MALDI imaging mass spectrometry for direct tissue analysis: a new frontier for molecular histology. Histochem Cell Biol. 2008;130:421-434. [PMID: 18618129 PMCID: PMC2522327 DOI: 10.1007/s00418-008-0469-9]

MALDI TIMS IMS Reveals Ganglioside Molecular Diversity within Murine S. aureus Kidney Tissue Abscesses

Gangliosides play important roles in innate and adaptive immunity. The high degree of structural heterogeneity results in significant variability in ganglioside expression patterns and greatly complicates linking structure and function. Structural characterization at the site of infection is essential in elucidating host ganglioside function in response to invading pathogens, such as Staphylococcus aureus (S. aureus). Matrix-assisted laser desorption/ionization imaging mass spectrometry (MALDI IMS) enables high-specificity spatial investigation of intact gangliosides. Here, ganglioside structural and spatial heterogeneity within an S. aureus-infected mouse kidney abscess was characterized. Differences in spatial distributions were observed for gangliosides of different classes and those that differ in ceramide chain composition and oligosaccharide-bound sialic acid. Furthermore, integrating trapped ion mobility spectrometry (TIMS) allowed for the gas-phase separation and visualization of monosialylated ganglioside isomers that differ in sialic acid type and position. The isomers differ in spatial distributions within the host-pathogen interface, where molecular patterns revealed new molecular zones in the abscess previously unidentified by traditional histology.

Application of spatial omics in gastric cancer

Gastric cancer (GC), a globally prevalent and lethal malignancy, continues to be a key research focus. However, due to its considerable heterogeneity and complex pathogenesis, the treatment and diagnosis of gastric cancer still face significant challenges. With the rapid development of spatial omics technology, which provides insights into the spatial information within tumor tissues, it has emerged as a significant tool in gastric cancer research. This technology affords new insights into the pathology and molecular biology of gastric cancer for scientists. This review discusses recent advances in spatial omics technology for gastric cancer research, highlighting its applications in the tumor microenvironment (TME), tumor heterogeneity, tumor genesis and development mechanisms, and the identification of potential biomarkers and therapeutic targets. Moreover, this article highlights spatial omics' potential in precision medicine and summarizes existing challenges and future directions. It anticipates spatial omics' continuing impact on gastric cancer research, aiming to improve diagnostic and therapeutic approaches for patients. With this review, we aim to offer a comprehensive overview to scientists and clinicians in gastric cancer research, motivating further exploration and utilization of spatial omics technology. Our goal is to improve patient outcomes, including survival rates and quality of life.

Mass spectrometry for metabolomics analysis: Applications in neonatal and cancer screening

Chemical analysis by analytical instrumentation has played a major role in disease diagnosis, which is a necessary step for disease treatment. While the treatment process often targets specific organs or compounds, the diagnostic step can occur through various means, including physical or chemical examination. Chemically, the genome may be evaluated to give information about potential genetic outcomes, the transcriptome to provide information about expression actively occurring, the proteome to offer insight on functions causing metabolite expression, or the metabolome to provide a picture of both past and ongoing physiological function in the body. Mass spectrometry (MS) has been elevated among other analytical instrumentation because it can be used to evaluate all four biological machineries of the body. In addition, MS provides enhanced sensitivity, selectivity, versatility, and speed for rapid turnaround time, qualities that are important for instance in clinical procedures involving the diagnosis of a pediatric patient in intensive care or a cancer patient undergoing surgery. In this review, we provide a summary of the use of MS to evaluate biomarkers for newborn screening and cancer diagnosis. As many reviews have recently appeared focusing on MS methods and instrumentation for metabolite analysis, we sought to describe the biological basis for many metabolomic and additional omics biomarkers used in newborn screening and how tandem MS methods have recently been applied, in comparison to traditional methods. Similar comparison is done for cancer screening, with emphasis on emerging MS approaches that allow biological fluids, tissues, and breath to be analyzed for the presence of diagnostic metabolites yielding insight for treatment options based on the understanding of prior and current physiological functions of the body.

Formation of multiple ion types during MALDI imaging mass spectrometry analysis of Mitragyna speciosa alkaloids in dosed rat brain tissue

Mitragyna speciosa, more commonly known as kratom, has emerged as an alternative to treat chronic pain and addiction. However, the alkaloid components of kratom, which are the major contributors to kratom's pharmaceutical properties, have not yet been fully investigated. In this study, matrix-assisted laser desorption/ionization (MALDI) imaging mass spectrometry was used to map the biodistribution of three alkaloids (corynantheidine, mitragynine, and speciogynine) in rat brain tissues. The alkaloids produced three main ion types during MALDI analysis: [M + H]+, [M - H]+, and [M - 3H]+. Contrary to previous reports suggesting that the [M - H]+ and [M - 3H]+ ion types form during laser ablation, these ion types can also be produced during the MALDI matrix application process. Several strategies are proposed to accurately map the biodistribution of the alkaloids. Due to differences in the relative abundances of the ions in different biological regions of the tissue, differences in ionization efficiencies of the ions, and potential overlap of the [M - H]+ and [M - 3H]+ ion types with endogenous metabolites of the same empirical formula, a matrix that mainly produces the [M + H]+ ion type is optimal for accurate mapping of the alkaloids. Alternatively, the most abundant ion type can be mapped or the intensities of all ion types can be summed together to generate a composite image. The accuracy of each of these approaches is explored and validated.

Machine learning integrative approaches to advance computational immunology

The study of immunology, traditionally reliant on proteomics to evaluate individual immune cells, has been revolutionized by single-cell RNA sequencing. Computational immunologists play a crucial role in analysing these datasets, moving beyond traditional protein marker identification to encompass a more detailed view of cellular phenotypes and their functional roles. Recent technological advancements allow the simultaneous measurements of multiple cellular components-transcriptome, proteome, chromatin, epigenetic modifications and metabolites-within single cells, including in spatial contexts within tissues. This has led to the generation of complex multiscale datasets that can include multimodal measurements from the same cells or a mix of paired and unpaired modalities. Modern machine learning (ML) techniques allow for the integration of multiple "omics" data without the need for extensive independent modelling of each modality. This review focuses on recent advancements in ML integrative approaches applied to immunological studies. We highlight the importance of these methods in creating a unified representation of multiscale data collections, particularly for single-cell and spatial profiling technologies. Finally, we discuss the challenges of these holistic approaches and how they will be instrumental in the development of a common coordinate framework for multiscale studies, thereby accelerating research and enabling discoveries in the computational immunology field.

Enhancing mass spectrometry imaging accessibility using convolutional autoencoders for deriving hypoxia-associated peptides from tumors

Mass spectrometry imaging (MSI) allows to study cancer's intratumoral heterogeneity through spatially-resolved peptides, metabolites and lipids. Yet, in biomedical research MSI is rarely used for biomarker discovery. Besides its high dimensionality and multicollinearity, mass spectrometry (MS) technologies typically output mass-to-charge ratio values but not the biochemical compounds of interest. Our framework makes particularly low-abundant signals in MSI more accessible. We utilized convolutional autoencoders to aggregate features associated with tumor hypoxia, a parameter with significant spatial heterogeneity, in cancer xenograft models. We highlight that MSI captures these low-abundant signals and that autoencoders can preserve them in their latent space. The relevance of individual hyperparameters is demonstrated through ablation experiments, and the contribution from original features to latent features is unraveled. Complementing MSI with tandem MS from the same tumor model, multiple hypoxia-associated peptide candidates were derived. Compared to random forests alone, our autoencoder approach yielded more biologically relevant insights for biomarker discovery.

Exploring the Potential of Bioactive Peptides: From Natural Sources to Therapeutics

Bioactive peptides, specific protein fragments with positive health effects, are gaining traction in drug development for advantages like enhanced penetration, low toxicity, and rapid clearance. This comprehensive review navigates the intricate landscape of peptide science, covering discovery to functional characterization. Beginning with a peptidomic exploration of natural sources, the review emphasizes the search for novel peptides. Extraction approaches, including enzymatic hydrolysis, microbial fermentation, and specialized methods for disulfide-linked peptides, are extensively covered. Mass spectrometric analysis techniques for data acquisition and identification, such as liquid chromatography, capillary electrophoresis, untargeted peptide analysis, and bioinformatics, are thoroughly outlined. The exploration of peptide bioactivity incorporates various methodologies, from in vitro assays to in silico techniques, including advanced approaches like phage display and cell-based assays. The review also discusses the structure-activity relationship in the context of antimicrobial peptides (AMPs), ACE-inhibitory peptides (ACEs), and antioxidative peptides (AOPs). Concluding with key findings and future research directions, this interdisciplinary review serves as a comprehensive reference, offering a holistic understanding of peptides and their potential therapeutic applications.

Metabolic heterogeneity in adrenocortical carcinoma impacts patient outcomes

Spatially resolved metabolomics enables the investigation of tumoral metabolites in situ. Inter- and intratumor heterogeneity are key factors associated with patient outcomes. Adrenocortical carcinoma (ACC) is an exceedingly rare tumor associated with poor survival. Its clinical prognosis is highly variable, but the contributions of tumor metabolic heterogeneity have not been investigated thus far to our knowledge. An in-depth understanding of tumor heterogeneity requires molecular feature-based identification of tumor subpopulations associated with tumor aggressiveness. Here, using spatial metabolomics by high-mass resolution MALDI Fourier transform ion cyclotron resonance mass spectrometry imaging, we assessed metabolic heterogeneity by de novo discovery of metabolic subpopulations and Simpson's diversity index. After identification of tumor subpopulations in 72 patients with ACC, we additionally performed a comparison with 25 tissue sections of normal adrenal cortex to identify their common and unique metabolic subpopulations. We observed variability of ACC tumor heterogeneity and correlation of high metabolic heterogeneity with worse clinical outcome. Moreover, we identified tumor subpopulations that served as independent prognostic factors and, furthermore, discovered 4 associated anticancer drug action pathways. Our research may facilitate comprehensive understanding of the biological implications of tumor subpopulations in ACC and showed that metabolic heterogeneity might impact chemotherapy.

Mass spectrometry imaging for biosolids characterization to assess ecological or health risks before reuse

Biosolids are byproducts of wastewater treatment. With the increasing global population, the amounts of wastewater to be treated are expanding, along with the amounts of biosolids generated. The reuse of biosolids is now accepted for diversified applications in fields such as agriculture, engineering, agro-forestry. However, biosolids are known to be potential carriers of compounds that can be toxic to living beings or alter the environment. Therefore, biosolid reuse is subject to regulations, mandatory analyses are performed on heavy metals, persistent organic pollutants or pathogens. Conventional methods for the analysis of heavy metals and persistent organic pollutants are demanding, lengthy, and sometimes unsafe. Here, we propose mass spectrometry imaging as a faster and safer method using small amounts of material to monitor heavy metals and persistent organic pollutants in different types of biosolids, allowing for ecological and health risk assessment before reuse. Our methodology can be extended to other soil-like matrices.

Histochemistry and Cell Biology-a glance into the past and a look ahead

At the occasion of the 65th anniversary of Histochemistry and Cell Biology, we browse through its first ten years of publication and highlight a selection of papers from the early days of enzyme, protein, and carbohydrate histochemistry. In addition, we narrate recent progress to identify, quantify, and precisely determine the tissue localization of proteins and lipids, and small molecules by the combination of spectroscopic techniques and histology.

How to sample a seizure plant: the role of the visualization spatial distribution analysis of Lophophora williamsii as an example

Natural compounds in plants are often unevenly distributed, and determining the best sampling locations to obtain the most representative results is technically challenging. Matrix-assisted laser desorption/ionization mass spectrometry imaging (MALDI-MSI) can provide the basis for formulating sampling guideline. For a succulent plant sample, ensuring the authenticity and in situ nature of the spatial distribution analysis results during MSI analysis also needs to be thoroughly considered. In this study, we developed a well-established and reliable MALDI-MSI method based on preservation methods, slice conditions, auxiliary matrices, and MALDI parameters to detect and visualize the spatial distribution of mescaline in situ in Lophophora williamsii. The MALDI-MSI results were validated using liquid chromatography-tandem mass spectrometry. Low-temperature storage at -80°C and drying of "bookmarks" were the appropriate storage methods for succulent plant samples and their flower samples, and cutting into 40 μm thick sections at -20°C using gelatin as the embedding medium is the appropriate sectioning method. The use of DCTB (trans-2-[3-(4-tert-butylphenyl)-2-methyl-2-propenylidene]malononitrile) as an auxiliary matrix and a laser intensity of 45 are favourable MALDI parameter conditions for mescaline analysis. The region of interest semi-quantitative analysis revealed that mescaline is concentrated in the epidermal tissues of L. williamsii as well as in the meristematic tissues of the crown. The study findings not only help to provide a basis for determining the best sampling locations for mescaline in L. williamsii, but they also provide a reference for the optimization of storage and preparation conditions for raw plant organs before MALDI detection.

Key Points

An accurate in situ MSI method for fresh water-rich succulent plants was obtained based on multi-parameter comparative experiments.Spatial imaging analysis of mescaline in Lophophora williamsii was performed using the above method.Based on the above results and previous results, a sampling proposal for forensic medicine practice is tentatively proposed.

Integration of whole transcriptome spatial profiling with protein markers

Spatial transcriptomics and proteomics provide complementary information that independently transformed our understanding of complex biological processes. However, experimental integration of these modalities is limited. To overcome this, we developed Spatial PrOtein and Transcriptome Sequencing (SPOTS) for high-throughput simultaneous spatial transcriptomics and protein profiling. Compared with unimodal measurements, SPOTS substantially improves signal resolution and cell clustering and enhances the discovery power in differential gene expression analysis across tissue regions.

Spatial metabolomics reveals glycogen as an actionable target for pulmonary fibrosis

Matrix assisted laser desorption/ionization imaging has greatly improved our understanding of spatial biology, however a robust bioinformatic pipeline for data analysis is lacking. Here, we demonstrate the application of high-dimensionality reduction/spatial clustering and histopathological annotation of matrix assisted laser desorption/ionization imaging datasets to assess tissue metabolic heterogeneity in human lung diseases. Using metabolic features identified from this pipeline, we hypothesize that metabolic channeling between glycogen and N-linked glycans is a critical metabolic process favoring pulmonary fibrosis progression. To test our hypothesis, we induced pulmonary fibrosis in two different mouse models with lysosomal glycogen utilization deficiency. Both mouse models displayed blunted N-linked glycan levels and nearly 90% reduction in endpoint fibrosis when compared to WT animals. Collectively, we provide conclusive evidence that lysosomal utilization of glycogen is required for pulmonary fibrosis progression. In summary, our study provides a roadmap to leverage spatial metabolomics to understand foundational biology in pulmonary diseases.

In situ droplet-based on-tissue chemical derivatization for lipid isomer characterization using LESA

In this work, we present an in situ droplet-based derivatization method for fast tissue lipid profiling at multiple isomer levels. On-tissue derivatization for isomer characterization was achieved in a droplet delivered by the TriVersa NanoMate LESA pipette. The derivatized lipids were then extracted and analyzed by the automated chip-based liquid extraction surface analysis (LESA) mass spectrometry (MS) followed by tandem MS to produce diagnostic fragment ions to reveal the lipid isomer structures. Three reactions, i.e., mCPBA epoxidation, photocycloaddition catalyzed by the photocatalyst Ir[dF(CF₃)ppy]₂(dtbbpy)PF₆, and Mn(II) lipid adduction, were applied using the droplet-based derivatization to provide lipid characterization at carbon-carbon double-bond positional isomer and sn-positional isomer levels. Relative quantitation of both types of lipid isomers was also achieved based on diagnostic ion intensities. This method provides the flexibility of performing multiple derivatizations at different spots in the same functional region of an organ for orthogonal lipid isomer analysis using a single tissue slide. Lipid isomers were profiled in the cortex, cerebellum, thalamus, hippocampus, and midbrain of the mouse brain and 24 double-bond positional isomers and 16 sn-positional isomers showed various distributions in those regions. This droplet-based derivatization of tissue lipids allows fast profiling of multi-level isomer identification and quantitation and has great potential in tissue lipid studies requiring rapid sample-to-result turnovers.

A Comparison of Different Sample Processing Protocols for MALDI Imaging Mass Spectrometry Analysis of Formalin-Fixed Multiple Myeloma Cells

Sample processing of formalin-fixed specimens constitutes a major challenge in molecular profiling efforts. Pre-analytical factors such as fixative temperature, dehydration, and embedding media affect downstream analysis, generating data dependent on technical processing rather than disease state. In this study, we investigated two different sample processing methods, including the use of the cytospin sample preparation and automated sample processing apparatuses for proteomic analysis of multiple myeloma (MM) cell lines using imaging mass spectrometry (IMS). In addition, two sample-embedding instruments using different reagents and processing times were considered. Three MM cell lines fixed in 4% paraformaldehyde were either directly centrifuged onto glass slides using cytospin preparation techniques or processed to create paraffin-embedded specimens with an automatic tissue processor, and further cut onto glass slides for IMS analysis. The number of peaks obtained from paraffin-embedded samples was comparable between the two different sample processing instruments. Interestingly, spectra profiles showed enhanced ion yield in cytospin compared to paraffin-embedded samples along with high reproducibility compared to the sample replicate.

Imaging Mass Spectrometry Reveals Complex Lipid Distributions Across Staphylococcus aureus Biofilm Layers

Introduction

Although Staphylococcus aureus is the leading cause of biofilm-related infections, the lipidomic distributions within these biofilms is poorly understood. Here, lipidomic mapping of S. aureus biofilm cross-sections was performed to investigate heterogeneity between horizontal biofilm layers.

Methods

S. aureus biofilms were grown statically, embedded in a mixture of carboxymethylcellulose/gelatin, and prepared for downstream matrix-assisted laser desorption/ionization imaging mass spectrometry (MALDI IMS). Trapped ion mobility spectrometry (TIMS) was also applied prior to mass analysis.

Results

Implementation of TIMS led to a ∼ threefold increase in the number of lipid species detected. Washing biofilm samples with ammonium formate (150 mM) increased signal intensity for some bacterial lipids by as much as tenfold, with minimal disruption of the biofilm structure. MALDI TIMS IMS revealed that most lipids localize primarily to a single biofilm layer, and species from the same lipid class such as cardiolipins CL(57:0) - CL(66:0) display starkly different localizations, exhibiting between 1.5 and 6.3-fold intensity differences between layers (n = 3, p < 0.03). No horizontal layers were observed within biofilms grown anaerobically, and lipids were distributed homogenously.

Conclusions

High spatial resolution analysis of S. aureus biofilm cross-sections by MALDI TIMS IMS revealed stark lipidomic heterogeneity between horizontal S. aureus biofilm layers demonstrating that each layer was molecularly distinct. Finally, this workflow uncovered an absence of layers in biofilms grown under anaerobic conditions, possibly indicating that oxygen contributes to the observed heterogeneity under aerobic conditions. Future applications of this workflow to study spatially localized molecular responses to antimicrobials could provide new therapeutic strategies.

Efficient Hydrogen-Deuterium Exchange in Matrix-Assisted Laser Desorption/Ionization Mass Spectrometry Imaging for Confident Metabolite Identification

Highly efficient hydrogen-deuterium exchange (HDX) is developed for mass spectrometry imaging (MSI) with low-vacuum matrix-assisted laser desorption/ionization (MALDI). A HDX efficiency of 73-85% is achieved by introducing D₂O vapor into a heated MALDI source in combination with a deuterium-labeled matrix, which allows correct determination of the number of possible H/D exchanges for up to 17 labile hydrogens. This provides valuable orthogonal information to supplement m/z, allowing for increased confidence in metabolite identification while retaining the spatial information MSI supplies. When combined with high-throughput METASPACE annotation, this approach can systematically improve untargeted metabolite annotations in MALDI-MS imaging. The developed method was applied to MALDI-MS imaging of the top surface, bottom surface, and middle section of Lemna minor fronds. Out of a total of 56 on-sample annotations made with the BraChem database using a 10% false discovery rate, 31 of these annotations (55%) matched our HDX data, providing additional confidence. For the remaining 45%, our data allowed us to narrow down structural possibilities and eliminate incorrect structures, greatly increasing confidence in metabolite identification.

Advances in phosphoproteomics and its application to COPD

INTRODUCTION

Chronic obstructive pulmonary disease (COPD) was the third leading cause of global death in 2019, causing a huge economic burden to society. Therefore, it is urgent to identify specific phenotypes of COPD patients through early detection, and to promptly treat exacerbations. The field of phosphoproteomics has been a massive advancement, compelled by the developments in mass spectrometry, enrichment strategies, algorithms, and tools. Modern mass spectrometry-based phosphoproteomics allows understanding of disease pathobiology, biomarker discovery, and predicting new therapeutic modalities.

AREAS COVERED

In this article, we present an overview of phosphoproteomic research and strategies for enrichment and fractionation of phosphopeptides, identification of phosphorylation sites, chromatographic separation and mass spectrometry detection strategies, and the potential application of phosphorylated proteomic analysis in the diagnosis, treatment, and prognosis of COPD disease.

EXPERT OPINION

The role of phosphoproteomics in COPD is critical for understanding disease pathobiology, identifying potential biomarkers, and predicting new therapeutic approaches. However, the complexity of COPD requires the more comprehensive understanding that can be achieved through integrated multi-omics studies. Phosphoproteomics, as a part of these multi-omics approaches, can provide valuable insights into the underlying mechanisms of COPD.

A synthetic synthesis to explore animal evolution and development

Identifying the general principles by which genotypes are converted into phenotypes remains a challenge in the post-genomic era. We still lack a predictive understanding of how genes shape interactions among cells and tissues in response to signalling and environmental cues, and hence how regulatory networks generate the phenotypic variation required for adaptive evolution. Here, we discuss how techniques borrowed from synthetic biology may facilitate a systematic exploration of evolvability across biological scales. Synthetic approaches permit controlled manipulation of both endogenous and fully engineered systems, providing a flexible platform for investigating causal mechanisms in vivo. Combining synthetic approaches with multi-level phenotyping (phenomics) will supply a detailed, quantitative characterization of how internal and external stimuli shape the morphology and behaviour of living organisms. We advocate integrating high-throughput experimental data with mathematical and computational techniques from a variety of disciplines in order to pursue a comprehensive theory of evolution.

This article is part of the theme issue ‘Genetic basis of adaptation and speciation: from loci to causative mutations’.

Spatial metabolomics for evaluating response to neoadjuvant therapy in non-small cell lung cancer patients

Background

The response to neoadjuvant chemotherapy (NAC) differs substantially among individual patients with non‐small cell lung cancer (NSCLC). Major pathological response (MPR) is a histomorphological read‐out used to assess treatment response and prognosis in patients NSCLC after NAC. Although spatial metabolomics is a promising tool for evaluating metabolic phenotypes, it has not yet been utilized to assess therapy responses in patients with NSCLC. We evaluated the potential application of spatial metabolomics in cancer tissues to assess the response to NAC, using a metabolic classifier that utilizes mass spectrometry imaging combined with machine learning.

Methods

Resected NSCLC tissue specimens obtained after NAC (n = 88) were subjected to high‐resolution mass spectrometry, and these data were used to develop an approach for assessing the response to NAC in patients with NSCLC. The specificities of the generated tumor cell and stroma classifiers were validated by applying this approach to a cohort of biologically matched chemotherapy‐naïve patients with NSCLC (n = 85).

Results

The developed tumor cell metabolic classifier stratified patients into different prognostic groups with 81.6% accuracy, whereas the stroma metabolic classifier displayed 78.4% accuracy. By contrast, the accuracies of MPR and TNM staging for stratification were 62.5% and 54.1%, respectively. The combination of metabolic and MPR classifiers showed slightly lower accuracy than either individual metabolic classifier. In multivariate analysis, metabolic classifiers were the only independent prognostic factors identified (tumor: P = 0.001, hazards ratio [HR] = 3.823, 95% confidence interval [CI] = 1.716–8.514; stroma: P = 0.049, HR = 2.180, 95% CI = 1.004–4.737), whereas MPR (P = 0.804; HR = 0.913; 95% CI = 0.445–1.874) and TNM staging (P = 0.078; HR = 1.223; 95% CI = 0.977–1.550) were not independent prognostic factors. Using Kaplan‐Meier survival analyses, both tumor and stroma metabolic classifiers were able to further stratify patients as NAC responders (P < 0.001) and non‐responders (P < 0.001).

Conclusions

Our findings indicate that the metabolic constitutions of both tumor cells and the stroma are valuable additions to the classical histomorphology‐based assessment of tumor response.

Mapping the Lipids of Skin Sebaceous Glands and Hair Follicles by High Spatial Resolution MALDI Imaging Mass Spectrometry

Matrix-assisted laser desorption/ionization (MALDI) imaging mass spectrometry (IMS) is a technology that utilizes the high sensitivity and specificity of mass spectrometry, combined with a high spatial resolution to characterize the molecular species present in skin tissue. In this article, we use MALDI IMS to map specific lipids characteristic of two important skin appendages in minipig skin: the sebaceous glands and hair follicles. A set of specific lipid markers linked to the synthesis of sebum, stages of sebum production, and the secretion of sebum for two different sebaceous gland subzones, the peripheral and central necrotic, were identified. Furthermore, biochemical pathway analysis of the identified markers provides potential drug-targeting strategies to reduce sebum overproduction in pathological conditions. In addition, specific lipid markers characteristic of the different layers in the hair follicle bulge area, including the outer root sheath, the inner root sheath, and the medulla that are associated with the growth cycles of the hair, were determined. This research highlights the ability of MALDI IMS to link a molecular distribution not only to the morphological features in skin tissue but to the physiological state as well. Thus, this platform can provide a basis for the investigation of biochemical pathways as well as the mechanisms of disease and pharmacology in the skin, which will ultimately be critical for drug discovery and the development of dermatology-related illnesses.

Reduced Hemoglobin Signal and Improved Detection of Endogenous Proteins in Blood-Rich Tissues for MALDI Mass Spectrometry Imaging

Mass spectrometry imaging provides a powerful approach for the direct analysis and spatial visualization of molecules in tissue sections. Using matrix-assisted laser desorption/ionization mass spectrometry, intact protein imaging has been widely investigated for biomarker analysis and diagnosis in a variety of tissue types and diseases. However, blood-rich or highly vascular tissues present a challenge in molecular imaging due to the high ionization efficiency of hemoglobin, which leads to ion suppression of endogenous proteins. Here, we describe a protocol to selectively reduce hemoglobin signal in blood-rich tissues that can easily be integrated into mass spectrometry imaging workflows.

Pharmacological inhibition of MERTK induces in vivo retinal degeneration: a multimodal imaging ocular safety assessment

The receptor tyrosine kinase, MERTK, plays an essential role in homeostasis of the retina via efferocytosis of shed outer nuclear segments of photoreceptors. The Royal College of Surgeons rat model of retinal degeneration has been linked to loss-of-function of MERTK, and together with the MERTK knock-out mouse, phenocopy retinitis pigmentosa in humans with MERTK mutations. Given recent efforts and interest in MERTK as a potential immuno-oncology target, development of a strategy to assess ocular safety at an early pre-clinical stage is critical. We have applied a state-of-the-art, multi-modal imaging platform to assess the in vivo effects of pharmacological inhibition of MERTK in mice. This involved the application of mass spectrometry imaging (MSI) to characterize the ocular spatial distribution of our highly selective MERTK inhibitor; AZ14145845, together with histopathology and transmission electron microscopy to characterize pathological and ultra-structural change in response to MERTK inhibition. In addition, we assessed the utility of a human retinal in vitro cell model to identify perturbation of phagocytosis post MERTK inhibition. We identified high localized total compound concentrations in the retinal pigment epithelium (RPE) and retinal lesions following 28 days of treatment with AZ14145845. These lesions were present in 4 of 8 treated animals, and were characterized by a thinning of the outer nuclear layer, loss of photoreceptors (PR) and accumulation of photoreceptor outer segments at the interface of the RPE and PRs. Furthermore, the lesions were very similar to that shown in the RCS rat and MERTK knock-out mouse, suggesting a MERTK-induced mechanism of PR cell death. This was further supported by the observation of reduced phagocytosis in the human retinal cell model following treatment with AZ14145845. Our study provides a viable, translational strategy to investigate the pre-clinical toxicity of MERTK inhibitors but is equally transferrable to novel chemotypes.

Peptide Signatures for Prognostic Markers of Pancreatic Cancer by MALDI Mass Spectrometry Imaging

Despite the overall poor prognosis of pancreatic cancer there is heterogeneity in clinical courses of tumors not assessed by conventional risk stratification. This yields the need of additional markers for proper assessment of prognosis and multimodal clinical management. We provide a proof of concept study evaluating the feasibility of Matrix-assisted laser desorption/ionization (MALDI) mass spectrometry imaging (MSI) to identify specific peptide signatures linked to prognostic parameters of pancreatic cancer. On 18 patients with exocrine pancreatic cancer after tumor resection, MALDI imaging analysis was performed additional to histopathological assessment. Principal component analysis (PCA) was used to explore discrimination of peptide signatures of prognostic histopathological features and receiver operator characteristic (ROC) to identify which specific m/z values are the most discriminative between the prognostic subgroups of patients. Out of 557 aligned m/z values discriminate peptide signatures for the prognostic histopathological features lymphatic vessel invasion (pL, 16 m/z values, eight proteins), nodal metastasis (pN, two m/z values, one protein) and angioinvasion (pV, 4 m/z values, two proteins) were identified. These results yield proof of concept that MALDI-MSI of pancreatic cancer tissue is feasible to identify peptide signatures of prognostic relevance and can augment risk assessment.

Peptidomics: A Review of Clinical Applications and Methodologies

Improvements in both liquid chromatography (LC) and mass spectrometry (MS) instrumentation have greatly enhanced proteomic and small molecule metabolomic analysis in recent years. Less focus has been on the improved capability to detect and quantify small bioactive peptides, even though the exact sequences of the peptide species produced can have important biological consequences. Endogenous bioactive peptide hormones, for example, are generated by the targeted and regulated cleavage of peptides from their prohormone sequence. This process may include organ specific variants, as proglucagon is converted to glucagon in the pancreas but glucagon-like peptide-1 (GLP-1) in the small intestine, with glucagon raising, whereas GLP-1, as an incretin, lowering blood glucose. Therefore, peptidomics workflows must preserve the structure of the processed peptide products to prevent the misidentification of ambiguous peptide species. The poor in vivo and in vitro stability of peptides in biological matrices is a major factor that needs to be considered when developing methods to study them. The bioinformatic analysis of peptidomics data sets requires the inclusion of specific post-translational modifications, which are critical for the function of many bioactive peptides. This review aims to discuss and contrast the various extraction, analytical, and bioinformatics approaches used for human peptidomics studies in a multitude of matrices.

High-Resolution Human Kidney Molecular Histology by Imaging Mass Spectrometry of Lipids

For many years, traditional histology has been the gold standard for the diagnosis of many diseases. However, alternative and powerful techniques have appeared in recent years that complement the information extracted from a tissue section. One of the most promising techniques is imaging mass spectrometry applied to lipidomics. Here, we demonstrate the capabilities of this technique to highlight the architectural features of the human kidney at a spatial resolution of 10 μm. Our data demonstrate that up to seven different segments of the nephron and the interstitial tissue can be readily identified in the sections according to their characteristic lipid fingerprints and that such fingerprints are maintained among different individuals (n = 32). These results set the foundation for further studies on the metabolic bases of the diseases affecting the human kidney.

Mass spectrometry imaging in zebrafish larvae for assessing drug safety and metabolism

Drug safety assessment in the early phases of drug discovery is critical to facilitate the rapid development of novel therapeutics. Recently, teleost zebrafish (Danio rerio) has emerged as a promising vertebrate model for the assessment of drug safety. Zebrafish is a convenient model because of its small size, high fecundity, embryo transparency, and ex utero development. In this study, we developed a matrix-assisted laser desorption/ionization mass spectrometry imaging (MALDI MSI) method applied to zebrafish larvae to investigate safety and metabolism of sahaquine (Sq), an anticancer agent inhibiting histone deacetylase 6. This technique improves on prior studies using liquid chromatography-mass spectrometry (LC-MS) by adding analysis of the drug spatial distribution. Using this method, it was determined that Sq dissolved in fish water (1-2000 μM) did not reach the larval body and was mainly distributed throughout the yolk. High Sq concentration (800 μM) administered intravenously allowed the compound to reach the larval body but did not induce phenotypic abnormalities. Sq was metabolized into its glucuronidated form within 24 h and was excreted within 72 h. MALDI MSI was instrumental in showing that Sq-glucuronide was mainly formed in the gut and slightly in yolk syncytial layer, and provided valuable insights into xenobiotics elimination in zebrafish larvae. This study indicates that Sq has a good safety profile and merits further investigations in other disease models. In addition, the optimized MALDI MSI protocol provided here can be widely applied to study distribution and metabolic fate of other structurally related molecules.

Mass spectrometry-based chemical mapping and profiling toward molecular understanding of diseases in precision medicine

Precision medicine has been strongly promoted in recent years. It is used in clinical management for classifying diseases at the molecular level and for selecting the most appropriate drugs or treatments to maximize efficacy and minimize adverse effects. In precision medicine, an in-depth molecular understanding of diseases is of great importance. Therefore, in the last few years, much attention has been given to translating data generated at the molecular level into clinically relevant information. However, current developments in this field lack orderly implementation. For example, high-quality chemical research is not well integrated into clinical practice, especially in the early phase, leading to a lack of understanding in the clinic of the chemistry underlying diseases. In recent years, mass spectrometry (MS) has enabled significant innovations and advances in chemical research. As reported, this technique has shown promise in chemical mapping and profiling for answering "what", "where", "how many" and "whose" chemicals underlie the clinical phenotypes, which are assessed by biochemical profiling, MS imaging, molecular targeting and probing, biomarker grading disease classification, etc. These features can potentially enhance the precision of disease diagnosis, monitoring and treatment and thus further transform medicine. For instance, comprehensive MS-based biochemical profiling of ovarian tumors was performed, and the results revealed a number of molecular insights into the pathways and processes that drive ovarian cancer biology and the ways that these pathways are altered in correspondence with clinical phenotypes. Another study demonstrated that quantitative biomarker mapping can be predictive of responses to immunotherapy and of survival in the supposedly homogeneous group of breast cancer patients, allowing for stratification of patients. In this context, our article attempts to provide an overview of MS-based chemical mapping and profiling, and a perspective on their clinical utility to improve the molecular understanding of diseases for advancing precision medicine.

Determination of Abundant Metabolite Matrix Adducts Illuminates the Dark Metabolome of MALDI-Mass Spectrometry Imaging Datasets

Spatial metabolomics using mass spectrometry imaging (MSI) is a powerful tool to map hundreds to thousands of metabolites in biological systems. One major challenge in MSI is the annotation of m/z values, which is substantially complicated by background ions introduced throughout the chemicals and equipment used during experimental procedures. Among many factors, the formation of adducts with sodium or potassium ions, or in case of matrix-assisted laser desorption ionization (MALDI)-MSI, the presence of abundant matrix clusters strongly increases total m/z peak counts. Currently, there is a limitation to identify the chemistry of the many unknown peaks to interpret their biological function. We took advantage of the co-localization of adducts with their parent ions and the accuracy of high mass resolution to estimate adduct abundance in 20 datasets from different vendors of mass spectrometers. Metabolites ranging from lipids to amines and amino acids form matrix adducts with the commonly used 2,5-dihydroxybenzoic acid (DHB) matrix like [M + (DHB-H₂O) + H]⁺ and [M + DHB + Na]⁺. Current data analyses neglect those matrix adducts and overestimate total metabolite numbers, thereby expanding the number of unidentified peaks. Our study demonstrates that MALDI-MSI data are strongly influenced by adduct formation across different sample types and vendor platforms and reveals a major influence of so far unrecognized metabolite–matrix adducts on total peak counts (up to one third). We developed a software package, mass2adduct, for the community for an automated putative assignment and quantification of metabolite–matrix adducts enabling users to ultimately focus on the biologically relevant portion of the MSI data.

Predictive Markers of Immunogenicity and Efficacy for Human Vaccines

Vaccines represent one of the major advances of modern medicine. Despite the many successes of vaccination, continuous efforts to design new vaccines are needed to fight "old" pandemics, such as tuberculosis and malaria, as well as emerging pathogens, such as Zika virus and severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2). Vaccination aims at reaching sterilizing immunity, however assessing vaccine efficacy is still challenging and underscores the need for a better understanding of immune protective responses. Identifying reliable predictive markers of immunogenicity can help to select and develop promising vaccine candidates during early preclinical studies and can lead to improved, personalized, vaccination strategies. A systems biology approach is increasingly being adopted to address these major challenges using multiple high-dimensional technologies combined with in silico models. Although the goal is to develop predictive models of vaccine efficacy in humans, applying this approach to animal models empowers basic and translational vaccine research. In this review, we provide an overview of vaccine immune signatures in preclinical models, as well as in target human populations. We also discuss high-throughput technologies used to probe vaccine-induced responses, along with data analysis and computational methodologies applied to the predictive modeling of vaccine efficacy.

Identification of a distinct lipidomic profile in the osteoarthritic synovial membrane by mass spectrometry imaging

OBJECTIVE

Synovial inflammation is one of the most characteristic events in different types of arthritis, including Osteoarthritis (OA). Emerging evidence also suggests the involvement of lipids in the regulation of inflammatory processes. The aim of this study was to elucidate the heterogeneity and spatial distribution of lipids in the OA synovial membrane and explore their putative involvement in inflammation.

METHOD

The abundance and distribution of lipids were examined in human synovial membranes. To this end, histological cuts from this tissue were analysed by matrix-assisted laser desorption ionization - mass spectrometry imaging (MALDI-MSI). The lipidomic profile of OA synovium was characterized and compared with healthy and other forms of inflammatory arthropathies as Rheumatoid Arthritis (RA) and Psoriatic Arthritis (PsA) using principal component analysis and discriminant analysis methods. Lipid identification was undertaken by tandem MS analyses and database queries.

RESULTS

Our results reveal differential and characteristic lipidomic profiles between OA and control samples. Specifically, we unveiled that OA synovium presents elevated levels of phosphatidylcholines, fatty acids and lysophosphatidic acids and lower levels of lysophosphatidylcholines compared to control tissues. The spatial distribution of particular glycerophospholipids was also correlated with hypertrophic, inflamed or vascularized synovial areas. Compared with other inflammatory arthritis, the OA tissue showed lower amounts of phosphatidylethanolamine-based plasmalogens.

CONCLUSIONS

This study provides a novel insight into the lipid profiles of synovial membrane and differences in abundance between OA and control tissues. The lipidomic alterations improves understanding of the pathogenic mechanisms of OA and may be important for its diagnosis.

Quantitative Mass Spectrometry Imaging of Biological Systems

Mass spectrometry imaging (MSI) is a powerful, label-free technique that provides detailed maps of hundreds of molecules in complex samples with high sensitivity and subcellular spatial resolution. Accurate quantification in MSI relies on a detailed understanding of matrix effects associated with the ionization process along with evaluation of the extraction efficiency and mass-dependent ion losses occurring in the analysis step. We present a critical summary of approaches developed for quantitative MSI of metabolites, lipids, and proteins in biological tissues and discuss their current and future applications.

The Lichens' Microbiota, Still a Mystery?

Lichens represent self-supporting symbioses, which occur in a wide range of terrestrial habitats and which contribute significantly to mineral cycling and energy flow at a global scale. Lichens usually grow much slower than higher plants. Nevertheless, lichens can contribute substantially to biomass production. This review focuses on the lichen symbiosis in general and especially on the model species Lobaria pulmonaria L. Hoffm., which is a large foliose lichen that occurs worldwide on tree trunks in undisturbed forests with long ecological continuity. In comparison to many other lichens, L. pulmonaria is less tolerant to desiccation and highly sensitive to air pollution. The name-giving mycobiont (belonging to the Ascomycota), provides a protective layer covering a layer of the green-algal photobiont (Dictyochloropsis reticulata) and interspersed cyanobacterial cell clusters (Nostoc spec.). Recently performed metaproteome analyses confirm the partition of functions in lichen partnerships. The ample functional diversity of the mycobiont contrasts the predominant function of the photobiont in production (and secretion) of energy-rich carbohydrates, and the cyanobiont's contribution by nitrogen fixation. In addition, high throughput and state-of-the-art metagenomics and community fingerprinting, metatranscriptomics, and MS-based metaproteomics identify the bacterial community present on L. pulmonaria as a surprisingly abundant and structurally integrated element of the lichen symbiosis. Comparative metaproteome analyses of lichens from different sampling sites suggest the presence of a relatively stable core microbiome and a sampling site-specific portion of the microbiome. Moreover, these studies indicate how the microbiota may contribute to the symbiotic system, to improve its health, growth and fitness.

Classification of Molecular Subtypes of High-Grade Serous Ovarian Cancer by MALDI-Imaging

Simple Summary

High-grade serous ovarian cancer (HGSOC) accounts for 70% of ovarian carcinomas with sobering survival rates. The mechanisms mediating treatment efficacy are still poorly understood with no adequate biomarkers of response to treatment and risk assessment. This variability of treatment response might be due to its molecular heterogeneity. Therefore, identification of biomarkers or molecular signatures to stratify patients and offer personalized treatment is of utmost priority. Currently, comprehensive gene expression profiling is time- and cost-extensive and limited by tissue heterogeneity. Thus, it has not been implemented into clinical practice. This study demonstrates for the first time a spatially resolved, time- and cost-effective approach to stratifying HGSOC patients by combining novel matrix-assisted laser desorption/ionization imaging mass spectrometry (MALDI-IMS) technology with machine-learning algorithms. Eventually, MALDI-derived predictive signatures for treatment efficacy, recurrent risk, or, as demonstrated here, molecular subtypes might be utilized for emerging clinical challenges to ultimately improve patient outcomes.

Abstract

Despite the correlation of clinical outcome and molecular subtypes of high-grade serous ovarian cancer (HGSOC), contemporary gene expression signatures have not been implemented in clinical practice to stratify patients for targeted therapy. Hence, we aimed to examine the potential of unsupervised matrix-assisted laser desorption/ionization imaging mass spectrometry (MALDI-IMS) to stratify patients who might benefit from targeted therapeutic strategies. Molecular subtyping of paraffin-embedded tissue samples from 279 HGSOC patients was performed by NanoString analysis (ground truth labeling). Next, we applied MALDI-IMS paired with machine-learning algorithms to identify distinct mass profiles on the same paraffin-embedded tissue sections and distinguish HGSOC subtypes by proteomic signature. Finally, we devised a novel approach to annotate spectra of stromal origin. We elucidated a MALDI-derived proteomic signature (135 peptides) able to classify HGSOC subtypes. Random forest classifiers achieved an area under the curve (AUC) of 0.983. Furthermore, we demonstrated that the exclusion of stroma-associated spectra provides tangible improvements to classification quality (AUC = 0.988). Moreover, novel MALDI-based stroma annotation achieved near-perfect classifications (AUC = 0.999). Here, we present a concept integrating MALDI-IMS with machine-learning algorithms to classify patients according to distinct molecular subtypes of HGSOC. This has great potential to assign patients for personalized treatment.

Spatially-resolved proteomics and transcriptomics: An emerging digital spatial profiling approach for tumor microenvironment

Digital spatial profiling (DSP) is an emerging powerful technology for proteomics and transcriptomics analyses in a spatially resolved manner for formalin-fixed paraffin-embedded (FFPE) samples developed by nanoString Technologies. DSP applies several advanced technologies, including high-throughput readout technologies (digital optical barcodes by nCounter instruments or next generation sequencing (NGS)), programmable digital micromirror device (DMD) technology, and microfluidic sampling technologies into traditional immunohistochemistry (IHC) and RNA in situ hybridization (ISH) approaches, creating an innovative tool for discovery, translational research, and clinical uses. Since its launch in 2019, DSP has been rapidly adopted, especially in immuno-oncology and tumor microenvironment research areas, and has revealed valuable information that was inaccessible before. In this article, we report the successful setup and validation of the first DSP technology platform in China. Both DSP spatial protein and RNA profiling approaches were validated using FFPE colorectal cancer tissues. Regions of interest (ROIs) were selected in the areas enriched with tumor cells, stroma/immune cells, or normal epithelial cells, and multiplex spatial profiling of both proteins and RNAs were performed. DSP spatial profiling data were processed and normalized accordingly, validating the high quality and consistency of the data. Unsupervised hierarchical clustering as well as principal component analysis (PCA) grouped tumor, stroma/immune cells, and normal epithelial cells into distinct clusters, indicating that the DSP approach effectively captured the spatially resolved proteomics and transcriptomics profiles of different compartments within the tumor microenvironment. In summary, the results confirmed the expected sensitivity and robustness of the DSP approach in profiling both proteins and RNAs in a spatially resolved manner. As a novel technology in highly complex spatial analyses, DSP endows refined analytical power from the tumor microenvironment perspective with the potential of scaling up to more analyzable targets at relatively low cell input levels. We expect that the DSP technology will greatly advance a wide range of biomedical research, especially in immuno-oncology and tumor microenvironment research areas.

Evaluation of the Aortopathy in the Ascending Aorta: The Novelty of Using Matrix-Assisted Laser Desorption/Ionization Imaging

PURPOSE

Histopathological evaluation presents conflicting reports regarding aortic abnormalities. The authors aim to present proof-of-concept study to explore the feasibility of matrix-assisted laser desorption/ionization imaging mass spectrometry (MALDI-IMS) in combination with histopathology for characterizing alterations in the aneurysmal ascending formalin-fixed paraffin-embedded (FFPE) aorta tissue.

EXPERIMENTAL DESIGN

The authors assess FFPE specimens from patients with a dilated aorta and bicuspid aortic valve (BAV), those with a standard tricuspid aortic valve (TAV), and those with Marfan syndrome (MFS) via histopathology and grade the conditions for elastic fiber fragmentation (EFF) and MALDI-IMS. The proteins using liquid chromatographic-mass spectrometry are identified and the results are confirmed by immunohistochemistry.

RESULTS

There is significant difference in terms of EFF between MFS and BAV, and TAV and BAV. Characteristic peptide signatures and m/z values in the EFF facilitate the characterization among the aortic specimens of BAV, MFS, and TAV. The m/z values from the aortic alpha smooth muscle actin and myosin heavy chains significantly increase in BAV compared with MFS and TAV. These findings are confirmed by immunohistochemistry.

CONCLUSION

The results represent a strategy that uses MALDI-IMS in combination with histopathology as promising approaches to characterize spatial alteration in the structure of the aneurysmal ascending aorta.

MALDI-IMS as a Tool to Determine the Myocardial Response to Syndecan-2-Selected Mesenchymal Stromal Cell Application in an Experimental Model of Diabetic Cardiomyopathy

PURPOSE

Mesenchymal stromal cells (MSC) are an attractive tool for treatment of diabetic cardiomyopathy. Syndecan-2/CD362 has been identified as a functional marker for MSC isolation. Imaging mass spectrometry (IMS) allows for the characterization of therapeutic responses in the left ventricle. This study aims to investigate whether IMS can assess the therapeutic effect of CD362+ -selected MSC on early onset experimental diabetic cardiomyopathy.

EXPERIMENTAL DESIGN

1 × 106 wild type (WT), CD362- , or CD362+ MSC are intravenously injected into db/db mice. Four weeks later, mice are hemodynamically characterized and subsequently sacrificed for IMS combined with bottom-up mass spectrometry, and isoform and phosphorylation analyses of cardiac titin.

RESULTS

Overall alterations of the cardiac proteome signatures, especially titin, are observed in db/db compared to control mice. Interestingly, only CD362+ MSC can overcome the reduced titin intensity distribution and shifts the isoform ratio toward the more compliant N2BA form. In contrast, WT and CD362- MSCs improve all-titin phosphorylation and protein kinase G activity, which is reflected in an improvement in diastolic performance.

CONCLUSIONS AND CLINICAL RELEVANCE

IMS enables the characterization of differences in titin intensity distribution following MSC application. However, further analysis of titin phosphorylation is needed to allow for the assessment of the therapeutic efficacy of MSC.

Spatially resolved 3D metabolomic profiling in tissues

Spatially resolved RNA and protein molecular analyses have revealed unexpected heterogeneity of cells. Metabolic analysis of individual cells complements these single-cell studies. Here, we present a three-dimensional spatially resolved metabolomic profiling framework (3D-SMF) to map out the spatial organization of metabolic fragments and protein signatures in immune cells of human tonsils. In this method, 3D metabolic profiles were acquired by time-of-flight secondary ion mass spectrometry to profile up to 189 compounds. Ion beams were used to measure sub-5-nanometer layers of tissue across 150 sections of a tonsil. To incorporate cell specificity, tonsil tissues were labeled by an isotope-tagged antibody library. To explore relations of metabolic and cellular features, we carried out data reduction, 3D spatial correlations and classifications, unsupervised K-means clustering, and network analyses. Immune cells exhibited spatially distinct lipidomic fragment distributions in lymphatic tissue. The 3D-SMF pipeline affects studying the immune cells in health and disease.

Lactic Acidosis Interferes With Toxicity of Perifosine to Colorectal Cancer Spheroids: Multimodal Imaging Analysis

Colorectal cancer (CRC) is a disease with constantly increasing incidence and high mortality. The treatment efficacy could be curtailed by drug resistance resulting from poor drug penetration into tumor tissue and the tumor-specific microenvironment, such as hypoxia and acidosis. Furthermore, CRC tumors can be exposed to different pH depending on the position in the intestinal tract. CRC tumors often share upregulation of the Akt signaling pathway. In this study, we investigated the role of external pH in control of cytotoxicity of perifosine, the Akt signaling pathway inhibitor, to CRC cells using 2D and 3D tumor models. In 3D settings, we employed an innovative strategy for simultaneous detection of spatial drug distribution and biological markers of proliferation/apoptosis using a combination of mass spectrometry imaging and immunohistochemistry. In 3D conditions, low and heterogeneous penetration of perifosine into the inner parts of the spheroids was observed. The depth of penetration depended on the treatment duration but not on the external pH. However, pH alteration in the tumor microenvironment affected the distribution of proliferation- and apoptosis-specific markers in the perifosine-treated spheroid. Accurate co-registration of perifosine distribution and biological response in the same spheroid section revealed dynamic changes in apoptotic and proliferative markers occurring not only in the perifosine-exposed cells, but also in the perifosine-free regions. Cytotoxicity of perifosine to both 2D and 3D cultures decreased in an acidic environment below pH 6.7. External pH affects cytotoxicity of the other Akt inhibitor, MK-2206, in a similar way. Our innovative approach for accurate determination of drug efficiency in 3D tumor tissue revealed that cytotoxicity of Akt inhibitors to CRC cells is strongly dependent on pH of the tumor microenvironment. Therefore, the effect of pH should be considered during the design and pre-clinical/clinical testing of the Akt-targeted cancer therapy.

A methodological approach to correlate tumor heterogeneity with drug distribution profile in mass spectrometry imaging data

BACKGROUND

Drug mass spectrometry imaging (MSI) data contain knowledge about drug and several other molecular ions present in a biological sample. However, a proper approach to fully explore the potential of such type of data is still missing. Therefore, a computational pipeline that combines different spatial and non-spatial methods is proposed to link the observed drug distribution profile with tumor heterogeneity in solid tumor. Our data analysis steps include pre-processing of MSI data, cluster analysis, drug local indicators of spatial association (LISA) map, and ions selection.

RESULTS

The number of clusters identified from different tumor tissues. The spatial homogeneity of the individual cluster was measured using a modified version of our drug homogeneity method. The clustered image and drug LISA map were simultaneously analyzed to link identified clusters with observed drug distribution profile. Finally, ions selection was performed using the spatially aware method.

CONCLUSIONS

In this paper, we have shown an approach to correlate the drug distribution with spatial heterogeneity in untargeted MSI data. Our approach is freely available in an R package 'CorrDrugTumorMSI'.

Optimization of a MALDI-Imaging protocol for studying adipose tissue-associated disorders

Matrix-assisted laser desorption ionization (MALDI) imaging mass spectrometry (IMS) is increasingly recognized for its potential in the discovery of novel biomarkers directly from tissue sections. However, there are no MALDI IMS studies as yet on the adipose tissue, a lipid-enriched tissue that plays a pivotal role in the development of obesity-associated disorders. Herein, we aimed at developing an optimized method for analyzing adipose tissue lipid composition under both physiological and pathological conditions by MALDI IMS. Our studies showed an exacerbated lipid delocalization from adipose tissue sections when conventional strategies were applied. However, our optimized method using conductive-tape sampling and 2,5-dihydroxybenzoic acid (DHB) as a matrix, preserved the anatomical organization and minimized lipid diffusion from sample sections. This method enabled the identification of a total of 625 down-regulated and 328 up-regulated m/z values in the adipose tissue from a rat model of extreme obesity as compared to lean animals. Combination of MALDI IMS and liquid chromatography (LC)-MS/MS data identified 44 differentially expressed lipid species between lean and obese animals, including phospholipids and sphingomyelins. Among the lipids identified, SM(d18:0_18:2), PE(P-16:0_20:0), and PC(O-16:0_16:1) showed a differential spatial distribution in the adipose tissue of lean vs. obese animals. In sum, our method provides a valuable new tool for research on adipose tissue that may pave the way for the identification of novel biomarkers of obesity and metabolic disease.

MALDI-MSI-A Step Forward in Overcoming the Diagnostic Challenges in Ovarian Tumors

This study presents the use of matrix-assisted laser desorption and ionization mass spectrometry imaging (MALDI-MSI) directly on the tissue of two ovarian tumors that often present a diagnostic challenge, a low-grade serous borderline ovarian tumor and ovarian fibrothecoma. Different spatial distribution of m/z values within the tissue samples was observed, and regiospecific peaks were identified. Among the 106 peaks in the borderline ovarian tumor five, regiospecific peaks (m/z: 2861.35; 2775.79; 3368.34; 3438.43; 4936.37) were selected using FlexImaging software. Subsequently, the distribution of those selected peaks was visualized on the fibrothecoma tissue section, which demonstrated the differences in the tissue homo-/heterogeneous structure of both tumors. The comparison with the histopathological staining of the ovarian borderline tumor tissue section, obtained during serial sectioning, showed a close correlation of the molecular map with the morphological and histopathological features of the tissue and allowed the identification of different tissue types within the sample. This study highlights the potential significance of MSI in enabling morphological characterization of ovarian tumors as well as correct diagnosis and further prognosis than thus far seen in the literature. Osteopontin, tropomyosin and orosomucoid are only a couple of the molecules investigated using MALDI-MSI in ovarian cancer research. This study, in line with the available literature, proves the potential of MALDI-MSI to overcome the current limitations of classic histopathological examination giving a more in-depth insight into the tissue structure and thus lead to the more accurate differential diagnosis of ovarian tumors, especially in the most challenging cases.

137Cs γ Ray and 28Si Irradiation Induced Murine Hepatocellular Carcinoma Lipid Changes in Liver Assessed by MALDI-MSI Combined with Spatial Shrunken Centroid Clustering Algorithm: A Pilot Study

Characterization of lipids by matrix-assisted laser desorption ionization mass spectrometry imaging (MALDI-MSI) is of great interest because not only are lipids important structural molecules in both the cell and internal organelle membranes, but they are also important signaling molecules. MALDI-MSI combined with spatial image segmentation has been previously used to identify tumor heterogeneities within tissues with distinct anatomical regions such as the brain. However, there has been no systematic study utilizing MALDI-MSI combined with spatial image segmentation to assess the tumor microenvironment in the liver. Here, we present that image segmentation can be used to evaluate the tumor microenvironment in the liver. In particular, to better understand the molecular mechanisms of irradiation-induced hepatic carcinogenesis, we used MALDI-MSI in the negative ion mode to identify lipid changes 12 months post exposure to low dose ²⁸Si and ¹³⁷Cs γ ray irradiation. We report here the changes in the lipid profiles of male C3H/HeNCrl mice liver tissues after exposure to irradiation and analyzed using the spatial shrunken centroid clustering algorithm. These findings provide valuable information as astronauts will be exposed to high-charge high-energy (HZE) particles and low-energy γ-ray irradiation during deep space travel. Even at low doses, exposure to these irradiations can lead to cancer. Previous studies infer that irradiation of mice with low-dose HZE particles induces oxidative damage and microenvironmental changes that are thought to play roles in the pathophysiology of hepatocellular carcinoma.

Multiplex enzyme activity imaging by MALDI-IMS of substrate library conversions

Enzymes are fundamental to biological processes and involved in most pathologies. Here we demonstrate the concept of simultaneously mapping multiple enzyme activities (EA) by applying enzyme substrate libraries to tissue sections and analyzing their conversion by matrix-assisted laser desorption/ionization (MALDI) imaging mass spectrometry (IMS). To that end, we spray-applied a solution of 20 naturally derived peptides that are known substrates for proteases, kinases, and phosphatases to zinc-fixed paraffin tissue sections of mouse kidneys. After enzyme conversion for 5 to 120 min at 37 °C and matrix application, the tissue sections were imaged by MALDI-IMS. We could image incubation time-dependently 16 of the applied substrates with differing signal intensities and 12 masses of expected products. Utilizing inherent enzyme amplification, EA-IMS can become a powerful tool to locally study multiple, potentially even lowly expressed, enzyme activities, networks, and their pharmaceutical modulation. Differences in the substrate detectability highlight the need for future optimizations.

Light sheet fluorescence microscopy guided MALDI-imaging mass spectrometry of cleared tissue samples

Light sheet fluorescence microscopy (LSFM) of optically cleared biological samples represents a powerful tool to analyze the 3-dimensional morphology of tissues and organs. Multimodal combinations of LSFM with additional analyses of the identical sample help to limit the consumption of restricted specimen and reduce inter-sample variation. Here, we demonstrate the proof-of-concept that LSFM of cleared brain tissue samples can be combined with Matrix Assisted Laser Desorption/Ionization-Mass Spectrometry Imaging (MALDI-MSI) for detection and quantification of proteins. Samples of freshly dissected murine brain and of archived formalin-fixed paraffin-embedded (FFPE) human brain tissue were cleared (3DISCO). Tissue regions of interest were defined by LSFM and excised, (re)-embedded in paraffin, and sectioned. Mouse sections were coated with sinapinic acid matrix. Human brain sections were pre-digested with trypsin and coated with α-cyano-4-hydroxycinnamic acid matrix. Subsequently, sections were subjected to MALDI-time-of-flight (TOF)-MSI in mass ranges between 0.8 to 4 kDa (human tissue sections), or 2.5–25 kDa (mouse tissue sections) with a lateral resolution of 50 µm. Protein- and peptide-identities corresponding to acquired MALDI-MSI spectra were confirmed by parallel liquid chromatography tandem mass spectrometry (LC–MS/MS) analysis. The spatial abundance- and intensity-patterns of established marker proteins detected by MALDI-MSI were also confirmed by immunohistochemistry.

Classification of Inflammatory Bowel Disease from Formalin-Fixed, Paraffin-Embedded Tissue Biopsies via Imaging Mass Spectrometry

PURPOSE

Discrimination between ulcerative colitis (UC) and Crohn's disease (CD) by histologic features alone can be challenging and often leads to inaccurate initial diagnoses in inflammatory bowel disease (IBD) patients. This is mostly due to an overlap of clinical and histologic features. However, exact diagnosis is not only important for patient treatment but it also has a socioeconomic impact. It is therefore important to develop and improve diagnostic tools complementing traditional histomorphological approaches.

EXPERIMENTAL DESIGN

In this retrospective proof-of-concept study, the utilization of MALDI imaging is explored in combination with multi-variate data analysis methods to classify formalin-fixed, paraffin-embedded (FFPE) colon biopsies from UC (87 biopsies, 14 patients), CD (71 biopsies, 14 patients), and normal colonic (21 biopsies, 14 patients) tissues.

RESULTS

The proposed method results in an overall balanced accuracy of 85.7% on patient and of 80.4% on sample level, thus demonstrating that the assessment of IBD from FFPE tissue specimens via MALDI imaging is feasible.

CONCLUSIONS AND CLINICAL RELEVANCE

The results emphasize the high potential of this method to distinguish IBD subtypes in FFPE tissue sections, which is a prerequisite for further investigations in retrospective multicenter studies, as well as for a future implementation into clinical routine.

Patient Management in Aortic Stenosis: Towards Precision Medicine Through Protein Analysis, Imaging and Diagnostic Tests

Aortic stenosis is the most frequent valvular disease in developed countries. It progresses from mild fibrocalcific leaflet changes to a more severe leaflet calcification at the end stages of the disease. Unfortunately, symptoms of aortic stenosis are unspecific and only appear when it is too late, complicating patients' management. The global impact of aortic stenosis is increasing due to the growing elderly population. The disease supposes a great challenge because of the multiple comorbidities of these patients. Nowadays, the only effective treatment is valve replacement, which has a high cost in both social and economic terms. For that reason, it is crucial to find potential diagnostic, prognostic and therapeutic indicators that could help us to detect this disease in its earliest stages. In this article, we comprehensively review several key observations and translational studies related to protein markers that are promising for being implemented in the clinical field as well as a discussion about the role of precision medicine in aortic stenosis.

Discovery of Prognostic Markers for Early-Stage High-Grade Serous Ovarian Cancer by Maldi-Imaging

With regard to relapse and survival, early-stage high-grade serous ovarian (HGSOC) patients comprise a heterogeneous group and there is no clear consensus on first-line treatment. Currently, no prognostic markers are available for risk assessment by standard targeted immunohistochemistry and novel approaches are urgently required. Here, we applied MALDI-imaging mass spectrometry (MALDI-IMS), a new method to identify distinct mass profiles including protein signatures on paraffin-embedded tissue sections. In search of prognostic biomarker candidates, we compared proteomic profiles of primary tumor sections from early-stage HGSOC patients with either recurrent (RD) or non-recurrent disease (N = 4; each group) as a proof of concept study. In total, MALDI-IMS analysis resulted in 7537 spectra from the malignant tumor areas. Using receiver operating characteristic (ROC) analysis, 151 peptides were able to discriminate between patients with RD and non-RD (AUC > 0.6 or < 0.4; p < 0.01), and 13 of them could be annotated to proteins. Strongest expression levels of specific peptides linked to Keratin type1 and Collagen alpha-2(I) were observed and associated with poor prognosis (AUC > 0.7). These results confirm that in using IMS, we could identify new candidates to predict clinical outcome and treatment extent for patients with early-stage HGSOC.

The development and clinical applications of proteomics: an Indian perspective

INTRODUCTION

Proteomic research has been extensively used to identify potential biomarkers or targets for various diseases. Advances in mass spectrometry along with data analytics have led proteomics to become a powerful tool for exploring the critical molecular players associated with diseases, thereby, playing a significant role in the development of proteomic applications for the clinic.

AREAS COVERED

This review presents recent advances in the development and clinical applications of proteomics in India toward understanding various diseases including cancer, metabolic diseases, and reproductive diseases. Keywords combined with 'clinical proteomics in India' 'proteomic research in India' and 'mass spectrometry' were used to search PubMed.

EXPERT OPINION

The past decade has seen a significant increase in research in clinical proteomics in India. This approach has resulted in the development of proteomics-based marker technologies for disease management in the country. The majority of these investigations are still in the discovery phase and efforts have to be made to address the intended clinical use so that the identified potential biomarkers reach the clinic. To move toward this necessity, there is a pressing need to establish some key infrastructure requirements and meaningful collaborations between the clinicians and scientists which will enable more effective solutions to address health issues specific to India.