Resolving brain regions using nanostructure initiator mass spectrometry imaging of phospholipids

Surface-assisted laser desorption/ionization mass spectrometry imaging: A review

In the last decades, surface-assisted laser desorption/ionization mass spectrometry (SALDI-MS) has attracted increasing interest due to its unique capabilities, achievable through the nanostructured substrates used to promote the analyte desorption/ionization. While the most widely recognized asset of SALDI-MS is the untargeted analysis of small molecules, this technique also offers the possibility of targeted approaches. In particular, the implementation of SALDI-MS imaging (SALDI-MSI), which is the focus of this review, opens up new opportunities. After a brief discussion of the nomenclature and the fundamental mechanisms associated with this technique, which are still highly controversial, the analytical strategies to perform SALDI-MSI are extensively discussed. Emphasis is placed on the sample preparation but also on the selection of the nanosubstrate (in terms of chemical composition and morphology) as well as its functionalization possibilities for the selective analysis of specific compounds in targeted approaches. Subsequently, some selected applications of SALDI-MSI in various fields (i.e., biomedical, biological, environmental, and forensic) are presented. The strengths and the remaining limitations of SALDI-MSI are finally summarized in the conclusion and some perspectives of this technique, which has a bright future, are proposed in this section.

ADVANCES IN HIGH-RESOLUTION MALDI MASS SPECTROMETRY FOR NEUROBIOLOGY

Research in the field of neurobiology and neurochemistry has seen a rapid expansion in the last several years due to advances in technologies and instrumentation, facilitating the detection of biomolecules critical to the complex signaling of neurons. Part of this growth has been due to the development and implementation of high-resolution Fourier transform (FT) mass spectrometry (MS), as is offered by FT ion cyclotron resonance (FTICR) and Orbitrap mass analyzers, which improves the accuracy of measurements and helps resolve the complex biological mixtures often analyzed in the nervous system. The coupling of matrix-assisted laser desorption/ionization (MALDI) with high-resolution MS has drastically expanded the information that can be obtained with these complex samples. This review discusses notable technical developments in MALDI-FTICR and MALDI-Orbitrap platforms and their applications toward molecules in the nervous system, including sequence elucidation and profiling with de novo sequencing, analysis of post-translational modifications, in situ analysis, key advances in sample preparation and handling, quantitation, and imaging. Notable novel applications are also discussed to highlight key developments critical to advancing our understanding of neurobiology and providing insight into the exciting future of this field. © 2020 John Wiley & Sons Ltd. Mass Spec Rev.

Imaging lipids in biological samples with surface-assisted laser desorption/ionization mass spectrometry: A concise review of the last decade

Knowing the spatial location of the lipid species present in biological samples is of paramount importance for the elucidation of pathological and physiological processes. In this context, mass spectrometry imaging (MSI) has emerged as a powerful technology allowing the visualization of the spatial distributions of biomolecules, including lipids, in complex biological samples. Among the different ionization methods available, the emerging surface-assisted laser desorption/ionization (SALDI) MSI offers unique capabilities for the study of lipids. This review describes the specific advantages of SALDI-MSI for lipid analysis, including the ability to perform analyses in both ionization modes with the same nanosubstrate, the detection of lipids characterized by low ionization efficiency in MALDI-MS, and the possibilities of surface modification to improve the detection of lipids. The complementarity of SALDI and MALDI-MSI is also discussed. Finally, this review presents data processing strategies applied in SALDI-MSI of lipids, as well as examples of applications of SALDI-MSI in biomedical lipidomics.

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

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

Charting Metabolism Heterogeneity by Nanostructure Imaging Mass Spectrometry: From Biological Systems to Subcellular Functions

The study of metabolism heterogeneity is essential to understand the role of metabolites in supporting and regulating biological functions. To this end, several mass spectrometry imaging (MSI) approaches have been proposed for the detection of small molecule metabolites. However, high noise from the ionization matrix and low metabolome coverage hinder their applicability for untargeted metabolomics studies across space. In this context, nanostructure imaging (/initiator) mass spectrometry (NIMS) and NIMS with fluorinated gold nanoparticles (f-AuNPs) are attractive strategies for comprehensive MSI of metabolites in biological systems, which can provide heterogeneous metabolome coverage, ultrahigh sensitivity, and high lateral resolution. In particular, NIMS with f-AuNPs permits the simultaneous detection of polar metabolites and lipids in a single and cohesive analytical session, thus allowing the systems-level interpretation of metabolic changes. In this Perspective article, we discuss the use of NIMS and f-AuNPs in the exploration of metabolism heterogeneity and provide a critical outlook on future applications of this technology for revealing the metabolic architecture that supports biological functions in health and disease, from whole organisms to tissues, single cells, and subcellular compartments.

rMSIKeyIon: An Ion Filtering R Package for Untargeted Analysis of Metabolomic LDI-MS Images

Many MALDI-MS imaging experiments make a case versus control studies of different tissue regions in order to highlight significant compounds affected by the variables of study. This is a challenge because the tissue samples to be compared come from different biological entities, and therefore they exhibit high variability. Moreover, the statistical tests available cannot properly compare ion concentrations in two regions of interest (ROIs) within or between images. The high correlation between the ion concentrations due to the existence of different morphological regions in the tissue means that the common statistical tests used in metabolomics experiments cannot be applied. Another difficulty with the reliability of statistical tests is the elevated number of undetected MS ions in a high percentage of pixels. In this study, we report a procedure for discovering the most important ions in the comparison of a pair of ROIs within or between tissue sections. These ROIs were identified by an unsupervised segmentation process, using the popular k-means algorithm. Our ion filtering algorithm aims to find the up or down-regulated ions between two ROIs by using a combination of three parameters: (a) the percentage of pixels in which a particular ion is not detected, (b) the Mann–Whitney U ion concentration test, and (c) the ion concentration fold-change. The undetected MS signals (null peaks) are discarded from the histogram before the calculation of (b) and (c) parameters. With this methodology, we found the important ions between the different segments of a mouse brain tissue sagittal section and determined some lipid compounds (mainly triacylglycerols and phosphatidylcholines) in the liver of mice exposed to thirdhand smoke.

Recent advances in single-cell analysis by mass spectrometry

Cells are the most basic structural units that play vital roles in the functioning of living organisms. Analysis of the chemical composition and content of a single cell plays a vital role in ensuring precise investigations of cellular metabolism, and is a crucial aspect of lipidomic and proteomic studies. In addition, structural knowledge provides a better understanding of cell behavior as well as the cellular and subcellular mechanisms. However, single-cell analysis can be very challenging due to the very small size of each cell as well as the large variety and extremely low concentrations of substances found in individual cells. On account of its high sensitivity and selectivity, mass spectrometry holds great promise as an effective technique for single-cell analysis. Numerous mass spectrometric techniques have been developed to elucidate the molecular profiles at the cellular level, including electrospray ionization mass spectrometry (ESI-MS), secondary ion mass spectrometry (SIMS), laser-based mass spectrometry and inductively coupled plasma mass spectrometry (ICP-MS). In this review, the recent advances in single-cell analysis by mass spectrometry are summarized. The strategies of different ionization modes to achieve single-cell analysis are classified and discussed in detail.

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.

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.

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.

Tissue spray ionization mass spectrometry for rapid recognition of human lung squamous cell carcinoma

Tissue spray ionization mass spectrometry (TSI-MS) directly on small tissue samples has been shown to provide highly specific molecular information. In this study, we apply this method to the analysis of 38 pairs of human lung squamous cell carcinoma tissue (cancer) and adjacent normal lung tissue (normal). The main components of pulmonary surfactants, dipalmitoyl phosphatidylcholine (DPPC, m/z 757.47), phosphatidylcholine (POPC, m/z 782.52), oleoyl phosphatidylcholine (DOPC, m/z 808.49), and arachidonic acid stearoyl phosphatidylcholine (SAPC, m/z 832.43), were identified using high-resolution tandem mass spectrometry. Monte Carlo sampling partial least squares linear discriminant analysis (PLS-LDA) was used to distinguish full-mass-range mass spectra of cancer samples from the mass spectra of normal tissues. With 5 principal components and 30-40 Monte Carlo samplings, the accuracy of cancer identification in matched tissue samples reached 94.42%. Classification of a tissue sample required less than 1 min, which is much faster than the analysis of frozen sections. The rapid, in situ diagnosis with minimal sample consumption provided by TSI-MS is advantageous for surgeons. TSI-MS allows them to make more informed decisions during surgery.

Current status and future prospects of mass spectrometry imaging of small molecules

In the field of small-molecule studies, vast efforts have been put forth in order to comprehensively characterize and quantify metabolites formed from complex mechanistic pathways within biochemical and biological organisms. Many technologies and methodologies have been developed to aid understanding of the inherent complexities within biological metabolomes. Specifically, mass spectroscopy imaging (MSI) has emerged as a foundational technique in gaining insight into the molecular entities within cells, tissues, and whole-body samples. In this chapter we provide a brief overview of major technical components involved in MSI, including topics such as sample preparation, analyte ionization, ion detection, and data analysis. Emerging applications are briefly summarized as well, but details will be presented in the following chapters.

Laser desorption ionization (LDI) silicon nanopost array chips fabricated using deep UV projection lithography and deep reactive ion etching

A nanofabricated laser desorption ionization mass spectrometry (LDI-MS) chip for quantitation of small molecules.

Brain region mapping using global metabolomics

Historically, studies of brain metabolism have been based on targeted analyses of a limited number of metabolites. Here we present an untargeted mass spectrometry-based metabolomic strategy that has successfully uncovered differences in a broad array of metabolites across anatomical regions of the mouse brain. The NSG immunodeficient mouse model was chosen because of its ability to undergo humanization leading to numerous applications in oncology and infectious disease research. Metabolic phenotyping by hydrophilic interaction liquid chromatography and nanostructure imaging mass spectrometry revealed both water-soluble and lipid metabolite patterns across brain regions. Neurochemical differences in metabolic phenotypes were mainly defined by various phospholipids and several intriguing metabolites including carnosine, cholesterol sulfate, lipoamino acids, uric acid, and sialic acid, whose physiological roles in brain metabolism are poorly understood. This study helps define regional homeostasis for the normal mouse brain to give context to the reaction to pathological events.

Metabolic imaging using nanostructure-initiator mass spectrometry (NIMS)

Nanostructure-initiator mass spectrometry (NIMS) imaging using soft laser desorption/ionization has proven to be a powerful tool in localizing the spatial distribution of intact biomolecules. NIMS specifically has been demonstrated to have high sensitivity and low background, particularly in the low mass range <1,000 Da, making this technique well suited for metabolic imaging studies. Here, we describe NIMS imaging for direct analysis of metabolite composition across a sectioned biospecimen.

"Replica-extraction-transfer" nanostructure-initiator mass spectrometry imaging of acoustically printed bacteria

Traditionally, microbes are studied under controlled laboratory conditions as isolates in planktonic culture. However, this is a vast extrapolation from their natural state; development of new techniques is required to decipher the largely unknown world of microbial chemical interactions in more realistic environments. The field of mass spectrometry imaging has made significant progress in localizing metabolites in and around bacterial colonies, primarily by using MALDI and ESI-based techniques that interrogate the top surface of the sample. Unfortunately, surface-based laser-desorption techniques, such as nanostructure-initiator mass spectrometry (NIMS), which has advantages in detection of small metabolite compounds and low background, has not been suitable for direct microbe imaging because desorption/ionization occurs on the bottom of the sample. Here, we describe a "replica-extraction-transfer" (REX) technique that overcomes this barrier by transferring biomolecules from agar cultures of spatially arrayed bacterial colonies onto NIMS surfaces; further, we demonstrate that acoustic printing of bacteria can be used to create complex colony geometries to probe microbial interactions with NIMS imaging. REX uses a solvent-laden semisolid (e.g., gel) to first extract metabolites from a microbial sample, such as a biofilm or agar culture; the metabolites are then replica "stamped" onto the NIMS surface. Using analytical standards we show that REX-NIMS effectively transfers and detects a range of small molecule compounds including amino acids and polyamines. This approach is then used to analyze the metabolite composition of streaked Shewanella oneidensis MR1 and Pseudomonas stutzeri RCH2 colonies and further resolve complex patterns produced by acoustic printing of liquid microbial cultures. Applying multivariate statistical analysis of the NIMS imaging data identified ions that were localized to different regions between and within colonies, as well as to the agar gel. Subsequent high-resolution tandem mass spectrometry was used to characterize two species-specific lipids that correlated with the spatial location of each microbial species and were found to be highly abundant in cell extracts. Overall, the use of acoustic printing of bacteria with REX-NIMS imaging will extend the range of analytical capabilities available for characterization of microbial interactions with mass spectrometry.

Surface analysis of lipids by mass spectrometry: more than just imaging

Mass spectrometry is now an indispensable tool for lipid analysis and is arguably the driving force in the renaissance of lipid research. In its various forms, mass spectrometry is uniquely capable of resolving the extensive compositional and structural diversity of lipids in biological systems. Furthermore, it provides the ability to accurately quantify molecular-level changes in lipid populations associated with changes in metabolism and environment; bringing lipid science to the "omics" age. The recent explosion of mass spectrometry-based surface analysis techniques is fuelling further expansion of the lipidomics field. This is evidenced by the numerous papers published on the subject of mass spectrometric imaging of lipids in recent years. While imaging mass spectrometry provides new and exciting possibilities, it is but one of the many opportunities direct surface analysis offers the lipid researcher. In this review we describe the current state-of-the-art in the direct surface analysis of lipids with a focus on tissue sections, intact cells and thin-layer chromatography substrates. The suitability of these different approaches towards analysis of the major lipid classes along with their current and potential applications in the field of lipid analysis are evaluated.

Comparison of NIMS and MALDI platforms for neuropeptide and lipid mass spectrometric imaging in C. borealis brain tissue

Nanostructure-initiator mass spectrometry (NIMS) is a recently developed matrix-free laser desorption/ionization technique that has shown promise for peptide analyses. It is also useful in mass spectrometric imaging (MSI) studies of small molecule drugs, metabolites, and lipids, minimizing analyte diffusion caused by matrix application. In this study, NIMS and matrix-assisted laser desorption/ionization (MALDI) MSI of a crustacean model organism Cancer borealis brain were compared. MALDI was found to perform better than NIMS in these neuropeptide imaging experiments. Twelve neuropeptides were identified in MALDI MSI experiments whereas none were identified in NIMS MSI experiments. In addition, lipid profiles were compared using each ionization method. Both techniques provided similar lipid profiles in the m/z range 700 - 900.

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

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