MALDI Imaging mass spectrometry: current frontiers and perspectives in pathology research and practice

Spatially resolved distribution of pancreatic hormones proteoforms by MALDI-imaging mass spectrometry

Zinc plays a crucial role both in the immune system and endocrine processes. Zinc restriction in the diet has been shown to lead to degeneration of the endocrine pancreas, resulting in hormonal imbalance within the β-cells. Proteostasismay vary depending on the stage of a pathophysiological process, which underscores the need for tools aimed at directly analyzing biological status. Among proteomics methods, MALDI-ToF-MS can serve as a rapid peptidomics tool for analyzing extracts or by histological imaging. Here we report the optimization of MALDI imaging mass spectrometry analysis of histological thin sections from mouse pancreas. This optimization enables the identification of the major islet peptide hormones as well as the major accumulated precursors and/or proteolytic products of peptide hormones. Cross-validation of the identified peptide hormones was performed by LC-ESI-MS from pancreatic islet extracts. Mice subjected to a zinc-restricted diet exhibited a relatively lower amount of peptide intermediates compared to the control group. These findings provide evidence for a complex modulation of proteostasis by micronutrients imbalance, a phenomenon directly accessed by MALDI-MSI.

Neurolipidomic insights into anxiety disorders: Uncovering lipid dynamics for potential therapeutic advances

Anxiety disorders constitute a spectrum of psychological conditions affecting millions of individuals worldwide, imposing a significant health burden. Historically, the development of anxiolytic medications has been largely focused on neurotransmitter function and modulation. However, in recent years, neurolipids emerged as a prime target for understanding psychiatric pathogenesis and developing novel medications. Neurolipids influence various neural activities such as neurotransmission and cellular functioning, as well as maintaining cell membrane integrity. Therefore, this review aims to elucidate the alterations in neurolipids associated with an anxious mental state and explore their potential as targets of novel anxiolytic medications. Existing evidence tentatively associates dysregulated neurolipid levels with the etiopathology of anxiety disorders. Notably, preclinical investigations suggest that several neurolipids, including endocannabinoids and polyunsaturated fatty acids, may hold promise as potential pharmacological targets. Overall, the current literature tentatively suggests the involvement of lipids in the pathogenesis of anxiety disorders, hinting at potential prospects for future pharmacological interventions.

Electromagnetic Field-Assisted Frozen Tissue Planarization Enhances MALDI-MSI in Plant Spatial Omics

Plant samples with irregular morphology are challenging for longitudinal tissue sectioning. This has restricted the ability to gain insight into some plants using matrix-assisted laser desorption/ionization mass spectrometry imaging (MALDI-MSI). Herein, we develop a novel technique termed electromagnetic field-assisted frozen tissue planarization (EMFAFTP). This technique involves using a pair of adjustable electromagnets on both sides of a plant tissue. Under an optimized electromagnetic field strength, nondestructive planarization and regularization of the frozen tissue is induced, allowing the longitudinal tissue sectioning that favors subsequent molecular profiling by MALDI-MSI. As a proof of concept, flowers, leaves and roots with irregular morphology from six plant species are chosen to evaluate the performance of EMFAFTP for MALDI-MSI of secondary metabolites, amino acids, lipids, and proteins among others in the plant samples. The significantly enhanced MALDI-MSI capabilities of these endogenous molecules demonstrate the robustness of EMFAFTP and suggest it has the potential to become a standard technique for advancing MALDI-MSI into a new era of plant spatial omics.

Development of benzimidazole derivatives as efficient matrices for the analysis of acidic small-molecule compounds using matrix-assisted laser desorption/ionisation time-of-flight mass spectrometry in negative ion mode

RATIONALE

With the development of matrix-assisted laser desorption/ionisation (MALDI) mass spectrometry (MS) in spatial localisation omics research on small molecules, the detection sensitivity of the matrix must increase. However, the types of matrices suitable for detecting acidic small molecules in (-) MALDI-MS mode are very limited and are either not sensitive enough or difficult to obtain.

METHODS

More than 10 commercially available benzimidazole and benzothiazole derivatives were selected as MALDI matrices in negative ion mode. MALDI-MS analysis was performed on 38 acidic small molecules and mouse serum, and the matrix effects were compared with those of the common commercial matrices 9-aminoacridine (9AA), 1,5-naphthalenediamine (DAN) and 3-aminoquinoline (3AQ). Moreover, the proton affinity (PA) of the selected potential matrix was calculated, and the relationships among the compound structure, PA value and matrix effect were discussed.

RESULTS

In (-) MALDI-MS mode, a higher PA value generally indicates a better matrix effect. Amino-substituted 2-phenyl-1H-benzo[d]imidazole derivatives had well-defined matrix effects on all analytes and were generally superior to the commonly used matrices 9AA, DAN and 3AQ. Among them, 2-(4-(dimethylamino-phenyl)-1H-benzo[d]imidazole-5-amine (E-4) has the best sensitivity and versatility for detecting different analytes and has the best ability to detect fatty acids in mouse serum; moreover, the limit of detection (LOD) of some analytes can reach as low as ng/L.

CONCLUSIONS

Compared to 9AA, DAN and 3AQ, matrix E-4 is more effective at detecting low-molecular-weight acidic compounds in (-) MALDI-MS mode, with higher sensitivity and better versatility. In addition, there is a clear correlation between compound structure, PA and matrix effects, which provides a basis for designing more efficient matrices.

Proteomic analysis of granulomas from cattle and pigs naturally infected with Mycobacterium tuberculosis complex by MALDI imaging

Matrix-assisted laser desorption/ionization mass spectrometry imaging (MALDI-MSI) has recently gained prominence for its ability to provide molecular and spatial information in tissue sections. This technology has the potential to uncover novel insights into proteins and other molecules in biological and immunological pathways activated along diseases with a complex host-pathogen interaction, such as animal tuberculosis. Thus, the present study conducted a data analysis of protein signature in granulomas of cattle and pigs naturally infected with the Mycobacterium tuberculosis complex (MTC), identifying biological and immunological signaling pathways activated throughout the disease. Lymph nodes from four pigs and four cattle, positive for the MTC by bacteriological culture and/or real-time PCR, were processed for histopathological examination and MALDI-MSI. Protein identities were assigned using the MaTisse database, and protein-protein interaction networks were visualized using the STRING database. Gene Ontology (GO) analysis was carried out to determine biological and immunological signaling pathways in which these proteins could participate together with Kyoto Encyclopedia of Genes and Genomes (KEGG) analysis. Distinct proteomic profiles between cattle and pig granulomas were displayed. Noteworthy, the GO analysis revealed also common pathways among both species, such as "Complement activation, alternative pathway" and "Tricarboxylic acid cycle", which highlight pathways that are conserved among different species infected by the MTC. In addition, species-specific terms were identified in the current study, such as "Natural killer cell degranulation" in cattle or those related to platelet and neutrophil recruitment and activation in pigs. Overall, this study provides insights into the immunopathogenesis of tuberculosis in cattle and pigs, opening new areas of research and highlighting the importance, among others, of the complement activation pathway and the regulation of natural killer cell- and neutrophil-mediated immunity in this disease.

Mass spectrometry imaging highlights dynamic patterns of lipid co-expression with Aβ plaques in mouse and human brains

Lipids play crucial roles in the susceptibility and brain cellular responses to Alzheimer's disease (AD) and are increasingly considered potential soluble biomarkers in cerebrospinal fluid (CSF) and plasma. To delineate the pathological correlations of distinct lipid species, we conducted a comprehensive characterization of both spatially localized and global differences in brain lipid composition in AppNL-G-F mice with spatial and bulk mass spectrometry lipidomic profiling, using human amyloid-expressing (h-Aβ) and WT mouse brains controls. We observed age-dependent increases in lysophospholipids, bis(monoacylglycerol) phosphates, and phosphatidylglycerols around Aβ plaques in AppNL-G-F mice. Immunohistology-based co-localization identified associations between focal pro-inflammatory lipids, glial activation, and autophagic flux disruption. Likewise, in human donors with varying Braak stages, similar studies of cortical sections revealed co-expression of lysophospholipids and ceramides around Aβ plaques in AD (Braak stage V/VI) but not in earlier Braak stage controls. Our findings in mice provide evidence of temporally and spatially heterogeneous differences in lipid composition as local and global Aβ-related pathologies evolve. Observing similar lipidomic changes associated with pathological Aβ plaques in human AD tissue provides a foundation for understanding differences in CSF lipids with reported clinical stage or disease severity.

Advancing rare cancer research by MALDI mass spectrometry imaging: Applications, challenges, and future perspectives in sarcoma

MALDI mass spectrometry imaging (MALDI imaging) uniquely advances cancer research, by measuring spatial distribution of endogenous and exogenous molecules directly from tissue sections. These molecular maps provide valuable insights into basic and translational cancer research, including tumor biology, tumor microenvironment, biomarker identification, drug treatment, and patient stratification. Despite its advantages, MALDI imaging is underutilized in studying rare cancers. Sarcomas, a group of malignant mesenchymal tumors, pose unique challenges in medical research due to their complex heterogeneity and low incidence, resulting in understudied subtypes with suboptimal management and outcomes. In this review, we explore the applicability of MALDI imaging in sarcoma research, showcasing its value in understanding this highly heterogeneous and challenging rare cancer. We summarize all MALDI imaging studies in sarcoma to date, highlight their impact on key research fields, including molecular signatures, cancer heterogeneity, and drug studies. We address specific challenges encountered when employing MALDI imaging for sarcomas, and propose solutions, such as using formalin-fixed paraffin-embedded tissues, and multiplexed experiments, and considerations for multi-site studies and digital data sharing practices. Through this review, we aim to spark collaboration between MALDI imaging researchers and clinical colleagues, to deploy the unique capabilities of MALDI imaging in the context of sarcoma.

Characterization of Humanized Mouse Model of Organophosphate Poisoning and Detection of Countermeasures via MALDI-MSI

Organophosphoate (OP) chemicals are known to inhibit the enzyme acetylcholinesterase (AChE). Studying OP poisoning is difficult because common small animal research models have serum carboxylesterase, which contributes to animals' resistance to OP poisoning. Historically, guinea pigs have been used for this research; however, a novel genetically modified mouse strain (KIKO) was developed with nonfunctional serum carboxylase (Es1 KO) and an altered acetylcholinesterase (AChE) gene, which expresses the amino acid sequence of the human form of the same protein (AChE KI). KIKO mice were injected with 1xLD50 of an OP nerve agent or vehicle control with or without atropine. After one to three minutes, animals were injected with 35 mg/kg of the currently fielded Reactivator countermeasure for OP poisoning. Postmortem brains were imaged on a Bruker RapifleX ToF/ToF instrument. Data confirmed the presence of increased acetylcholine in OP-exposed animals, regardless of treatment or atropine status. More interestingly, we detected a small amount of Reactivator within the brain of both exposed and unexposed animals; it is currently debated if reactivators can cross the blood-brain barrier. Further, we were able to simultaneously image acetylcholine, the primary affected neurotransmitter, as well as determine the location of both Reactivator and acetylcholine in the brain. This study, which utilized sensitive MALDI-MSI methods, characterized KIKO mice as a functional model for OP countermeasure development.

Metabolomics at the tumor microenvironment interface: Decoding cellular conversations

Cancer heterogeneity remains a significant challenge for effective cancer treatments. Altered energetics is one of the hallmarks of cancer and influences tumor growth and drug resistance. Studies have shown that heterogeneity exists within the metabolic profile of tumors, and personalized-combination therapy with relevant metabolic interventions could improve patient response. Metabolomic studies are identifying novel biomarkers and therapeutic targets that have improved treatment response. The spatial location of elements in the tumor microenvironment are becoming increasingly important for understanding disease progression. The evolution of spatial metabolomics analysis now allows scientists to deeply understand how metabolite distribution contributes to cancer biology. Recently, these techniques have spatially resolved metabolite distribution to a subcellular level. It has been proposed that metabolite mapping could improve patient outcomes by improving precision medicine, enabling earlier diagnosis and intraoperatively identifying tumor margins. This review will discuss how altered metabolic pathways contribute to cancer progression and drug resistance and will explore the current capabilities of spatial metabolomics technologies and how these could be integrated into clinical practice to improve patient outcomes.

Multiple ion isolation and accumulation events for selective chemical noise reduction and dynamic range enhancement in MALDI imaging mass spectrometry

Abundant chemical noise in MALDI imaging mass spectrometry experiments can impede the detection of less abundant compounds of interest. This chemical noise commonly originates from the MALDI matrix as well as other endogenous compounds present in high concentrations and/or with high ionization efficiencies. MALDI imaging mass spectrometry of biological tissues measures numerous biomolecular compounds that exist in a wide range of concentrations in vivo. When ion trapping instruments are used, highly abundant ions can dominate the charge capacity and lead to space charge effects that hinder the dynamic range and detection of lowly abundant compounds of interest. Gas-phase fractionation has been previously utilized in mass spectrometry to isolate and enrich target analytes. Herein, we have characterized the use of multiple continuous accumulations of selected ions (Multi CASI) to reduce the abundance of chemical noise and diminish the effects of space charge in MALDI imaging mass spectrometry experiments. Multi CASI utilizes the mass-resolving capability of a quadrupole mass filter to perform multiple sequential ion isolation events prior to a single mass analysis of the combined ion population. Multi CASI was used to improve metabolite and lipid detection in the MALDI imaging mass spectrometry analysis of rat brain tissue.

Imaging Mass Spectrometry of Isotopically Resolved Intact Proteins on a Trapped Ion-Mobility Quadrupole Time-of-Flight Mass Spectrometer

In this work, we demonstrate rapid, high spatial, and high spectral resolution imaging of intact proteins by matrix-assisted laser desorption/ionization (MALDI) imaging mass spectrometry (IMS) on a hybrid quadrupole-reflectron time-of-flight (qTOF) mass spectrometer equipped with trapped ion mobility spectrometry (TIMS). Historically, untargeted MALDI IMS of proteins has been performed on TOF mass spectrometers. While advances in TOF instrumentation have enabled rapid, high spatial resolution IMS of intact proteins, TOF mass spectrometers generate relatively low-resolution mass spectra with limited mass accuracy. Conversely, the implementation of MALDI sources on high-resolving power Fourier transform (FT) mass spectrometers has allowed IMS experiments to be conducted with high spectral resolution with the caveat of increasingly long data acquisition times. As illustrated here, qTOF mass spectrometers enable protein imaging with the combined advantages of TOF and FT mass spectrometers. Protein isotope distributions were resolved for both a protein standard mixture and proteins detected from a whole-body mouse pup tissue section. Rapid (∼10 pixels/s) 10 μm lateral spatial resolution IMS was performed on a rat brain tissue section while maintaining isotopic spectral resolution. Lastly, proof-of-concept MALDI-TIMS data was acquired from a protein mixture to demonstrate the ability to differentiate charge states by ion mobility. These experiments highlight the advantages of qTOF and timsTOF platforms for resolving and interpreting complex protein spectra generated from tissue by IMS.

Metabolic heterogeneity affects trastuzumab response and survival in HER2-positive advanced gastric cancer

BACKGROUND

Trastuzumab is the only first-line treatment targeted against the human epidermal growth factor receptor 2 (HER2) approved for patients with HER2-positive advanced gastric cancer. The impact of metabolic heterogeneity on trastuzumab treatment efficacy remains unclear.

METHODS

Spatial metabolomics via high mass resolution imaging mass spectrometry was performed in pretherapeutic biopsies of patients with HER2-positive advanced gastric cancer in a prospective multicentre observational study. The mass spectra, representing the metabolic heterogeneity within tumour areas, were grouped by K-means clustering algorithm. Simpson's diversity index was applied to compare the metabolic heterogeneity level of individual patients.

RESULTS

Clustering analysis revealed metabolic heterogeneity in HER2-positive gastric cancer patients and uncovered nine tumour subpopulations. High metabolic heterogeneity was shown as a factor indicating sensitivity to trastuzumab (p = 0.008) and favourable prognosis at trend level. Two of the nine tumour subpopulations associated with favourable prognosis and trastuzumab sensitivity, and one subpopulation associated with poor prognosis and trastuzumab resistance.

CONCLUSIONS

This work revealed that tumour metabolic heterogeneity associated with prognosis and trastuzumab response based on tissue metabolomics of HER2-positive gastric cancer. Tumour metabolic subpopulations may provide an association with trastuzumab therapy efficacy.

CLINICAL TRIAL REGISTRATION

The patient cohort was conducted from a multicentre observational study (VARIANZ;NCT02305043).

Advances in mass spectrometry imaging for spatial cancer metabolomics

Mass spectrometry (MS) has become a central technique in cancer research. The ability to analyze various types of biomolecules in complex biological matrices makes it well suited for understanding biochemical alterations associated with disease progression. Different biological samples, including serum, urine, saliva, and tissues have been successfully analyzed using mass spectrometry. In particular, spatial metabolomics using MS imaging (MSI) allows the direct visualization of metabolite distributions in tissues, thus enabling in-depth understanding of cancer-associated biochemical changes within specific structures. In recent years, MSI studies have been increasingly used to uncover metabolic reprogramming associated with cancer development, enabling the discovery of key biomarkers with potential for cancer diagnostics. In this review, we aim to cover the basic principles of MSI experiments for the nonspecialists, including fundamentals, the sample preparation process, the evolution of the mass spectrometry techniques used, and data analysis strategies. We also review MSI advances associated with cancer research in the last 5 years, including spatial lipidomics and glycomics, the adoption of three-dimensional and multimodal imaging MSI approaches, and the implementation of artificial intelligence/machine learning in MSI-based cancer studies. The adoption of MSI in clinical research and for single-cell metabolomics is also discussed. Spatially resolved studies on other small molecule metabolites such as amino acids, polyamines, and nucleotides/nucleosides will not be discussed in the context.

Recent Developments and Application of Mass Spectrometry Imaging in N-Glycosylation Studies: An Overview

Among the most typical posttranslational modifications is glycosylation, which often involves the covalent binding of an oligosaccharide (glycan) to either an asparagine (N-linked) or a serine/threonine (O-linked) residue. Studies imply that the N-glycan portion of a glycoprotein could serve as a particular disease biomarker rather than the protein itself because N-linked glycans have been widely recognized to evolve with the advancement of tumors and other diseases. N-glycans found on protein asparagine sites have been especially significant. Since N-glycans play clearly defined functions in the folding of proteins, cellular transport, and transmission of signals, modifications to them have been linked to several illnesses. However, because these N-glycans' production is not template driven, they have a substantial morphological range, rendering it difficult to distinguish the species that are most relevant to biology and medicine using standard techniques. Mass spectrometry (MS) techniques have emerged as effective analytical tools for investigating the role of glycosylation in health and illness. This is due to developments in MS equipment, data collection, and sample handling techniques. By recording the spatial dimension of a glycan's distribution in situ, mass spectrometry imaging (MSI) builds atop existing methods while offering added knowledge concerning the structure and functionality of biomolecules. In this review article, we address the current development of glycan MSI, starting with the most used tissue imaging techniques and ionization sources before proceeding on to a discussion on applications and concluding with implications for clinical research.

Label-free biomolecular and cellular methods in small molecule epigallocatechin-gallate research

Small molecule natural compounds are gaining popularity in biomedicine due to their easy access to wide structural diversity and their proven health benefits in several case studies. Affinity measurements of small molecules below 100 Da molecular weight in a label-free and automatized manner using small amounts of samples have now become a possibility and reviewed in the present work. We also highlight novel label-free setups with excellent time resolution, which is important for kinetic measurements of biomolecules and living cells. We summarize how molecular-scale affinity data can be obtained from the in-depth analysis of cellular kinetic signals. Unlike traditional measurements, label-free biosensors have made such measurements possible, even without the isolation of specific cellular receptors of interest. Throughout this review, we consider epigallocatechin gallate (EGCG) as an exemplary compound. EGCG, a catechin found in green tea, is a well-established anti-inflammatory and anti-cancer agent. It has undergone extensive examination in numerous studies, which typically rely on fluorescent-based methods to explore its effects on both healthy and tumor cells. The summarized research topics range from molecular interactions with proteins and biological films to the kinetics of cellular adhesion and movement on novel biomimetic interfaces in the presence of EGCG. While the direct impact of small molecules on living cells and biomolecules is relatively well investigated in the literature using traditional biological measurements, this review also highlights the indirect influence of these molecules on the cells by modifying their nano-environment. Moreover, we underscore the significance of novel high-throughput label-free techniques in small molecular measurements, facilitating the investigation of both molecular-scale interactions and cellular processes in one single experiment. This advancement opens the door to exploring more complex multicomponent models that were previously beyond the reach of traditional assays.

Current Challenges and Future Directions in Peptidomics

The field of peptidomics has been under development since its start more than 20 years ago. In this chapter we provide a personal outlook for future directions in this field. The applications of peptidomics technologies are spreading more and more from classical research of peptide hormones and neuropeptides towards commercial applications in plant and food-science. Many clinical applications have been developed to analyze the complexity of biofluids, which are being addressed with new instrumentation, automization, and data processing. Additionally, the newly developed field of immunopeptidomics is showing promise for cancer therapies. In conclusion, peptidomics will continue delivering important information in classical fields like neuropeptides and peptide hormones, benefiting from improvements in state-of-the-art technologies. Moreover, new directions of research such as immunopeptidomics will further complement classical omics technologies and may become routine clinical procedures. Taken together, discoveries of new substances, networks, and applications of peptides can be expected in different disciplines.

Recent Advances in Drug Discovery Toxicology

Major advances in scientific discovery and insights that stem from the development and use of new techniques and models can bring remarkable progress to conventional toxicology. Although animal testing is still considered as the "gold standard" in traditional toxicity testing, there is a necessity for shift from animal testing to alternative methods regarding the drug safety testing owing to the emerging state-of-art techniques and the proposal of 3Rs (replace, reduce, and refine) towards animal welfare. This review describes some recent research methods in drug discovery toxicology, including in vitro cell and organ-on-a-chip, imaging systems, model organisms (C. elegans, Danio rerio, and Drosophila melanogaster), and toxicogenomics in modern toxicology testing.

Ascorbate deficiency increases progression of shigellosis in guinea pigs and mice infection models

Shigella spp. are the causative agents of bacterial dysentery and shigellosis, mainly in children living in developing countries. The study of Shigella entire life cycle in vivo and the evaluation of vaccine candidates' protective efficacy have been hampered by the lack of a suitable animal model of infection. None of the studies evaluated so far (rabbit, guinea pig, mouse) allowed the recapitulation of full shigellosis symptoms upon Shigella oral challenge. Historical reports have suggested that dysentery and scurvy are both metabolic diseases associated with ascorbate deficiency. Mammals, which are susceptible to Shigella infection (humans, non-human primates and guinea pigs) are among the few species unable to synthesize ascorbate. We optimized a low-ascorbate diet to induce moderate ascorbate deficiency, but not scurvy, in guinea pigs to investigate whether poor vitamin C status increases the progression of shigellosis. Moderate ascorbate deficiency increased shigellosis symptom severity during an extended period of time (up to 48 h) in all strains tested (Shigella sonnei, Shigella flexneri 5a, and 2a). At late time points, an important influx of neutrophils was observed both within the disrupted colonic mucosa and in the luminal compartment, although Shigella was able to disseminate deep into the organ to reach the sub-mucosal layer and the bloodstream. Moreover, we found that ascorbate deficiency also increased Shigella penetration into the colon epithelium layer in a Gulo-/- mouse infection model. The use of these new rodent models of shigellosis opens new doors for the study of both Shigella infection strategies and immune responses to Shigella infection.

FluoMALDI Microscopy: Matrix Co-Crystallization Simultaneously Enhances Fluorescence and MALDI Imaging

Here, the authors report that co-crystallization of fluorophores with matrix-assisted laser desorption/ionization (MALDI) imaging matrices significantly enhances fluorophore brightness up to 79-fold, enabling the amplification of innate tissue autofluorescence. This discovery facilitates FluoMALDI, the imaging of the same biological sample by both fluorescence microscopy and MALDI imaging. The approach combines the high spatial resolution and specific labeling capabilities of fluorescence microscopy with the inherently multiplexed, versatile imaging capabilities of MALDI imaging. This new paradigm simplifies registration by avoiding physical changes between fluorescence and MALDI imaging, allowing to image the exact same cells in tissues with both modalities. Matrix-fluorophore co-crystallization also facilitates applications with insufficient fluorescence brightness. The authors demonstrate  feasibility of FluoMALDI imaging with endogenous and exogenous fluorophores and autofluorescence-based FluoMALDI of brain and kidney tissue sections. FluoMALDI will advance structural-functional microscopic imaging in cell biology, biomedicine, and pathology.

Discovery adductomics provides a comprehensive portrait of tissue-, age- and sex-specific DNA modifications in rodents and humans

DNA damage causes genomic instability underlying many diseases, with traditional analytical approaches providing minimal insight into the spectrum of DNA lesions in vivo. Here we used untargeted chromatography-coupled tandem mass spectrometry-based adductomics (LC-MS/MS) to begin to define the landscape of DNA modifications in rat and human tissues. A basis set of 114 putative DNA adducts was identified in heart, liver, brain, and kidney in 1-26-month-old rats and 111 in human heart and brain by 'stepped MRM' LC-MS/MS. Subsequent targeted analysis of these species revealed species-, tissue-, age- and sex-biases. Structural characterization of 10 selected adductomic signals as known DNA modifications validated the method and established confidence in the DNA origins of the signals. Along with strong tissue biases, we observed significant age-dependence for 36 adducts, including N2-CMdG, 5-HMdC and 8-Oxo-dG in rats and 1,N6-ϵdA in human heart, as well as sex biases for 67 adducts in rat tissues. These results demonstrate the potential of adductomics for discovering the true spectrum of disease-driving DNA adducts. Our dataset of 114 putative adducts serves as a resource for characterizing dozens of new forms of DNA damage, defining mechanisms of their formation and repair, and developing them as biomarkers of aging and disease.

Clinical Proteomics for Solid Organ Tissues

The evaluation of biopsied solid organ tissue has long relied on visual examination using a microscope. Immunohistochemistry is critical in this process, labeling and detecting cell lineage markers and therapeutic targets. However, while the practice of immunohistochemistry has reshaped diagnostic pathology and facilitated improvements in cancer treatment, it has also been subject to pervasive challenges with respect to standardization and reproducibility. Efforts are ongoing to improve immunohistochemistry, but for some applications, the benefit of such initiatives could be impeded by its reliance on monospecific antibody-protein reagents and limited multiplexing capacity. This perspective surveys the relevant challenges facing traditional immunohistochemistry and describes how mass spectrometry, particularly liquid chromatography-tandem mass spectrometry, could help alleviate problems. In particular, targeted mass spectrometry assays could facilitate measurements of individual proteins or analyte panels, using internal standards for more robust quantification and improved interlaboratory reproducibility. Meanwhile, untargeted mass spectrometry, showcased to date clinically in the form of amyloid typing, is inherently multiplexed, facilitating the detection and crude quantification of 100s to 1000s of proteins in a single analysis. Further, data-independent acquisition has yet to be applied in clinical practice, but offers particular strengths that could appeal to clinical users. Finally, we discuss the guidance that is needed to facilitate broader utilization in clinical environments and achieve standardization.

Next-generation pathology practices with mass spectrometry imaging

Mass spectrometry imaging (MSI) is a powerful technique that reveals the spatial distribution of various molecules in biological samples, and it is widely used in pathology-related research. In this review, we summarize common MSI techniques, including matrix-assisted laser desorption/ionization and desorption electrospray ionization MSI, and their applications in pathological research, including disease diagnosis, microbiology, and drug discovery. We also describe the improvements of MSI, focusing on the accumulation of imaging data sets, expansion of chemical coverage, and identification of biological significant molecules, that have prompted the evolution of MSI to meet the requirements of pathology practices. Overall, this review details the applications and improvements of MSI techniques, demonstrating the potential of integrating MSI techniques into next-generation pathology practices.

A Comprehensive Comparison of Tissue Processing Methods for High-Quality MALDI Imaging of Lipids in Reconstructed Human Epidermis

Matrix-assisted laser desorption/ionization (MALDI) mass spectrometry imaging (MSI) has become an important tool for skin analysis, as it allows the simultaneous detection and localization of diverse molecular species within a sample. The use of in vivo and ex vivo human skin models is costly and presents ethical issues; therefore, reconstructed human epidermis (RHE) models, which mimic the upper part of native human skin, represent a suitable alternative to investigate adverse effects of chemicals applied to the skin. However, there are few publications investigating the feasibility of using MALDI MSI on RHE models. Therefore, the aim of this study was to investigate the effect of sample preparation techniques, i.e., substrate, sample thickness, washing, and matrix recrystallization, on the quality of MALDI MSI for lipids analysis of the SkinEthic RHE model. Images were generated using an atmospheric pressure MALDI source coupled to a high-resolution mass spectrometer with a pixel size of 5 μm. Masses detected in a defined region of interest were analyzed and annotated using the LipostarMSI platform. The results indicated that the combination of (1) coated metallic substrates, such as APTES-coated stainless-steel plates, (2) tissue sections of 6 μm thickness, and (3) aqueous washing before HCCA matrix spraying (without recrystallization), resulted in images with a significant signal intensity as well as numerous m/z values. This refined methodology using AP-MALDI coupled to a high-resolution mass spectrometer should improve the current sample preparation workflow to evaluate changes in skin composition after application of dermatocosmetics.

Unlocking the Hidden Depths: Multi-Modal Integration of Imaging Mass Spectrometry-Based and Molecular Imaging Techniques

Multimodal imaging (MMI) has emerged as a powerful tool in clinical research, combining different imaging modes to acquire comprehensive information and enabling scientists and surgeons to study tissue identification, localization, metabolic activity, and molecular discovery, thus aiding in disease progression analysis. While multimodal instruments are gaining popularity, challenges such as non-standardized characteristics, custom software, inadequate commercial support, and integration issues with other instruments need to be addressed. The field of multimodal imaging or multiplexed imaging allows for simultaneous signal reproduction from multiple imaging strategies. Intraoperatively, MMI can be integrated into frameless stereotactic surgery. Recent developments in medical imaging modalities such as magnetic resonance imaging (MRI), and Positron Emission Topography (PET) have brought new perspectives to multimodal imaging, enabling early cancer detection, molecular tracking, and real-time progression monitoring. Despite the evidence supporting the role of MMI in surgical decision-making, there is a need for comprehensive studies to validate and perform integration at the intersection of multiple imaging technologies. They were integrating mass spectrometry-based technologies (e.g., imaging mass spectrometry (IMS), imaging mass cytometry (IMC), and Ion mobility mass spectrometry ((IM-IM) with medical imaging modalities, offering promising avenues for molecular discovery and clinical applications. This review emphasizes the potential of multi-omics approaches in tissue mapping using MMI integrated into desorption electrospray ionization (DESI) and matrix-assisted laser desorption ionization (MALDI), allowing for sequential analyses of the same section. By addressing existing knowledge gaps, this review encourages future research endeavors toward multi-omics approaches, providing a roadmap for future research and enhancing the value of MMI in molecular pathology for diagnosis.

Multielement Z-tag imaging by X-ray fluorescence microscopy for next-generation multiplex imaging

Rapid, highly multiplexed, nondestructive imaging that spans the molecular to the supra-cellular scale would be a powerful tool for tissue analysis. However, the physical constraints of established imaging methods limit the simultaneous improvement of these parameters. Whole-organism to atomic-level imaging is possible with tissue-penetrant, picometer-wavelength X-rays. To enable highly multiplexed X-ray imaging, we developed multielement Z-tag X-ray fluorescence (MEZ-XRF) that can operate at kHz speeds when combined with signal amplification by exchange reaction (SABER)-amplified Z-tag reagents. We demonstrated parallel imaging of 20 Z-tag or SABER Z-tag reagents at subcellular resolution in cell lines and multiple human tissues. We benchmarked MEZ-XRF against imaging mass cytometry and demonstrated the nondestructive multiscale repeat imaging capabilities of MEZ-XRF with rapid tissue overview scans, followed by slower, more sensitive imaging of low-abundance markers such as immune checkpoint proteins. The unique multiscale, nondestructive nature of MEZ-XRF, combined with SABER Z-tags for high sensitivity or enhanced speed, enables highly multiplexed bioimaging across biological scales.

Mass spectrometry methods for analysis of extracellular matrix components in neurological diseases

The brain extracellular matrix (ECM) is a highly glycosylated environment and plays important roles in many processes including cell communication, growth factor binding, and scaffolding. The formation of structures such as perineuronal nets (PNNs) is critical in neuroprotection and neural plasticity, and the formation of molecular networks is dependent in part on glycans. The ECM is also implicated in the neuropathophysiology of disorders such as Alzheimer's disease (AD), Parkinson's disease (PD), and Schizophrenia (SZ). As such, it is of interest to understand both the proteomic and glycomic makeup of healthy and diseased brain ECM. Further, there is a growing need for site-specific glycoproteomic information. Over the past decade, sample preparation, mass spectrometry, and bioinformatic methods have been developed and refined to provide comprehensive information about the glycoproteome. Core ECM molecules including versican, hyaluronan and proteoglycan link proteins, and tenascin are dysregulated in AD, PD, and SZ. Glycomic changes such as differential sialylation, sulfation, and branching are also associated with neurodegeneration. A more thorough understanding of the ECM and its proteomic, glycomic, and glycoproteomic changes in brain diseases may provide pathways to new therapeutic options.

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.

Digital Histopathology by Infrared Spectroscopic Imaging

Infrared (IR) spectroscopic imaging records spatially resolved molecular vibrational spectra, enabling a comprehensive measurement of the chemical makeup and heterogeneity of biological tissues. Combining this novel contrast mechanism in microscopy with the use of artificial intelligence can transform the practice of histopathology, which currently relies largely on human examination of morphologic patterns within stained tissue. First, this review summarizes IR imaging instrumentation especially suited to histopathology, analyses of its performance, and major trends. Second, an overview of data processing methods and application of machine learning is given, with an emphasis on the emerging use of deep learning. Third, a discussion on workflows in pathology is provided, with four categories proposed based on the complexity of methods and the analytical performance needed. Last, a set of guidelines, termed experimental and analytical specifications for spectroscopic imaging in histopathology, are proposed to help standardize the diversity of approaches in this emerging area.

A novel visualization method for the composition analysis of processed garlic by MALDI-TOF imaging mass spectrometry (MSI) and Q-TOF LC-MS/MS

Laba garlic is a kind of vinegar processed garlic (Allium sativum L.) product with multiple health effects. This study applied matrix-assisted laser desorption/ionization imaging mass spectrometry (MALDI-TOF MSI) and Q-TOF LC-MS/MS for the first time to investigate the garlic tissue spatial distribution changes of low molecular weight compounds during the Laba garlic processing. The distribution characteristics of the compounds were observed in processed and unprocessed garlic including amino acids and derivatives, organosulfur compounds, pigment precursors, polysaccharides and saponins. During Laba garlic processing, some bioactive compounds such as alliin and saponins were lost because they were transformed into other compounds or leached into the acetic acid solution, and some new compounds including pigments-related compounds occurred. This study provided a basis for the spatial distributions and changes of compounds in garlic tissue during Laba garlic processing, which suggested that the bioactivities of garlic might be changed after processing owing to the transformation and change of the constituents.

Single-Cell Proteomics with Spatial Attributes: Tools and Techniques

Now-a-days, the single-cell proteomics (SCP) concept is attracting interest, especially in clinical research, because it can identify the proteomic signature specific to diseased cells. This information is very essential when dealing with the progression of certain diseases, such as cancer, diabetes, Alzheimer's, etc. One of the major drawbacks of conventional destructive proteomics is that it gives an average idea about the protein expression profile in the disease condition. During the extraction of the protein from a biopsy or blood sample, proteins may come from both diseased cells and adjacent normal cells or any other cells from the disease environment. Again, SCP along with spatial attributes is utilized to learn about the heterogeneous function of a single protein. Before performing SCP, it is necessary to isolate single cells. This can be done by various techniques, including fluorescence-activated cell sorting (FACS), magnetic-activated cell sorting (MACS), laser capture microdissection (LCM), microfluidics, manual cell picking/micromanipulation, etc. Among the different approaches for proteomics, mass spectrometry-based proteomics tools are widely used for their high resolution as well as sensitivity. This Review mainly focuses on the mass spectrometry-based approaches for the study of single-cell proteomics.

Rapid Multivariate Analysis Approach to Explore Differential Spatial Protein Profiles in Tissue

Spatially targeted proteomics analyzes the proteome of specific cell types and functional regions within tissue. While spatial context is often essential to understanding biological processes, interpreting sub-region-specific protein profiles can pose a challenge due to the high-dimensional nature of the data. Here, we develop a multivariate approach for rapid exploration of differential protein profiles acquired from distinct tissue regions and apply it to analyze a published spatially targeted proteomics data set collected from Staphylococcus aureus-infected murine kidney, 4 and 10 days postinfection. The data analysis process rapidly filters high-dimensional proteomic data to reveal relevant differentiating species among hundreds to thousands of measured molecules. We employ principal component analysis (PCA) for dimensionality reduction of protein profiles measured by microliquid extraction surface analysis mass spectrometry. Subsequently, k-means clustering of the PCA-processed data groups samples by chemical similarity. Cluster center interpretation revealed a subset of proteins that differentiate between spatial regions of infection over two time points. These proteins appear involved in tricarboxylic acid metabolomic pathways, calcium-dependent processes, and cytoskeletal organization. Gene ontology analysis further uncovered relationships to tissue damage/repair and calcium-related defense mechanisms. Applying our analysis in infectious disease highlighted differential proteomic changes across abscess regions over time, reflecting the dynamic nature of host-pathogen interactions.

Imaging the metabolic reprograming of fatty acid synthesis pathway enables new diagnostic and therapeutic opportunity for breast cancer

BACKGROUND

Reprogrammed metabolic network is a key hallmark of cancer. Profiling cancer metabolic alterations with spatial signatures not only provides clues for understanding cancer biochemical heterogeneity, but also helps to decipher the possible roles of metabolic reprogramming in cancer development.

METHODS

Matrix-assisted laser desorption/ionization mass spectrometry imaging (MALDI-MSI) technique was used to characterize the expressions of fatty acids in breast cancer tissues. Specific immunofluorescence staining was further carried out to investigate the expressions of fatty acid synthesis-related enzymes.

RESULTS

The distributions of 23 fatty acids in breast cancer tissues have been mapped, and the levels of most fatty acids in cancer tissues are significantly higher than those in adjacent normal tissues. Two metabolic enzymes, fatty acid synthase (FASN) and acetyl CoA carboxylase (ACC), which being involved in the de novo synthesis of fatty acid were found to be up-regulated in breast cancer. Targeting the up-regulation of FASN and ACC is an effective approach to limiting the growth, proliferation, and metastasis of breast cancer cells.

CONCLUSIONS

These spatially resolved findings enhance our understanding of cancer metabolic reprogramming and give an insight into the exploration of metabolic vulnerabilities for better cancer treatment.

Label-free, multi-parametric assessments of cell metabolism and matrix remodeling within human and early-stage murine osteoarthritic articular cartilage

Osteoarthritis (OA) is characterized by the progressive deterioration of articular cartilage, involving complicated cell-matrix interactions. Systematic investigations of dynamic cellular and matrix changes during OA progression are lacking. In this study, we use label-free two-photon excited fluorescence (TPEF) and second harmonic generation (SHG) imaging to assess cellular and extracellular matrix features of murine articular cartilage during several time points at early stages of OA development following destabilization of medial meniscus surgery. We detect significant changes in the organization of collagen fibers and crosslink-associated fluorescence of the superficial zone as early as one week following surgery. Such changes become significant within the deeper transitional and radial zones at later time-points, highlighting the importance of high spatial resolution. Cellular metabolic changes exhibit a highly dynamic behavior, and indicate metabolic reprogramming from enhanced oxidative phosphorylation to enhanced glycolysis or fatty acid oxidation over the ten-week observation period. The optical metabolic and matrix changes detected within this mouse model are consistent with differences identified in excised human cartilage specimens from OA and healthy cartilage specimens. Thus, our studies reveal important cell-matrix interactions at the onset of OA that may enable improved understanding of OA development and identification of new potential treatment targets.

Lipidomic Profiling of PFOA-Exposed Mouse Liver by Multi-Modal Mass Spectrometry Analysis

Perfluorooctanoic acid (PFOA) is a synthetic perfluorinated chemical classified as a persistent organic pollutant. PFOA has been linked to many toxic effects, including liver injury. Many studies report that PFOA exposure alters serum and hepatic lipid metabolism. However, lipidomic pathways altered by PFOA exposure are largely unknown and only a few lipid classes, mostly triacylglycerol (TG), are usually considered in lipid analysis. Here, we performed a global lipidomic analysis on the liver of PFOA-exposed (high-dose and short-duration) and control mice by combining three mass spectrometry (MS) techniques: liquid chromatography with tandem mass spectrometry (LC-MS/MS), matrix-assisted laser desorption ionization mass spectrometry imaging (MALDI-MSI), and time-of-flight secondary ion mass spectrometry (TOF-SIMS). Among all hepatic lipids identified by LC-MS/MS analysis, more than 350 were statistically impacted (increased or decreased levels) after PFOA exposure, as confirmed by multi-variate data analysis. The levels of many lipid species from different lipid classes, most notably phosphatidylethanolamine (PE), phosphatidylcholine (PC), and TG, were significantly altered. Subsequent lipidomic analysis highlights the pathways significantly impacted by PFOA exposure, with the glycerophospholipid metabolism being the most impacted, and the changes in the lipidome network, which connects all the lipid species together. MALDI-MSI displays the heterogeneous distribution of the affected lipids and PFOA, revealing different areas of lipid expression linked to PFOA localization. TOF-SIMS localizes PFOA at the cellular level, supporting MALDI-MSI results. This multi-modal MS analysis unveils the lipidomic impact of PFOA in the mouse liver after high-dose and short-term exposure and opens new opportunities in toxicology.

A Multimodal SIMS/MALDI Mass Spectrometry Imaging Source with Secondary Electron Imaging Capabilities for Use with timsTOF Instruments

Mass spectrometry imaging (MSI) is a surface analysis technique that produces chemical images and is commonly used for biological and biomedical research. Multimodal imaging combines multiple imaging modes in order to get a more comprehensive view of a sample. Multimodal MSI images are often acquired using multiple MSI instruments, which leads to issues regarding image registration and increases the chance of sample damage or degradation during sample transfer. These problems can be solved by using a single instrument that can image in multiple modes. In order to improve the efficiency of multimodal imaging and investigate complementary modes of MSI, we have modified a prototype Bruker timsTOF fleX by adding secondary ion mass spectrometry (SIMS) and secondary electron (SE) imaging capabilities while preserving the ability to perform matrix-assisted laser desorption/ionization (MALDI). We show multimodal images collected on this instrument that required only trivial registration and were acquired without sample transfer between imaging trials. Furthermore, we characterize the performance of SIMS, SE, and MALDI imaging and compare the performance of the modified instrument to a commercial timsTOF fleX.

Intracellular Amyloid-β Detection from Human Brain Sections Using a Microfluidic Immunoassay in Tandem with MALDI-MS

Alzheimer's disease (AD) currently affects more than 30 million people worldwide. The lack of understanding of AD's physiopathology limits the development of therapeutic and diagnostic tools. Soluble amyloid-β peptide (Aβ) oligomers that appear as intermediates along the Aβ aggregation into plaques are considered among the main AD neurotoxic species. Although a wealth of data are available about Aβ from in vitro and animal models, there is little known about intracellular Aβ in human brain cells, mainly due to the lack of technology to assess the intracellular protein content. The elucidation of the Aβ species in specific brain cell subpopulations can provide insight into the role of Aβ in AD and the neurotoxic mechanism involved. Here, we report a microfluidic immunoassay for in situ mass spectrometry analysis of intracellular Aβ species from archived human brain tissue. This approach comprises the selective laser dissection of individual pyramidal cell bodies from tissues, their transfer to the microfluidic platform for sample processing on-chip, and mass spectrometric characterization. As a proof-of-principle, we demonstrate the detection of intracellular Aβ species from as few as 20 human brain cells.

The Blood-Brain Barrier: Implications for Experimental Cancer Therapeutics

The blood-brain barrier is critically important for the treatment of both primary and metastatic cancers of the central nervous system (CNS). Clinical outcomes for patients with primary CNS tumors are poor and have not significantly improved in decades. As treatments for patients with extracranial solid tumors improve, the incidence of CNS metastases is on the rise due to suboptimal CNS exposure of otherwise systemically active agents. Despite state-of-the art surgical care and increasingly precise radiation therapy, clinical progress is limited by the ability to deliver an effective dose of a therapeutic agent to all cancerous cells. Given the tremendous heterogeneity of CNS cancers, both across cancer subtypes and within a single tumor, and the range of diverse therapies under investigation, a nuanced examination of CNS drug exposure is needed. With a shared goal, common vocabulary, and interdisciplinary collaboration, the field is poised for renewed progress in the treatment of CNS cancers.

Correlative Chemical Imaging and Spatial Chemometrics Delineate Alzheimer Plaque Heterogeneity at High Spatial Resolution

We present a novel, correlative chemical imaging strategy based on multimodal matrix-assisted laser desorption/ionization (MALDI) mass spectrometry imaging (MSI), hyperspectral microscopy, and spatial chemometrics. Our workflow overcomes challenges associated with correlative MSI data acquisition and alignment by implementing 1 + 1-evolutionary image registration for precise geometric alignment of multimodal imaging data and their integration in a common, truly multimodal imaging data matrix with maintained MSI resolution (10 μm). This enabled multivariate statistical modeling of multimodal imaging data using a novel multiblock orthogonal component analysis approach to identify covariations of biochemical signatures between and within imaging modalities at MSI pixel resolution. We demonstrate the method's potential through its application toward delineating chemical traits of Alzheimer's disease (AD) pathology. Here, trimodal MALDI MSI of transgenic AD mouse brain delineates beta-amyloid (Aβ) plaque-associated co-localization of lipids and Aβ peptides. Finally, we establish an improved image fusion approach for correlative MSI and functional fluorescence microscopy. This allowed for high spatial resolution (300 nm) prediction of correlative, multimodal MSI signatures toward distinct amyloid structures within single plaque features critically implicated in Aβ pathogenicity.

To metabolomics and beyond: a technological portfolio to investigate cancer metabolism

Tumour cells have exquisite flexibility in reprogramming their metabolism in order to support tumour initiation, progression, metastasis and resistance to therapies. These reprogrammed activities include a complete rewiring of the bioenergetic, biosynthetic and redox status to sustain the increased energetic demand of the cells. Over the last decades, the cancer metabolism field has seen an explosion of new biochemical technologies giving more tools than ever before to navigate this complexity. Within a cell or a tissue, the metabolites constitute the direct signature of the molecular phenotype and thus their profiling has concrete clinical applications in oncology. Metabolomics and fluxomics, are key technological approaches that mainly revolutionized the field enabling researchers to have both a qualitative and mechanistic model of the biochemical activities in cancer. Furthermore, the upgrade from bulk to single-cell analysis technologies provided unprecedented opportunity to investigate cancer biology at cellular resolution allowing an in depth quantitative analysis of complex and heterogenous diseases. More recently, the advent of functional genomic screening allowed the identification of molecular pathways, cellular processes, biomarkers and novel therapeutic targets that in concert with other technologies allow patient stratification and identification of new treatment regimens. This review is intended to be a guide for researchers to cancer metabolism, highlighting current and emerging technologies, emphasizing advantages, disadvantages and applications with the potential of leading the development of innovative anti-cancer therapies.

Applications and continued evolution of glycan imaging mass spectrometry

Glycosylation is an important posttranslational modifier of proteins and lipid conjugates critical for the stability and function of these macromolecules. Particularly important are N-linked glycans attached to asparagine residues in proteins. N-glycans have well-defined roles in protein folding, cellular trafficking and signal transduction, and alterations to them are implicated in a variety of diseases. However, the non-template driven biosynthesis of these N-glycans leads to significant structural diversity, making it challenging to identify the most biologically and clinically relevant species using conventional analyses. Advances in mass spectrometry instrumentation and data acquisition, as well as in enzymatic and chemical sample preparation strategies, have positioned mass spectrometry approaches as powerful analytical tools for the characterization of glycosylation in health and disease. Imaging mass spectrometry expands upon these strategies by capturing the spatial component of a glycan's distribution in-situ, lending additional insight into the organization and function of these molecules. Herein we review the ongoing evolution of glycan imaging mass spectrometry beginning with widely adopted tissue imaging approaches and expanding to other matrices and sample types with potential research and clinical implications. Adaptations of these techniques, along with their applications to various states of disease, are discussed. Collectively, glycan imaging mass spectrometry analyses broaden our understanding of the biological and clinical relevance of N-glycosylation to human disease.

In Situ Imaging of O-Linked β-N-Acetylglucosamine Using On-Tissue Hydrolysis and MALDI Mass Spectrometry

Post-translational O-glycosylation of proteins via the addition of N-acetylglucosamine (O-GlcNAc) is a regulator of many aspects of cellular physiology. Processes driven by perturbed dynamics of O-GlcNAcylation modification have been implicated in cancer development. Variability in O-GlcNAcylation is emerging as a metabolic biomarker of many cancers. Here, we evaluate the use of MALDI-mass spectrometry imaging (MSI) to visualize the location of O-GlcNAcylated proteins in tissue sections by mapping GlcNAc that has been released by the enzymatic hydrolysis of glycoproteins using an O-GlcNAc hydrolase. We use this strategy to monitor O-GlcNAc within hepatic VX2 tumor tissue. We show that increased O-GlcNAc is found within both viable tumor and tumor margin regions, implicating GlcNAc in tumor progression.

High-multiplex tissue imaging in routine pathology-are we there yet?

High-multiplex tissue imaging (HMTI) approaches comprise several novel immunohistological methods that enable in-depth, spatial single-cell analysis. Over recent years, studies in tumor biology, infectious diseases, and autoimmune conditions have demonstrated the information gain accessible when mapping complex tissues with HMTI. Tumor biology has been a focus of innovative multiparametric approaches, as the tumor microenvironment (TME) contains great informative value for accurate diagnosis and targeted therapeutic approaches: unraveling the cellular composition and structural organization of the TME using sophisticated computational tools for spatial analysis has produced histopathologic biomarkers for outcomes in breast cancer, predictors of positive immunotherapy response in melanoma, and histological subgroups of colorectal carcinoma. Integration of HMTI technologies into existing clinical workflows such as molecular tumor boards will contribute to improve patient outcomes through personalized treatments tailored to the specific heterogeneous pathological fingerprint of cancer, autoimmunity, or infection. Here, we review the advantages and limitations of existing HMTI technologies and outline how spatial single-cell data can improve our understanding of pathological disease mechanisms and determinants of treatment success. We provide an overview of the analytic processing and interpretation and discuss how HMTI can improve future routine clinical diagnostic and therapeutic processes.

Multiple two-sample testing under arbitrary covariance dependency with an application in imaging mass spectrometry

Large-scale hypothesis testing has become a ubiquitous problem in high-dimensional statistical inference, with broad applications in various scientific disciplines. One relevant application is constituted by imaging mass spectrometry (IMS) association studies, where a large number of tests are performed simultaneously in order to identify molecular masses that are associated with a particular phenotype, for example, a cancer subtype. Mass spectra obtained from matrix-assisted laser desorption/ionization (MALDI) experiments are dependent, when considered as statistical quantities. False discovery proportion (FDP) estimation and  control under arbitrary dependency structure among test statistics is an active topic in modern multiple testing research. In this context, we are concerned with the evaluation of associations between the binary outcome variable (describing the phenotype) and multiple predictors derived from MALDI measurements. We propose an inference procedure in which the correlation matrix of the test statistics is utilized. The approach is based on multiple marginal models. Specifically, we fit a marginal logistic regression model for each predictor individually. Asymptotic joint normality of the stacked vector of the marginal regression coefficients is established under standard regularity assumptions, and their (limiting) correlation matrix is estimated. The proposed method extracts common factors from the resulting empirical correlation matrix. Finally, we estimate the realized FDP of a thresholding procedure for the marginal p-values. We demonstrate a practical application of the proposed workflow to MALDI IMS data in an oncological context.

Classification of Pancreatic Ductal Adenocarcinoma Using MALDI Mass Spectrometry Imaging Combined with Neural Networks

Despite numerous diagnostic and therapeutic advances, pancreatic ductal adenocarcinoma (PDAC) has a high mortality rate, and is the fourth leading cause of cancer death in developing countries. Besides its increasing prevalence, pancreatic malignancies are characterized by poor prognosis. Omics technologies have potential relevance for PDAC assessment but are time-intensive and relatively cost-intensive and limited by tissue heterogeneity. Matrix-assisted laser desorption/ionization mass spectrometry imaging (MALDI-MSI) can obtain spatially distinct peptide-signatures and enables tumor classification within a feasible time with relatively low cost. While MALDI-MSI data sets are inherently large, machine learning methods have the potential to greatly decrease processing time. We present a pilot study investigating the potential of MALDI-MSI in combination with neural networks, for classification of pancreatic ductal adenocarcinoma. Neural-network models were trained to distinguish between pancreatic ductal adenocarcinoma and other pancreatic cancer types. The proposed methods are able to correctly classify the PDAC types with an accuracy of up to 86% and a sensitivity of 82%. This study demonstrates that machine learning tools are able to identify different pancreatic carcinoma from complex MALDI data, enabling fast prediction of large data sets. Our results encourage a more frequent use of MALDI-MSI and machine learning in histopathological studies in the future.

Divide and Conquer: A Flexible Deep Learning Strategy for Exploring Metabolic Heterogeneity from Mass Spectrometry Imaging Data

Research on metabolic heterogeneity provides an important basis for the study of the molecular mechanism of a disease and personalized treatment. The screening of metabolism-related sub-regions that affect disease development is essential for the more focused exploration on disease progress aberrant phenotypes, even carcinogenesis and metastasis. The mass spectrometry imaging (MSI) technique has distinct advantages to reveal the heterogeneity of an organism based on in situ molecular profiles. The challenge of heterogeneous analysis has been to perform an objective identification among biological tissues with different characteristics. By introducing the divide-and-conquer strategy to architecture design and application, we establish here a flexible unsupervised deep learning model, called divide-and-conquer (dc)-DeepMSI, for metabolic heterogeneity analysis from MSI data without prior knowledge of histology. dc-DeepMSI can be used to identify either spatially contiguous regions of interest (ROIs) or spatially sporadic ROIs by designing two specific modes, spat-contig and spat-spor. Comparison results on fetus mouse data demonstrate that the dc-DeepMSI outperforms state-of-the-art MSI segmentation methods. We demonstrate that the novel learning strategy successfully obtained sub-regions that are statistically linked to the invasion status and molecular phenotypes of breast cancer as well as organizing principles during developmental phase.

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.

Neurotrauma investigation through spatial omics guided by mass spectrometry imaging: Target identification and clinical applications

Traumatic brain injury (TBI) represents one of the major public health concerns worldwide due to the increase in TBI incidence as a result of injuries from daily life accidents such as sports and motor vehicle transportation as well as military-related practices. This type of central nervous system trauma is known to predispose patients to several neurological disorders such as Parkinson's disease, Alzheimer's disease, chronic trauamatic encephalopathy, and age-related Dementia. Recently, several proteomic and lipidomic platforms have been applied on different TBI studies to investigate TBI-related mechanisms that have broadened our understanding of its distinct neuropathological complications. In this study, we provide an updated comprehensive overview of the current knowledge and novel perspectives of the spatially resolved microproteomics and microlipidomics approaches guided by mass spectrometry imaging used in TBI studies and its applications in the neurotrauma field. In this regard, we will discuss the use of the spatially resolved microproteomics and assess the different microproteomic sampling methods such as laser capture microdissection, parafilm assisted microdissection, and liquid microjunction extraction as accurate and precise techniques in the field of neuroproteomics. Additionally, we will highlight lipid profiling applications and their prospective potentials in characterizing molecular processes involved in the field of TBI. Specifically, we will discuss the phospholipid metabolism acting as a precursor for proinflammatory molecules such as eicosanoids. Finally, we will survey the current state of spatial neuroproteomics and microproteomics applications and present the various studies highlighting their findings in these fields.

Mass spectrometry-based strategies for single-cell metabolomics

Single cell analysis has drawn increasing interest from the research community due to its capability to interrogate cellular heterogeneity, allowing refined tissue classification and facilitating novel biomarker discovery. With the advancement of relevant instruments and techniques, it is now possible to perform multiple omics including genomics, transcriptomics, metabolomics or even proteomics at single cell level. In comparison with other omics studies, single-cell metabolomics (SCM) represents a significant challenge since it involves many types of dynamically changing compounds with a wide range of concentrations. In addition, metabolites cannot be amplified. Although difficult, considerable progress has been made over the past decade in mass spectrometry (MS)-based SCM in terms of processing technologies and biochemical applications. In this review, we will summarize recent progress in the development of promising MS platforms, sample preparation methods and SCM analysis of various cell types (including plant cell, cancer cell, neuron, embryo cell, and yeast cell). Current limitations and future research directions in the field of SCM will also be discussed.