3-D imaging mass spectrometry of protein distributions in mouse Neurofibromatosis 1 (NF1)-associated optic glioma

Spatially Resolved Top-Down Proteomics of Tissue Sections Based on a Microfluidic Nanodroplet Sample Preparation Platform

Conventional proteomic approaches measure the averaged signal from mixed cell populations or bulk tissues, leading to the dilution of signals arising from subpopulations of cells that might serve as important biomarkers. Recent developments in bottom-up proteomics have enabled spatial mapping of cellular heterogeneity in tissue microenvironments. However, bottom-up proteomics cannot unambiguously define and quantify proteoforms, which are intact (i.e., functional) forms of proteins capturing genetic variations, alternatively spliced transcripts and posttranslational modifications. Herein, we described a spatially resolved top-down proteomics (TDP) platform for proteoform identification and quantitation directly from tissue sections. The spatial TDP platform consisted of a nanodroplet processing in one pot for trace samples-based sample preparation system and an laser capture microdissection-based cell isolation system. We improved the nanodroplet processing in one pot for trace samples sample preparation by adding benzonase in the extraction buffer to enhance the coverage of nucleus proteins. Using ∼200 cultured cells as test samples, this approach increased total proteoform identifications from 493 to 700; with newly identified proteoforms primarily corresponding to nuclear proteins. To demonstrate the spatial TDP platform in tissue samples, we analyzed laser capture microdissection-isolated tissue voxels from rat brain cortex and hypothalamus regions. We quantified 509 proteoforms within the union of top-down mass spectrometry-based proteoform identification and characterization and TDPortal identifications to match with features from protein mass extractor. Several proteoforms corresponding to the same gene exhibited mixed abundance profiles between two tissue regions, suggesting potential posttranslational modification-specific spatial distributions. The spatial TDP workflow has prospects for biomarker discovery at proteoform level from small tissue sections.

An overview of image registration for aligning mass spectrometry imaging with clinically relevant imaging modalities

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Mass spectrometry imaging (MSI) is a powerful molecular imaging technique.

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Integration with other imaging modalities is essential in clinical MSI.

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Image integration is performed by image registration techniques.

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Technical potential of image registration in MSI has not been fully exploited.

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Roadmap proposed to improve registration accuracy.

Mass spectrometry imaging (MSI) is used in many aspects of clinical research, including pharmacokinetics, toxicology, personalised medicine, and surgical decision-making. Maximising its potential requires the spatial integration of MSI images with imaging data from existing clinical imaging modalities, such as histology and MRI. To ensure that the information is properly integrated, all contributing images must be accurately aligned. This process is called image registration and is the focus of this review. In light of the ever-increasing spatial resolution of MSI instrumentation and a diversification of multi-modal MSI studies (e.g., spatial omics, 3D-MSI), the accuracy, versatility, and precision of image registration must increase accordingly. We review the application of image registration to align MSI data with different clinically relevant ex vivo and in vivo imaging techniques. Based on this, we identify steps in the current image registration processes where there is potential for improvement. Finally, we propose a roadmap for community efforts to address these challenges in order to increase registration quality and help MSI to fully exploit its multi-modal potential.

Experimental and Data Analysis Considerations for Three-Dimensional Mass Spectrometry Imaging in Biomedical Research

Mass spectrometry imaging (MSI) enables the visualization of molecular distributions on complex surfaces. It has been extensively used in the field of biomedical research to investigate healthy and diseased tissues. Most of the MSI studies are conducted in a 2D fashion where only a single slice of the full sample volume is investigated. However, biological processes occur within a tissue volume and would ideally be investigated as a whole to gain a more comprehensive understanding of the spatial and molecular complexity of biological samples such as tissues and cells. Mass spectrometry imaging has therefore been expanded to the 3D realm whereby molecular distributions within a 3D sample can be visualized. The benefit of investigating volumetric data has led to a quick rise in the application of single-sample 3D-MSI investigations. Several experimental and data analysis aspects need to be considered to perform successful 3D-MSI studies. In this review, we discuss these aspects as well as ongoing developments that enable 3D-MSI to be routinely applied to multi-sample studies.

Lipid Landscape of the Human Retina and Supporting Tissues Revealed by High-Resolution Imaging Mass Spectrometry

The human retina provides vision at light levels ranging from starlight to sunlight. Its supporting tissues regulate plasma-delivered lipophilic essentials for vision, including retinoids. The macula is an anatomic specialization for high-acuity and color vision that is also vulnerable to prevalent blinding diseases. The retina's exquisite architecture comprises numerous cell types that are aligned horizontally, yielding structurally distinct cell, synaptic, and vascular layers that are visible in histology and in diagnostic clinical imaging. MALDI imaging mass spectrometry (IMS) is now capable of uniting low micrometer spatial resolution with high levels of chemical specificity. In this study, a multimodal imaging approach fortified with accurate multi-image registration was used to localize lipids in human retina tissue at laminar, cellular, and subcellular levels. Multimodal imaging results indicate differences in distributions and abundances of lipid species across and within single cell types. Of note are distinct localizations of signals within specific layers of the macula. For example, phosphatidylethanolamine and phosphatidylinositol lipids were localized to central RPE cells, whereas specific plasmalogen lipids were localized to cells of the perifoveal RPE and Henle fiber layer. Subcellular compartments of photoreceptors were distinguished by PE(20:0_22:5) in the outer nuclear layer, PE(18:0_22:6) in outer and inner segments, and cardiolipin CL(70:5) in the mitochondria-rich inner segments. Several lipids, differing by a single double bond, have markedly different distributions between the central fovea and the ganglion cell and inner nuclear layers. A lipid atlas, initiated in this study, can serve as a reference database for future examination of diseased tissues.

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.

Dual strategy for reduced signal-suppression effects in matrix-assisted laser desorption/ionization mass spectrometry imaging

RATIONALE

The molecular complexity of tissue features several signal-suppression effects which reduce the ionization of analytes significantly and thereby weakens the quality of matrix-assisted laser desorption/ionization (MALDI) mass spectrometry (MS) imaging (MALDI imaging). We report a novel approach in MALDI imaging by reducing signal-suppression effects for the analysis of beta-amyloid (Aβ) plaques, one pathological hallmark of Alzheimer's disease (AD).

METHODS

We analyzed Aβ proteoforms from postmortem AD brains and brains from transgenic mice (APPPS1-21) overexpressing familial AD mutations by combining two techniques: (1) laser capture microdissection (LCM) to accumulate Aβ plaques and (2) phosphoric acid (PA) as additive to the super-2,5-dihydroxybenzoic acid matrix.

RESULTS

LCM and MALDI-MS enabled tandem mass spectrometric fragmentation of stained Aβ plaques. PA improved the signal-to-noise (S/N) ratio, especially of the Aβ1-42 peptide, by three-fold compared with the standard matrix additive trifluoroacetic acid. The beneficial effect of the PA matrix additive in MALDI imaging was particularly important for AD brain tissue. We identified several significant differences in Aβ plaque composition from AD compared with APPPS1-21, underlining the value of reducing signal-suppressing effects in MALDI imaging.

CONCLUSIONS

We present a novel strategy for overcoming signal-suppression effects in MALDI imaging of Aβ proteoforms.

Proteome Imaging: From Classic to Modern Mass Spectrometry-Based Molecular Histology

In order to overcome the limitations of classic imaging in Histology during the actually era of multiomics, the multi-color "molecular microscope" by its emerging "molecular pictures" offers quantitative and spatial information about thousands of molecular profiles without labeling of potential targets. Healthy and diseased human tissues, as well as those of diverse invertebrate and vertebrate animal models, including genetically engineered species and cultured cells, can be easily analyzed by histology-directed MALDI imaging mass spectrometry. The aims of this review are to discuss a range of proteomic information emerging from MALDI mass spectrometry imaging comparative to classic histology, histochemistry and immunohistochemistry, with applications in biology and medicine, concerning the detection and distribution of structural proteins and biological active molecules, such as antimicrobial peptides and proteins, allergens, neurotransmitters and hormones, enzymes, growth factors, toxins and others. The molecular imaging is very well suited for discovery and validation of candidate protein biomarkers in neuroproteomics, oncoproteomics, aging and age-related diseases, parasitoproteomics, forensic, and ecotoxicology. Additionally, in situ proteome imaging may help to elucidate the physiological and pathological mechanisms involved in developmental biology, reproductive research, amyloidogenesis, tumorigenesis, wound healing, neural network regeneration, matrix mineralization, apoptosis and oxidative stress, pain tolerance, cell cycle and transformation under oncogenic stress, tumor heterogeneity, behavior and aggressiveness, drugs bioaccumulation and biotransformation, organism's reaction against environmental penetrating xenobiotics, immune signaling, assessment of integrity and functionality of tissue barriers, behavioral biology, and molecular origins of diseases. MALDI MSI is certainly a valuable tool for personalized medicine and "Eco-Evo-Devo" integrative biology in the current context of global environmental challenges.

The Ups and Downs of Repeated Cleavage and Internal Fragment Production in Top-Down Proteomics

Analysis of whole proteins by mass spectrometry, or top-down proteomics, has several advantages over methods relying on proteolysis. For example, proteoforms can be unambiguously identified and examined. However, from a gas-phase ion-chemistry perspective, proteins are enormous molecules that present novel challenges relative to peptide analysis. Herein, the statistics of cleaving the peptide backbone multiple times are examined to evaluate the inherent propensity for generating internal versus terminal ions. The raw statistics reveal an inherent bias favoring production of terminal ions, which holds true regardless of protein size. Importantly, even if the full suite of internal ions is generated by statistical dissociation, terminal ions are predicted to account for at least 50% of the total ion current, regardless of protein size, if there are three backbone dissociations or fewer. Top-down analysis should therefore be a viable approach for examining proteins of significant size. Comparison of the purely statistical analysis with actual top-down data derived from ultraviolet photodissociation (UVPD) and higher-energy collisional dissociation (HCD) reveals that terminal ions account for much of the total ion current in both experiments. Terminal ion production is more favored in UVPD relative to HCD, which is likely due to differences in the mechanisms controlling fragmentation. Importantly, internal ions are not found to dominate from either the theoretical or experimental point of view. Graphical abstract ᅟ.

Matrix-Assisted Laser Desorption/Ionisation Mass Spectrometry Imaging in the Study of Gastric Cancer: A Mini Review

Gastric cancer (GC) is one of the leading causes of cancer-related deaths worldwide and the disease outcome commonly depends upon the tumour stage at the time of diagnosis. However, this cancer can often be asymptomatic during the early stages and remain undetected until the later stages of tumour development, having a significant impact on patient prognosis. However, our comprehension of the mechanisms underlying the development of gastric malignancies is still lacking. For these reasons, the search for new diagnostic and prognostic markers for gastric cancer is an ongoing pursuit. Modern mass spectrometry imaging (MSI) techniques, in particular matrix-assisted laser desorption/ionisation (MALDI), have emerged as a plausible tool in clinical pathology as a whole. More specifically, MALDI-MSI is being increasingly employed in the study of gastric cancer and has already elucidated some important disease checkpoints that may help us to better understand the molecular mechanisms underpinning this aggressive cancer. Here we report the state of the art of MALDI-MSI approaches, ranging from sample preparation to statistical analysis, and provide a complete review of the key findings that have been reported in the literature thus far.

3D MALDI Mass Spectrometry Imaging of a Single Cell: Spatial Mapping of Lipids in the Embryonic Development of Zebrafish

The zebrafish (Danio rerio) has been widely used as a model vertebrate system to study lipid metabolism, the roles of lipids in diseases, and lipid dynamics in embryonic development. Here, we applied high-spatial resolution matrix-assisted laser desorption/ionization (MALDI)-mass spectrometry imaging (MSI) to map and visualize the three-dimensional spatial distribution of phospholipid classes, phosphatidylcholine (PC), phosphatidylethanolamines (PE), and phosphatidylinositol (PI), in newly fertilized individual zebrafish embryos. This is the first time MALDI-MSI has been applied for three dimensional chemical imaging of a single cell. PC molecular species are present inside the yolk in addition to the blastodisc, while PE and PI species are mostly absent in the yolk. Two-dimensional MSI was also studied for embryos at different cell stages (1-, 2-, 4-, 8-, and 16-cell stage) to investigate the localization changes of some lipids at various cell developmental stages. Four different normalization approaches were compared to find reliable relative quantification in 2D- and 3D- MALDI MSI data sets.

Advances in MALDI imaging mass spectrometry of proteins in cardiac tissue, including the heart valve

Significant progress has been made for tissue imaging of proteins using matrix-assisted laser desorption ionization imaging mass spectrometry (MALDI IMS). These advancements now facilitate mapping of a wide range of proteins, peptides, and post-translational modifications in a wide variety of tissues; however, the use of MALDI IMS to detect proteins from cardiac tissue is limited. This review discusses the most recent advances in protein imaging and demonstrates application to cardiac tissue, including the heart valve. Protein imaging by MALDI IMS allows multiplexed histological mapping of proteins and protein components that are inaccessible by antibodies and should be considered an important tool for basic and clinical cardiovascular research. This article is part of a Special Issue entitled: MALDI Imaging, edited by Dr. Corinna Henkel and Prof. Peter Hoffmann.

Recent advancements in matrix-assisted laser desorption/ionization mass spectrometry imaging

Matrix assisted laser desorption/ionization mass spectrometry imaging (MALDI-MSI) is a robust tool for spatially resolved analysis of biomolecules in situ. Recent advances in high ionization-efficiency MALDI matrices, new matrix deposition procedures, and the development of high spatial-resolution and high sensitivity MS instruments continue to drive new applications of MALDI-MSI, along with other MSI techniques, which allow us to visualize and determine the regio-specific and temporal changes in proteins, peptides, lipids, drug molecules, and metabolites within the tissues, cells and microorganisms. These provide researchers with a new route to the discovery of potential biomarkers of human disease and elucidation of the underlying biology of metabolic regulation, thus bringing our understanding of human health to a new level.