Analysis of tissue specimens by matrix-assisted laser desorption/ionization imaging mass spectrometry in biological and clinical research

Discrimination and prediction of Qingzhuan tea storage year using quantitative chemical profile combined with multivariate analysis: Advantages of MRMHR based targeted quantification metabolomics

The duration of storage significantly influences the quality and market value of Qingzhuan tea (QZT). Herein, a high-resolution multiple reaction monitoring (MRMHR) quantitative method for markers of QZT storage year was developed. Quantitative data alongside multivariate analysis were employed to discriminate and predict the storage year of QZT. Furthermore, the content of the main biochemical ingredients, catechins and alkaloids, and free amino acids (FAA) were assessed for this purpose. The results show that targeted marker-based models exhibited superior discrimination and prediction performance among four datasets. The R2Xcum, R2Ycum and Q2cum of orthogonal projection to latent structure-discriminant analysis discrimination model were close to 1. The correlation coefficient (R2) and the root mean square error of prediction of the QZT storage year prediction model were 0.9906 and 0.63, respectively. This study provides valuable insights into tea storage quality and highlights the potential application of targeted markers in food quality evaluation.

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

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

Advancing mass spectrometry-based chemical imaging: A noncontact continuous flow surface probe in mass spectrometry for enhanced signal detection and spatial resolution

A new method has been developed for mass spectrometric imaging of small molecules and proteins on tissue or in thinly sliced materials. A laser desorption Venturi electrospray ionization-mass spectrometer was developed for molecular imaging. This method combines laser desorption (LD) and electrospray ionization (ESI) systems before a mass spectrometer (MS). To carry out laser desorption, samples are excited with a laser from the back side of a glass substrate. The desorbed molecules or particles are then captured by a solvent flow. In the ESI system, these desorbed particles and molecules are ionized. The spray part of the solvent system consists of two capillaries: one delivers solvent to the sample plate sides to capture desorbed molecules and particles, and the other carries the solution to the mass spectrometry side using the Venturi effect. A 2D stage facilitates sampling. The system is designed to minimize the sample size after desorption using a 355 nm diode laser, and it is optimized for molecules of various sizes, including organic molecules, amino acids, and proteins. Despite challenging atmospheric conditions for protein desorption, this specialized design enables the collection of protein spectra. The amino acids and other small molecules showed high sensitivity in the MSI measurements. This innovative MS imaging system can be directly applied to real tissue systems and other plant samples to visualize the molecular level distributions.

MALDI-mass spectrometry imaging as a new technique for detecting non-heme iron in peripheral tissues via caudal vein injection of deferoxamine

As one of the most common iron-chelating agents, deferoxamine (DFO) rapidly chelates iron in the body. Moreover, it does not compete for the iron characteristic of hemoglobin in the blood cells, which is common in the clinical treatment of iron poisoning. Iron is a trace element necessary to maintain organism normal life activities. Iron deficiency can lead to anemia, whereas iron overload can cause elevated levels of cellular oxidative stress and cell damage. As a consequence, detection of the iron content in tissues and blood is of great significance. The traditional techniques for detecting the iron content include inductively coupled plasma-mass spectrometry and atomic absorption spectrometry, which cannot be used for imaging purposes. Laser ablation-ICP-MS and synchrotron radiation micro-X-ray fluorescence can map the concentration and distribution of iron in tissues. However, these methods can only be used to measure the total iron levels in blood or tissues. In recent years, due to the deepening understanding of iron metabolism, diseases related to iron overload have attracted increasing attention. Therefore, we took advantage of the properties of DFO in terms of chelating iron and investigated different sampling times following DFO injection in the tail vein of mice. We used mass spectrometry imaging (MSI) technology to detect the DFO and ferrioxamine content in the blood and different tissues to indirectly characterize the non-heme iron content.

Deep learning-assisted mass spectrometry imaging for preliminary screening and pre-classification of psychoactive substances

Currently, it is of great urgency to develop a rapid pre-classification and screening method for suspected drugs as the constantly springing up of new psychoactive substances. In most researches, psychoactive substances classification approaches depended on the similar chemical structures and pharmacological action with known drugs. Such approaches could not face the complicated circumstance of emerging new psychoactive substances. Herein, mass spectrometry imaging and convolutional neural networks (CNN) were used for preliminary screening and pre-classification of suspected psychoactive substances. Mass spectrometry imaging was performed simultaneously on two brain slices as one was from blank group and another one was from psychoactive substance-induced group. Then, fused neurotransmitter variation mass spectrometry images (Nv-MSIs) reflecting the difference of neurotransmitters between two slices were achieved through two homemade programs. A CNN model was developed to classify the Nv-MSIs. Compared with traditional classification methods, CNN achieved better estimation accuracy and required minimal data preprocessing. Also, the specific region on Nv-MSIs and weight of each neurotransmitter that affected the classification most could be unraveled by CNN. Finally, the method was successfully applied to assist the identification of a new psychoactive substance seized recently. This sample was identified as cannabinoids, which greatly promoted the screening process.

Imaging with mass spectrometry: Which ionization technique is best?

The use of mass spectrometry (MS) to acquire molecular images of biological tissues and other substrates has developed into an indispensable analytical tool over the past 25 years. Imaging mass spectrometry technologies are widely used today to study the in situ spatial distributions for a variety of analytes. Early MS images were acquired using secondary ion mass spectrometry and matrix-assisted laser desorption/ionization. Researchers have also designed and developed other ionization techniques in recent years to probe surfaces and generate MS images, including desorption electrospray ionization (DESI), nanoDESI, laser ablation electrospray ionization, and infrared matrix-assisted laser desorption electrospray ionization. Investigators now have a plethora of ionization techniques to select from when performing imaging mass spectrometry experiments. This brief perspective will highlight the utility and relative figures of merit of these techniques within the context of their use in imaging mass spectrometry.

Novel Small Molecule Matrix Screening for Simultaneous MALDI Mass Spectrometry Imaging of Multiple Lipids and Phytohormones

Most of the traditional matrices cannot simultaneously image multiple lipids and phytohormones, so screening and discovery of novel matrices stand as essential approaches for broadening the application scope of matrix-assisted laser desorption/ionization mass spectrometry imaging (MALDI-MSI). In this work, 12 organic small molecule compounds were comprehensively screened and investigated as potential MALDI matrices for simultaneous imaging analysis of various lipids and phytohormones. In the positive ionization mode, p-nitroaniline, m-nitroaniline, and 2-aminoterephthalic acid displayed good performance for the highly sensitive detection of lysophosphatidylcholines (LPCs), phosphatidylcholines (PCs), and triacylglycerols (TGs). Furthermore, p-nitroaniline possessed excellent characteristics of strong ultraviolet absorption and homogeneous cocrystallization, making it a desirable matrix for MALDI-MSI analysis of eight plant hormones. Compared with conventional matrices (2,5-dihydroxybenzoic acid (DHB), α-cyano-4-hydroxycinnamic acid (CHCA), and 9-aminoacridine (9-AA), the use of p-nitroaniline resulted in higher ionization efficiency, superior sensitivity, and clearer imaging images in dual polarity mode. Our research offers valuable guidance and new ideas for future endeavors in matrix screening.

Spatial omics techniques and data analysis for cancer immunotherapy applications

In-depth profiling of cancer cells/tissues is expanding our understanding of the genomic, epigenomic, transcriptomic, and proteomic landscape of cancer. However, the complexity of the cancer microenvironment, particularly its immune regulation, has made it difficult to exploit the potential of cancer immunotherapy. High-throughput spatial omics technologies and analysis pipelines have emerged as powerful tools for tackling this challenge. As a result, a potential revolution in cancer diagnosis, prognosis, and treatment is on the horizon. In this review, we discuss the technological advances in spatial profiling of cancer around and beyond the central dogma to harness the full benefits of immunotherapy. We also discuss the promise and challenges of spatial data analysis and interpretation and provide an outlook for the future.

A multi-modal image fusion workflow incorporating MALDI imaging mass spectrometry and microscopy for the study of small pharmaceutical compounds

Multi-modal imaging analyses of dosed tissue samples can provide more comprehensive insight into the effects of a therapeutically active compound on a target tissue compared to single-modal imaging. For example, simultaneous spatial mapping of pharmaceutical compounds and endogenous macromolecule receptors is difficult to achieve in a single imaging experiment. Herein, we present a multi-modal workflow combining imaging mass spectrometry with immunohistochemistry (IHC) fluorescence imaging and brightfield microscopy imaging. Imaging mass spectrometry enables direct mapping of pharmaceutical compounds and metabolites, IHC fluorescence imaging can visualize large proteins, and brightfield microscopy imaging provides tissue morphology information. Single-cell resolution images are generally difficult to acquire using imaging mass spectrometry, but are readily acquired with IHC fluorescence and brightfield microscopy imaging. Spatial sharpening of mass spectrometry images would thus allow for higher fidelity co-registration with higher resolution microscopy images. Imaging mass spectrometry spatial resolution can be predicted to a finer value via a computational image fusion workflow, which models the relationship between the intensity values in the mass spectrometry image and the features of a high spatial resolution microscopy image. As a proof of concept, our multi-modal workflow was applied to brain tissue extracted from a Sprague Dawley rat dosed with a kratom alkaloid, corynantheidine. Four candidate mathematical models including linear regression, partial least squares regression (PLS), random forest regression, and two-dimensional convolutional neural network (2-D CNN), were tested. The random forest and 2-D CNN models most accurately predicted the intensity values at each pixel as well as the overall patterns of the mass spectrometry images, while also providing the best spatial resolution enhancements. Herein, image fusion enabled predicted mass spectrometry images of corynantheidine, GABA, and glutamine to approximately 2.5 μm spatial resolutions, a significant improvement compared to the original images acquired at 25 μm spatial resolution. The predicted mass spectrometry images were then co-registered with an H&E image and IHC fluorescence image of the μ-opioid receptor to assess co-localization of corynantheidine with brain cells. Our study also provides insight into the different evaluation parameters to consider when utilizing image fusion for biological applications.

Molecular analysis of the extracellular microenvironment: from form to function

The extracellular matrix (ECM) proteome represents an important component of the tissue microenvironment that controls chemical flux and induces cell signaling through encoded structure. The analysis of the ECM represents an analytical challenge through high levels of post-translational modifications, protease-resistant structures, and crosslinked, insoluble proteins. This review provides a comprehensive overview of the analytical challenges involved in addressing the complexities of spatially profiling the extracellular matrix proteome. A synopsis of the process of synthesizing the ECM structure, detailing inherent chemical complexity, is included to present the scope of the analytical challenge. Current chromatographic and spatial techniques addressing these challenges are detailed. Capabilities for multimodal multiplexing with cellular populations are discussed with a perspective on developing a holistic view of disease processes that includes both the cellular and extracellular microenvironment.

Lipid Isobar and Isomer Imaging Using Nanospray Desorption Electrospray Ionization Combined with Triple Quadrupole Mass Spectrometry

Mass spectrometry imaging (MSI) is widely used for examining the spatial distributions of molecules in biological samples. Conventional MSI approaches, in which molecules extracted from the sample are distinguished based on their mass-to-charge ratio, cannot distinguish between isomeric species and some closely spaced isobars. To facilitate isobar separation, MSI is typically performed using high-resolution mass spectrometers. Nevertheless, the complexity of the mixture of biomolecules observed in each pixel of the image presents a challenge, even for modern mass spectrometers with the highest resolving power. Herein, we implement nanospray desorption electrospray ionization (nano-DESI) MSI on a triple quadrupole (QqQ) mass spectrometer for the spatial mapping of isobaric and isomeric species in biological tissues. We use multiple reaction monitoring acquisition mode (MRM) with unit mass resolution to demonstrate the performance of this new platform by imaging lipids in mouse brain and rat kidney tissues. We demonstrate that imaging in MRM mode may be used to distinguish between isobaric phospholipids requiring a mass resolving power of 3,800,000. Additionally, we have been able to image eicosanoid isomers, a largely unexplored class of signaling molecules present in tissues at low concentrations, in rat kidney tissue. This new capability substantially enhances the specificity and selectivity of MSI, enabling spatial localization of species that remain unresolved in conventional MSI experiments.

Toward Omics-Scale Quantitative Mass Spectrometry Imaging of Lipids in Brain Tissue Using a Multiclass Internal Standard Mixture

Mass spectrometry imaging (MSI) has accelerated our understanding of lipid metabolism and spatial distribution in tissues and cells. However, few MSI studies have approached lipid imaging quantitatively and those that have focused on a single lipid class. We overcome this limitation by using a multiclass internal standard (IS) mixture sprayed homogeneously over the tissue surface with concentrations that reflect those of endogenous lipids. This enabled quantitative MSI (Q-MSI) of 13 lipid classes and subclasses representing almost 200 sum-composition lipid species using both MALDI (negative ion mode) and MALDI-2 (positive ion mode) and pixel-wise normalization of each lipid species in a manner analogous to that widely used in shotgun lipidomics. The Q-MSI approach covered 3 orders of magnitude in dynamic range (lipid concentrations reported in pmol/mm2) and revealed subtle changes in distribution compared to data without normalization. The robustness of the method was evaluated by repeating experiments in two laboratories using both timsTOF and Orbitrap mass spectrometers with an ∼4-fold difference in mass resolution power. There was a strong overall correlation in the Q-MSI results obtained by using the two approaches. Outliers were mostly rationalized by isobaric interferences or the higher sensitivity of one instrument for a particular lipid species. These data provide insight into how the mass resolving power can affect Q-MSI data. This approach opens up the possibility of performing large-scale Q-MSI studies across numerous lipid classes and subclasses and revealing how absolute lipid concentrations vary throughout and between biological tissues.

Improving the Signal Intensity of Cryosections Using a Conductive Adhesive Film in Matrix-Assisted Laser Desorption/Ionization Mass Spectrometry Imaging

The matrix-assisted laser desorption/ionization mass spectrometry imaging (MALDI-MSI) technique was used to obtain the molecular images of cryosections without labeling. Although MALDI-MSI has been widely used to detect small molecules from biological tissues, issues remain due to the technical process of cryosectioning and limited mass spectrometry parameters. The use of a conductive adhesive film is a unique method to obtain high-quality sections from cutting tissue, such as bone, muscle, adipose tissue, and whole body of mice or fish, and we have reported the utilization of the film for MALDI-MSI in previous. However, some signal of the small molecules using the conductive adhesive films was still lower than on the indium tin oxide (ITO) glass slide. Here, the sample preparation and analytical conditions for MALDI-MSI using an advanced conductive adhesive film were optimized to obtain strong signals from whole mice heads. The effects of tissue thickness and laser ionization power on signal intensity were verified using MALDI-MSI. The phospholipid signal intensity was measured for samples with three tissue thicknesses (5, 10, and 20 μm); compared to the signals from the samples on the ITO glass slides, the signals with conductive adhesive films exhibited significantly higher intensities when a laser with a higher range of power was used to ionize the small molecules. Thus, the technique using the advanced conductive adhesive film showed an improvement in MALDI-MSI analysis.

Application of surface-layer matrix-assisted laser desorption/ionization mass spectrometry imaging to pharmaceutical-loaded poly(ester urea) films

Polymer thin films are often used in transdermal patches as a method of continuous drug administration for patients with chronic illness. Understanding the drug segregation and distribution within these films is important for monitoring proper drug release over time. Surface-layer matrix-assisted laser desorption/ionization mass spectrometry imaging (SL-MALDI-MSI) is a unique analytical technique that provides an optical representation of chemical compositions that exist at the surface of polymeric materials. Solvent-free sublimation is employed for application of matrix to the sample surface, so that only molecules in direct contact with the matrix layer are detected. Here, these methodologies are utilized to visualize variations in drug concentration at both the air and substrate interface in pharmaceutical-loaded polymer films.

A multimodal pipeline for image correction and registration of mass spectrometry imaging with microscopy

The integration of matrix-assisted laser desorption/ionization mass spectrometry imaging (MALDI MSI) and histology plays a pivotal role in advancing our understanding of complex heterogeneous tissues, which provides a comprehensive description of biological tissue with both wide molecule coverage and high lateral resolution. Herein, we proposed a novel strategy for the correction and registration of MALDI MSI data with hematoxylin & eosin (H&E) staining images. To overcome the challenges of discrepancies in spatial resolution towards the unification of the two imaging modalities, a deep learning-based interpolation algorithm for MALDI MSI data was constructed, which enables spatial coherence and the following orientation matching between images. Coupled with the affine transformation (AT) and the subsequent moving least squares algorithm, the two types of images from one rat brain tissue section were aligned automatically with high accuracy. Moreover, we demonstrated the practicality of the developed pipeline by projecting it to a rat cerebral ischemia-reperfusion injury model, which would help decipher the link between molecular metabolism and pathological interpretation towards microregion. This new approach offers the chance for other types of bioimaging to boost the field of multimodal image fusion.

Integrated mass spectrometry imaging reveals spatial-metabolic alteration in diabetic cardiomyopathy and the intervention effects of ferulic acid

Diabetic cardiomyopathy (DCM) is a metabolic disease and a leading cause of heart failure among people with diabetes. Mass spectrometry imaging (MSI) is a versatile technique capable of combining the molecular specificity of mass spectrometry (MS) with the spatial information of imaging. In this study, we used MSI to visualize metabolites in the rat heart with high spatial resolution and sensitivity. We optimized the air flow-assisted desorption electrospray ionization (AFADESI)-MSI platform to detect a wide range of metabolites, and then used matrix-assisted laser desorption ionization (MALDI)-MSI for increasing metabolic coverage and improving localization resolution. AFADESI-MSI detected 214 and 149 metabolites in positive and negative analyses of rat heart sections, respectively, while MALDI-MSI detected 61 metabolites in negative analysis. Our study revealed the heterogenous metabolic profile of the heart in a DCM model, with over 105 region-specific changes in the levels of a wide range of metabolite classes, including carbohydrates, amino acids, nucleotides, and their derivatives, fatty acids, glycerol phospholipids, carnitines, and metal ions. The repeated oral administration of ferulic acid during 20 weeks significantly improved most of the metabolic disorders in the DCM model. Our findings provide novel insights into the molecular mechanisms underlying DCM and the potential of ferulic acid as a therapeutic agent for treating this condition.

Changes in physical architecture and lipids compounds in skeletal muscle from Pekin duck and Liancheng white duck

As a complex food, meat displays various biochemical properties that are determined to a great extent by physical architecture and lipid metabolites. Pekin duck and Liancheng white duck are elite breeds with distinct characteristics. Here, we explored the development of the muscle fibers from embryonic stage to 10-wk after birth, and muscle fibers grow slowly after 8-wk. We investigated the meat quality, ultrastructure, lipidomics profiling, and lipids spatial distribution of skeletal muscle at 8 wk. Pekin duck has lower Warner-Bratzler shear force (WBSF) (P < 0.05), high intramuscular fat (IMF) (P < 0.01), longer and wider sarcomere, and higher mitochondrial density (P < 0.001). Liancheng white duck with tighter collagen architecture. A total of 950 lipids from 6 lipid classes identified with lipidomics were analyzed, the levels of GP, GL, and PR were significantly higher in Pekin duck (P < 0.05), SL and ST were significantly higher in Liancheng white duck (P < 0.05). There were 333 significantly different lipids (|log2(Fold Change)| ≥ 1 and FDR < 0.05) screened, most lipids distributed in the muscle tissue were uniform, but some specifically distributed in connective tissue. To some extent, the results demonstrate the high lipid deposition capacity of Pekin duck and the high medicinal function of Liancheng white duck. Our study provides new insights into the relationship between skeletal muscle architecture and meat toughness, which increased the knowledge of lipidomic characteristics and provide a basis for duck meat authentication.

Sample Preparation Method for MALDI Mass Spectrometry Imaging of Fresh-Frozen Spines

Technologies assessing the lipidomics, genomics, epigenomics, transcriptomics, and proteomics of tissue samples at single-cell resolution have deepened our understanding of physiology and pathophysiology at an unprecedented level of detail. However, the study of single-cell spatial metabolomics in undecalcified bones faces several significant challenges, such as the fragility of bone, which often requires decalcification or fixation leading to the degradation or removal of lipids and other molecules. As such, we describe a method for performing mass spectrometry imaging on undecalcified spine that is compatible with other spatial omics measurements. In brief, we use fresh-frozen rat spines and a system of carboxyl methylcellulose embedding, cryofilm, and polytetrafluoroethylene rollers to maintain tissue integrity while avoiding signal loss from variations in laser focus and artifacts from traditional tissue processing. This reveals various tissue types and lipidomic profiles of spinal regions at 10 μm spatial resolutions using matrix-assisted laser desorption/ionization mass spectrometry imaging. We expect this method to be adapted and applied to the analysis of the spinal cord, shedding light on the mechanistic aspects of cellular heterogeneity, development, and disease pathogenesis underlying different bone-related conditions and diseases. This study furthers the methodology for high spatial metabolomics of spines and adds to the collective efforts to achieve a holistic understanding of diseases via single-cell spatial multiomics.

Simultaneous Mapping of Amino Neurotransmitters and Nucleoside Neuromodulators on Brain Tissue Sections by On-Tissue Chemoselective Derivatization and MALDI-MSI

Neurotransmitters (NTs) and neuromodulators (NMs) are two of the most important neurochemicals in the brain, and their imbalances in specific brain regions are thought to underlie certain neurological disorders. We present an on-tissue chemoselective derivatization mass spectrometry imaging (OTCD-MSI) method for the simultaneous mapping of NTs and NMs. Our derivatization system consists of a pyridiniumyl-benzylboronic acid based derivatization reagent and pyrylium salt, which facilitate covalent charge labeling of molecules containing cis-diol and primary amino, respectively. These derivatization systems improved the detection sensitivity of matrix-assisted laser desorption/ionization (MALDI)-MSI and simplified the identification of amino NTs and nucleoside NMs by the innate chemoselectivity of derivatization reagents and the unique isotopic pattern of boron-derivative reagents. We demonstrated the ability of the developed method on brain sections from a hypoxia mouse model and control. The simultaneous imaging of NTs and NMs provided a method for exploring how hypoxic stress and drugs affect specific brain regions through neurotransmitter modulation.

In situ profiling reveals metabolic alterations in the tumor microenvironment of ovarian cancer after chemotherapy

In this study, we investigated the metabolic alterations associated with clinical response to chemotherapy in patients with ovarian cancer. Pre- and post-neoadjuvant chemotherapy (NACT) tissues from patients with high-grade serous ovarian cancer (HGSC) who had poor response (PR) or excellent response (ER) to NACT were examined. Desorption electrospray ionization mass spectrometry (DESI-MS) was performed on sections of HGSC tissues collected according to a rigorous laparoscopic triage algorithm. Quantitative MS-based proteomics and phosphoproteomics were performed on a subgroup of pre-NACT samples. Highly abundant metabolites in the pre-NACT PR tumors were related to pyrimidine metabolism in the epithelial regions and oxygen-dependent proline hydroxylation of hypoxia-inducible factor alpha in the stromal regions. Metabolites more abundant in the epithelial regions of post-NACT PR tumors were involved in the metabolism of nucleotides, and metabolites more abundant in the stromal regions of post-NACT PR tumors were related to aspartate and asparagine metabolism, phenylalanine and tyrosine metabolism, nucleotide biosynthesis, and the urea cycle. A predictive model built on ions with differential abundances allowed the classification of patients' tumor responses as ER or PR with 75% accuracy (10-fold cross-validation ridge regression model). These findings offer new insights related to differential responses to chemotherapy and could lead to novel actionable targets.

Spatial metabolomics identifies distinct tumor-specific and stroma-specific subtypes in patients with lung squamous cell carcinoma

Molecular subtyping of lung squamous cell carcinoma (LUSC) has been performed at the genomic, transcriptomic, and proteomic level. However, LUSC stratification based on tissue metabolomics is still lacking. Combining high-mass-resolution imaging mass spectrometry with consensus clustering, four tumor- and four stroma-specific subtypes with distinct metabolite patterns were identified in 330 LUSC patients. The first tumor subtype T1 negatively correlated with DNA damage and immunological features including CD3, CD8, and PD-L1. The same features positively correlated with the tumor subtype T2. Tumor subtype T4 was associated with high PD-L1 expression. Compared with the status of subtypes T1 and T4, patients with subtype T3 had improved prognosis, and T3 was an independent prognostic factor with regard to UICC stage. Similarly, stroma subtypes were linked to distinct immunological features and metabolic pathways. Stroma subtype S4 had a better prognosis than S2. Subsequently, analyses based on an independent LUSC cohort treated by neoadjuvant therapy revealed that the S2 stroma subtype was associated with chemotherapy resistance. Clinically relevant patient subtypes as determined by tissue-based spatial metabolomics are a valuable addition to existing molecular classification systems. Metabolic differences among the subtypes and their associations with immunological features may contribute to the improvement of personalized therapy.

RaMALDI: Enabling simultaneous Raman and MALDI imaging of the same tissue section

Multimodal tissue imaging techniques that integrate two complementary modalities are powerful discovery tools for unraveling biological processes and identifying biomarkers of disease. Combining Raman spectroscopic imaging (RSI) and matrix-assisted laser-desorption/ionization (MALDI) mass spectrometry imaging (MSI) to obtain fused images with the advantages of both modalities has the potential of providing spatially resolved, sensitive, specific biomolecular information, but has so far involved two separate sample preparations, or even consecutive tissue sections for RSI and MALDI MSI, resulting in images with inherent disparities. We have developed RaMALDI, a streamlined, integrated, multimodal imaging workflow of RSI and MALDI MSI, performed on a single tissue section with one sample preparation protocol. We show that RaMALDI imaging of various tissues effectively integrates molecular information acquired from both RSI and MALDI MSI of the same sample, which will drive discoveries in cell biology, biomedicine, and pathology, and advance tissue diagnostics.

Non-Mass Spectrometric Targeted Single-Cell Metabolomics

Metabolic assays serve as pivotal tools in biomedical research, offering keen insights into cellular physiological and pathological states. While mass spectrometry (MS)-based metabolomics remains the gold standard for comprehensive, multiplexed analyses of cellular metabolites, innovative technologies are now emerging for the targeted, quantitative scrutiny of metabolites and metabolic pathways at the single-cell level. In this review, we elucidate an array of these advanced methodologies, spanning synthetic and surface chemistry techniques, imaging-based methods, and electrochemical approaches. We summarize the rationale, design principles, and practical applications for each method, and underscore the synergistic benefits of integrating single-cell metabolomics (scMet) with other single-cell omics technologies. Concluding, we identify prevailing challenges in the targeted scMet arena and offer a forward-looking commentary on future avenues and opportunities in this rapidly evolving field.

Clinical Proteomics for Solid Organ Tissues

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

Next-generation pathology practices with mass spectrometry imaging

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

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

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

A monolithic microfluidic probe for ambient mass spectrometry imaging of biological tissues

Ambient mass spectrometry imaging (MSI) is a powerful technique that allows for the simultaneous mapping of hundreds of molecules in biological samples under atmospheric conditions, requiring minimal sample preparation. We have developed nanospray desorption electrospray ionization (nano-DESI), a liquid extraction-based ambient ionization technique, which has proven to be sensitive and capable of achieving high spatial resolution. We have previously described an integrated microfluidic probe, which simplifies the nano-DESI setup, but is quite difficult to fabricate. Herein, we introduce a facile and scalable strategy for fabricating microfluidic devices for nano-DESI MSI applications. Our approach involves the use of selective laser-assisted etching (SLE) of fused silica to create a monolithic microfluidic probe (SLE-MFP). Unlike the traditional photolithography-based fabrication, SLE eliminates the need for the wafer bonding process and allows for automated, scalable fabrication of the probe. The chamfered design of the sampling port and ESI emitter significantly reduces the amount of polishing required to fine-tune the probe thereby streamlining and simplifying the fabrication process. We have also examined the performance of a V-shaped probe, in which only the sampling port is fabricated using SLE technology. The V-shaped design of the probe is easy to fabricate and provides an opportunity to independently optimize the size and shape of the electrospray emitter. We have evaluated the performance of SLE-MFP by imaging mouse tissue sections. Our results demonstrate that SLE technology enables the fabrication of robust monolithic microfluidic probes for MSI experiments. This development expands the capabilities of nano-DESI MSI and makes the technique more accessible to the broader scientific community.

Imaging and Structural Characterization of Phosphatidylcholine Isomers from Rat Brain Tissue Using Sequential Collision-Induced Dissociation/Electron-Induced Dissociation

The chemical complexity of biological tissues creates challenges in the analysis of lipids via imaging mass spectrometry. The presence of isobaric and isomeric compounds introduces chemical noise that makes it difficult to unambiguously identify and accurately map the spatial distributions of these compounds. Electron-induced dissociation (EID) has previously been shown to profile phosphatidylcholine (PCs) sn-isomers directly from rat brain tissue in matrix-assisted laser desorption/ionization (MALDI) imaging mass spectrometry. However, the acquisition of true pixel-by-pixel images, as opposed to regional profiling measurements, using EID is difficult due to low fragmentation efficiency and precursor ion signal dilution into multiple fragment ion channels, resulting in low sensitivity. In this work, we have developed a sequential collision-induced dissociation (CID)/EID method to visualize the distribution of sn-isomers in MALDI imaging mass spectrometry experiments. Briefly, CID is performed on sodium-adducted PCs, which results in facile loss of the phosphocholine headgroup. This ion is then subjected to an EID analysis. Since the lipid headgroup is removed prior to EID, a major fragmentation pathway common to EID ion activation is eliminated, resulting in a more sensitive analysis. This sequential CID/EID workflow generates sn-specific fragment ions allowing for the assignment of the sn-positions. Carbon-carbon double-bond (C═C) positions are also localized along the fatty acyl tails by the presence of a 2 Da shift pattern in the fragment ions arising from carbon-carbon bond cleavages. Moreover, the integration of the CID/EID method into MALDI imaging mass spectrometry enables the mapping of the absolute and relative distribution of sn-isomers at every pixel. The localized relative abundances of sn-isomers vary throughout brain substructures and likely reflect different biological functions and metabolism.

High-resolution integrated microfluidic probe for mass spectrometry imaging of biological tissues

Nanospray desorption electrospray ionization (nano-DESI) is an ambient ionization technique that enables molecular imaging of biological samples with high spatial resolution. We have recently developed an integrated microfluidic probe (iMFP) for nano-DESI mass spectrometry imaging (MSI) that significantly enhances the robustness of the technique. In this study, we designed a new probe that enables imaging of biological samples with high spatial resolution. The new probe design features smaller primary and spray channels and an entirely new configuration of the sampling port that enables robust imaging of tissues with a spatial resolution of 8-10 μm. We demonstrate the spatial resolution, sensitivity, durability, and throughput of the iMFP by imaging mouse uterine and brain tissue sections. The robustness of the high-resolution iMFP allowed us to perform first imaging experiments with both high spatial resolution and high throughput, which is particularly advantageous for high-resolution imaging of large tissue sections of interest to most MSI applications. Overall, the new probe design opens opportunities for mapping of biomolecules in biological samples with high throughput and cellular resolution, which is important for understanding biological systems.

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.

Acidic Methanol Treatment Facilitates Matrix-Assisted Laser Desorption Ionization-Mass Spectrometry Imaging of Energy Metabolism

Detection of small molecule metabolites (SMM), particularly those involved in energy metabolism using MALDI-mass spectrometry imaging (MSI), is challenging due to factors including ion suppression from other analytes present (e.g., proteins and lipids). One potential solution to enhance SMM detection is to remove analytes that cause ion suppression from tissue sections before matrix deposition through solvent washes. Here, we systematically investigated solvent treatment conditions to improve SMM signal and preserve metabolite localization. Washing with acidic methanol significantly enhances the detection of phosphate-containing metabolites involved in energy metabolism. The improved detection is due to removing lipids and highly polar metabolites that cause ion suppression and denaturing proteins that release bound phosphate-containing metabolites. Stable isotope infusions of [13C6]nicotinamide coupled to MALDI-MSI ("Iso-imaging") in the kidney reveal patterns that indicate blood vessels, medulla, outer stripe, and cortex. We also observed different ATP:ADP raw signals across mouse kidney regions, consistent with regional differences in glucose metabolism favoring either gluconeogenesis or glycolysis. In mouse muscle, Iso-imaging using [13C6]glucose shows high glycolytic flux from infused circulating glucose in type 1 and 2a fibers (soleus) and relatively lower glycolytic flux in type 2b fiber type (gastrocnemius). Thus, improved detection of phosphate-containing metabolites due to acidic methanol treatment combined with isotope tracing provides an improved way to probe energy metabolism with spatial resolution in vivo.

Enhancing Ion Signals and Improving Matrix Selection in Time-of-Flight Secondary Ion Mass Spectrometry with Microvolume Expansion Using Large Argon Clusters

The molecular environment has an important impact on the ionization mechanism in time-of-flight secondary ion mass spectrometry (ToF-SIMS). In complex samples, desorption/ionization, and thus the detection of a molecular signal, can be hampered by molecular entanglement, ionization-suppressive neighbors, or even an unfavorable sample substrate. Here, a method called microvolume expansion is developed to overcome these negative effects. Large argon clusters are able to transfer biomolecules from a target to a collector in vacuum. In this study, argon gas cluster ion beams (Arn+-GCIB with n centered around 3000 or 5000) are used to expand a microvolume from the sample to a collector, which is a material ideally enhancing the ionization yield. The collector is then analyzed using a liquid metal ion gun. The signal amplification factor corresponding to the expansion of phosphatidylcholine (PC) lipid on collectors partially covered with acidic matrices was evaluated as an initial proof of concept. In one experiment, the PC expansion on a pattern of four drop-casted matrix-assisted laser desorption/ionization matrices led to the selection of α-cyano-4-hydroxycinnamic (CHCA) as the optimal candidate for cationic PC detection. The ion signal is increased by at least three orders of magnitude when PC was expanded using 10 keV Ar3000+ and Ar5000+ on a sublimated layer of CHCA. Finally, the expansion of the gray matter of a mouse on different materials (Si, Au-coated Si, CHCA, and polyethylene) was achieved with varying degrees of success, demonstrating the potential of the method to further analyze complex and fragile biological assemblies.

Patterns of Carbon-Bound Exogenous Compounds Impact Disease Pathophysiology in Lung Cancer Subtypes in Different Ways

Carbon-bound exogenous compounds, such as polycyclic aromatic hydrocarbons (PAHs), tobacco-specific nitrosamines, aromatic amines, and organohalogens, are known to affect both tumor characteristics and patient outcomes in lung squamous cell carcinoma (LUSC); however, the roles of these compounds in lung adenocarcinoma (LUAD) remain unclear. We analyzed 11 carbon-bound exogenous compounds in LUAD and LUSC samples using in situ high mass-resolution matrix-assisted laser desorption/ionization Fourier-transform ion cyclotron resonance mass spectrometry imaging and performed a cluster analysis to compare the patterns of carbon-bound exogenous compounds between these two lung cancer subtypes. Correlation analyses were conducted to investigate associations among exogenous compounds, endogenous metabolites, and clinical data, including patient survival outcomes and smoking behaviors. Additionally, we examined differences in exogenous compound patterns between normal and tumor tissues. Our analyses revealed that PAHs, aromatic amines, and organohalogens were more abundant in LUAD than in LUSC, whereas the tobacco-specific nitrosamine nicotine-derived nitrosamine ketone was more abundant in LUSC. Patients with LUAD and LUSC could be separated according to carbon-bound exogenous compound patterns detected in the tumor compartment. The same compounds had differential impacts on patient outcomes, depending on the cancer subtype. Correlation and network analyses indicated substantial differences between LUAD and LUSC metabolomes, associated with substantial differences in the patterns of the carbon-bound exogenous compounds. These data suggest that the contributions of these carcinogenic compounds to cancer biology may differ according to the cancer subtypes.

Spatial multimodal analysis of transcriptomes and metabolomes in tissues

We present a spatial omics approach that combines histology, mass spectrometry imaging and spatial transcriptomics to facilitate precise measurements of mRNA transcripts and low-molecular-weight metabolites across tissue regions. The workflow is compatible with commercially available Visium glass slides. We demonstrate the potential of our method using mouse and human brain samples in the context of dopamine and Parkinson's disease.

Spatially Resolved Metabolites in Stable and Unstable Human Atherosclerotic Plaques Identified by Mass Spectrometry Imaging

BACKGROUND

Impairments in carbohydrate, lipid, and amino acid metabolism drive features of plaque instability. However, where these impairments occur within the atheroma remains largely unknown. Therefore, we sought to characterize the spatial distribution of metabolites within stable and unstable atherosclerosis in both the fibrous cap and necrotic core.

METHODS

Atherosclerotic tissue specimens from 9 unmatched individuals were scored based on the Stary classification scale and subdivided into stable and unstable atheromas. After performing mass spectrometry imaging on these samples, we identified over 850 metabolite-related peaks. Using MetaboScape, METASPACE, and Human Metabolome Database, we confidently annotated 170 of these metabolites and found over 60 of these were different between stable and unstable atheromas. We then integrated these results with an RNA-sequencing data set comparing stable and unstable human atherosclerosis.

RESULTS

Upon integrating our mass spectrometry imaging results with the RNA-sequencing data set, we discovered that pathways related to lipid metabolism and long-chain fatty acids were enriched in stable plaques, whereas reactive oxygen species, aromatic amino acid, and tryptophan metabolism were increased in unstable plaques. In addition, acylcarnitines and acylglycines were increased in stable plaques whereas tryptophan metabolites were enriched in unstable plaques. Evaluating spatial differences in stable plaques revealed lactic acid in the necrotic core, whereas pyruvic acid was elevated in the fibrous cap. In unstable plaques, 5-hydroxyindoleacetic acid was enriched in the fibrous cap.

CONCLUSIONS

Our work here represents the first step to defining an atlas of metabolic pathways involved in plaque destabilization in human atherosclerosis. We anticipate this will be a valuable resource and open new avenues of research in cardiovascular disease.

Spatial lipidomics of fresh-frozen spines

Technologies assessing the lipidomics, genomics, epigenomics, transcriptomics, and proteomics of tissue samples at single-cell resolution have deepened our understanding of physiology and pathophysiology at an unprecedented level of detail. However, the study of single-cell spatial metabolomics in undecalcified bones faces several significant challenges, such as the fragility of bone which often requires decalcification or fixation leading to the degradation or removal of lipids and other molecules and. As such, we describe a method for performing mass spectrometry imaging on undecalcified spine that is compatible with other spatial omics measurements. In brief, we use fresh-freeze rat spines and a system of carboxyl methylcellulose embedding, cryofilm, and polytetrafluoroethylene rollers to maintain tissue integrity, while avoiding signal loss from variations in laser focus and artifacts from traditional tissue processing. This reveals various tissue types and lipidomic profiles of spinal regions at 10 μm spatial resolutions using matrix-assisted laser desorption/ionization mass spectrometry imaging. We expect this method to be adapted and applied to the analysis of spinal cord, shedding light on the mechanistic aspects of cellular heterogeneity, development, and disease pathogenesis underlying different bone-related conditions and diseases. This study furthers the methodology for high spatial metabolomics of spines, as well as adds to the collective efforts to achieve a holistic understanding of diseases via single-cell spatial multi-omics.

Metabolic heterogeneity in adrenocortical carcinoma impacts patient outcomes

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

Nanospray Desorption Electrospray Ionization (Nano-DESI) Mass Spectrometry Imaging with High Ion Mobility Resolution

Untargeted separation of isomeric and isobaric species in mass spectrometry imaging (MSI) is challenging. The combination of ion mobility spectrometry (IMS) with MSI has emerged as an effective strategy for differentiating isomeric and isobaric species, which substantially enhances the molecular coverage and specificity of MSI experiments. In this study, we have implemented nanospray desorption electrospray ionization (nano-DESI) MSI on a trapped ion mobility spectrometry (TIMS) mass spectrometer. A new nano-DESI source was constructed, and a specially designed inlet extension was fabricated to accommodate the new source. The nano-DESI-TIMS-MSI platform was evaluated by imaging mouse brain tissue sections. We achieved high ion mobility resolution by utilizing three narrow mobility scan windows that covered the majority of the lipid molecules. Notably, the mobility resolution reaching up to 300 in this study is much higher than the resolution obtained in our previous study using drift tube IMS. High-resolution TIMS successfully separated lipid isomers and isobars, revealing their distinct localizations in tissue samples. Our results further demonstrate the power of high-mobility-resolution IMS for unraveling the complexity of biomolecular mixtures analyzed in MSI experiments.

Rethinking our approach to cancer metabolism to deliver patient benefit

Altered cellular metabolism is a major mechanism by which tumours support nutrient consumption associated with increased cellular proliferation. Selective dependency on specific metabolic pathways provides a therapeutic vulnerability that can be targeted in cancer therapy. Anti-metabolites have been used clinically since the 1940s and several agents targeting nucleotide metabolism are now well established as standard of care treatment in a range of indications. However, despite great progress in our understanding of the metabolic requirements of cancer and non-cancer cells within the tumour microenvironment, there has been limited clinical success for novel agents targeting pathways outside of nucleotide metabolism. We believe that there is significant therapeutic potential in targeting metabolic processes within cancer that is yet to be fully realised. However, current approaches to identify novel targets, test novel therapies and select patient populations most likely to benefit are sub-optimal. We highlight recent advances in technologies and understanding that will support the identification and validation of novel targets, re-evaluation of existing targets and design of optimal clinical positioning strategies to deliver patient benefit.

Untangling Alzheimer's disease with spatial multi-omics: a brief review

Alzheimer's disease (AD) is the most common form of neurological dementia, specified by extracellular β-amyloid plaque deposition, neurofibrillary tangles, and cognitive impairment. AD-associated pathologies like cerebral amyloid angiopathy (CAA) are also affiliated with cognitive impairment and have overlapping molecular drivers, including amyloid buildup. Discerning the complexity of these neurological disorders remains a significant challenge, and the spatiomolecular relationships between pathogenic features of AD and AD-associated pathologies remain poorly understood. This review highlights recent developments in spatial omics, including profiling and molecular imaging methods, and how they are applied to AD. These emerging technologies aim to characterize the relationship between how specific cell types and tissue features are organized in combination with mapping molecular distributions to provide a systems biology view of the tissue microenvironment around these neuropathologies. As spatial omics methods achieve greater resolution and improved molecular coverage, they are enabling deeper characterization of the molecular drivers of AD, leading to new possibilities for the prediction, diagnosis, and mitigation of this debilitating disease.

Single-filament imaging mass spectrometry lipidomics in Arthrospira platensis

RATIONALE

Elucidating intra-organismal biochemical and lipid organization in photosynthetic biological cell factories of filamentous cyanobacteria, such as Arthrospira platensis (Spirulina), is important for tracking physiological response mechanisms during growth. Little is known about the filaments' biochemical organization and cellular structure and no label-free imaging techniques exist that provide molecular mapping.

METHODS

We applied ultrahigh-resolution mass spectrometry (MS) with matrix-assisted laser desorption ionization (MALDI) imaging to immobilized Spirulina filaments to investigate the localization of lipids across distinct physiological regions. We optimized matrix selection and deposition methods with the goal of facilitating high spatial, and intra-filament, resolution using untargeted multivariate statistical spectral deconvolution across MS pixels.

RESULTS

Our results demonstrate an improved two-step matrix application with an optimized procedure for intra-organismal lipid profiling to improve analyte sensitivity and achieve higher spatial resolution. We evaluate several conventional matrices, namely 2,5-dihydroxybenzoic acid (DHB), superDHB (sDHB), 1,5-diaminonaphthalene (DAN), and a 50:50 mix of DHB and sDHB, and compare delineation and pixel-based elucidation of intra-filament lipidomics. We identified a total of 1626 features that could be putatively assigned a lipid-like formula based on database query and 46 unique features, with associated lipid assignments that were significantly distinct in their intra-filament location.

CONCLUSIONS

MALDI imaging MS with untargeted statistical spectral deconvolution was used to visualize intra-filament lipidomics organization in Spirulina filaments. Improvements in matrix deposition, including sequential sublimation and pneumatic spraying, increased signal abundance at high spatial resolution and allowed for identification of distinct lipid composition regions. This work outlines a methodology that may be used for micro-ecological untargeted molecular phenotyping.

Dissecting the brain with spatially resolved multi-omics

Recent studies have highlighted spatially resolved multi-omics technologies, including spatial genomics, transcriptomics, proteomics, and metabolomics, as powerful tools to decipher the spatial heterogeneity of the brain. Here, we focus on two major approaches in spatial transcriptomics (next-generation sequencing-based technologies and image-based technologies), and mass spectrometry imaging technologies used in spatial proteomics and spatial metabolomics. Furthermore, we discuss their applications in neuroscience, including building the brain atlas, uncovering gene expression patterns of neurons for special behaviors, deciphering the molecular basis of neuronal communication, and providing a more comprehensive explanation of the molecular mechanisms underlying central nervous system disorders. However, further efforts are still needed toward the integrative application of multi-omics technologies, including the real-time spatial multi-omics analysis in living cells, the detailed gene profile in a whole-brain view, and the combination of functional verification.

Advances and Emerging Medical Applications of Direct Mass Spectrometry Technologies for Tissue Analysis

Offering superb speed, chemical specificity, and analytical sensitivity, direct mass spectrometry (MS) technologies are highly amenable for the molecular analysis of complex tissues to aid in disease characterization and help identify new diagnostic, prognostic, and predictive markers. By enabling detection of clinically actionable molecular profiles from tissues and cells, direct MS technologies have the potential to guide treatment decisions and transform sample analysis within clinical workflows. In this review, we highlight recent health-related developments and applications of direct MS technologies that exhibit tangible potential to accelerate clinical research and disease diagnosis, including oncological and neurodegenerative diseases and microbial infections. We focus primarily on applications that employ direct MS technologies for tissue analysis, including MS imaging technologies to map spatial distributions of molecules in situ as well as handheld devices for rapid in vivo and ex vivo tissue analysis.

Spatially resolved multi-omics highlights cell-specific metabolic remodeling and interactions in gastric cancer

Mapping tumor metabolic remodeling and their spatial crosstalk with surrounding non-tumor cells can fundamentally improve our understanding of tumor biology, facilitates the designing of advanced therapeutic strategies. Here, we present an integration of mass spectrometry imaging-based spatial metabolomics and lipidomics with microarray-based spatial transcriptomics to hierarchically visualize the intratumor metabolic heterogeneity and cell metabolic interactions in same gastric cancer sample. Tumor-associated metabolic reprogramming is imaged at metabolic-transcriptional levels, and maker metabolites, lipids, genes are connected in metabolic pathways and colocalized in the heterogeneous cancer tissues. Integrated data from spatial multi-omics approaches coherently identify cell types and distributions within the complex tumor microenvironment, and an immune cell-dominated "tumor-normal interface" region where tumor cells contact adjacent tissues are characterized with distinct transcriptional signatures and significant immunometabolic alterations. Our approach for mapping tissue molecular architecture provides highly integrated picture of intratumor heterogeneity, and transform the understanding of cancer metabolism at systemic level.

Development and application of lipidomics for food research

Lipidomics is an emerging and promising omics derived from metabolomics to comprehensively analyze all of lipid molecules in biological matrices. The purpose of this chapter is to introduce the development and application of lipidomics for food research. First, three aspects of sample preparation are introduced: food sampling, lipid extraction, and transportation and storage. Second, five types of instruments for data acquisition are summarized: direct infusion-mass spectrometry (MS), chromatographic separation-MS, ion mobility-MS, MS imaging, and nuclear magnetic resonance spectroscopy. Third, data acquisition and analysis software are described for the lipidomics software development. Fourth, the application of lipidomics for food research is discussed such as food origin and adulteration analysis, food processing research, food preservation research, and food nutrition and health research. All the contents suggest that lipidomics is a powerful tool for food research based on its ability of lipid component profile analysis.

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

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

MALDI IMS-Derived Molecular Contour Maps: Augmenting Histology Whole-Slide Images

Imaging mass spectrometry (IMS) provides untargeted, highly multiplexed maps of molecular distributions in tissue. Ion images are routinely presented as heatmaps and can be overlaid onto complementary microscopy images that provide greater context. However, heatmaps use transparency blending to visualize both images, obscuring subtle quantitative differences and distribution gradients. Here, we developed a contour mapping approach that combines information from IMS ion intensity distributions with that of stained microscopy. As a case study, we applied this approach to imaging data from Staphylococcus aureus-infected murine kidney. In a univariate, or single molecular species, use-case of the contour map representation of IMS data, certain lipids colocalizing with regions of infection were selected using Pearson's correlation coefficient. Contour maps of these lipids overlaid with stained microscopy showed enhanced visualization of lipid distributions and spatial gradients in and around the bacterial abscess as compared to traditional heatmaps. The full IMS data set comprising hundreds of individual ion images was then grouped into a smaller subset of representative patterns using non-negative matrix factorization (NMF). Contour maps of these multivariate NMF images revealed distinct molecular profiles of the major abscesses and surrounding immune response. This contour mapping workflow also enabled a molecular visualization of the transition zone at the host-pathogen interface, providing potential clues about the spatial molecular dynamics beyond what histological staining alone provides. In summary, we developed a new IMS-based contour mapping approach to augment classical stained microscopy images, providing an enhanced and more interpretable visualization of IMS-microscopy multimodal molecular imaging data sets.

Spatial Proteomics for the Molecular Characterization of Breast Cancer

Breast cancer (BC) is a major global health issue, affecting a significant proportion of the female population and contributing to high rates of mortality. One of the primary challenges in the treatment of BC is the disease's heterogeneity, which can lead to ineffective therapies and poor patient outcomes. Spatial proteomics, which involves the study of protein localization within cells, offers a promising approach for understanding the biological processes that contribute to cellular heterogeneity within BC tissue. To fully leverage the potential of spatial proteomics, it is critical to identify early diagnostic biomarkers and therapeutic targets, and to understand protein expression levels and modifications. The subcellular localization of proteins is a key factor in their physiological function, making the study of subcellular localization a major challenge in cell biology. Achieving high resolution at the cellular and subcellular level is essential for obtaining an accurate spatial distribution of proteins, which in turn can enable the application of proteomics in clinical research. In this review, we present a comparison of current methods of spatial proteomics in BC, including untargeted and targeted strategies. Untargeted strategies enable the detection and analysis of proteins and peptides without a predetermined molecular focus, whereas targeted strategies allow the investigation of a predefined set of proteins or peptides of interest, overcoming the limitations associated with the stochastic nature of untargeted proteomics. By directly comparing these methods, we aim to provide insights into their strengths and limitations and their potential applications in BC research.

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

BACKGROUND

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

METHODS

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

RESULTS

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

CONCLUSIONS

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