Recent advances in mass spectrometry imaging for multiomics application in neurology

Mass spectrometry imaging for spatially resolved multi-omics molecular mapping

The recent upswing in the integration of spatial multi-omics for conducting multidimensional information measurements is opening a new chapter in biological research. Mapping the landscape of various biomolecules including metabolites, proteins, nucleic acids, etc., and even deciphering their functional interactions and pathways is believed to provide a more holistic and nuanced exploration of the molecular intricacies within living systems. Mass spectrometry imaging (MSI) stands as a forefront technique for spatially mapping the metabolome, lipidome, and proteome within diverse tissue and cell samples. In this review, we offer a systematic survey delineating different MSI techniques for spatially resolved multi-omics analysis, elucidating their principles, capabilities, and limitations. Particularly, we focus on the advancements in methodologies aimed at augmenting the molecular sensitivity and specificity of MSI; and depict the burgeoning integration of MSI-based spatial metabolomics, lipidomics, and proteomics, encompassing the synergy with other imaging modalities. Furthermore, we offer speculative insights into the potential trajectory of MSI technology in the future.

Application of clustering strategy for automatic segmentation of tissue regions in mass spectrometry imaging

RATIONALE

Mass spectrometry imaging (MSI) has been widely used in biomedical research fields. Each pixel in MSI consists of a mass spectrum that reflects the molecule feature of the tissue spot. Because MSI contains high-dimensional datasets, it is highly desired to develop computational methods for data mining and constructing tissue segmentation maps.

METHODS

To visualize different tissue regions based on mass spectrum features and improve the efficiency in processing enormous data, we proposed a computational strategy that consists of four procedures including preprocessing, data reduction, clustering, and quantitative validation.

RESULTS

In this study, we examined the combination of t-distributed stochastic neighbor embedding (t-SNE) and hierarchical clustering (HC) for MSI data analysis. Using publicly available MSI datasets, one dataset of mouse urinary bladder, and one dataset of human colorectal cancer, we demonstrated that the generated tissue segmentation maps from this combination were superior to other data reduction and clustering algorithms. Using the staining image as a reference, we assessed the performance of clustering algorithms with external and internal clustering validation measures, including purity, adjusted Rand index (ARI), Davies-Bouldin index (DBI), and spatial aggregation index (SAI). The result indicated that SAI delivered excellent performance for automatic segmentation of tissue regions in MSI.

CONCLUSIONS

We used a clustering algorithm to construct tissue automatic segmentation in MSI datasets. The performance was evaluated by comparing it with the stained image and calculating clustering validation indexes. The results indicated that SAI is important for automatic tissue segmentation in MSI, different from traditional clustering validation measures. Compared to the reports that used internal clustering validation measures such as DBI, our method offers more effective evaluation of clustering results for MSI segmentation. We envision that the proposed automatic image segmentation strategy can facilitate deep learning in molecular feature extraction and biomarker discovery for the biomedical applications of MSI.

Time of acquisition and high spatial resolution mass spectrometry imaging

The field of mass spectrometry imaging (MSI) is constantly evolving to analyze a diverse array of biological systems. A common goal is the need to resolve cellular and subcellular heterogeneity with high spatial resolution. As the field continues to progress towards high spatial resolution, other parameters must be considered when developing a practical method. Here, we discuss the impacts of high spatial resolution on the time of acquisition and the associated implications they have on an MSI analysis (e.g., area of the region of interest). This work presents a brief tutorial serving to evaluate high spatial resolution MSI relative to time of acquisition and data file size.

Interrogating the Metabolomic Profile of Amyotrophic Lateral Sclerosis in the Post-Mortem Human Brain by Infrared Matrix-Assisted Laser Desorption Electrospray Ionization (IR-MALDESI) Mass Spectrometry Imaging (MSI)

Amyotrophic lateral sclerosis (ALS) is an idiopathic, fatal neurodegenerative disease characterized by progressive loss of motor function with an average survival time of 2-5 years after diagnosis. Due to the lack of signature biomarkers and heterogenous disease phenotypes, a definitive diagnosis of ALS can be challenging. Comprehensive investigation of this disease is imperative to discovering unique features to expedite the diagnostic process and improve diagnostic accuracy. Here, we present untargeted metabolomics by mass spectrometry imaging (MSI) for comparing sporadic ALS (sALS) and C9orf72 positive (C9Pos) post-mortem frontal cortex human brain tissues against a control cohort. The spatial distribution and relative abundance of metabolites were measured by infrared matrix-assisted laser desorption electrospray ionization (IR-MALDESI) MSI for association to biological pathways. Proteomic studies on the same patients were completed via LC-MS/MS in a previous study, and results were integrated with imaging metabolomics results to enhance the breadth of molecular coverage. Utilizing METASPACE annotation platform and MSiPeakfinder, nearly 300 metabolites were identified across the sixteen samples, where 25 were identified as dysregulated between disease cohorts. The dysregulated metabolites were further examined for their relevance to alanine, aspartate, and glutamate metabolism, glutathione metabolism, and arginine and proline metabolism. The dysregulated pathways discussed are consistent with reports from other ALS studies. To our knowledge, this work is the first of its kind, reporting on the investigation of ALS post-mortem human brain tissue analyzed by multiomic MSI.

Multimodal Mass Spectrometry Imaging of Rat Brain Using IR-MALDESI and NanoPOTS-LC-MS/MS

Multimodal mass spectrometry imaging (MSI) is a critical technique used for deeply investigating biological systems by combining multiple MSI platforms in order to gain the maximum molecular information about a sample that would otherwise be limited by a single analytical technique. The aim of this work was to create a multimodal MSI approach that measures metabolomic and proteomic data from a single biological organ by combining infrared matrix-assisted laser desorption electrospray ionization (IR-MALDESI) for metabolomic MSI and nanodroplet processing in one pot for trace samples (nanoPOTS) LC-MS/MS for spatially resolved proteome profiling. Adjacent tissue sections of rat brain were analyzed by each platform, and each data set was individually analyzed using previously optimized workflows. IR-MALDESI data sets were annotated by accurate mass and spectral accuracy using HMDB, METLIN, and LipidMaps databases, while nanoPOTS-LC-MS/MS data sets were searched against the rat proteome using the Sequest HT algorithm and filtered with a 1% FDR. The combined data revealed complementary molecular profiles distinguishing the corpus callosum against other sampled regions of the brain. A multiomic pathway integration showed a strong correlation between the two data sets when comparing average abundances of metabolites and corresponding enzymes in each brain region. This work demonstrates the first steps in the creation of a multimodal MSI technique that combines two highly sensitive and complementary imaging platforms. Raw data files are available in METASPACE (https://metaspace2020.eu/project/pace-2021) and MassIVE (identifier: MSV000088211).

Recent advances in metabolomics analysis for early drug development

The pharmaceutical industry adapted proteomics and other 'omics technologies for drug research early following their initial introduction. Although metabolomics lacked behind in this development, it has now become an accepted and widely applied approach in early drug development. Over the past few decades, metabolomics has evolved from a pure exploratory tool to a more mature and quantitative biochemical technology. Several metabolomics-based platforms are now applied during the early phases of drug discovery. Metabolomics analysis assists in the definition of the physiological response and target engagement (TE) markers as well as elucidation of the mode of action (MoA) of drug candidates under investigation. In this review, we highlight recent examples and novel developments of metabolomics analyses applied during early drug development.

Harnessing Metabolomics to Advance Epilepsy Research

Metabolomics is the laboratory analysis and scientific study of the metabolome—that is, the entire collection of small molecule chemicals in an organism. The metabolome represents the functional state of an organism and provides a multifaceted readout of the aggregate activity of endogenous (cellular) and exogenous (environmental) processes. In this review, we discuss how the integrative and dynamic properties of the metabolome create unique opportunities to study complex pathologies that evolve and oscillate over time, like epilepsy. We explain how the scientific progress and clinical applications of metabolomics remain hampered by biological and technical challenges, and we propose best practices to overcome these challenges so that metabolomics can be used in a rigorous and effective manner to further epilepsy research.

The foundations and development of lipidomics

For over a century, the importance of lipid metabolism in biology was recognized but difficult to mechanistically understand due to the lack of sensitive and robust technologies for identification and quantification of lipid molecular species. The enabling technological breakthroughs emerged in the 1980s with the development of soft ionization methods (Electrospray Ionization and Matrix Assisted Laser Desorption/Ionization) that could identify and quantify intact individual lipid molecular species. These soft ionization technologies laid the foundations for what was to be later named the field of lipidomics. Further innovative advances in multistage fragmentation, dramatic improvements in resolution and mass accuracy, and multiplexed sample analysis fueled the early growth of lipidomics through the early 1990s. The field exponentially grew through the use of a variety of strategic approaches, which included direct infusion, chromatographic separation, and charge-switch derivatization, which facilitated access to the low abundance species of the lipidome. In this Thematic Review, we provide a broad perspective of the foundations, enabling advances, and predicted future directions of growth of the lipidomics field.

The circadian clock in the mouse habenula is set by catecholamines

Circadian rhythms are those variations in behavioral and molecular processes of organisms that follow roughly 24 h cycles in the absence of any external cue. The hypothalamic suprachiasmatic nucleus (SCN) harbors the principal brain pacemaker driving circadian rhythms. The epithalamic habenula (Hb) contains a self-sustained circadian clock functionally coupled to the SCN. Anatomically, the Hb projects to the midbrain dopamine (DA) and serotonin (5-HT) systems, and it receives inputs from the forebrain, midbrain, and brainstem. The SCN is set by internal signals such as 5-HT or melatonin from the raphe nuclei and pineal gland, respectively. However, how the Hb clock is set by internal cues is not well characterized. Hence, in the present study, we determined whether DA, noradrenaline (NA), 5-HT, and the neuropeptides orexin (ORX) and vasopressin influence the Hb circadian clock. Using PER2::Luciferase transgenic mice, we found that the amplitude of the PER2 protein circadian oscillations from Hb explants was strongly affected by DA and NA. Importantly, these effects were dose-and region (rostral vs. caudal) dependent for NA, with a main effect in the caudal part of the Hb. Furthermore, ORX also induced a significant change in the amplitude of PER2 protein oscillations in the caudal Hb. In conclusion, catecholaminergic (DA, NA) and ORXergic transmission impacts the clock properties of the Hb clock likely contributing to the circadian regulation of motivated behaviors. Accordingly, pathological conditions that lead in alterations of catecholamine or ORX activity (drug intake, compulsive feeding) might affect the Hb clock and conduct to circadian disturbances.

Successive High-Resolution (H2O)n-GCIB and C60-SIMS Imaging Integrates Multi-Omics in Different Cell Types in Breast Cancer Tissue

The temporo-spatial organization of different cells in the tumor microenvironment (TME) is the key to understanding their complex communication networks and the immune landscape that exists within compromised tissues. Multi-omics profiling of single-interacting cells in the native TME is critical for providing further information regarding the reprograming mechanisms leading to immunosuppression and tumor progression. This requires new technologies for biomolecular profiling of phenotypically heterogeneous cells on the same tissue sample. Here, we developed a new methodology for comprehensive lipidomic and metabolomic profiling of individual cells on frozen-hydrated tissue sections using water gas cluster ion beam secondary ion mass spectrometry ((H2O)n-GCIB-SIMS) (at 1.6 μm beam spot size), followed by profiling cell-type specific lanthanide antibodies on the same tissue section using C60-SIMS (at 1.1 μm beam spot size). We revealed distinct variations of distribution and intensities of >150 key ions (e.g., lipids and important metabolites) in different types of the TME individual cells, such as actively proliferating tumor cells as well as infiltrating immune cells. The demonstrated feasibility of SIMS imaging to integrate the multi-omics profiling in the same tissue section at the single-cell level will lead to new insights into the role of lipid reprogramming and metabolic response in normal regulation or pathogenic discoordination of cell-cell interactions in a variety of tissue microenvironments.

Multiomics Imaging Using High-Energy Water Gas Cluster Ion Beam Secondary Ion Mass Spectrometry [(H2O)n-GCIB-SIMS] of Frozen-Hydrated Cells and Tissue

Integration of multiomics at the single-cell level allows the unambiguous dissecting of phenotypic heterogeneity at different states such as health, disease, and biomedical response. Imaging mass spectrometry holds the promise of being able to measure multiple types of biomolecules in parallel in the same cell. We have explored the possibility of using water gas cluster ion beam secondary ion mass spectrometry [(H2O)n-GCIB-SIMS] as an analytical tool for multiomics assay. (H2O)n-GCIB has been hailed as an ideal ionization source for biological sampling owing to the enhanced chemical sensitivity and reduced matrix effect. Taking advantage of 1 μm spatial resolution by using a high-energy beam system, we have clearly shown the enhancement of multiple intact biomolecules up to a few hundredfold in single cells. Coupled with the cryogenic sample preparation/measurement, the lipids and metabolites were imaged simultaneously within the cellular region, uncovering the pristine chemistry for integrated omics in the same sample. We have demonstrated that double-charged myelin protein fragments and single-charged multiple lipids and metabolites can be localized in the same cells/tissue with a single acquisition. Our exploration has also been extended to the capability of (H2O)n-GCIB in the generation of multiple charged peptides on protein standards. Frozen hydration combined with (H2O)n-GCIB provides the possibility of universal enhancement for the ionization of multiple bio-molecules, including peptides/proteins which has allowed "omics" to become feasible in the same sample using SIMS.

Nanocomposite-Based Matrices in Laser Desorption/Ionization Mass Spectrometry for Small-Molecule Analysis

The efficient detection of small molecules is of significance for environmental monitoring, pharmacology, metabolomics, and lipidomics. The laser desorption/ionization mass spectrometry (LDI MS) platform enables high sensitivity, accuracy, resolution, and throughput in molecular analysis, but its analytical capability with respect to small molecules is limited due to inherent drawbacks arising from conventional organic matrices. The selection of an appropriate matrix is thus a precondition for small molecule detection by LDI MS. To date, various inorganic matrices have been developed, with a growing interest in composite materials displaying synergetic effects. This Minireview highlights the development of nanocomposites as LDI matrices driven by numerous innovations in material science, and their emerging use in small-molecule analysis.

Natural Products, the Fourth Industrial Revolution, and the Quintuple Helix

The profound interconnectedness of the sciences and technologies embodied in the Fourth Industrial Revolution is discussed in terms of the global role of natural products, and how that interplays with the development of sustainable and climate-conscious practices of cyberecoethnopharmacolomics within the Quintuple Helix for the promotion of a healthier planet and society.

A Picture Worth a Thousand Molecules-Integrative Technologies for Mapping Subcellular Molecular Organization and Plasticity in Developing Circuits

A key challenge in developmental neuroscience is identifying the local regulatory mechanisms that control neurite and synaptic refinement over large brain volumes. Innovative molecular techniques and high-resolution imaging tools are beginning to reshape our view of how local protein translation in subcellular compartments drives axonal, dendritic, and synaptic development and plasticity. Here we review recent progress in three areas of neurite and synaptic study in situ-compartment-specific transcriptomics/translatomics, targeted proteomics, and super-resolution imaging analysis of synaptic organization and development. We discuss synergies between sequencing and imaging techniques for the discovery and validation of local molecular signaling mechanisms regulating synaptic development, plasticity, and maintenance in circuits.

Multimodal Imaging Mass Spectrometry: Next Generation Molecular Mapping in Biology and Medicine

Imaging mass spectrometry has become a mature molecular mapping technology that is used for molecular discovery in many medical and biological systems. While powerful by itself, imaging mass spectrometry can be complemented by the addition of other orthogonal, chemically informative imaging technologies to maximize the information gained from a single experiment and enable deeper understanding of biological processes. Within this review, we describe MALDI, SIMS, and DESI imaging mass spectrometric technologies and how these have been integrated with other analytical modalities such as microscopy, transcriptomics, spectroscopy, and electrochemistry in a field termed multimodal imaging. We explore the future of this field and discuss forthcoming developments that will bring new insights to help unravel the molecular complexities of biological systems, from single cells to functional tissue structures and organs.

Circulating Metabolites as Potential Biomarkers for Neurological Disorders-Metabolites in Neurological Disorders

There are, still, limitations to predicting the occurrence and prognosis of neurological disorders. Biomarkers are molecules that can change in different conditions, a feature that makes them potential tools to improve the diagnosis of disease, establish a prognosis, and monitor treatments. Metabolites can be used as biomarkers, and are small molecules derived from the metabolic process found in different biological media, such as tissue samples, cells, or biofluids. They can be identified using various strategies, targeted or untargeted experiments, and by different techniques, such as high-performance liquid chromatography, mass spectrometry, or nuclear magnetic resonance. In this review, we aim to discuss the current knowledge about metabolites as biomarkers for neurological disorders. We will present recent developments that show the need and the feasibility of identifying such biomarkers in different neurological disorders, as well as discuss relevant research findings in the field of metabolomics that are helping to unravel the mechanisms underlying neurological disorders. Although several relevant results have been reported in metabolomic studies in patients with neurological diseases, there is still a long way to go for the clinical use of metabolites as potential biomarkers in these disorders, and more research in the field is needed.

Analysis of neurotransmitters in rat placenta exposed to flame retardants using IR-MALDESI mass spectrometry imaging

Chemical exposures can adversely impact fetal development. For many compounds, including common flame retardants, the mechanisms by which this occurs remain unclear, but emerging evidence suggests that disruption at the level of the placenta may play a role. Understanding how the placenta might be vulnerable to chemical exposures is challenging due to its complex structure. The primary objective of this study was to develop a method for detecting placental neurotransmitters and related metabolites without chemical derivatization so changes in the abundance and spatial distribution of neurotransmitters in rat placenta following chemical exposure could be determined using infrared matrix-assisted laser desorption electrospray ionization (IR-MALDESI) mass spectrometry imaging. Without chemical derivatization, 49 neurotransmitters and their related metabolites were putatively identified in untreated rat placenta sections using mass measurement accuracy and spectral accuracy. A few neurotransmitters were less abundant in placentas that were exposed to various flame retardants and were further investigated by KEGG metabolic pathway analysis. Many of these downregulated neurotransmitters shared the same enzyme responsible for metabolism, aromaticl-amino acid decarboxylase, suggesting a mechanistic role. These data constitute a new approach that could help identify novel mechanisms of toxicity in complex tissues. Graphical abstract.

Rise of Systems Glycobiology and Personalized Glycomedicine: Why and How to Integrate Glycomics with Multiomics Science?

Glycomics is a rapidly emerging subspecialty of system sciences that evaluates the structures and functions of glycans in biological systems. Moreover, glycomics informs allied scholarships such as systems glycobiology and personalized glycomedicine that collectively aim to explain the role of glycans in person-to-person and between-population variations in disease susceptibility and response to health interventions such as drugs, nutrition, and vaccines. For glycomics to make greater, systems-scale, contributions to biology and medical research, it is facing a new developmental challenge: transition from single omics to multiomics integrative technology platforms. A comprehensive map of all possible connections between glycomics and other omics types has not yet been developed. Glycomics aims to discover a complex interplay of molecular interactions; however, little is known about the regulatory networks controlling these complex processes. In addition, the glycomics knowledgebase is presently scattered across various publications and databases, and therefore does not enable a holistic or systems view of this study field. Therefore, researchers are not always aware, for example, that a given analyzed genetic locus is linked with glycans, and that there are also glycomics determinants of complex phenotypes in health and biology. This review presents several published examples of glycomics science in association with other omics levels, such as genomics, transcriptomics, proteomics, metabolomics, epigenomics, ncRNomics, lipidomics, and interactomics. I also highlight the salient knowledge gaps and suggest future research directions. Understanding the interconnections of glycomics with other omics technologies will facilitate multiomics science and knowledge integration, enhance development of systems glycobiology and personalized glycomedicine.