Multimodal MALDI Imaging Mass Spectrometry Reveals Spatially Correlated Lipid and Protein Changes in Mouse Heart with Acute Myocardial Infarction

Intersection of Immunology and Metabolism in Myocardial Disease

Immunometabolism is an emerging field at the intersection of immunology and metabolism. Immune cell activation plays a critical role in the pathogenesis of cardiovascular diseases and is integral for regeneration during cardiac injury. We currently possess a limited understanding of the processes governing metabolic interactions between immune cells and cardiomyocytes. The impact of this intercellular crosstalk can manifest as alterations to the steady state flux of metabolites and impact cardiac contractile function. Although much of our knowledge is derived from acute inflammatory response, recent work emphasizes heterogeneity and flexibility in metabolism between cardiomyocytes and immune cells during pathological states, including ischemic, cardiometabolic, and cancer-associated disease. Metabolic adaptation is crucial because it influences immune cell activation, cytokine release, and potential therapeutic vulnerabilities. This review describes current concepts about immunometabolic regulation in the heart, focusing on intercellular crosstalk and intrinsic factors driving cellular regulation. We discuss experimental approaches to measure the cardio-immunologic crosstalk, which are necessary to uncover unknown mechanisms underlying the immune and cardiac interface. Deeper insight into these axes holds promise for therapeutic strategies that optimize cardioimmunology crosstalk for cardiac health.

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.

A Machine Learning-Driven Comparison of Ion Images Obtained by MALDI and MALDI-2 Mass Spectrometry Imaging

Matrix-assisted laser desorption ionization mass spectrometry imaging (MALDI-MSI) enables label-free imaging of biomolecules in biological tissues. However, many species remain undetected due to their poor ionization efficiencies. MALDI-2 (laser-induced post-ionization) is the most widely used post-ionization method for improving analyte ionization efficiencies. Mass spectra acquired using MALDI-2 constitute a combination of ions generated by both MALDI and MALDI-2 processes. Until now, no studies have focused on a detailed comparison between the ion images (as opposed to the generated m/z values) produced by MALDI and MALDI-2 for mass spectrometry imaging (MSI) experiments. Herein, we investigated the ion images produced by both MALDI and MALDI-2 on the same tissue section using correlation analysis (to explore similarities in ion images for ions common to both MALDI and MALDI-2) and a deep learning approach. For the latter, we used an analytical workflow based on the Xception convolutional neural network, which was originally trained for human-like natural image classification but which we adapted to elucidate similarities and differences in ion images obtained using the two MSI techniques. Correlation analysis demonstrated that common ions yielded similar spatial distributions with low-correlation species explained by either poor signal intensity in MALDI or the generation of additional unresolved signals using MALDI-2. Using the Xception-based method, we identified many regions in the t-SNE space of spatially similar ion images containing MALDI and MALDI-2-related signals. More notably, the method revealed distinct regions containing only MALDI-2 ion images with unique spatial distributions that were not observed using MALDI. These data explicitly demonstrate the ability of MALDI-2 to reveal molecular features and patterns as well as histological regions of interest that are not visible when using conventional MALDI.

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.

Optimized combination of MALDI MSI and immunofluorescence for neuroimaging of lipids within cellular microenvironments

Proper neurological function relies on the cellular and molecular microenvironment of the brain, with perturbations of this environment leading to neurological disorders. However, studying the microenvironments of neurological tissue has proven difficult because of its inherent complexity. Both the cell type and metabolomic underpinnings of the cell have crucial functional roles, thus making multimodal characterization methods key to acquiring a holistic view of the brain's microenvironment. This study investigates methods for combining matrix-assisted laser desorption/ionization mass spectrometry imaging (MALDI MSI) and immunofluorescence (IF) microscopy to enable the concurrent investigation of cell types and lipid profiles on the same sample. In brief, 1,5-diaminonaphthalene (DAN), α-cyano-4-hydroxy-cinnamic acid (CHCA), and 2,5-dihydroxybenzoic acid (DHB) were tested in addition to instrument-specific parameters for compatibility with IF. Alternatively, the effects of IF protocols on MALDI MSI were also tested, showing significant signal loss with all tested permutations. Ultimately, the use of CHCA for MALDI MSI resulted in the best IF images, while the use of DAN gave the lowest quality IF images. Overall, increasing the laser power and number of shots per laser burst resulted in the most tissue ablation. However, optimized parameter settings allowed for minimal tissue ablation while maintaining sufficient MALDI MSI signal.

Systems Immunology Approaches to Metabolism

Over the last decade, immunometabolism has emerged as a novel interdisciplinary field of research and yielded significant fundamental insights into the regulation of immune responses. Multiple classical approaches to interrogate immunometabolism, including bulk metabolic profiling and analysis of metabolic regulator expression, paved the way to appreciating the physiological complexity of immunometabolic regulation in vivo. Studying immunometabolism at the systems level raised the need to transition towards the next-generation technology for metabolic profiling and analysis. Spatially resolved metabolic imaging and computational algorithms for multi-modal data integration are new approaches to connecting metabolism and immunity. In this review, we discuss recent studies that highlight the complex physiological interplay between immune responses and metabolism and give an overview of technological developments that bear the promise of capturing this complexity most directly and comprehensively.

Emerging Computational Methods in Mass Spectrometry Imaging

Mass spectrometry imaging (MSI) is a powerful analytical technique that generates maps of hundreds of molecules in biological samples with high sensitivity and molecular specificity. Advanced MSI platforms with capability of high-spatial resolution and high-throughput acquisition generate vast amount of data, which necessitates the development of computational tools for MSI data analysis. In addition, computation-driven MSI experiments have recently emerged as enabling technologies for further improving the MSI capabilities with little or no hardware modification. This review provides a critical summary of computational methods and resources developed for MSI data analysis and interpretation along with computational approaches for improving throughput and molecular coverage in MSI experiments. This review is focused on the recently developed artificial intelligence methods and provides an outlook for a future paradigm shift in MSI with transformative computational methods.

Protein Alterations in Cardiac Ischemia/Reperfusion Revealed by Spatial-Omics

Myocardial infarction is the most common cause of death worldwide. An understanding of the alterations in protein pathways is needed in order to develop strategies that minimize myocardial damage. To identify the protein signature of cardiac ischemia/reperfusion (I/R) injury in rats, we combined, for the first time, protein matrix-assisted laser desorption/ionization mass spectrometry imaging (MALDI-MSI) and label-free proteomics on the same tissue section placed on a conductive slide. Wistar rats were subjected to I/R surgery and sacrificed after 24 h. Protein MALDI-MSI data revealed ischemia specific regions, and distinct profiles for the infarct core and border. Firstly, the infarct core, compared to histologically unaffected tissue, showed a significant downregulation of cardiac biomarkers, while an upregulation was seen for coagulation and immune response proteins. Interestingly, within the infarct tissue, alterations in the cytoskeleton reorganization and inflammation were found. This work demonstrates that a single tissue section can be used for protein-based spatial-omics, combining MALDI-MSI and label-free proteomics. Our workflow offers a new methodology to investigate the mechanisms of cardiac I/R injury at the protein level for new strategies to minimize damage after MI.

Applications of MALDI-MS/MS-Based Proteomics in Biomedical Research

Matrix-assisted laser desorption/ionization (MALDI) mass spectrometry (MS) is one of the most widely used techniques in proteomics to achieve structural identification and characterization of proteins and peptides, including their variety of proteoforms due to post-translational modifications (PTMs) or protein–protein interactions (PPIs). MALDI-MS and MALDI tandem mass spectrometry (MS/MS) have been developed as analytical techniques to study small and large molecules, offering picomole to femtomole sensitivity and enabling the direct analysis of biological samples, such as biofluids, solid tissues, tissue/cell homogenates, and cell culture lysates, with a minimized procedure of sample preparation. In the last decades, structural identification of peptides and proteins achieved by MALDI-MS/MS helped researchers and clinicians to decipher molecular function, biological process, cellular component, and related pathways of the gene products as well as their involvement in pathogenesis of diseases. In this review, we highlight the applications of MALDI ionization source and tandem approaches for MS for analyzing biomedical relevant peptides and proteins. Furthermore, one of the most relevant applications of MALDI-MS/MS is to provide “molecular pictures”, which offer in situ information about molecular weight proteins without labeling of potential targets. Histology-directed MALDI-mass spectrometry imaging (MSI) uses MALDI-ToF/ToF or other MALDI tandem mass spectrometers for accurate sequence analysis of peptide biomarkers and biological active compounds directly in tissues, to assure complementary and essential spatial data compared with those obtained by LC-ESI-MS/MS technique.

Comparing DESI-MSI and MALDI-MSI Mediated Spatial Metabolomics and Their Applications in Cancer Studies

Metabolic heterogeneity of cancer contributes significantly to its poor treatment outcomes and prognosis. As a result, studies continue to focus on identifying new biomarkers and metabolic vulnerabilities, both of which depend on the understanding of altered metabolism in cancer. In the recent decades, the rise of mass spectrometry imaging (MSI) enables the in situ detection of large numbers of small molecules in tissues. Therefore, researchers look to using MSI-mediated spatial metabolomics to further study the altered metabolites in cancer patients. In this review, we examined the two most commonly used spatial metabolomics techniques, MALDI-MSI and DESI-MSI, and some recent highlights of their applications in cancer studies. We also described AFADESI-MSI as a recent variation from the DESI-MSI and compare it with the two major techniques. Specifically, we discussed spatial metabolomics results in four types of heterogeneous malignancies, including breast cancer, esophageal cancer, glioblastoma and lung cancer. Multiple studies have effectively classified cancer tissue subtypes using altered metabolites information. In addition, distribution trends of key metabolites such as fatty acids, high-energy phosphate compounds, and antioxidants were identified. Therefore, while the visualization of finer distribution details requires further improvement of MSI techniques, past studies have suggested spatial metabolomics to be a promising direction to study the complexity of cancer pathophysiology.

Mass Spectrometry Imaging Spatial Tissue Analysis toward Personalized Medicine

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

Evaluation of 6 MALDI-Matrices for 10 μm Lipid Imaging and On-Tissue MSn with AP-MALDI-Orbitrap

Mass spectrometry imaging is a technique uniquely suited to localize and identify lipids in a tissue sample. Using an atmospheric pressure (AP-) matrix-assisted laser desorption ionization (MALDI) source coupled to an Orbitrap Elite, numerous lipid locations and structures can be determined in high mass resolution spectra and at cellular spatial resolution, but careful sample preparation is necessary. We tested 11 protocols on serial brain sections for the commonly used MALDI matrices CHCA, norharmane, DHB, DHAP, THAP, and DAN in combination with tissue washing and matrix additives to determine the lipid coverage, signal intensity, and spatial resolution achievable with AP-MALDI. In positive-ion mode, the most lipids could be detected with CHCA and THAP, while THAP and DAN without additional treatment offered the best signal intensities. In negative-ion mode, DAN showed the best lipid coverage and DHAP performed superiorly for gangliosides. DHB produced intense cholesterol signals in the white matter. One hundred fifty-five lipids were assigned in positive-ion mode (THAP) and 137 in negative-ion mode (DAN), and 76 peaks were identified using on-tissue tandem-MS. The spatial resolution achievable with DAN was 10 μm, confirmed with on tissue line-scans. This enabled the association of lipid species to single neurons in AP-MALDI images. The results show that the performance of AP-MALDI is comparable to vacuum MALDI techniques for lipid imaging.

Referenced Kendrick Mass Defect Annotation and Class-Based Filtering of Imaging MS Lipidomics Experiments

Because of their diverse functionalities in cells, lipids are of primary importance when characterizing molecular profiles of physiological and disease states. Imaging mass spectrometry (IMS) provides the spatial distributions of lipid populations in tissues. Referenced Kendrick mass defect (RKMD) analysis is an effective mass spectrometry (MS) data analysis tool for classification and annotation of lipids. Herein, we extend the capabilities of RKMD analysis and demonstrate an integrated method for lipid annotation and chemical structure-based filtering for IMS datasets. Annotation of lipid features with lipid molecular class, radyl carbon chain length, and degree of unsaturation allows image reconstruction and visualization based on each structural characteristic. We show a proof-of-concept application of the method to a computationally generated IMS dataset and validate that the RKMD method is highly specific for lipid components in the presence of confounding background ions. Moreover, we demonstrate an application of the RKMD-based annotation and filtering to matrix-assisted laser desorption/ionization (MALDI) IMS lipidomic data from human kidney tissue analysis.

Graphene Quantum Dots-Based Electrochemical Biosensing Platform for Early Detection of Acute Myocardial Infarction

Heart failure resulting from acute myocardial infarction (AMI) is an important global health problem. Treatments of heart failure and AMI have improved significantly over the past two decades; however, the available diagnostic tests only give limited insights into these heterogeneous conditions at a reversible stage and are not precise enough to evaluate the status of the tissue at high risk. Innovative diagnostic tools for more accurate, more reliable, and early diagnosis of AMI are urgently needed. A promising solution is the timely identification of prognostic biomarkers, which is crucial for patients with AMI, as myocardial dysfunction and infarction lead to more severe and irreversible changes in the cardiovascular system over time. The currently available biomarkers for AMI detection include cardiac troponin I (cTnI), cardiac troponin T (cTnT), myoglobin, lactate dehydrogenase, C-reactive protein, and creatine kinase and myoglobin. Most recently, electrochemical biosensing technologies coupled with graphene quantum dots (GQDs) have emerged as a promising platform for the identification of troponin and myoglobin. The results suggest that GQDs-integrated electrochemical biosensors can provide useful prognostic information about AMI at an early, reversible, and potentially curable stage. GQDs offer several advantages over other nanomaterials that are used for the electrochemical detection of AMI such as strong interactions between cTnI and GQDs, low biomarker consumption, and reusability of the electrode; graphene-modified electrodes demonstrate excellent electrochemical responses due to the conductive nature of graphene and other features of GQDs (e.g., high specific surface area, π-π interactions with the analyte, facile electron-transfer mechanisms, size-dependent optical features, interplay between bandgap and photoluminescence, electrochemical luminescence emission capability, biocompatibility, and ease of functionalization). Other advantages include the presence of functional groups such as hydroxyl, carboxyl, carbonyl, and epoxide groups, which enhance the solubility and dispersibility of GQDs in a wide variety of solvents and biological media. In this perspective article, we consider the emerging knowledge regarding the early detection of AMI using GQDs-based electrochemical sensors and address the potential role of this sensing technology which might lead to more efficient care of patients with AMI.

A new update of MALDI-TOF mass spectrometry in lipid research

Matrix-assisted laser desorption and ionization (MALDI) mass spectrometry (MS) is an indispensable tool in modern lipid research since it is fast, sensitive, tolerates sample impurities and provides spectra without major analyte fragmentation. We will discuss some methodological aspects, the related ion-forming processes and the MALDI MS characteristics of the different lipid classes (with the focus on glycerophospholipids) and the progress, which was achieved during the last ten years. Particular attention will be given to quantitative aspects of MALDI MS since this is widely considered as the most serious drawback of the method. Although the detailed role of the matrix is not yet completely understood, it will be explicitly shown that the careful choice of the matrix is crucial (besides the careful evaluation of the positive and negative ion mass spectra) in order to be able to detect all lipid classes of interest. Two developments will be highlighted: spatially resolved Imaging MS is nowadays well established and the distribution of lipids in tissues merits increasing interest because lipids are readily detectable and represent ubiquitous compounds. It will also be shown that a combination of MALDI MS with thin-layer chromatography (TLC) enables a fast spatially resolved screening of an entire TLC plate which makes the method competitive with LC/MS.

Molecular Histology Analysis of Cryopreserved Tissue Using Peptide/Protein MALDI-TOF Imaging Mass Spectrometry (MALDI-IMS)

Matrix-assisted laser desorption/ionization (MALDI) imaging mass spectrometry (IMS) has emerged as a powerful tool for analyzing the spatial distribution of peptides, small proteins, and other molecules within biological tissues. The obtained signals can be correlated with underlying tissue architecture, without any geometrical distortion, enabling the so-called molecular histology. Here, we analyzed cryopreserved tissue samples employing the MALDI-IMS for proteins and peptides. We used a nonstandard OCT-free cryo-slicing protocol, followed by Carnoy delipidation. Automated matrix spray was utilized to circumvent some of MALDI-IMS technology drawbacks in protein and peptide analysis.

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

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

Matrix-Assisted Laser Desorption/Ionization Mass Spectrometry Imaging of Phospholipid Changes in a Drosophila Model of Early Amyotrophic Lateral Sclerosis

Amyotrophic lateral sclerosis (ALS) is a degenerative disease caused by motor neuron damage in the central nervous system, and it is difficult to diagnose early. Drosophila melanogaster is widely used to investigate disease mechanisms and discover biomarkers because it is easy to induce disease in Drosophila through genetic engineering. We performed matrix-assisted laser desorption/ionization-mass spectrometry imaging (MALDI-MSI) to investigate changes in phospholipid distribution in the brain tissue of an ALS-induced Drosophila model. Fly brain tissues of several hundred micrometers or less were sampled using a fly collar to obtain reproducible tissue sections of similar sizes. MSI of brain tissues of Drosophila cultured for 1 or 10 days showed that the distribution of phospholipids, including phosphatidylcholine (PC), phosphatidylethanolamine (PE), phosphatidic acid (PA), phosphatidylserine (PS), and phosphatidylinositol (PI), was significantly different between the control group and the ALS group. In addition, the lipid profile according to phospholipids differed as the culture time increased from 1 to 10 days. These results suggest that disease indicators based on lipid metabolites can be discovered by performing MALDI-MSI on very small brain tissue samples from the Drosophila disease model to ultimately assess the phospholipid changes that occur in early-stage ALS.

A Caffeic Acid Matrix Improves In Situ Detection and Imaging of Proteins with High Molecular Weight Close to 200,000 Da in Tissues by Matrix-Assisted Laser Desorption/Ionization Mass Spectrometry Imaging

To our knowledge, this was the first study in which caffeic acid (CA) was successfully evaluated as a matrix to enhance the in situ detection and imaging of endogenous proteins in three biological tissue sections (i.e., a rat brain and Capparis masaikai and germinating soybean seeds) by matrix-assisted laser desorption/ionization mass spectrometry imaging (MALDI-MSI). Our results show several properties of CA, including strong ultraviolet absorption, a super-wide MS detection mass range close to 200,000 Da, micrometer-sized matrix crystals, uniform matrix deposition, and high ionization efficiency. More high-molecular-weight (HMW) protein ion signals (m/z > 30,000) could be clearly detected in biological tissues with the use of CA, compared to two commonly used MALDI matrices, i.e., sinapinic acid (SA) and ferulic acid (FA). Notably, CA shows excellent performance for HMW protein in situ detection from biological tissues in the mass range m/z > 80,000, compared to the use of SA and FA. Furthermore, the use of a CA matrix also significantly enhanced the imaging of proteins on the surface of selected biological tissue sections. Three HMW protein ion signals (m/z 50,419, m/z 65,874, and m/z 191,872) from a rat brain, two sweet proteins (mabinlin-2 and mabinlin-4) from a Capparis masaikai seed, and three HMW protein ion signals (m/z 94,838, m/z 134,204, and m/z 198,738) from a germinating soybean seed were successfully imaged for the first time. Our study proves that CA has the potential to become a standard organic acid matrix for enhanced tissue imaging of HMW proteins by MALDI-MSI in both animal and plant tissues.

M2aia-Interactive, fast, and memory-efficient analysis of 2D and 3D multi-modal mass spectrometry imaging data

BACKGROUND

Mass spectrometry imaging (MSI) is a label-free analysis method for resolving bio-molecules or pharmaceuticals in the spatial domain. It offers unique perspectives for the examination of entire organs or other tissue specimens. Owing to increasing capabilities of modern MSI devices, the use of 3D and multi-modal MSI becomes feasible in routine applications-resulting in hundreds of gigabytes of data. To fully leverage such MSI acquisitions, interactive tools for 3D image reconstruction, visualization, and analysis are required, which preferably should be open-source to allow scientists to develop custom extensions.

FINDINGS

We introduce M2aia (MSI applications for interactive analysis in MITK), a software tool providing interactive and memory-efficient data access and signal processing of multiple large MSI datasets stored in imzML format. M2aia extends MITK, a popular open-source tool in medical image processing. Besides the steps of a typical signal processing workflow, M2aia offers fast visual interaction, image segmentation, deformable 3D image reconstruction, and multi-modal registration. A unique feature is that fused data with individual mass axes can be visualized in a shared coordinate system. We demonstrate features of M2aia by reanalyzing an N-glycan mouse kidney dataset and 3D reconstruction and multi-modal image registration of a lipid and peptide dataset of a mouse brain, which we make publicly available.

CONCLUSIONS

To our knowledge, M2aia is the first extensible open-source application that enables a fast, user-friendly, and interactive exploration of large datasets. M2aia is applicable to a wide range of MSI analysis tasks.

Electroblotting through Enzymatic Membranes to Enhance Molecular Tissue Imaging

MALDI-TOF mass spectrometry imaging (MSI) is a powerful tool for studying biomolecule localization in tissue. Protein distributions in tissue provide important histological information; however, large proteins exhibit a high limit of detection in MALDI-MS when compared to their corresponding smaller proteolytic peptides. As a result, several techniques have emerged to digest proteins into more detectable peptides for imaging. Digestion is typically accomplished through trypsin deposition on the tissue, but this technique increases the complexity of the tissue microenvironment, which can limit the number of detectable species. This proof-of-principle study explores tryptic tissue digestion during electroblotting through a trypsin-containing membrane. This approach actively extracts and enzymatically digests proteins from mouse brain tissue sections while simultaneously reducing the complexity of the tissue microenvironment (compared to trypsin deposition on the surface) to obtain an increased number of detectable peptide fragments. The method does not greatly compromise spatial location or require expensive devices to uniformly deposit trypsin on tissue. Using electrodigestion through membranes, we detected and tentatively identified several tryptic peptides that were not observed after on-tissue digestion. Moreover, the use of pepsin rather than trypsin in digestion membranes allows extraction and digestion at low pH to detect peptides from a complementary subset of tissue proteins. Future studies will aim to further improve the method, including changing the substrate membrane to increase spatial resolution and the number of detected peptides.

Engineering of injectable hydrogels associate with Adipose-Derived stem cells delivery for anti-cardiac hypertrophy agents

Adipose-derived stem cells (ADSCs) treatment offers support to new methods of transporting baseline cell protein endothelial cells in alginate (A)/silk sericin (SS) lamellar-coated antioxidant system (ASS@L) to promote acute myocardial infarction. In the synthesized frames of ASS, the ratio of fixity modules, pores, the absorption and inflammation was detected at ka (65ka), 151 ± 40.12 μm, 92.8%, 43.2 ± 2.58 and 30.10 ± 2.1. In this context, ADSC-ASS@L was developed and the corresponding material was stable and physically chemical for the development of cardiac regenerative applications. ADSC-ASS@L injectable hydrogels in vitro examination demonstrated higher cell survival rates and pro-angiogenic and pro-Inflammatory expression factors, demonstrating the favorable effect of fractional ejections, fibre-areas, and low infracture vessel densities. In successful cardiac damage therapy in acute myocardial infarction the innovative ADSC injection hydrogel approach may be helpful. The approach could also be effective during coronary artery hypertrophy for successful heart damage treatment.

Detecting early myocardial ischemia in rat heart by MALDI imaging mass spectrometry

Diagnostics of myocardial infarction in human post-mortem hearts can be achieved only if ischemia persisted for at least 6–12 h when certain morphological changes appear in myocardium. The initial 4 h of ischemia is difficult to diagnose due to lack of a standardized method. Developing a panel of molecular tissue markers is a promising approach and can be accelerated by characterization of molecular changes. This study is the first untargeted metabolomic profiling of ischemic myocardium during the initial 4 h directly from tissue section. Ischemic hearts from an ex-vivo Langendorff model were analysed using matrix assisted laser desorption/ionization imaging mass spectrometry (MALDI IMS) at 15 min, 30 min, 1 h, 2 h, and 4 h. Region-specific molecular changes were identified even in absence of evident histological lesions and were segregated by unsupervised cluster analysis. Significantly differentially expressed features were detected by multivariate analysis starting at 15 min while their number increased with prolonged ischemia. The biggest significant increase at 15 min was observed for m/z 682.1294 (likely corresponding to S-NADHX—a damage product of nicotinamide adenine dinucleotide (NADH)). Based on the previously reported role of NAD⁺/NADH ratio in regulating localization of the sodium channel (Na_v1.5) at the plasma membrane, Na_v1.5 was evaluated by immunofluorescence. As expected, a fainter signal was observed at the plasma membrane in the predicted ischemic region starting 30 min of ischemia and the change became the most pronounced by 4 h. Metabolomic changes occur early during ischemia, can assist in identifying markers for post-mortem diagnostics and improve understanding of molecular mechanisms.

Multimodal chemical imaging of a single brain tissue section using ToF-SIMS, MALDI-ToF and immuno/histochemical staining

Cluster ion beam ToF-SIMS and/or MALDI-ToF mass spectrometry imaging (using 1,5-DAN matrix via sublimation) of a single coronal rat brain tissue section followed by classical- or immuno- histochemical staining faclilated a new multimodal chemical imaging workflow allowing complementary correlation of the lipid molecular ion images with the immuno/histological features within cerebellum region of the same brain tisue section.