MALDI imaging mass spectrometry of human tissue: method challenges and clinical perspectives

Resolving multi-image spatial lipidomic responses to inhaled toxicants by machine learning

Regional responses to inhaled toxicants are essential to understand the pathogenesis of lung disease under exposure to air pollution. We evaluated the effect of combined allergen sensitization and ozone exposure on eliciting spatial differences in lipid distribution in the mouse lung that may contribute to ozone-induced exacerbations in asthma. Lung lobes from male and female BALB/c mice were cryosectioned and acquired by high resolution mass spectrometry imaging (MSI). Processed MSI peak annotations were validated by LC-MS/MS data from scraped tissue slides and microdissected lung tissue. Images were normalized and segmented into clusters. Interestingly, segmented clusters overlapped with stained serial tissue sections, enabling statistical analysis across biological replicates for morphologically relevant lung regions. Spatially distinct lipids had higher overall degree of unsaturated fatty acids in distal lung regions compared to proximal regions. Furthermore, the airway and alveolar epithelium exhibited significantly decreased sphingolipid and glycerophospholipid abundance in females, but not in males. We demonstrate the potential role of lipid saturation in healthy lung function and highlight sex differences in regional lung lipid distribution following ozone exposure. Our study provides a framework for future MSI experiments capable of relative quantification across biological replicates and expansion to multiple sample types, including human tissue.

The Role of Lipid Metabolism Disorders in the Development of Thyroid Cancer

Thyroid cancer (TC) is a neoplasm with an increasing incidence worldwide. Its etiology is complex and based on a multi-layered interplay of factors. Among these, disorders of lipid metabolism have emerged as an important area of investigation. Cancer cells are metabolically reprogrammed to promote their rapid growth, proliferation, and survival. This reprogramming is associated with significant changes at the level of lipids, mainly fatty acids (FA), as they play a critical role in maintaining cell structure, facilitating signaling pathways, and providing energy. These lipid-related changes help cancer cells meet the increased demands of continued growth and division while adapting to the tumor microenvironment. In this review, we examine lipid metabolism at different stages, including synthesis, transport, and oxidation, in the context of TC and the effects of obesity and hormones on TC development. Recent scientific efforts have revealed disturbances in lipid homeostasis that are specific to thyroid cancer, opening up potential avenues for early detection and targeted therapeutic interventions. Understanding the intricate metabolic pathways involved in FA metabolism may provide insights into potential interventions to prevent cancer progression and mitigate its effects on surrounding tissues.

Mass Spectrometry Imaging Techniques: Non-Ambient and Ambient Ionization Approaches

Molecular information can be acquired from sample surfaces in real time using a revolutionary molecular imaging technique called mass spectrometry imaging (MSI). The technique can concurrently provide high spatial resolution information on the spatial distribution and relative proportion of many different compounds. Thus, many scientists have been drawn to the innovative capabilities of the MSI approach, leading to significant focus in various fields during the past few decades. This review describes the sampling protocol, working principle and applications of a few non-ambient and ambient ionization mass spectrometry imaging techniques. The non-ambient techniques include secondary ionization mass spectrometry and matrix-assisted laser desorption ionization, while the ambient techniques include desorption electrospray ionization, laser ablation electrospray ionization, probe electro-spray ionization, desorption atmospheric pressure photo-ionization and femtosecond laser desorption ionization. The review additionally addresses the advantages and disadvantages of ambient and non-ambient MSI techniques in relation to their suitability, particularly for biological samples used in tissue diagnostics. Last but not least, suggestions and conclusions are made regarding the challenges and future prospects of MSI.

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

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

Special Issue with Research Topics on "Recent Analysis and Applications of Mass Spectra on Biochemistry"

Analytical mass spectrometry applies irreplaceable mass spectrometric (MS) methods to analytical chemistry and chemical analysis, among other areas of analytical science [...].

Applying Multimodal Mass Spectrometry to Image Tumors Undergoing Ferroptosis Following In Vivo Treatment with a Ferroptosis Inducer

Epithelial ovarian cancer (EOC) is the most common form of ovarian cancer. The poor prognosis generally associated with this disease has led to the search for improved therapies such as ferroptosis-inducing agents. Ferroptosis is a form of regulated cell death that is dependent on iron and is characterized by lipid peroxidation. Precise mapping of lipids and iron within tumors exposed to ferroptosis-inducing agents may provide insight into processes of ferroptosis in vivo and ultimately assist in the optimal deployment of ferroptosis inducers in cancer therapy. In this work, we present a method for combining matrix-assisted laser desorption/ionization (MALDI) mass spectrometry imaging (MSI) with secondary ion mass spectrometry (SIMS) to analyze changes in spatial lipidomics and metal composition, respectively, in ovarian tumors following exposure to a ferroptosis inducer. Tumors were obtained by injecting human ovarian cancer tumor-initiating cells into mice, followed by treatment with the ferroptosis inducer erastin. SIMS imaging detected iron accumulation in the tumor tissue, and sequential MALDI-MS imaging of the same tissue section displayed two chemically distinct regions of lipids. One region was associated with the iron-rich area detected with SIMS, and the other region encompassed the remainder of the tissue section. Bulk lipidomics confirmed the lipid assignments putatively assigned from the MALDI-MS data. Overall, we demonstrate the ability of multimodal MSI to identify the spatial locations of iron and lipids in the same tissue section and associate these regions with clinical pathology.

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

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

Microlensed fiber allows subcellular imaging by laser-based mass spectrometry

Mass spectrometry imaging (MSI) enables the chemical mapping of molecules and elements in a label-free, high-throughput manner. Because this approach can be accomplished rapidly, it also enables chemical changes to be monitored. Here, we describe a protocol for MSI with subcellular spatial resolution. This is achieved by using a microlensed fiber, which is made by grinding an optical fiber. It is a universal and economic technique that can be adapted to most laser-based mass spectrometry methods. In this protocol, the output of laser radiation from the microlensed fiber causes laser ablation of the sample, and the resulting plume is mass spectrometrically analyzed. The microlensed fiber can be used with matrix-assisted laser desorption ionization, laser desorption ionization, laser ablation electrospray desorption ionization and laser ablation inductively coupled plasma, in each case to achieve submicroscale imaging of single cells and biological tissues. This report provides a detailed introduction of the microlensed fiber design and working principles, sample preparation, microlensed fiber ion source setup and multiple MSI platforms with different kinds of mass spectrometers. A researcher with a little background (such as a trained graduate student) is able to complete all the steps for the experimental setup in ~2 h, including fiber test, laser coupling and ion source modification. The imaging time spent mainly depends on the size of the imaging area. It is suggested that most existing laser-based MSI platforms, especially atmospheric pressure applications, can achieve breakthroughs in spatial resolution by introducing a microlensed fiber module.

Desorption electrospray ionization and matrix-assisted laser desorption/ionization as imaging approaches for biological samples analysis

The imaging of biological tissues can offer valuable information about the sample composition, which improves the understanding of analyte distribution in such complex samples. Different approaches using mass spectrometry imaging (MSI), also known as imaging mass spectrometry (IMS), enabled the visualization of the distribution of numerous metabolites, drugs, lipids, and glycans in biological samples. The high sensitivity and multiple analyte evaluation/visualization in a single sample provided by MSI methods lead to various advantages and overcome drawbacks of classical microscopy techniques. In this context, the application of MSI methods, such as desorption electrospray ionization-MSI (DESI-MSI) and matrix-assisted laser desorption/ionization-MSI (MALDI-MSI), has significantly contributed to this field. This review discusses the evaluation of exogenous and endogenous molecules in biological samples using DESI and MALDI imaging. It offers rare technical insights not commonly found in the literature (scanning speed and geometric parameters), making it a comprehensive guide for applying these techniques step-by-step. Furthermore, we provide an in-depth discussion of recent research findings on using these methods to study biological tissues.

Nano-DESI Mass Spectrometry Imaging of Proteoforms in Biological Tissues with High Spatial Resolution

Mass spectrometry imaging (MSI) is a powerful tool for label-free mapping of the spatial distribution of proteins in biological tissues. We have previously demonstrated imaging of individual proteoforms in biological tissues using nanospray desorption electrospray ionization (nano-DESI), an ambient liquid extraction-based MSI technique. Nano-DESI MSI generates multiply charged protein ions, which is advantageous for their identification using top-down proteomics analysis. In this study, we demonstrate proteoform mapping in biological tissues with a spatial resolution down to 7 μm using nano-DESI MSI. A substantial decrease in protein signals observed in high-spatial-resolution MSI makes these experiments challenging. We have enhanced the sensitivity of nano-DESI MSI experiments by optimizing the design of the capillary-based probe and the thickness of the tissue section. In addition, we demonstrate that oversampling may be used to further improve spatial resolution at little or no expense to sensitivity. These developments represent a new step in MSI-based spatial proteomics, which complements targeted imaging modalities widely used for studying biological systems.

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

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

Revealing the In Situ Behavior of Aggregation-Induced Emission Nanoparticles and Their Biometabolic Effects via Mass Spectrometry Imaging

Simultaneous imaging of exogenous nanomaterials and endogenous metabolites in situ remains challenging and is beneficial for a systemic understanding of the biological behavior of nanomaterials at the molecular level. Here, combined with label-free mass spectrometry imaging, visualization and quantification of the aggregation-induced emission nanoparticles (NPs) in tissue were realized as well as related endogenous spatial metabolic changes simultaneously. Our approach enables us to identify the heterogeneous deposition and clearance behavior of nanoparticles in organs. The accumulation of nanoparticles in normal tissues results in distinct endogenous metabolic changes such as oxidative stress as indicated by glutathione depletion. The low passive delivery efficiency of nanoparticles to tumor foci suggested that the enrichment of NPs in tumors did not benefit from the abundant tumor vessels. Moreover, spatial-selective metabolic changes upon NPs mediated photodynamic therapy was identified, which enables understanding of the NPs induced apoptosis in the process of cancer therapy. This strategy allows us to simultaneously detect exogenous nanomaterials and endogenous metabolites in situ, hence to decipher spatial selective metabolic changes in drug delivery and cancer therapy processes.

Mobility-resolved broadband dissociation and parallel reaction monitoring for laser desorption/ionization-mass spectrometry - Tattoo pigment identification supported by trapped ion mobility spectrometry

In this work, trapped ion mobility spectrometry (TIMS) was introduced to facilitate tandem mass spectrometry (MS²) experiments for laser desorption/ionization-mass spectrometry (LDI-MS) as mobility-resolved fragmentation. The mobility separation of desorbed ions was followed by subsequent fragmentation using data-independent broadband collision-induced dissociation (bbCID) or targeted fragmentation through a prototypic version of parallel reaction monitoring-parallel accumulation serial fragmentation (prm-PASEF) for LDI. Both mobility-resolved fragmentation options, TIMS-bbCID and prm-PASEF, were applied to LDI point measurements to identify organic pigments in tattoo inks. Furthermore, the prototypic prm-PASEF algorithm was used in imaging applications to increase confidence in annotating organic tattoo pigments in skin samples with adverse reactions. Due to less complex spectra in matrix-free LDI, both fragmentation methods yielded fast and reliable MS² identification workflows. TIMS-bbCID was especially beneficial for the rapid acquisition of multiple fragment spectra. For the targeted prm-PASEF approach, analytes' mobilities needed to be collected prior to simplified fragmentation. Therefore, a reference list for 14 pigments was created. The possible number of experiments per thin section and the associated savings in analysis time compared to conventional MS² were particularly suitable for the imaging application. Furthermore, the mobility dimension enabled a new orthogonal identification parameter, increasing the annotation confidence of tattoo pigments through compound specific mobilities.

Pyrylium-Based Derivatization for Rapid Labeling and Enhanced Detection of Cholesterol in Mass Spectrometry Imaging

Cholesterol in the central nervous system has been increasingly found to be closely related to neurodegenerative diseases. Defects in cholesterol metabolism can cause structural and functional disorders of the central nervous system. The detection of abnormal cholesterol is of great significance for the cognition of physiological and pathological states of organisms, and the spatial distribution of cholesterol can also provide more clues for our understanding of the complex mechanism of disease. Here, we developed a novel pyrylium-based derivatization reagent combined with matrix-assisted laser desorption/ionization mass spectrometry imaging (MALDI-MSI) to visualize cholesterol in biological tissues. A new class of charged hydroxyl derivatization reagents was designed and synthesized, and finally 1-(carboxymethyl)-2,4,6-trimethylpyridinium (CTMP) was screened for tissue derivatization of cholesterol. Different from the shortcomings of traditional hydroxyl labeling methods such as harsh reaction conditions and long reaction time, in our study, we combined the advantages of CTMP itself and the EDCl/HOBt reaction system to achieve instant labeling of cholesterol on tissues through two-step activation. In addition, we also reported changes in cholesterol content in different stages and different brain regions during disease development in SOD1 mutant mouse model. The cholesterol derivatization method we developed provides an efficient way to explore the distribution and spatial metabolic network of cholesterol in biological tissues.

Versatile Mass Spectrometry-Based Intraoperative Diagnosis of Liver Tumor in a Multiethnic Cohort

Currently used techniques for intraoperative assessment of tumor resection margins are time-consuming and laborious and, more importantly, lack specificity. Moreover, pathological diagnosis during surgery does not often give a clear outcome. Recent advances in mass spectrometry (MS) and instrumentation have made it possible to obtain detailed molecular information from tissue specimens in real-time, with minimal sample pre-treatment. Probe Electro Spray Ionization MS (PESI-MS), combined with artificial intelligence (AI), has demonstrated its effectiveness in distinguishing liver cancer tissues from healthy tissues in a large Italian population group. As the MS profile can reflect the patient’s ethnicity, dietary habits, or particular operating room procedures, the AI algorithm must be well trained to distinguish different groups. We used a large dataset composed of liver tumor and healthy specimens, from the Italian and Japanese populations, to develop a versatile algorithm free from ethnic bias. The system can classify tissues with discrepancies <5% from the pathologist’s diagnosis. These results demonstrate the potential of the PESI-MS system to distinguish tumor from surrounding non-tumor tissues in patients, with minimal bias from race/ethnicity or etiological characteristics or operating room procedures.

MALDI-TOF Mass Spectrometry Analysis and Human Post-Mortem Microbial Community: A Pilot Study

Introduction: The human post-mortem microbiome (HPM) plays a major role in the decomposition process. Successional changes in post-mortem bacterial communities have been recently demonstrated using high throughput metagenomic sequencing techniques, showing great potential as a post-mortem interval (PMI) predictor. The aim of this study is to verify the application of the mass spectrometry technique, better known as MALDI-TOF MS (matrix-assisted laser desorption/ionization time-of-flight mass spectrometry), as a cheap and quick method for microbe taxonomic identification and for studying the PM microbiome. Methods: The study was carried out on 18 human bodies, ranging from 4 months to 82 years old and with a PMI range from 24 h up to 15 days. The storage time interval in the coolers was included in the final PMI estimates. Using the PMI, the sample study was divided into three main groups: seven cases with a PMI < 72 h; six cases with a PMI of 72−168 h and five cases with a PMI > 168 h. For each body, microbiological swabs were sampled from five external anatomical sites (eyes, ears, nose, mouth, and rectum) and four internal organs (brain, spleen, liver, and heart). Results: The HPM became increasingly different from the starting communities over time in the internal organs as well as at skin sites; the HPM microbiome was mostly dominated by Firmicutes and Proteobacteria phyla; and a PM microbial turnover existed during decomposition, evolving with the PMI. Conclusions: MALDI-TOF is a promising method for PMI estimation, given its sample handling, good reproducibility, and high speed and throughput. Although several intrinsic and extrinsic factors can affect the structure of the HPM, MALDI-TOF can detect the overall microbial community turnover of most prevalent phyla during decomposition. Limitations are mainly related to its sensitivity due to the culture-dependent method and bias in the identification of new isolates.

Molecular histoproteomy by MALDI mass spectrometry imaging to uncover markers of the impact of Nosema on Apis mellifera

Matrix-assisted laser desorption/ionization imaging mass spectrometry (MALDI IMS) is a powerful technology used to investigate the spatio-temporal distribution of a huge number of molecules throughout a body/tissue section. In this paper, we report the use of MALDI IMS to follow the molecular impact of an experimental infection of Apis mellifera with the microsporidia Nosema ceranae. We performed representative molecular mass fingerprints of selected tissues obtained by dissection. This was followed by MALDI IMS workflows optimization including specimen embedding and positioning as well as washing and matrix application. We recorded the local distribution of peptides/proteins within different tissues from experimentally infected versus non infected honeybees. As expected, a distinction in these molecular profiles between the two conditions was recorded from different anatomical sections of the gut tissue. More importantly, we observed differences in the molecular profiles in the brain, thoracic ganglia, hypopharyngeal glands, and hemolymph. We introduced MALDI IMS as an effective approach to monitor the impact of N. ceranae infection on A. mellifera. This opens perspectives for the discovery of molecular changes in peptides/proteins markers that could contribute to a better understanding of the impact of stressors and toxicity on different tissues of a bee in a single experiment.

Data parsing in mass spectrometry imaging using R Studio and Cardinal: A tutorial

Mass spectrometry imaging (MSI) has emerged as a rapidly expanding field in the MS community. The analysis of large spectral data is further complicated by the added spatial dimension of MSI. A plethora of resources exist for expert users to begin parsing MSI data in R, but there is a critical lack of guidance for absolute beginners. This tutorial is designed to serve as a one-stop guide to start using R with MSI data and describe the possibilities that data science can bring to MSI analysis.

Short communication: Distribution of phospholipids in parotid cancer by matrix-assisted laser desorption/ionization imaging mass spectrometry

Background

Parotid cancer is relatively rare, and malignancy varies; therefore, novel markers are needed to predict prognosis. Recent advances in matrix-assisted laser desorption/ionization imaging mass spectrometry (MALDI-IMS), useful for visualization of lipid molecules, have revealed the relationship between cancer and lipid metabolism, indicating the potential of lipids as biomarkers. However, the distribution and importance of phospholipids in parotid cancer remain unclear.

Objective

This study aimed to use MALDI-IMS to comprehensively investigate the spatial distribution of phospholipids characteristically expressed in human parotid cancer tissues.

Methods

Tissue samples were surgically collected from two patients with parotid cancer (acinic cell carcinoma and mucoepidermoid carcinoma). Frozen sections of the samples were assessed using MALDI-IMS in both positive and negative ion modes, with an m/z range of 600–1000. The mass spectra obtained in the tumor and non-tumor regions were compared and analyzed. Ion images corresponding to the peak characteristics of the tumor regions were visualized.

Results

Several candidate phospholipids with significantly different expression levels were detected between the tumor and non-tumor regions. The number of unique lipid peaks with significantly different intensities between the tumor and non-tumor regions was 95 and 85 for Cases 1 and 2, respectively, in positive ion mode, and 99 and 97 for Cases 1 and 2, respectively, in negative ion mode. Imaging differentiated the characteristics that phospholipids were heterogeneously distributed in the tumor regions.

Conclusion

Phospholipid candidates that are characteristically expressed in human parotid cancer tissues were found, demonstrating the localization of their expression. These findings are notable for further investigation of alterations in lipid metabolism of parotid cancer and may have potential for the development of phospholipids as biomarkers.

Unveiling spatial metabolome of Paeonia suffruticosa and Paeonia lactiflora roots using MALDI MS imaging

Paeonia suffruticosa (PS) and Paeonia lactiflora (PL) belong to the only genus in the family Paeoniaceae. Comparative analysis of the spatial metabolomes of PS and PL has rarely been performed. In this work, combined with multiple matrixes and dual-polarity detection, high mass resolution matrix-assisted laser desorption/ionization MS imaging (MALDI MSI) and MALDI tandem MSI were performed on the root sections of the two Paeonia species. The spatial distributions of many metabolites including monoterpene and paeonol glycosides, tannins, flavonoids, saccharides and lipids were systematically characterized. The ambiguous tissue distribution of the two isomers paeoniflorin and albiflorin were distinguished by tandem MSI using lithium salt doped 2,5-dihydroxybenzoate matrix. In addition, the major intermediates involved in the biosynthetic pathway of gallotannins were successfully localized and visualized in the root sections. High-mass resolution MALDI full-scan MSI provides comprehensive and accurate spatial distribution of metabolites. The analytical power of the technique was further tested in the tandem MSI of two isomers. The ion images of individual metabolites provide chemical and microscopic characteristics beyond morphological identification, and the detailed spatiochemical information could not only improve our understanding of the biosynthetic pathway of hydrolyzable tannins, but also ensure the safety and effectiveness of their medicinal use.

In Situ Localization of Plant Lipid Metabolites by Matrix-Assisted Laser Desorption/Ionization Mass Spectrometry Imaging (MALDI-MSI)

Matrix-assisted laser desorption/ionization mass spectrometry imaging (MALDI-MSI) has emerged as a major analytical platform for the determination and localization of lipid metabolites directly from tissue sections. Unlike analysis of lipid extracts, where lipid localizations are lost due to homogenization and/ or solvent extraction, MALDI-MSI analysis is capable of revealing spatial localization of metabolites while simultaneously collecting high chemical resolution mass spectra. Important considerations for obtaining high quality MALDI-MS images include tissue preservation, section preparation, MS data collection and data processing. Errors in any of these steps can lead to poor quality metabolite images and increases the chance for metabolite misidentification and/ or incorrect localization. Here, we present detailed methods and recommendations for specimen preparation, MALDI-MS instrument parameters, software analysis platforms for data processing, and practical considerations for each of these steps to ensure acquisition of high-quality chemical and spatial resolution data for reconstructing MALDI-MS images of plant tissues.

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.

Regional Lipid Expression Abnormalities Identified Using MALDI IMS Correspond to MRI-Defined White Matter Hyperintensities within Post-mortem Human Brain Tissues

Periventricular white matter hyperintensities (pvWMHs) are a neurological feature detected with magnetic resonance imaging that are clinically associated with an increased risk of stroke and dementia. pvWMHs represent white matter lesions characterized by regions of myelin and axon rarefaction and as such likely involve changes in lipid composition; however, these alterations remain unknown. Lipids are critical in determining cell function and survival. Perturbations in lipid expression have previously been associated with neurological disorders. Matrix-assisted laser desorption/ionization (MALDI) imaging mass spectrometry (IMS) is an emerging technique for untargeted, high-throughput investigation of lipid expression and spatial distribution in situ; however, the use of MALDI IMS has been previously been limited by the need for non-embedded, non-fixed, fresh-frozen samples. In the current study, we demonstrate the novel use of MALDI IMS to distinguish regional lipid abnormalities that correlate with magnetic resonance imaging (MRI) defined pvWMHs within ammonium formate washed, formalin-fixed human archival samples. MALDI IMS scans were conducted in positive or negative ion detection mode on tissues sublimated with 2,5-dihydroxybenzoic acid or 1,5-diaminonaphthalene matrices, respectively. Using a broad, untargeted approach to lipid analysis, we consistently detected 116 lipid ion species in 21 tissue blocks from 11 different post-mortem formalin-fixed human brains. Comparing the monoisotopic mass peaks of these lipid ions elucidated significant differences in lipid expression between pvWMHs and NAWM for 31 lipid ion species. Expanding our understanding of alterations in lipid composition will provide greater knowledge of molecular mechanisms underpinning ischemic white matter lesions and provides the potential for novel therapeutic interventions targeting lipid composition abnormalities.

Label-free lipidome study of paraventricular thalamic nucleus (PVT) of rat brain with post-traumatic stress injury by Raman imaging

Post-traumatic stress disorder (PTSD) is a widespread psychiatric injury that develops serious life-threatening symptoms like substance abuse, severe depression, cognitive impairments, and persistent anxiety. However, the mechanisms of post-traumatic stress injury in brain are poorly understood due to the lack of practical methods to reveal biochemical alterations in various brain regions affected by this type of injury. Here, we introduce a novel method that provides quantitative results from Raman maps in the paraventricular nucleus of the thalamus (PVT) region. By means of this approach, we have shown a lipidome comparison in PVT regions of control and PTSD rat brains. Matrix-assisted laser desorption/ionization (MALDI) mass spectrometry was also employed for validation of the Raman results. Lipid alterations can reveal invaluable information regarding the PTSD mechanisms in affected regions of brain. We have showed that the concentration of cholesterol, cholesteryl palmitate, phosphatidylinositol, phosphatidylserine, phosphatidylethanolamine, sphingomyelin, ganglioside, glyceryl tripalmitate and sulfatide changes in the PVT region of PTSD compared to control rats. A higher concentration of cholesterol suggests a higher level of corticosterone in the brain. Moreover, concentration changes of phospholipids and sphingolipids suggest the alteration of phospholipase A2 (PLA2) which is associated with inflammatory processes in the brain. Our results have broadened the understanding of biomolecular mechanisms for PTSD in the PVT region of the brain. This is the first report regarding the application of Raman spectroscopy for PTSD studies. This method has a wide spectrum of applications and can be applied to various other brain related disorders or other regions of the brain.

Rapid automated diagnosis of primary hepatic tumour by mass spectrometry and artificial intelligence

BACKGROUND AND AIMS

Complete surgical resection with negative margin is one of the pillars in treatment of liver tumours. However, current techniques for intra-operative assessment of tumour resection margins are time-consuming and empirical. Mass spectrometry (MS) combined with artificial intelligence (AI) is useful for classifying tissues and provides valuable prognostic information. The aim of this study was to develop a MS-based system for rapid and objective liver cancer identification and classification.

METHODS

A large dataset derived from 222 patients with hepatocellular carcinoma (HCC, 117 tumours and 105 non-tumours) and 96 patients with mass-forming cholangiocarcinoma (MFCCC, 50 tumours and 46 non-tumours) were analysed by Probe Electrospray Ionization (PESI) MS. AI by means of support vector machine (SVM) and random forest (RF) algorithms was employed. For each classifier, sensitivity, specificity and accuracy were calculated.

RESULTS

The overall diagnostic accuracy exceeded 94% in both the AI algorithms. For identification of HCC vs non-tumour tissue, RF was the best, with 98.2% accuracy, 97.4% sensitivity and 99% specificity. For MFCCC vs non-tumour tissue, both algorithms gave 99.0% accuracy, 98% sensitivity and 100% specificity.

CONCLUSIONS

The herein reported MS-based system, combined with AI, permits liver cancer identification with high accuracy. Its bench-top size, minimal sample preparation and short working time are the main advantages. From diagnostics to therapeutics, it has the potential to influence the decision-making process in real-time with the ultimate aim of improving cancer patient cure.

Stochastic dynamic mass spectrometric quantification of steroids in mixture - Part II

This paper deals with quantification of the following steroids in mixture: hydrocortisone (1), deoxycorticosterone (2), progesterone (3) and methyltestosterone (4) by means of mass spectrometry and implementing our innovative stochatic dynamic functional relationship between the analyte concentration in solution and the experimental variable intensity. The mass spectrometric data are correlated independently using chromatography. Chemometric analysis is carried out.

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.

Classification of Thyroid Tumors Based on Mass Spectrometry Imaging of Tissue Microarrays; a Single-Pixel Approach

The primary diagnosis of thyroid tumors based on histopathological patterns can be ambiguous in some cases, so proper classification of thyroid diseases might be improved if molecular biomarkers support cytological and histological assessment. In this work, tissue microarrays representative for major types of thyroid malignancies—papillary thyroid cancer (classical and follicular variant), follicular thyroid cancer, anaplastic thyroid cancer, and medullary thyroid cancer—and benign thyroid follicular adenoma and normal thyroid were analyzed by mass spectrometry imaging (MSI), and then different computation approaches were implemented to test the suitability of the registered profiles of tryptic peptides for tumor classification. Molecular similarity among all seven types of thyroid specimens was estimated, and multicomponent classifiers were built for sample classification using individual MSI spectra that corresponded to small clusters of cells. Moreover, MSI components showing the most significant differences in abundance between the compared types of tissues detected and their putative identity were established by annotation with fragments of proteins identified by liquid chromatography-tandem mass spectrometry in corresponding tissue lysates. In general, high accuracy of sample classification was associated with low inter-tissue similarity index and a high number of components with significant differences in abundance between the tissues. Particularly, high molecular similarity was noted between three types of tumors with follicular morphology (adenoma, follicular cancer, and follicular variant of papillary cancer), whose differentiation represented the major classification problem in our dataset. However, low level of the intra-tissue heterogeneity increased the accuracy of classification despite high inter-tissue similarity (which was exemplified by normal thyroid and benign adenoma). We compared classifiers based on all detected MSI components (n = 1536) and the subset of the most abundant components (n = 147). Despite relatively higher contribution of components with significantly different abundance and lower overall inter-tissue similarity in the latter case, the precision of classification was generally higher using all MSI components. Moreover, the classification model based on individual spectra (a single-pixel approach) outperformed the model based on mean spectra of tissue cores. Our result confirmed the high feasibility of MSI-based approaches to multi-class detection of cancer types and proved the good performance of sample classification based on individual spectra (molecular image pixels) that overcame problems related to small amounts of heterogeneous material, which limit the applicability of classical proteomics.

Discovery of a new antifungal lipopeptaibol from Purpureocillium lilacinum using MALDI-TOF-IMS

Fungi are considered to be rich in biologically active natural products for agricultural and medicinal purposes. The discovery and accurate identification of the bioactive fungal natural products is important for their efficient utilization. During the course of our continuing search for the new natural products from the fungal agents, we found the well-known bio-control fungus Purpureocillium lilacinum showed in vitro activity against Botrytis cinerea, an airborne plant pathogenic fungus causing gray mold disease in many vegetables and fruits. The co-culture of two fungi on agar plate showed that P. lilacinum inhibited the growth of B. cinerea which means P. lilacinum has potential to produce some bioactive secondary metabolites against B. cinerea. In this study, we applied matrix-assisted laser desorption ionization-time of flight mass spectrometry imaging mass spectrometry (MALDI-TOF-IMS), as a fast identification tool, for the discovery of a new antifungal lipopeptaibol (leucinostatin Z) from P. lilacinum against B. cinerea. The planar structure of leucinostatin Z was further established by using the LC-HRESI-MS-MS analysis. MALDI-TOF-IMS is becoming a new approach that allows us to observe the bioactive natural products directly on growth media between the colonies of two fungi, which is faster and more effective than the traditional techniques to discover new bioactive compounds in fungi.

Identification and Validation of VEGFR2 Kinase as a Target of Voacangine by a Systematic Combination of DARTS and MSI

Although natural products are an important source of drugs and drug leads, identification and validation of their target proteins have proven difficult. Here, we report the development of a systematic strategy for target identification and validation employing drug affinity responsive target stability (DARTS) and mass spectrometry imaging (MSI) without modifying or labeling natural compounds. Through a validation step using curcumin, which targets aminopeptidase N (APN), we successfully standardized the systematic strategy. Using label-free voacangine, an antiangiogenic alkaloid molecule as the model natural compound, DARTS analysis revealed vascular endothelial growth factor receptor 2 (VEGFR2) as a target protein. Voacangine inhibits VEGFR2 kinase activity and its downstream signaling by binding to the kinase domain of VEGFR2, as was revealed by docking simulation. Through cell culture assays, voacangine was found to inhibit the growth of glioblastoma cells expressing high levels of VEGFR2. Specific localization of voacangine to tumor compartments in a glioblastoma xenograft mouse was revealed by MSI analysis. The overlap of histological images with the MSI signals for voacangine was intense in the tumor regions and showed colocalization of voacangine and VEGFR2 in the tumor tissues by immunofluorescence analysis of VEGFR2. The strategy employing DARTS and MSI to identify and validate the targets of a natural compound as demonstrated for voacangine in this study is expected to streamline the general approach of drug discovery and validation using other biomolecules including natural products.

Ganglioside Detection from Formalin-Fixed Human Brain Tissue Utilizing MALDI Imaging Mass Spectrometry

Matrix assisted laser desorption ionization (MALDI) imaging mass spectrometry (IMS) is used to perform mass spectrometric analysis directly on biological samples providing visual and anatomical spatial information on molecules within tissues. A current obscuration of MALDI-IMS is that it is largely performed on fresh frozen tissue, whereas clinical tissue samples stored long-term are fixed in formalin, and the fixation process is thought to cause signal suppression for lipid molecules. Studies have shown that fresh frozen tissue sections applied with an ammonium formate (AF) wash prior to matrix application in the MALDI-IMS procedure display an increase in observed signal intensity and sensitivity for lipid molecules detected within the brain while maintaining the spatial distribution of molecules throughout the tissue. In this work, we investigate the viability of formalin-fixed tissue imaging in a clinical setting by comparing MALDI data of fresh frozen and postfixed rat brain samples, along with postfixed human brain samples washed with AF to assess the capabilities of ganglioside analysis in MALDI imaging of formalin-fixed tissue. Results herein demonstrate that MALDI-IMS spectra for gangliosides, including GM1, were significantly enhanced in fresh frozen rat brain, formalin-fixed rat brain, and formalin-fixed human brain samples through the use of an AF wash. Improvements in MALDI-IMS image quality were demonstrated, and the spatial distribution of molecules was retained. Results indicate that this method will allow for the analysis of gangliosides from formalin-fixed clinical samples, which can open additional avenues for neurodegenerative disease research.

Immersion by rotation-based application of the matrix for fast and reproducible sample preparations and robust results in mass spectrometry imaging

Automated matrix deposition for matrix-assisted laser desorption/ionization (MALDI) mass spectrometry imaging (MSI) is crucial for producing reproducible analyte ion signals. Here we report an innovative method employing an automated immersion apparatus, which enables a robust matrix deposition within 5 minutes and with scalable throughput by using MAPS matrix and non-polar solvents. MSI results received from mouse heart and rat brain tissues were qualitatively similar to those from nozzle sprayed samples with respect to peak number and quality of the ion images. Overall, the immersion-method enables a fast and careful matrix deposition and has the future potential for implementation in clinical tissue diagnostics.

Differentiation of lymphocytic-plasmacytic enteropathy and small cell lymphoma in cats using histology-guided mass spectrometry

BACKGROUND

Differentiation of lymphocytic-plasmacytic enteropathy (LPE) from small cell lymphoma (SCL) in cats can be challenging.

HYPOTHESIS/OBJECTIVE

Histology-guided mass spectrometry (HGMS) is a suitable method for the differentiation of LPE from SCL in cats.

ANIMALS

Forty-one cats with LPE and 52 cats with SCL.

METHODS

This is a retrospective clinicopathologic study. Duodenal tissue samples of 17 cats with LPE and 22 cats with SCL were subjected to HGMS, and the acquired data were used to develop a linear discriminate analysis (LDA) machine learning algorithm. The algorithm was subsequently validated using a separate set of 24 cats with LPE and 30 cats with SCL. Cases were classified as LPE or SCL based on a consensus by an expert panel consisting of 5-7 board-certified veterinary specialists. Histopathology, immunohistochemistry, and clonality testing were available for all cats. The panel consensus classification served as a reference for the calculation of test performance parameters.

RESULTS

Relative sensitivity, specificity, and accuracy of HGMS were 86.7% (95% confidence interval [CI]: 74.5%-98.8%), 91.7% (95% CI: 80.6%-100%), and 88.9% (95% CI: 80.5%-97.3%), respectively. Comparatively, the clonality testing had a sensitivity, specificity, and accuracy of 85.7% (95% CI: 72.8%-98.7%), 33.3% (95% CI: 14.5%-52.2%), and 61.5% (95% CI: 48.3%-74.8%) relative to the panel decision.

CONCLUSIONS AND CLINICAL IMPORTANCE

Histology-guided mass spectrometry was a reliable technique for the differentiation of LPE from SCL in duodenal formalin-fixed paraffin-embedded samples of cats and might have advantages over tests currently considered state of the art.

Spatially Controlled Molecular Analysis of Biological Samples Using Nanodroplet Arrays and Direct Droplet Aspiration

Mass spectrometry (MS) has emerged as a valuable technology for molecular and spatial evaluation of biological samples. Ambient ionization MS techniques, in particular, allow direct analysis of tissue samples with minimal pretreatment. Here, we describe the design and optimization of an alternative ambient liquid extraction MS approach for metabolite and lipid profiling and imaging from biological samples. The system combines a piezoelectric picoliter dispenser to form solvent nanodroplets onto the sample surface with controlled and tunable spatial resolution and a conductive capillary to directly aspirate/ionize the nanodroplets for efficient analyte transmission and detection. Using this approach, we performed spatial profiling of mouse brain tissue sections with different droplet sizes (390, 420, and 500 μm). MS analysis of normal and cancerous human brain and ovarian tissues yielded rich metabolic profiles that were characteristic of disease state and enabled visualization of tissue regions with different histologic composition. This method was also used to analyze the lipid profiles of human ovarian cell lines. Overall, our results demonstrate the capabilities of this system for spatially controlled MS analysis of biological samples.

Sample preparation of formalin-fixed paraffin-embedded tissue sections for MALDI-mass spectrometry imaging

Matrix-assisted laser desorption/ionization (MALDI) mass spectrometry imaging (MALDI MSI) has become a powerful tool with a high potential relevance for the analysis of biomolecules in tissue samples in the context of diseases like cancer and cardiovascular or cardiorenal diseases. In recent years, significant progress has been made in the technology of MALDI MSI. However, a more systematic optimization of sample preparation would likely achieve an increase in the molecular information derived from MALDI MSI. Therefore, we have employed a systematic approach to develop, establish and validate an optimized "standard operating protocol" (SOP) for sample preparation in MALDI MSI of formalin-fixed paraffin-embedded (FFPE) tissue sample analyses within this study. The optimized parameters regarding the impact on the resulting signal-to-noise (S/N) ratio were as follows: (i) trypsin concentration, solvents, deposition method, and incubation time; (ii) tissue washing procedures and drying processes; and (iii) spray flow rate, number of layers of trypsin deposition, and grid size. The protocol was evaluated on interday variability and its applicability for analyzing the mouse kidney, aorta, and heart FFPE tissue samples. In conclusion, an optimized SOP for MALDI MSI of FFPE tissue sections was developed to generate high sensitivity, to enhance spatial resolution and reproducibility, and to increase its applicability for various tissue types. This optimized SOP will further increase the molecular information content and intensify the use of MSI in future basic research and diagnostic applications. Graphical Abstract.

Use of physiological information based on grayscale images to improve mass spectrometry imaging data analysis from biological tissues

The characterization of cancer tissues by matrix-assisted laser desorption ionization-mass spectrometry images (MALDI-MSI) is of great interest because of the power of MALDI-MS to understand the composition of biological samples and the imaging side that allows for setting spatial boundaries among tissues of different nature based on their compositional differences. In tissue-based cancer research, information on the spatial location of necrotic/tumoral cell populations can be approximately known from grayscale images of the scanned tissue slices. This study proposes as a major novelty the introduction of this physiologically-based information to help in the performance of unmixing methods, oriented to extract the MS signatures and distribution maps of the different tissues present in biological samples. Specifically, the information gathered from grayscale images will be used as a local rank constraint in Multivariate Curve Resolution-Alternating Least Squares (MCR-ALS) for the analysis of MALDI-MSI of cancer tissues. The use of this constraint, setting absence of certain kind of tissues only in clear zones of the image, will help to improve the performance of MCR-ALS and to provide a more reliable definition of the chemical MS fingerprint and location of the tissues of interest. The general strategy to address the analysis of MALDI-MSI of cancer tissues will involve the study of the MCR-ALS results and the posterior use of MCR-ALS scores as dimensionality reduction for image segmentation based on K-means clustering. The resolution method will provide the MS signatures and their distribution maps for each tissue in the sample. Then, the resolved distribution maps for each biological component (MCR scores) will be submitted as initial information to K-means clustering for image segmentation to obtain information on the boundaries of the different tissular regions in the samples studied. MCR-ALS prior to K-means not only provides the desired dimensionality reduction, but additionally resolved non-biological signal contributions are not used and the weight given to the different biological components in the segmentation process can be modulated by suitable preprocessing methods.

Correlative microscopy of freeze-dried cells and studies on intracellular calcium stores with imaging secondary ion mass spectrometry (SIMS)

Secondary ion mass spectrometry (SIMS)-based imaging techniques have become effective tools for studies of elements and molecules in biological samples. In the current work, a correlative microscopy approach was applied to cryogenically prepared fractured freeze-dried cells for organelle-level imaging of chemical composition using SIMS. A CAMECA IMS-3f SIMS ion microscope was used for studying the effect of microtubule-perturbing agents, specifically nocodazole and taxol, on intracellular calcium stores. The perturbation of microtubules in renal epithelial LLC-PK1 cells resulted in significant loss of total calcium in both the nucleus and cytoplasm. In another study, the stable isotope 44Ca was used for imaging the influx of calcium in resting and stimulated LLC-PK1 cells. SIMS imaging of two calcium isotopes, 44Ca and 40Ca, in the same cell revealed the distribution of calcium influx in the 44Ca image and endogenous calcium in the 40Ca image. An arginine-vasopressin treatment of cells showed that the Golgi apparatus is sensitive to hormonal stimulation.

Current Progress of Lipid Analysis in Metabolic Diseases by Mass Spectrometry Methods

BACKGROUND

Obesity, insulin resistance, diabetes, and metabolic syndrome are associated with lipid alterations, and they affect the risk of long-term cardiovascular disease. A reliable analytical instrument to detect changes in the composition or structures of lipids and the tools allowing to connect changes in a specific group of lipids with a specific disease and its progress, is constantly lacking. Lipidomics is a new field of medicine based on the research and identification of lipids and lipid metabolites present in human organism. The primary aim of lipidomics is to search for new biomarkers of different diseases, mainly civilization diseases.

OBJECTIVE

We aimed to review studies reporting the application of mass spectrometry for lipid analysis in metabolic diseases.

METHOD

Following an extensive search of peer-reviewed articles on the mass spectrometry analysis of lipids the literature has been discussed in this review article.

RESULTS

The lipid group contains around 1.7 million species; they are totally different, in terms of the length of aliphatic chain, amount of rings, additional functional groups. Some of them are so complex that their complex analyses are a challenge for analysts. Their qualitative and quantitative analysis of is based mainly on mass spectrometry.

CONCLUSION

Mass spectrometry techniques are excellent tools for lipid profiling in complex biological samples and the combination with multivariate statistical analysis enables the identification of potential diagnostic biomarkers.

Discrimination of normal oral mucosa from oral cancer by mass spectrometry imaging of proteins and lipids

Identification of biomarkers for molecular classification of cancer and for differentiation between cancerous and normal epithelium remains a vital issue in the field of head and neck cancer. Here we aimed to compare the ability of proteome and lipidome components to discriminate oral cancer from normal mucosa. Tissue specimens including squamous cell cancer and normal epithelium were analyzed by MALDI mass spectrometry imaging. Two molecular domains of tissue components were imaged in serial sections-peptides (resulting from trypsin-processed proteins) and lipids (primarily zwitterionic phospholipids), then regions of interest corresponding to cancer and normal epithelium were compared. Heterogeneity of cancer regions was higher than the heterogeneity of normal epithelium, and the distribution of peptide components was more heterogeneous than the distribution of lipid components. Moreover, there were more peptide components than lipid components that showed significantly different abundance between cancer and normal epithelium (median of the Cohen's effect was 0.49 and 0.31 in case of peptide and lipid components, respectively). Multicomponent cancer classifier was tested (vs. normal epithelium) using tissue specimens from three patients and then validated with a tissue specimen from the fourth patient. Peptide-based signature and lipid-based signature allowed cancer classification with a weighted accuracy of 0.85 and 0.69, respectively. Nevertheless, both classifiers had very high precision (0.98 and 0.94, respectively). We concluded that though molecular differences between cancerous and normal mucosa were higher in the proteome domain than in the analyzed lipidome subdomain, imaging of lipidome components also enabled discrimination of oral cancer and normal epithelium. Therefore, both cancer proteome and lipidome are promising sources of biomarkers of oral malignancies.

Biological tissue sample preparation for time-of-flight secondary ion mass spectrometry (ToF-SIMS) imaging

Time-of-flight secondary ion mass spectrometry (ToF-SIMS) imaging is an analytical technique rapidly expanding in use in biological studies. This technique is based on high spatial resolution (50-100 nm), high surface sensitivity (1-2 nm top-layer), and statistical analytic power. In mass spectrometry imaging (MSI), sample preparation is a crucial step to maintaining the natural state of the biomolecules and providing accurate spatial information. However, a number of problems associated with temperature changes in tissue samples such as loss of original distribution due to undesired molecular migration during the sample preparation or reduced ionization efficiency make it difficult to accurately perform MSI. Although frozen hydrate analysis is the ideal sample preparation method to eliminate the effects of temperature, this approach is hindered by mechanical limitations. Alternatively, an adhesive-tape-supported mounting and freeze-drying preparation has been proposed. This paper provides a concise review of the sample preparation procedures, a review of current issues, and proposes efficacious solutions for ToF-SIMS imaging in biological research.

MALDI MS imaging investigation of the host response to visceral leishmaniasis

Mass spectrometry imaging (MSI) of animal tissues has become an important tool for in situ molecular analyses and biomarker studies in several clinical areas, but there are few applications in parasitological studies. Leishmaniasis is a neglected tropical disease, and experimental mouse models have been essential to evaluate pathological and immunological processes and to develop diagnostic methods. Herein we have employed MALDI MSI to examine peptides and low molecular weight proteins (2 to 20 kDa) differentially expressed in the liver during visceral leishmaniasis in mice models. We analyzed liver sections of Balb/c mice infected with Leishmania infantum using the SCiLS Lab software for statistical analysis, which facilitated data interpretation and thus highlighted several key proteins and/or peptides. We proposed a decision tree classification for visceral leishmaniasis with distinct phases of the disease, which are named here as healthy, acute infection and chronic infection. Among others, the ion of m/z 4963 was the most important to identify acute infection and was tentatively identified as Thymosin β4. This peptide was previously established as a recovery factor in the human liver and might participate in the response of mice to Leishmania infection. This preliminary investigation shows the potential of MALDI MSI to complement classical compound selective imaging techniques and to explore new features not yet recognized by these approaches.

Tissue and serum lipidome shows altered lipid composition with diagnostic potential in mycosis fungoides

Mycosis fungoides (MF) is the most common type of cutaneous T cell lymphoma. In this study, we used matrix-assisted laser desorption/ionization-Fourier transform ion cyclotron resonance mass spectrometry (MALDI-FTICR-MS) to perform lipidomic profiling of 5 MF tissue samples and 44 serum samples (22 from MF patients and 22 from control subjects). Multivariate statistical analysis of the mass spectral data showed that MF tissues had altered levels of seven lipids and MF sera had altered levels of twelve. Among these, six phosphotidylcholines, PC (34:2), PC (34:1), PC (36:3), PC (36:2), PC (32:0), and PC (38:4) and one sphingomyelin, SM (16:0) were altered in both MF tissues and sera. PC (34:2), PC (34:1), PC (36:3), and PC (36:2) levels were increased in both tissues and sera from MF patients, whereas SM (16:0), PC (32:0), and PC (38:4) levels were increased in MF sera but were decreased in MF tissues. We have thus identified multiple lipids that are altered in MF tissues and sera. This suggests serological and tissue lipidomic profiling could be an effective approach to screening for diagnostic biomarkers of MF.

LRP6 is identified as a potential prognostic marker for oral squamous cell carcinoma via MALDI-IMS

Oral squamous cell carcinoma (OSCC) is a leading cause of cancer-related deaths worldwide, with 500 000 new cases each year. However, the mechanisms underlying OSCC development are relatively unknown. In this study, matrix-assisted laser desorption ionization imaging mass spectrometry (MALDI-IMS)-based proteomic strategy was used to profile the differentially expressed peptides/proteins between OSCC tissues and their adjacent noncancerous tissues. Sixty-seven unique peptide peaks and five distinct proteins were identified with changed expression levels. Among them, LRP6 expression was found to be upregulated in OSCC tissues, and correlated with a cluster of clinicopathologic parameters, including smoking, drinking, tumor differentiation status, lymph node metastasis and survival time. Notably, knockdown of LRP6 inhibited the proliferation ability of OSCC cells. Furthermore, we demonstrated that the expression of LRP6 in OSCC cells is positively correlated with its downstream oncogene, FGF8. The present study suggests that LRP6 could be a potential biomarker for OSCC patients, and might further assist in the therapeutic decisions in OSCC treatment.