Multimodal Imaging Mass Spectrometry: Next Generation Molecular Mapping in Biology and Medicine

Delineating regions of interest for mass spectrometry imaging by multimodally corroborated spatial segmentation

Mass spectrometry imaging (MSI), which localizes molecules in a tag-free, spatially resolved manner, is a powerful tool for the understanding of underlying biochemical mechanisms of biological phenomena. When analyzing MSI data, it is essential to delineate regions of interest (ROIs) that correspond to tissue areas of different anatomical or pathological labels. Spatial segmentation, obtained by clustering MSI pixels according to their mass spectral similarities, is a popular approach to automate ROI definition. However, how to select the number of clusters (#Clusters), which determines the granularity of segmentation, remains to be resolved, and an inappropriate #Clusters may lead to ROIs not biologically real. Here we report a multimodal fusion strategy to enable an objective and trustworthy selection of #Clusters by utilizing additional information from corresponding histology images. A deep learning-based algorithm is proposed to extract "histomorphological feature spectra" across an entire hematoxylin and eosin image. Clustering is then similarly performed to produce histology segmentation. Since ROIs originating from instrumental noise or artifacts would not be reproduced cross-modally, the consistency between histology and MSI segmentation becomes an effective measure of the biological validity of the results. So, #Clusters that maximize the consistency is deemed as most probable. We validated our strategy on mouse kidney and renal tumor specimens by producing multimodally corroborated ROIs that agreed excellently with ground truths. Downstream analysis based on the said ROIs revealed lipid molecules highly specific to tissue anatomy or pathology. Our work will greatly facilitate MSI-mediated spatial lipidomics, metabolomics, and proteomics research by providing intelligent software to automatically and reliably generate ROIs.

Exploring New Methods to Study and Moderate Proton Beam Damage for Multimodal Imaging on a Single Tissue Section

Characterizing proton beam damage in biological materials is of interest to enable the integration of proton microprobe elemental mapping techniques with other imaging modalities. It is also of relevance to obtain a deeper understanding of mechanical damage to lipids in tissues during proton beam cancer therapy. We have developed a novel strategy to characterize proton beam damage to lipids in biological tissues based on mass spectrometry imaging. This methodology is applied to characterize changes to lipids in tissues ex vivo, irradiated under different conditions designed to mitigate beam damage. This work shows that performing proton beam irradiation at ambient pressure, as well as including the application of an organic matrix prior to irradiation, can reduce damage to lipids in tissues. We also discovered that, irrespective of proton beam irradiation, placing a sample in a vacuum prior to desorption electrospray ionization imaging can enhance lipid signals, a conclusion that may be of future benefit to the mass spectrometry imaging community.

Analysis of carbohydrates and glycoconjugates by matrix-assisted laser desorption/ionization mass spectrometry: An update for 2019-2020

This review is the tenth update of the original article published in 1999 on the application of matrix-assisted laser desorption/ionization (MALDI) mass spectrometry to the analysis of carbohydrates and glycoconjugates and brings coverage of the literature to the end of 2020. Also included are papers that describe methods appropriate to analysis by MALDI, such as sample preparation techniques, even though the ionization method is not MALDI. The review is basically divided into three sections: (1) general aspects such as theory of the MALDI process, matrices, derivatization, MALDI imaging, fragmentation, quantification and the use of arrays. (2) Applications to various structural types such as oligo- and polysaccharides, glycoproteins, glycolipids, glycosides and biopharmaceuticals, and (3) other areas such as medicine, industrial processes and glycan synthesis where MALDI is extensively used. Much of the material relating to applications is presented in tabular form. The reported work shows increasing use of incorporation of new techniques such as ion mobility and the enormous impact that MALDI imaging is having. MALDI, although invented nearly 40 years ago is still an ideal technique for carbohydrate analysis and advancements in the technique and range of applications show little sign of diminishing.

Desorption Electrospray Ionization Mass Spectrometry Imaging Allows Spatial Localization of Changes in Acetaminophen Metabolism in the Liver after Intervention with 4-Methylpyrazole

Acetaminophen (APAP) overdose is the most common cause of acute liver failure in the US, and hepatotoxicity is initiated by a reactive metabolite which induces characteristic centrilobular necrosis. The only clinically available antidote is N-acetylcysteine, which has limited efficacy, and we have identified 4-methylpyrazole (4MP, Fomepizole) as a strong alternate therapeutic option, protecting against generation and downstream effects of the cytotoxic reactive metabolite in the clinically relevant C57BL/6J mouse model and in humans. However, despite the regionally restricted necrosis after APAP, our earlier studies on APAP metabolites in biofluids or whole tissue homogenate lack the spatial information needed to understand region-specific consequences of reactive metabolite formation after APAP overdose. Thus, to gain insight into the regional variation in APAP metabolism and study the influence of 4MP, we established a desorption electrospray ionization mass spectrometry imaging (DESI-MSI) platform for generation of ion images for APAP and its metabolites under ambient air, without chemical labeling or a prior coating of tissue which reduces chemical interference and perturbation of small molecule tissue localization. The spatial intensity and distribution of both oxidative and nonoxidative APAP metabolites were determined from mouse liver sections after a range of APAP overdoses. Importantly, exclusive differential signal intensities in metabolite abundance were noted in the tissue microenvironment, and 4MP treatment substantially influenced this topographical distribution.

Spatially resolved characterization of tissue metabolic compartments in fasted and high-fat diet livers

Cells adapt their metabolism to physiological stimuli, and metabolic heterogeneity exists between cell types, within tissues, and subcellular compartments. The liver plays an essential role in maintaining whole-body metabolic homeostasis and is structurally defined by metabolic zones. These zones are well-understood on the transcriptomic level, but have not been comprehensively characterized on the metabolomic level. Mass spectrometry imaging (MSI) can be used to map hundreds of metabolites directly from a tissue section, offering an important advance to investigate metabolic heterogeneity in tissues compared to extraction-based metabolomics methods that analyze tissue metabolite profiles in bulk. We established a workflow for the preparation of tissue specimens for matrix-assisted laser desorption/ionization (MALDI) MSI that can be implemented to achieve broad coverage of central carbon, nucleotide, and lipid metabolism pathways. Herein, we used this approach to visualize the effect of nutrient stress and excess on liver metabolism. Our data revealed a highly organized metabolic tissue compartmentalization in livers, which becomes disrupted under high fat diet. Fasting caused changes in the abundance of several metabolites, including increased levels of fatty acids and TCA intermediates while fatty livers had higher levels of purine and pentose phosphate-related metabolites, which generate reducing equivalents to counteract oxidative stress. This spatially conserved approach allowed the visualization of liver metabolic compartmentalization at 30 μm pixel resolution and can be applied more broadly to yield new insights into metabolic heterogeneity in vivo.

Mass Spectrometry Imaging Spatial Tissue Analysis toward Personalized Medicine

Novel profiling methodologies are redefining the diagnostic capabilities and therapeutic approaches towards more precise and personalized healthcare. Complementary information can be obtained from different omic approaches in combination with the traditional macro- and microscopic analysis of the tissue, providing a more complete assessment of the disease. Mass spectrometry imaging, as a tissue typing approach, provides information on the molecular level directly measured from the tissue. Lipids, metabolites, glycans, and proteins can be used for better understanding imbalances in the DNA to RNA to protein translation, which leads to aberrant cellular behavior. Several studies have explored the capabilities of this technology to be applied to tumor subtyping, patient prognosis, and tissue profiling for intraoperative tissue evaluation. In the future, intercenter studies may provide the needed confirmation on the reproducibility, robustness, and applicability of the developed classification models for tissue characterization to assist in disease management.

The Advances and Biomedical Applications of Imageable Nanomaterials

Nanomedicine shows great potential in screening, diagnosing and treating diseases. However, given the limitations of current technology, detection of some smaller lesions and drugs’ dynamic monitoring still need to be improved. With the advancement of nanotechnology, researchers have produced various nanomaterials with imaging capabilities which have shown great potential in biomedical research. Here, we summarized the researches based on the characteristics of imageable nanomaterials, highlighted the advantages and biomedical applications of imageable nanomaterials in the diagnosis and treatment of diseases, and discussed current challenges and prospects.

Metabolomics-based mass spectrometry methods to analyze the chemical content of 3D organoid models

Metabolomics, the study of metabolites present in biological samples, can provide a global view of sample state as well as insights into biological changes caused by disease or environmental interactions. Mass spectrometry (MS) is commonly used for metabolomics analysis given its high-throughput capabilities, high sensitivity, and capacity to identify multiple compounds in complex samples simultaneously. MS can be coupled to separation methods that can handle small volumes, making it well suited for analyzing the metabolome of organoids, miniaturized three-dimensional aggregates of stem cells that model in vivo organs. Organoids are being used in research efforts to study human disease and development, and in the design of personalized drug treatments. For organoid models to be useful, they need to recapitulate morphological and chemical aspects, such as the metabolome, of the parent tissue. This review highlights the separation- and imaging-based MS-based metabolomics methods that have been used to analyze the chemical contents of organoids. Future perspectives on how MS techniques can be optimized to determine the accuracy of organoid models and expand the field of organoid research are also discussed.

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.

Human lymph node immune dynamics as driver of vaccine efficacy: an understudied aspect of immune responses

INTRODUCTION

During the last century, changes in hygiene, sanitation, and the advent of childhood vaccination have resulted in profound reductions in mortality from infectious diseases. Despite this success, infectious diseases remain an enigmatic public health threat, where effective vaccines for influenza, human immunodeficiency virus (HIV), tuberculosis, and malaria, among others remain elusive.

AREA COVERED

In addition to the immune evasion tactics employed by complex pathogens, our understanding of immunopathogenesis and the development of effective vaccines is also complexified by the inherent variability of human immune responses. Lymph nodes (LNs) are the anatomical sites where B cell responses develop. An important, but understudied component of immune response complexity is variation in LN immune dynamics and in particular variation in germinal center follicular helper T cells (Tfh) and B cells which can be impacted by genetic variation, aging, the microbiome and chronic infection.

EXPERT OPINION

This review describes the contribution of genetic variation, aging, microbiome and chronic infection on LN immune dynamics and associated Tfh responses and offers perspective on how inclusion of LN immune subset and cytoarchitecture analyses, along with peripheral blood biomarkers can supplement systems vaccinology or immunology approaches for the development of vaccines or other interventions to prevent infectious diseases.

Host-Guest Chemistry for Simultaneous Imaging of Endogenous Alkali Metals and Metabolites with Mass Spectrometry

Sodium and potassium are biological alkali metal ions that are essential for the physiological processes of cells and organisms. In combination with small-molecule metabolite information, disturbances in sodium and potassium tissue distributions can provide a further understanding of the biological processes in diseases. However, methods using mass spectrometry are generally tailored toward either elemental or molecular detection, which limits simultaneous quantitative mass spectrometry imaging of alkali metal ions and molecular ions. Here, we provide a new method by including crown ether molecules in the solvent for nanospray desorption electrospray ionization mass spectrometry imaging (nano-DESI MSI) that combines host-guest chemistry targeting sodium and potassium ions and quantitative imaging of endogenous lipids and metabolites. After evaluation and optimization, the method was applied to an ischemic stroke model, which has highly dynamic tissue sodium and potassium concentrations, and we report 2 times relative increase in the detected sodium concentration in the ischemic region compared to healthy tissue. Further, in the same experiment, we showed the accumulation and depletion of lipids, neurotransmitters, and amino acids using relative quantitation with internal standards spiked in the nano-DESI solvent. Overall, we demonstrate a new method that with a simple modification in liquid extraction MSI techniques using host-guest chemistry provides the added dimension of alkali metal ion imaging to provide unique insights into biological processes.

PALP: A rapid imaging technique for stratifying ferroptosis sensitivity in normal and tumor tissues in situ

Ferroptosis is an emerging cancer suppression strategy. However, how to select cancer patients for treating with ferroptosis inducers remains challenging. Here, we develop photochemical activation of membrane lipid peroxidation (PALP), which uses targeted lasers to induce localized polyunsaturated fatty acyl (PUFA)-lipid peroxidation for reporting ferroptosis sensitivity in cells and tissues. PALP captured by BODIPY-C11 can be suppressed by lipophilic antioxidants and iron chelation, and is dependent on PUFA-lipid levels. Moreover, we develop PALPv2, for studying lipid peroxidation on selected membranes along the z axis in live cells using two-photon microscopes. Using PALPv1, we detect PUFA-lipids in multiple tissues, and validate a PUFA-phospholipid reduction during muscle aging as previously reported. Patterns of PALPv1 signals across multiple cancer cell types in vitro and in vivo are concordant with their ferroptosis susceptibility and PUFA-phospholipid levels. We envision that PALP will enable rapid stratification of ferroptosis sensitivity in cancer patients and facilitate PUFA-lipid research.

Utilizing imaging mass spectrometry to analyze microbial biofilm chemical responses to exogenous compounds

Matrix-assisted laser desorption/ionization imaging mass spectrometry (MALDI-IMS) is an appealing label-free method for imaging biological samples which focuses on the spatial distribution of chemical signals. This approach has been used to study the chemical ecology of microbes and can be applied to study the chemical responses of microbes to treatment with exogenous compounds. Specific conjugated cholic acids such as taurocholic acid (TCA), have been shown to inhibit biofilm formation in the enteric pathogen Vibrio cholerae and MALDI-IMS can be used to directly observe the chemical responses of V. cholerae biofilm colonies to treatment with TCA. A major challenge of MALDI-IMS is optimizing the sample preparation and drying for a particular growth condition and microbial strain. Here we demonstrate how V. cholerae is cultured and prepared for MALDI-IMS analysis and highlight critical steps to ensure proper sample adherence to a MALDI target plate and maintain spatial distributions when applying this technique to any microbial strain. We additionally show how to use both manual interrogation and statistical analyses of MALDI-IMS data to establish the adequacy of the sample preparation protocol. This protocol can serve as a guideline for the development of sample preparation techniques and the acquisition of high quality MALDI-IMS data.

MALDI imaging mass spectrometry: an emerging tool in neurology

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

LA-ICP-MS and MALDI-MS image registration for correlating nanomaterial biodistributions and their biochemical effects

Laser ablation inductively-coupled plasma mass spectrometry (LA-ICP-MS) imaging and matrix assisted laser desorption ionization mass spectrometry imaging (MALDI-MSI) are complementary methods that measure distributions of elements and biomolecules in tissue sections. Quantitative correlations of the information provided by these two imaging modalities requires that the datasets be registered in the same coordinate system, allowing for pixel-by-pixel comparisons. We describe here a computational workflow written in Python that accomplishes this registration, even for adjacent tissue sections, with accuracies within ±50 μm. The value of this registration process is demonstrated by correlating images of tissue sections from mice injected with gold nanomaterial drug delivery systems. Quantitative correlations of the nanomaterial delivery vehicle, as detected by LA-ICP-MS imaging, with biochemical changes, as detected by MALDI-MSI, provide deeper insight into how nanomaterial delivery systems influence lipid biochemistry in tissues. Moreover, the registration process allows the more precise images associated with LA-ICP-MS imaging to be leveraged to achieve improved segmentation in MALDI-MS images, resulting in the identification of lipids that are most associated with different sub-organ regions in tissues.

A Mass Spectrometry Imaging Based Approach for Prognosis Prediction in UICC Stage I/II Colon Cancer

Simple Summary

Tumor treatment is heavily dictated by the tumor progression status. However, in colon cancer, it is difficult to predict disease progression in the early stages. In this study, we have employed a proteomic analysis using matrix-assisted laser desorption/ionization mass spectrometry imaging (MALDI-MSI). MALDI-MSI is a technique that measures the molecular content of (tumor) tissue. We analyzed tumor samples of 276 patients. If the patients developed distant metastasis, they were considered to have a more aggressive tumor type than the patients that did not. In this comparative study, we have developed bioinformatics methods that can predict the tendency of tumor progression and advance a couple of molecules that could be used as prognostic markers of colon cancer. The prediction of tumor progression can help to choose a more adequate treatment for each individual patient.

Abstract

Currently, pathological evaluation of stage I/II colon cancer, following the Union Internationale Contre Le Cancer (UICC) guidelines, is insufficient to identify patients that would benefit from adjuvant treatment. In our study, we analyzed tissue samples from 276 patients with colon cancer utilizing mass spectrometry imaging. Two distinct approaches are herein presented for data processing and analysis. In one approach, four different machine learning algorithms were applied to predict the tendency to develop metastasis, which yielded accuracies over 90% for three of the models. In the other approach, 1007 m/z features were evaluated with regards to their prognostic capabilities, yielding two m/z features as promising prognostic markers. One feature was identified as a fragment from collagen (collagen 3A1), hinting that a higher collagen content within the tumor is associated with poorer outcomes. Identification of proteins that reflect changes in the tumor and its microenvironment could give a very much-needed prediction of a patient’s prognosis, and subsequently assist in the choice of a more adequate treatment.

Membranous nephropathy

Membranous nephropathy (MN) is a glomerular disease that can occur at all ages. In adults, it is the most frequent cause of nephrotic syndrome. In ~80% of patients, there is no underlying cause of MN (primary MN) and the remaining cases are associated with medications or other diseases such as systemic lupus erythematosus, hepatitis virus infection or malignancies. MN is an autoimmune disease characterized by a thickening of the glomerular capillary walls due to immune complex deposition. Identification of the phospholipase A2 receptor (PLA2R) as the major antigen in adults in 2009 induced a paradigm shift in disease diagnosis and monitoring and several other antigens have since been characterized. Disease outcome is difficult to predict and around one-third of patients will undergo spontaneous remission. In those at high risk of progression, immunosuppressive therapy with cyclophosphamide plus corticosteroids has substantially reduced the need for kidney replacement therapy. Owing to carcinogenic risk, other treatments (calcineurin inhibitors and CD20-targeted B cell depletion therapy (rituximab)) have been developed. However, disease relapses are frequent when calcineurin inhibitors are stopped and the remission rate with rituximab is lower than with cyclophosphamide, particularly in patients with high PLA2R antibody titres. Other new drugs are already available and antigen-specific immunotherapies are being developed.

Protocol for multimodal analysis of human kidney tissue by imaging mass spectrometry and CODEX multiplexed immunofluorescence

Here, we describe the preservation and preparation of human kidney tissue for interrogation by histopathology, imaging mass spectrometry, and multiplexed immunofluorescence. Custom image registration and integration techniques are used to create cellular and molecular atlases of this organ system. Through careful optimization, we ensure high-quality and reproducible datasets suitable for cross-patient comparisons that are essential to understanding human health and disease. Moreover, each of these steps can be adapted to other organ systems or diseases, enabling additional atlas efforts.

Visualization of HIV-1 reservoir: an imaging perspective

PURPOSE OF REVIEW

The persistence of HIV-1-infected cells, despite the introduction of the combinatorial antiretroviral therapy, is a major obstacle to HIV-1 eradication. Understanding the nature of HIV reservoir will lead to novel therapeutic approaches for the functional cure or eradication of the virus. In this review, we will update the recent development in imaging applications toward HIV-1/simian immunodeficiency virus (SIV) viral reservoirs research and highlight some of their limitations.

RECENT FINDINGS

CD4 T cells are the primary target of HIV-1/SIV and the predominant site for productive and latent reservoirs. This viral reservoir preferentially resides in lymphoid compartments that are difficult to access, which renders sampling and measurements problematical and a hurdle for understanding HIV-1 pathogenicity. Novel noninvasive technologies are needed to circumvent this and urgently help to find a cure for HIV-1. Recent technological advancements have had a significant impact on the development of imaging methodologies allowing the visualization of relevant biomarkers with high resolution and analytical capacity. Such methodologies have provided insights into our understanding of cellular and molecular interactions in health and disease.

SUMMARY

Imaging of the HIV-1 reservoir can provide significant insights for the nature (cell types), spatial distribution, and the role of the tissue microenvironment for its in vivo dynamics and potentially lead to novel targets for the virus elimination.

Automated annotation and visualisation of high-resolution spatial proteomic mass spectrometry imaging data using HIT-MAP

Spatial proteomics has the potential to significantly advance our understanding of biology, physiology and medicine. Matrix-assisted laser desorption/ionisation mass spectrometry imaging (MALDI-MSI) is a powerful tool in the spatial proteomics field, enabling direct detection and registration of protein abundance and distribution across tissues. MALDI-MSI preserves spatial distribution and histology allowing unbiased analysis of complex, heterogeneous tissues. However, MALDI-MSI faces the challenge of simultaneous peptide quantification and identification. To overcome this, we develop and validate HIT-MAP (High-resolution Informatics Toolbox in MALDI-MSI Proteomics), an open-source bioinformatics workflow using peptide mass fingerprint analysis and a dual scoring system to computationally assign peptide and protein annotations to high mass resolution MSI datasets and generate customisable spatial distribution maps. HIT-MAP will be a valuable resource for the spatial proteomics community for analysing newly generated and retrospective datasets, enabling robust peptide and protein annotation and visualisation in a wide array of normal and disease contexts.

Streamlined Multimodal DESI and MALDI Mass Spectrometry Imaging on a Singular Dual-Source FT-ICR Mass Spectrometer

The study of biological specimens by mass spectrometry imaging (MSI) has had a profound influence in the various forms of spatial-omics over the past two decades including applications for the identification of clinical biomarker analysis; the metabolic fingerprinting of disease states; treatment with therapeutics; and the profiling of lipids, peptides and proteins. No singular approach is able to globally map all biomolecular classes simultaneously. This led to the development of many complementary multimodal imaging approaches to solve analytical problems: fusing multiple ionization techniques, imaging microscopy or spectroscopy, or local extractions into robust multimodal imaging methods. However, each fusion typically requires the melding of analytical information from multiple commercial platforms, and the tandem utilization of multiple commercial or third-party software platforms—even in some cases requiring computer coding. Herein, we report the use of matrix-assisted laser desorption/ionization (MALDI) in tandem with desorption electrospray ionization (DESI) imaging in the positive ion mode on a singular commercial orthogonal dual-source Fourier transform ion cyclotron resonance (FT-ICR) instrument for the complementary detection of multiple analyte classes by MSI from tissue. The DESI source was 3D printed and the commercial Bruker Daltonics software suite was used to generate mass spectrometry images in tandem with the commercial MALDI source. This approach allows for the generation of multiple modes of mass spectrometry images without the need for third-party software and a customizable platform for ambient ionization imaging. Highlighted is the streamlined workflow needed to obtain phospholipid profiles, as well as increased depth of coverage of both annotated phospholipid, cardiolipin, and ganglioside species from rat brain with both high spatial and mass resolution.