including mutants), the monitoring of plant growth, root morphology, exudate composition, and microbial localization under controlled environmental conditions. We anticipate that these detailed protocols will serve as an important starting point for other researchers, ideally helping create standardized experimental systems for investigating plant-microbe interactions.

BASIS: High-performance bioinformatics platform for processing of large-scale mass spectrometry imaging data in chemically augmented histology

Mass Spectrometry Imaging (MSI) holds significant promise in augmenting digital histopathologic analysis by generating highly robust big data about the metabolic, lipidomic and proteomic molecular content of the samples. In the process, a vast quantity of unrefined data, that can amount to several hundred gigabytes per tissue section, is produced. Managing, analysing and interpreting this data is a significant challenge and represents a major barrier to the translational application of MSI. Existing data analysis solutions for MSI rely on a set of heterogeneous bioinformatics packages that are not scalable for the reproducible processing of large-scale (hundreds to thousands) biological sample sets. Here, we present a computational platform (pyBASIS) capable of optimized and scalable processing of MSI data for improved information recovery and comparative analysis across tissue specimens using machine learning and related pattern recognition approaches. The proposed solution also provides a means of seamlessly integrating experimental laboratory data with downstream bioinformatics interpretation/analyses, resulting in a truly integrated system for translational MSI.

BASTet: Shareable and Reproducible Analysis and Visualization of Mass Spectrometry Imaging Data via OpenMSI

Mass spectrometry imaging (MSI) is a transformative imaging method that supports the untargeted, quantitative measurement of the chemical composition and spatial heterogeneity of complex samples with broad applications in life sciences, bioenergy, and health. While MSI data can be routinely collected, its broad application is currently limited by the lack of easily accessible analysis methods that can process data of the size, volume, diversity, and complexity generated by MSI experiments. The development and application of cutting-edge analytical methods is a core driver in MSI research for new scientific discoveries, medical diagnostics, and commercial-innovation. However, the lack of means to share, apply, and reproduce analyses hinders the broad application, validation, and use of novel MSI analysis methods. To address this central challenge, we introduce the Berkeley Analysis and Storage Toolkit (BASTet), a novel framework for shareable and reproducible data analysis that supports standardized data and analysis interfaces, integrated data storage, data provenance, workflow management, and a broad set of integrated tools. Based on BASTet, we describe the extension of the OpenMSI mass spectrometry imaging science gateway to enable web-based sharing, reuse, analysis, and visualization of data analyses and derived data products. We demonstrate the application of BASTet and OpenMSI in practice to identify and compare characteristic substructures in the mouse brain based on their chemical composition measured via MSI.

Quantitative analysis of multiple high-resolution mass spectrometry images using chemometric methods: quantitation of chlordecone in mouse liver

In this work, a chemometrics-based strategy is developed for quantitative mass spectrometry imaging (MSI).

The arc of Mass Spectrometry Exchange Formats is long, but it bends toward HDF5

The evolution of data exchange in Mass Spectrometry spans decades and has ranged from human-readable text files representing individual scans or collections thereof (McDonald et al., 2004) through the official standard XML-based (Harold, Means, & Udemadu, 2005) data interchange standard (Deutsch, 2012), to increasingly compressed (Teleman et al., 2014) variants of this standard sometimes requiring purely binary adjunct files (Römpp et al., 2011). While the desire to maintain even partial human readability is understandable, the inherent mismatch between XML's textual and irregular format relative to the numeric and highly regular nature of actual spectral data, along with the explosive growth in dataset scales and the resulting need for efficient (binary and indexed) access has led to a phenomenon referred to as "technical drift" (Davis, 2013). While the drift is being continuously corrected using adjunct formats, compression schemes, and programs (Röst et al., 2015), we propose that the future of Mass Spectrometry Exchange Formats lies in the continued reliance and development of the PSI-MS (Mayer et al., 2014) controlled vocabulary, along with an expedited shift to an alternative, thriving and well-supported ecosystem for scientific data-exchange, storage, and access in binary form, namely that of HDF5 (Koranne, 2011). Indeed, pioneering efforts to leverage this universal, binary, and hierarchical data-format have already been published (Wilhelm et al., 2012; Rübel et al., 2013) though they have under-utilized self-description, a key property shared by HDF5 and XML. We demonstrate that a straightforward usage of plain ("vanilla") HDF5 yields immediate returns including, but not limited to, highly efficient data access, platform independent data viewers, a variety of libraries (Collette, 2014) for data retrieval and manipulation in many programming languages and remote data access through comprehensive RESTful data-servers. © 2016 Wiley Periodicals, Inc. Mass Spec Rev 36:668-673, 2017.

From chromatogram to analyte to metabolite. How to pick horses for courses from the massive web resources for mass spectral plant metabolomics

The grand challenge currently facing metabolomics is the expansion of the coverage of the metabolome from a minor percentage of the metabolic complement of the cell toward the level of coverage afforded by other post-genomic technologies such as transcriptomics and proteomics. In plants, this problem is exacerbated by the sheer diversity of chemicals that constitute the metabolome, with the number of metabolites in the plant kingdom generally considered to be in excess of 200 000. In this review, we focus on web resources that can be exploited in order to improve analyte and ultimately metabolite identification and quantification. There is a wide range of available software that not only aids in this but also in the related area of peak alignment; however, for the uninitiated, choosing which program to use is a daunting task. For this reason, we provide an overview of the pros and cons of the software as well as comments regarding the level of programing skills required to effectively exploit their basic functions. In addition, the torrent of available genome and transcriptome sequences that followed the advent of next-generation sequencing has opened up further valuable resources for metabolite identification. All things considered, we posit that only via a continued communal sharing of information such as that deposited in the databases described within the article are we likely to be able to make significant headway toward improving our coverage of the plant metabolome.

Morphology-Driven Control of Metabolite Selectivity Using Nanostructure-Initiator Mass Spectrometry

Nanostructure-initiator mass spectrometry (NIMS) is a laser desorption/ionization analysis technique based on the vaporization of a nanostructure-trapped liquid "initiator" phase. Here we report an intriguing relationship between NIMS surface morphology and analyte selectivity. Scanning electron microscopy and spectroscopic ellipsometry were used to characterize the surface morphologies of a series of NIMS substrates generated by anodic electrochemical etching. Mass spectrometry imaging was applied to compare NIMS sensitivity of these various surfaces toward the analysis of diverse analytes. The porosity of NIMS surfaces was found to increase linearly with etching time where the pore size ranged from 4 to 12 nm with corresponding porosities estimated to be 7-70%. Surface morphology was found to significantly and selectively alter NIMS sensitivity. The small molecule (<2k Da) sensitivity was found to increase with increased porosity, whereas low porosity had the highest sensitivity for the largest molecules examined. Estimation of molecular sizes showed that this transition occurs when the pore size is <3× the maximum of molecular dimensions. While the origins of selectivity are unclear, increased signal from small molecules with increased surface area is consistent with a surface area restructuring-driven desorption/ionization process where signal intensity increases with porosity. In contrast, large molecules show highest signal for the low-porosity and small-pore-size surfaces. We attribute this to strong interactions between the initiator-coated pore structures and large molecules that hinder desorption/ionization by trapping large molecules. This finding may enable us to design NIMS surfaces with increased specificity to molecules of interest.

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

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

On-chip integration of droplet microfluidics and nanostructure-initiator mass spectrometry for enzyme screening

Biological assays often require expensive reagents and tedious manipulations. These shortcomings can be overcome using digitally operated microfluidic devices that require reduced sample volumes to automate assays. One particular challenge is integrating bioassays with mass spectrometry based analysis. Towards this goal we have developed μNIMS, a highly sensitive and high throughput technique that integrates droplet microfluidics with nanostructure-initiator mass spectrometry (NIMS). Enzyme reactions are carried out in droplets that can be arrayed on discrete NIMS elements at defined time intervals for subsequent mass spectrometry analysis, enabling time resolved enzyme activity assay. We apply the μNIMS platform for kinetic characterization of a glycoside hydrolase enzyme (CelE-CMB3A), a chimeric enzyme capable of deconstructing plant hemicellulose into monosaccharides for subsequent conversion to biofuel. This study reveals NIMS nanostructures can be fabricated into arrays for microfluidic droplet deposition, NIMS is compatible with droplet and digital microfluidics, and can be used on-chip to assay glycoside hydrolase enzyme in vitro.

From single cells to our planet-recent advances in using mass spectrometry for spatially resolved metabolomics

Spatial information in the form of 3D digital content has been increasingly integrated into our daily lives. Metabolomic studies parallel this trend with spatial and time resolved information being acquired. Mass spectrometry imaging (MSI), which combines qualitative and quantitative molecular information with spatial information, plays a crucial role in mass spectrometry-based metabolomics. The lateral spatial resolution obtained by MSI continues to improve and allows mass spectrometers to be used as molecular microscopes-enabling the exploration of the cellular and subcellular metabolome. Towards the other end of the scale, MS is also being used to map (image) molecules on our skin, habitats, and entire ecosystems. In this article, we provide a perspective of imaging mass spectrometry for metabolomic studies from the subcellular to planetary scale.

Methods for Specifying Scientific Data Standards and Modeling Relationships with Applications to Neuroscience

Neuroscience continues to experience a tremendous growth in data; in terms of the volume and variety of data, the velocity at which data is acquired, and in turn the veracity of data. These challenges are a serious impediment to sharing of data, analyses, and tools within and across labs. Here, we introduce BRAINformat, a novel data standardization framework for the design and management of scientific data formats. The BRAINformat library defines application-independent design concepts and modules that together create a general framework for standardization of scientific data. We describe the formal specification of scientific data standards, which facilitates sharing and verification of data and formats. We introduce the concept of Managed Objects, enabling semantic components of data formats to be specified as self-contained units, supporting modular and reusable design of data format components and file storage. We also introduce the novel concept of Relationship Attributes for modeling and use of semantic relationships between data objects. Based on these concepts we demonstrate the application of our framework to design and implement a standard format for electrophysiology data and show how data standardization and relationship-modeling facilitate data analysis and sharing. The format uses HDF5, enabling portable, scalable, and self-describing data storage and integration with modern high-performance computing for data-driven discovery. The BRAINformat library is open source, easy-to-use, and provides detailed user and developer documentation and is freely available at: https://bitbucket.org/oruebel/brainformat.

More than Pictures: When MS Imaging Meets Histology

Attaining high-resolution spatial information is a recurrent challenge in biological research, particularly in the case of small-molecule distribution. Mass spectrometry imaging (MSI) is an innovative molecular histology technique that could provide such information. It allows in situ and label-free measurement of both the abundance and distribution of a variety of molecules at the tissue or single cell level. The application of MSI in plant research has received considerable attention; thus, in this review, we describe the current state of MSI in plants. In particular, we present an overview of MSI approaches, highlight the recent technical and methodological developments, and discuss a range of applications contributing to the field of plant science.

Advanced Multidimensional Separations in Mass Spectrometry: Navigating the Big Data Deluge

Hybrid analytical instrumentation constructed around mass spectrometry (MS) is becoming the preferred technique for addressing many grand challenges in science and medicine. From the omics sciences to drug discovery and synthetic biology, multidimensional separations based on MS provide the high peak capacity and high measurement throughput necessary to obtain large-scale measurements used to infer systems-level information. In this article, we describe multidimensional MS configurations as technologies that are big data drivers and review some new and emerging strategies for mining information from large-scale datasets. We discuss the information content that can be obtained from individual dimensions, as well as the unique information that can be derived by comparing different levels of data. Finally, we summarize some emerging data visualization strategies that seek to make highly dimensional datasets both accessible and comprehensible.

msIQuant--Quantitation Software for Mass Spectrometry Imaging Enabling Fast Access, Visualization, and Analysis of Large Data Sets

This paper presents msIQuant, a novel instrument- and manufacturer-independent quantitative mass spectrometry imaging software suite that uses the standardized open access data format imzML. Its data processing structure enables rapid image display and the analysis of very large data sets (>50 GB) without any data reduction. In addition, msIQuant provides many tools for image visualization including multiple interpolation methods, low intensity transparency display, and image fusion. It also has a quantitation function that automatically generates calibration standard curves from series of standards that can be used to determine the concentrations of specific analytes. Regions-of-interest in a tissue section can be analyzed based on a number of quantities including the number of pixels, average intensity, standard deviation of intensity, and median and quartile intensities. Moreover, the suite's export functions enable simplified postprocessing of data and report creation. We demonstrate its potential through several applications including the quantitation of small molecules such as drugs and neurotransmitters. The msIQuant suite is a powerful tool for accessing and evaluating very large data sets, quantifying drugs and endogenous compounds in tissue areas of interest, and for processing mass spectra and images.

Mass Spectrometry in Plant-omics

Plant-omics is rapidly becoming an important field of study in the scientific community due to the urgent need to address many of the most important questions facing humanity today with regard to agriculture, medicine, biofuels, environmental decontamination, ecological sustainability, etc. High-performance mass spectrometry is a dominant tool for interrogating the metabolomes, peptidomes, and proteomes of a diversity of plant species under various conditions, revealing key insights into the functions and mechanisms of plant biochemistry.

Matrix assisted laser desorption/ionization-mass spectrometry imaging (MALDI-MSI) for direct visualization of plant metabolites in situ

Direct visualization of plant tissues by matrix assisted laser desorption ionization-mass spectrometry imaging (MALDI-MSI) has revealed key insights into the localization of metabolites in situ. Recent efforts have determined the spatial distribution of primary and secondary metabolites in plant tissues and cells. Strategies have been applied in many areas of metabolism including isotope flux analyses, plant interactions, and transcriptional regulation of metabolite accumulation. Technological advances have pushed achievable spatial resolution to subcellular levels and increased instrument sensitivity by several orders of magnitude. It is anticipated that MALDI-MSI and other MSI approaches will bring a new level of understanding to metabolomics as scientists will be encouraged to consider spatial heterogeneity of metabolites in descriptions of metabolic pathway regulation.

Mass spectrometry imaging for plant biology: a review

Mass spectrometry imaging (MSI) is a developing technique to measure the spatio-temporal distribution of many biomolecules in tissues. Over the preceding decade, MSI has been adopted by plant biologists and applied in a broad range of areas, including primary metabolism, natural products, plant defense, plant responses to abiotic and biotic stress, plant lipids and the developing field of spatial metabolomics. This review covers recent advances in plant-based MSI, general aspects of instrumentation, analytical approaches, sample preparation and the current trends in respective plant research.

An accessible, scalable ecosystem for enabling and sharing diverse mass spectrometry imaging analyses

Mass spectrometry imaging (MSI) is used in an increasing number of biological applications. Typical MSI datasets contain unique, high-resolution mass spectra from tens of thousands of spatial locations, resulting in raw data sizes of tens of gigabytes per sample. In this paper, we review technical progress that is enabling new biological applications and that is driving an increase in the complexity and size of MSI data. Handling such data often requires specialized computational infrastructure, software, and expertise. OpenMSI, our recently described platform, makes it easy to explore and share MSI datasets via the web - even when larger than 50 GB. Here we describe the integration of OpenMSI with IPython notebooks for transparent, sharable, and replicable MSI research. An advantage of this approach is that users do not have to share raw data along with analyses; instead, data is retrieved via OpenMSI's web API. The IPython notebook interface provides a low-barrier entry point for data manipulation that is accessible for scientists without extensive computational training. Via these notebooks, analyses can be easily shared without requiring any data movement. We provide example notebooks for several common MSI analysis types including data normalization, plotting, clustering, and classification, and image registration.

Dirigent Protein-Mediated Lignan and Cyanogenic Glucoside Formation in Flax Seed: Integrated Omics and MALDI Mass Spectrometry Imaging

An integrated omics approach using genomics, transcriptomics, metabolomics (MALDI mass spectrometry imaging, MSI), and bioinformatics was employed to study spatiotemporal formation and deposition of health-protecting polymeric lignans and plant defense cyanogenic glucosides. Intact flax (Linum usitatissimum) capsules and seed tissues at different development stages were analyzed. Transcriptome analyses indicated distinct expression patterns of dirigent protein (DP) gene family members encoding (-)- and (+)-pinoresinol-forming DPs and their associated downstream metabolic processes, respectively, with the former expressed at early seed coat development stages. Genes encoding (+)-pinoresinol-forming DPs were, in contrast, expressed at later development stages. Recombinant DP expression and DP assays also unequivocally established their distinct stereoselective biochemical functions. Using MALDI MSI and ion mobility separation analyses, the pinoresinol downstream derivatives, secoisolariciresinol diglucoside (SDG) and SDG hydroxymethylglutaryl ester, were localized and detectable only in early seed coat development stages. SDG derivatives were then converted into higher molecular weight phenolics during seed coat maturation. By contrast, the plant defense cyanogenic glucosides, the monoglucosides linamarin/lotaustralin, were detected throughout the flax capsule, whereas diglucosides linustatin/neolinustatin only accumulated in endosperm and embryo tissues. A putative biosynthetic pathway to the cyanogens is proposed on the basis of transcriptome coexpression data. Localization of all metabolites was at ca. 20 μm resolution, with the web based tool OpenMSI enabling not only resolution enhancement but also an interactive system for real-time searching for any ion in the tissue under analysis.

Potential use of multivariate curve resolution for the analysis of mass spectrometry images

The application of MCR-ALS to mass spectrometry imaging data provides spatial distribution and MS spectra of components, allowing compound identification.

MALDI mass spectrometric imaging meets “omics”: recent advances in the fruitful marriage

Matrix-assisted laser desorption/ionization mass spectrometric imaging (MALDI MSI) is a method that allows the investigation of the molecular content of surfaces, in particular, tissues, within its morphological context.