Atmospheric pressure MALDI mass spectrometry imaging of tissues and cells at 1.4-μm lateral resolution

Computational methods for alignment and integration of spatially resolved transcriptomics data

Most of the complex biological regulatory activities occur in three dimensions (3D). To better analyze biological processes, it is essential not only to decipher the molecular information of numerous cells but also to understand how their spatial contexts influence their behavior. With the development of spatially resolved transcriptomics (SRT) technologies, SRT datasets are being generated to simultaneously characterize gene expression and spatial arrangement information within tissues, organs or organisms. To fully leverage spatial information, the focus extends beyond individual two-dimensional (2D) slices. Two tasks known as slices alignment and data integration have been introduced to establish correlations between multiple slices, enhancing the effectiveness of downstream tasks. Currently, numerous related methods have been developed. In this review, we first elucidate the details and principles behind several representative methods. Then we report the testing results of these methods on various SRT datasets, and assess their performance in representative downstream tasks. Insights into the strengths and weaknesses of each method and the reasons behind their performance are discussed. Finally, we provide an outlook on future developments. The codes and details of experiments are now publicly available at https://github.com/YangLabHKUST/SRT_alignment_and_integration.

Proteomics mining of cancer hallmarks on a single-cell resolution

Dysregulated proteome is an essential contributor in carcinogenesis. Protein fluctuations fuel the progression of malignant transformation, such as uncontrolled proliferation, metastasis, and chemo/radiotherapy resistance, which severely impair therapeutic effectiveness and cause disease recurrence and eventually mortality among cancer patients. Cellular heterogeneity is widely observed in cancer and numerous cell subtypes have been characterized that greatly influence cancer progression. Population-averaged research may not fully reveal the heterogeneity, leading to inaccurate conclusions. Thus, deep mining of the multiplex proteome at the single-cell resolution will provide new insights into cancer biology, to develop prognostic biomarkers and treatments. Considering the recent advances in single-cell proteomics, herein we review several novel technologies with particular focus on single-cell mass spectrometry analysis, and summarize their advantages and practical applications in the diagnosis and treatment for cancer. Technological development in single-cell proteomics will bring a paradigm shift in cancer detection, intervention, and therapy.

MALDI-2-Enabled Oversampling for the Mass Spectrometry Imaging of Metabolites at Single-Cell Resolution

Matrix-assisted laser desorption/ionization mass spectrometry imaging (MALDI-MSI) can provide valuable insights into the metabolome of complex biological systems such as organ tissues and cells. However, obtaining metabolite data at single-cell spatial resolutions presents a few technological challenges. Generally, spatial resolution is defined by the increment the sample stage moves between laser ablation spots. Stage movements less than the diameter of the focused laser beam (i.e., oversampling) can improve spatial resolution; however, such oversampling conditions result in a reduction in sensitivity. To overcome this, we combine an oversampling approach with laser postionization (MALDI-2), which allows for both higher spatial resolution and improved analyte ionization efficiencies. This approach provides significant enhancements to sensitivity for various metabolite classes (e.g., amino acids, purines, carbohydrates etc.), with mass spectral intensities from 6 to 8 μm pixel sizes (from a laser spot size of ∼13 μm) being commensurate with or higher than those obtained by conventional MALDI at 20 μm pixel sizes for many different metabolites. This technique has been used to map the distribution of metabolites throughout mouse spinal cord tissue to observe how metabolite localizations change throughout specific anatomical regions, such as those distributed to the somatosensory area of the dorsal horn, white matter, gray matter, and ventral horn. Furthermore, this method is utilized for single-cell metabolomics of human iPSC-derived astrocytes at 10 μm pixel sizes whereby many different metabolites, including nucleotides, were detected from individual cells while providing insight into cellular localizations.

Applying Spatial Metabolomics To Investigate Age- and Drug-Induced Neurochemical Changes

In an era when population aging is increasing the burden of neurodegenerative conditions, deciphering the mechanisms underlying brain senescence is more important than ever. Here, we present a spatial metabolomics analysis of age-induced neurochemical alterations in the mouse brain using negative ionization mode mass spectrometry imaging. The age-dependent effects of the acetylcholinesterase inhibitor tacrine were simultaneously examined. For ultrahigh mass resolution analysis, we utilized a Fourier-transform ion cyclotron resonance spectrometer. To complement this, a trapped ion mobility spectrometry time-of-flight analyzer provided high speed and lateral resolution. The chosen approach facilitated the detection and identification of a wide range of metabolites, from amino acids to sphingolipids. We reported significant, age-dependent alterations in brain lipids which were most evident for sulfatides and lysophosphatidic acids. Sulfatide species, which are mainly localized to white matter, either increased or decreased with age, depending on the carbon chain length and hydroxylation stage. Lysophosphatidic acids were found to decrease with age in the detailed cortical and hippocampal subregions. An age-dependent increase in the glutamine/glutamate ratio, an indicator of glia-neuron interconnection and neurotoxicity, was detected after tacrine administration. The presented metabolic mapping approach was able to provide visualizations of the lipid signaling and neurotransmission alterations induced by early aging and can thus be beneficial to further elucidating age-related neurochemical pathways.

Oversampling for Enhanced Spatial Resolution of Zebrafish by Top-Hat IR-MALDESI-MSI

Mass spectrometry imaging (MSI) has become a significant tool for measuring chemical species in biological tissues, where much of the impact of these platforms lies in their capability to report the spatial distribution of analytes for correlation to sample morphology. As a result, enhancement of spatial resolution has become a frontier of innovation in the field, and necessary developments are dependent on the ionization source. More particularly, laser-based imaging sources may require modifications to the optical train or alternative sampling techniques. These challenges are heightened for systems with infrared (IR) lasers, as their operating wavelength generates spot sizes that are inherently larger than their ultraviolet counterparts. Recently, the infrared matrix-assisted laser desorption electrospray ionization (IR-MALDESI) source has shown the utility of a diffractive optical element (DOE) to produce square ablation patterns, termed top-hat IR-MALDESI. If the DOE optic is combined with oversampling methods, smaller ablation volumes can be sampled to render higher spatial resolution imaging experiments. Further, this approach enables reproducible spot sizes and ablation volumes for better comparison between scans. Herein, we investigate the utility of oversampling with top-hat IR-MALDESI to enhance the spatial resolution of measured lipids localized within the head of sectioned zebrafish tissue. Four different spatial resolutions were evaluated for data quality (e.g., mass measurement accuracy, spectral accuracy) and quantity of annotations. Other experimental parameters to consider for high spatial resolution imaging are also discussed. Ultimately, 20 μm spatial resolution was achieved in this work and supports feasibility for use in future IR-MALDESI studies.

Enhanced MALDI-2 Sensitivity with Reflecting Post-Ionization Laser for High-Resolution MS Imaging Combined with Real-Time Microscope Imaging

Laser-induced matrix-assisted laser desorption/ionization post-ionization (MALDI-2) could improve the MALDI sensitivity of biological metabolites by over 1 order of magnitude. Herein, we demonstrate that MALDI-2 sensitivity can be further enhanced with reflecting post-ionization laser that multiplies the intersection times between laser and MALDI plume. This method, which we named MALDI-2+, typically brought over 2 times sensitivity improvement from conventional MALDI-2. Advancing in sensitivity thereby prompted us to pursue higher mass spectrometry imaging (MSI) spatial resolution. A dedicated T-shaped ion guide was designed to allow perpendicular incidence of ablation laser in reflection geometry MALDI. Although 8-10 μm pixel was used in MALDI imaging due to the limited precision of the motorized stage, the laser spot diameter could be down to 2.5 μm for potentially higher spatial resolution. In addition, this ion source enabled real-time and high-quality microscope imaging from backward of the sample plate. Beneficially, we were able to monitor the actual laser spot condition in real time as well as obtain high-resolution microscopic sample images that inherently register with MSI images. All of these benefits have been demonstrated by analyzing standard samples and imaging of cells. We believe that the enhancement in sensitivity, spatial resolution, and microscope capacity of our design could facilitate spatial omics studies.

Multi-modal mass spectrometry imaging of a single tissue section

Mass spectrometry imaging (MSI) was developed to visualize spatial chemical information within tissues, thereby facilitating spatial multi-omic analysis. However, due to the limited spatial information provided by individual modal MSI, correlating various chemical data within tissues remains a significant challenge. In recent years, multimodal MSI has garnered considerable attention due to its ability to visualize the spatial distributions of multiple biomolecules within tissues. Among the strategies employed in this field, multimodal imaging on a single tissue section circumvents multiple issues introduced by integration of images of consecutive tissue sections. In this minireview, we provide an overview of multimodal MSI on a single tissue section, with a particular focus on the use of Matrix-Assisted Laser Desorption/Ionization-MSI for spatial multi-omic investigations that offer a comprehensive and in-depth elucidation of the biological state and activities, aiming to inspire the development of new approaches in this field.

Mass Spectrometry Imaging of Organoids to Improve Preclinical Research

Preclinical models are essential research tools before novel therapeutic or diagnostic methods can be applied to humans. These range from in vitro cell monocultures to vastly more complex animal models, but clinical translation to humans often fails to deliver significant results. Three-dimensional (3D) organoid systems are being increasingly studied to establish physiologically relevant in vitro platforms in a trade-off between the complexity of the research question and the complexity of practical experimental setups. The sensitivity and precision of analytical tools are yet another limiting factors in what can be investigated, and mass spectrometry (MS) is one of the most powerful analytical techniques available to the scientific community. Its innovative use to spatially resolve biological samples has opened many research avenues in the field of MS imaging (MSI). Here, this work aims to explore the current scientific landscape in the application of MSI on organoids, with an emphasis on their combined potential to facilitate and improve preclinical studies.

Mass spectrometry imaging as a promising analytical technique for herbal medicines: an updated review

Herbal medicines (HMs) have long played a pivotal role in preventing and treating various human diseases and have been studied widely. However, the complexities present in HM metabolites and their unclear mechanisms of action have posed significant challenges in the modernization of traditional Chinese medicine (TCM). Over the past two decades, mass spectrometry imaging (MSI) has garnered increasing attention as a robust analytical technique that enables the simultaneous execution of qualitative, quantitative, and localization analyses without complex sample pretreatment. With advances in technical solutions, MSI has been extensively applied in the field of HMs. MSI, a label-free ion imaging technique can comprehensively map the spatial distribution of HM metabolites in plant native tissues, thereby facilitating the effective quality control of HMs. Furthermore, the spatial dimension information of small molecule endogenous metabolites within animal tissues provided by MSI can also serve as a supplement to uncover pharmacological and toxicological mechanisms of HMs. In the review, we provide an overview of the three most common MSI techniques. In addition, representative applications in HM are highlighted. Finally, we discuss the current challenges and propose several potential solutions. We hope that the summary of recent findings will contribute to the application of MSI in exploring metabolites and mechanisms of action of HMs.

Single-cell omics: experimental workflow, data analyses and applications

Cells are the fundamental units of biological systems and exhibit unique development trajectories and molecular features. Our exploration of how the genomes orchestrate the formation and maintenance of each cell, and control the cellular phenotypes of various organismsis, is both captivating and intricate. Since the inception of the first single-cell RNA technology, technologies related to single-cell sequencing have experienced rapid advancements in recent years. These technologies have expanded horizontally to include single-cell genome, epigenome, proteome, and metabolome, while vertically, they have progressed to integrate multiple omics data and incorporate additional information such as spatial scRNA-seq and CRISPR screening. Single-cell omics represent a groundbreaking advancement in the biomedical field, offering profound insights into the understanding of complex diseases, including cancers. Here, we comprehensively summarize recent advances in single-cell omics technologies, with a specific focus on the methodology section. This overview aims to guide researchers in selecting appropriate methods for single-cell sequencing and related data analysis.

External Data Systems Enable Enhanced (and Sustainable) Fourier Transform Mass Spectrometry Imaging for Legacy Hybrid Linear Ion Trap-Orbitrap Platforms

Legacy Fourier transform (FT) mass spectrometers provide robust platforms for bioanalytical mass spectrometry (MS) yet lack the most modern performance capabilities. For many laboratories, the routine investment in next generation instrumentation is cost prohibitive. Field-based upgrades provide a direct path to extend the usable lifespan of MS platforms which may be considered antiquated based on performance specifications at the time of manufacture. Here we demonstrate and evaluate the performance of a hybrid linear ion trap (LTQ)-Orbitrap mass spectrometer that has been enhanced via an external high-performance data acquisition and processing system to provide true absorption mode FT processing during an experimental acquisition. For the application to mass spectrometry imaging, several performance metrics have been improved including mass resolving power, mass accuracy, and dynamic range to provide an FTMS system comparable to current platforms. We also demonstrate, perhaps, the unexpected ability of these legacy platforms to detect usable time-domain signals up to 5 s in duration to achieve a mass resolving power 8× higher than the original platform specification.

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.

Probe-based mass spectrometry approaches for single-cell and single-organelle measurements

Exploring the chemical content of individual cells not only reveals underlying cell-to-cell chemical heterogeneity but is also a key component in understanding how cells combine to form emergent properties of cellular networks and tissues. Recent technological advances in many analytical techniques including mass spectrometry (MS) have improved instrumental limits of detection and laser/ion probe dimensions, allowing the analysis of micron and submicron sized areas. In the case of MS, these improvements combined with MS's broad analyte detection capabilities have enabled the rise of single-cell and single-organelle chemical characterization. As the chemical coverage and throughput of single-cell measurements increase, more advanced statistical and data analysis methods have aided in data visualization and interpretation. This review focuses on secondary ion MS and matrix-assisted laser desorption/ionization MS approaches for single-cell and single-organelle characterization, which is followed by advances in mass spectral data visualization and analysis.

Metabolomic profiling and accurate diagnosis of basal cell carcinoma by MALDI imaging and machine learning

Basal cell carcinoma (BCC), the most common keratinocyte cancer, presents a substantial public health challenge due to its high prevalence. Traditional diagnostic methods, which rely on visual examination and histopathological analysis, do not include metabolomic data. This exploratory study aims to molecularly characterize BCC and diagnose tumour tissue by applying matrix-assisted laser desorption ionization mass spectrometry imaging (MALDI-MSI) and machine learning (ML). BCC tumour development was induced in a mouse model and tissue sections containing BCC (n = 12) were analysed. The study design involved three phases: (i) Model training, (ii) Model validation and (iii) Metabolomic analysis. The ML algorithm was trained on MS data extracted and labelled in accordance with histopathology. An overall classification accuracy of 99.0% was reached for the labelled data. Classification of unlabelled tissue areas aligned with the evaluation of a certified Mohs surgeon for 99.9% of the total tissue area, underscoring the model's high sensitivity and specificity in identifying BCC. Tentative metabolite identifications were assigned to 189 signals of importance for the recognition of BCC, each indicating a potential tumour marker of diagnostic value. These findings demonstrate the potential for MALDI-MSI coupled with ML to characterize the metabolomic profile of BCC and to diagnose tumour tissue with high sensitivity and specificity. Further studies are needed to explore the potential of implementing integrated MS and automated analyses in the clinical setting.

Mass spectrometry imaging of N-linked glycans: Fundamentals and recent advances

With implications in several medical conditions, N-linked glycosylation is one of the most important posttranslation modifications present in all living organisms. Due to their nontemplate synthesis, glycan structures are extraordinarily complex and require multiple analytical techniques for complete structural elucidation. Mass spectrometry is the most common way to investigate N-linked glycans; however, with techniques such as liquid-chromatography mass spectrometry, there is complete loss of spatial information. Mass spectrometry imaging is a transformative analytical technique that can visualize the spatial distribution of ions within a biological sample and has been shown to be a powerful tool to investigate N-linked glycosylation. This review covers the fundamentals of mass spectrometry imaging and N-linked glycosylation and highlights important findings of recent key studies aimed at expanding and improving the glycomics imaging field.

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

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

Gel-assisted mass spectrometry imaging enables sub-micrometer spatial lipidomics

A technique capable of label-free detection, mass spectrometry imaging (MSI) is a powerful tool for spatial investigation of native biomolecules in intact specimens. However, MSI has often been precluded from single-cell applications due to the spatial resolution limit set forth by the physical and instrumental constraints of the method. By taking advantage of the reversible interaction between the analytes and a superabsorbent hydrogel, we have developed a sample preparation and imaging workflow named Gel-Assisted Mass Spectrometry Imaging (GAMSI) to overcome the spatial resolution limits of modern mass spectrometers. With GAMSI, we show that the spatial resolution of MALDI-MSI can be enhanced ~3-6-fold to the sub-micrometer level without changing the existing mass spectrometry hardware or analysis pipeline. This approach will vastly enhance the accessibility of MSI-based spatial analysis at the cellular scale.

Single-cell proteomics by mass spectrometry: Advances and implications in cancer research

Cancer harbours extensive proteomic heterogeneity. Inspired by the prior success of single-cell RNA sequencing (scRNA-seq) in characterizing minute transcriptomics heterogeneity in cancer, researchers are now actively searching for information regarding the proteomics counterpart. Therefore recently, single-cell proteomics by mass spectrometry (SCP) has rapidly developed into state-of-the-art technology to cater the need. This review aims to summarize application of SCP in cancer research, while revealing current development progress of SCP technology. The review also aims to contribute ideas into research gaps and future directions, ultimately promoting the application of SCP in cancer research.

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.

Reproducible mass spectrometry data processing and compound annotation in MZmine 3

Untargeted mass spectrometry (MS) experiments produce complex, multidimensional data that are practically impossible to investigate manually. For this reason, computational pipelines are needed to extract relevant information from raw spectral data and convert it into a more comprehensible format. Depending on the sample type and/or goal of the study, a variety of MS platforms can be used for such analysis. MZmine is an open-source software for the processing of raw spectral data generated by different MS platforms. Examples include liquid chromatography-MS, gas chromatography-MS and MS-imaging. These data might typically be associated with various applications including metabolomics and lipidomics. Moreover, the third version of the software, described herein, supports the processing of ion mobility spectrometry (IMS) data. The present protocol provides three distinct procedures to perform feature detection and annotation of untargeted MS data produced by different instrumental setups: liquid chromatography-(IMS-)MS, gas chromatography-MS and (IMS-)MS imaging. For training purposes, example datasets are provided together with configuration batch files (i.e., list of processing steps and parameters) to allow new users to easily replicate the described workflows. Depending on the number of data files and available computing resources, we anticipate this to take between 2 and 24 h for new MZmine users and nonexperts. Within each procedure, we provide a detailed description for all processing parameters together with instructions/recommendations for their optimization. The main generated outputs are represented by aligned feature tables and fragmentation spectra lists that can be used by other third-party tools for further downstream analysis.

Hepatic Topology of Glycosphingolipids in Schistosoma mansoni-Infected Hamsters

Schistosomiasis is a neglected tropical disease caused by worm parasites of the genus Schistosoma. Upon infection, parasite eggs can lodge inside of host organs like the liver. This leads to granuloma formation, which is the main cause of the pathology of schistosomiasis. To better understand the different levels of host-pathogen interaction and pathology, our study focused on the characterization of glycosphingolipids (GSLs). For this purpose, GSLs in livers of infected and noninfected hamsters were studied by combining high-spatial-resolution atmospheric-pressure scanning microprobe matrix-assisted laser desorption/ionization mass spectrometry imaging (AP-SMALDI MSI) with nanoscale hydrophilic interaction liquid chromatography tandem mass spectrometry (nano-HILIC MS/MS). Nano-HILIC MS/MS revealed 60 GSL species with a distinct saccharide and ceramide composition. AP-SMALDI MSI measurements were conducted in positive- and negative-ion mode for the visualization of neutral and acidic GSLs. Based on nano-HILIC MS/MS results, we discovered no downregulated but 50 significantly upregulated GSLs in liver samples of infected hamsters. AP-SMALDI MSI showed that 44 of these GSL species were associated with the granulomas in the liver tissue. Our findings suggest an important role of GSLs during granuloma formation.

Development of Transmission Ambient Pressure Laser Desorption Ionization/Postphotoionization Mass Spectrometry Imaging

Laser-based high-resolution mass spectrometry imaging at ambient conditions has promising applications in life science. However, the ion yield during laser desorption/ablation is poor. Here, transmission atmospheric pressure laser desorption ionization combined with a compact postphotoionization (t-AP-LDI/PI) assembly with a krypton discharge lamp was developed for the untargeted imaging of various biomolecules. The spatial distributions of numerous lipid classes, fatty acids, neurotransmitters, and amino acids in the subregions of mouse cerebellum tissue were obtained. Compared with single laser ablation, the sensitivities for most analytes were increased by 1 to 3 orders of magnitude by dopant-assisted postphotoionization. After careful optimization, a spatial resolution of 4 μm could be achieved for the metabolites in mouse hippocampus tissue. Finally, the melanoma tissue slices were analyzed using t-AP-LDI/PI MSI, which revealed the metabolic heterogeneity of the melanoma microenvironment and exhibited the phenomenon of abnormal proliferation and invasion trends in tumor cells.

Recent Progress in Mass Spectrometry-Based Single-Cell Metabolic Analysis

Single-cell analysis enables the measurement of biomolecules at the level of individual cells, facilitating in-depth investigations into cellular heterogeneity and precise interpretation of the related biological mechanisms. Among these biomolecules, cellular metabolites exhibit remarkable sensitivity to environmental and biochemical changes, unveiling a hidden world underlying cellular heterogeneity and allowing for the determination of cell physiological states. However, the metabolic analysis of single cells is challenging due to the extremely low concentrations, substantial content variations, and rapid turnover rates of cellular metabolites. Mass spectrometry (MS), characterized by its high sensitivity, wide dynamic range, and excellent selectivity, is employed in single-cell metabolic analysis. This review focuses on recent advances and applications of MS-based single-cell metabolic analysis, encompassing three key steps of single-cell isolation, detection, and application. It is anticipated that MS will bring profound implications in biomedical practices, serving as advanced tools to depict the single-cell metabolic landscape.

Advances in mass spectrometry imaging for spatial cancer metabolomics

Mass spectrometry (MS) has become a central technique in cancer research. The ability to analyze various types of biomolecules in complex biological matrices makes it well suited for understanding biochemical alterations associated with disease progression. Different biological samples, including serum, urine, saliva, and tissues have been successfully analyzed using mass spectrometry. In particular, spatial metabolomics using MS imaging (MSI) allows the direct visualization of metabolite distributions in tissues, thus enabling in-depth understanding of cancer-associated biochemical changes within specific structures. In recent years, MSI studies have been increasingly used to uncover metabolic reprogramming associated with cancer development, enabling the discovery of key biomarkers with potential for cancer diagnostics. In this review, we aim to cover the basic principles of MSI experiments for the nonspecialists, including fundamentals, the sample preparation process, the evolution of the mass spectrometry techniques used, and data analysis strategies. We also review MSI advances associated with cancer research in the last 5 years, including spatial lipidomics and glycomics, the adoption of three-dimensional and multimodal imaging MSI approaches, and the implementation of artificial intelligence/machine learning in MSI-based cancer studies. The adoption of MSI in clinical research and for single-cell metabolomics is also discussed. Spatially resolved studies on other small molecule metabolites such as amino acids, polyamines, and nucleotides/nucleosides will not be discussed in the context.

Multiscale biochemical mapping of the brain through deep-learning-enhanced high-throughput mass spectrometry

Spatial omics technologies can reveal the molecular intricacy of the brain. While mass spectrometry imaging (MSI) provides spatial localization of compounds, comprehensive biochemical profiling at a brain-wide scale in three dimensions by MSI with single-cell resolution has not been achieved. We demonstrate complementary brain-wide and single-cell biochemical mapping using MEISTER, an integrative experimental and computational mass spectrometry (MS) framework. Our framework integrates a deep-learning-based reconstruction that accelerates high-mass-resolving MS by 15-fold, multimodal registration creating three-dimensional (3D) molecular distributions and a data integration method fitting cell-specific mass spectra to 3D datasets. We imaged detailed lipid profiles in tissues with millions of pixels and in large single-cell populations acquired from the rat brain. We identified region-specific lipid contents and cell-specific localizations of lipids depending on both cell subpopulations and anatomical origins of the cells. Our workflow establishes a blueprint for future development of multiscale technologies for biochemical characterization of the brain.

Spatial neurolipidomics-MALDI mass spectrometry imaging of lipids in brain pathologies

Given the complexity of nervous tissues, understanding neurochemical pathophysiology puts high demands on bioanalytical techniques with respect to specificity and sensitivity. Mass spectrometry imaging (MSI) has evolved to become an important, biochemical imaging technology for spatial biology in biological and translational research. The technique facilitates comprehensive, sensitive elucidation of the spatial distribution patterns of drugs, lipids, peptides, and small proteins in situ. Matrix-assisted laser desorption ionization (MALDI)-based MSI is the dominating modality due to its broad applicability and fair compromise of selectivity, sensitivity price, throughput, and ease of use. This is particularly relevant for the analysis of spatial lipid patterns, where no other comparable spatial profiling tools are available. Understanding spatial lipid biology in nervous tissue is therefore a key and emerging application area of MSI research. The aim of this review is to give a concise guide through the MSI workflow for lipid imaging in central nervous system (CNS) tissues and essential parameters to consider while developing and optimizing MSI assays. Further, this review provides a broad overview of key developments and applications of MALDI MSI-based spatial neurolipidomics to map lipid dynamics in neuronal structures, ultimately contributing to a better understanding of neurodegenerative disease pathology.

Advances in multimodal mass spectrometry for single-cell analysis and imaging enhancement

Multimodal mass spectrometry (MMS) incorporates an imaging modality with probe-based mass spectrometry (MS) to enable precise, targeted data acquisition and provide additional biological and chemical data not available by MS alone. Two categories of MMS are covered; in the first, an imaging modality guides the MS probe to target individual cells and to reduce acquisition time by automatically defining regions of interest. In the second category, imaging and MS data are coupled in the data analysis pipeline to increase the effective spatial resolution using a higher resolution imaging method, correct for tissue deformation, and incorporate fine morphological features in an MS imaging dataset. Recent methodological and computational developments are covered along with their application to single-cell and imaging analyses.

Combined Widely Targeted Metabolomic, Transcriptomic, and Spatial Metabolomic Analysis Reveals the Potential Mechanism of Coloration and Fruit Quality Formation in Actinidia chinensis cv. Hongyang

Postharvest kiwifruit (Actinidia chinensis cv. Hongyang) pulp is mainly composed of outer yellow-flesh (LR) and inner red-flesh (HR). However, information about the differences in coloration and fruit quality between these two parts are limited. In this study, widely targeted metabolomic, transcriptomic, and spatial metabolomic analyses were used to reveal the potential mechanism of coloration and fruit quality formation. The results show that a total of 1001 metabolites were identified in Hongyang kiwifruit, and the accumulation of 211 metabolites were significantly higher in the HR than LR, including 69 flavonoids, 53 phenolic acids, and 38 terpenoids. There were no significant differences in the content of citric acid, quinic acid, glucose, fructose, or sucrose between the LR and HR. These results were consistent with the results from the RNA-seq profile and spatial metabolomic analysis. In addition, a total of 23 key candidate genes related to flesh color and fruit quality formation were identified and validated by qRT-PCR analysis. This study provides a theoretical basis for elucidating the underlying mechanism of the formation of kiwifruit flesh color and fruit quality.

N-acetyldopamine oligomers from Periplaneta americana with anti-inflammatory and vasorelaxant effects and their spatial distribution visualized by mass spectrometry imaging

ETHNOPHARMACOLOGICAL RELEVANCE

Periplaneta americana is a medicinal insect that has been applied to promote blood circulation and remove blood stasis based on traditional Chinese medicine (TCM) for a long time. Its modern preparation, Xinmailong injection, was adopted for the treatment of congestive heart failure (CHF). The bioactive constituents of P. americana and their correlation with its traditional uses need further investigation.

AIM OF THE STUDY

This study aimed to elucidate the N-acetyldopamine (NADA) oligomers from P. american, determine their spatial distribution, and investigate their anti-inflammatory and vasorelaxant effects to provide scientific evidence supporting the clinical use of this medicinal insect.

MATERIAL AND METHODS

NADA oligomers were isolated from the CH2Cl2: CH3OH (2:1) extract of P. americana, through sequential chromatographic methods including silica gel, Sephadex LH-20, preparative HPLC, and chiral-phase separation. Their structures were determined by HRESIMS, 1D and 2D NMR spectroscopic analysis, chiral resolution, and calculated electronic circular dichroism analysis. With the aid of atmospheric pressure scanning matrix-assisted laser desorption/ionization mass spectrometry imaging (AP-SMALDI MSI), the isolated compounds in a spatial profile within P. americana were identified. NO production was measured to assess anti-inflammatory activity. Vasorelaxant activity assessments were performed on the norepinephrine-precontracted 3rd-order mesenteric arteries.

RESULTS

Seven new NADA trimers, peridopamines A-G (1-7), two new NADA dimers, peridopamines H and I (8 and 9), and six known NADA derivatives (10-15), were obtained from P. americana. The trimers and dimers were detected and showed similar pattern of distribution with accumulation in peripheral and rigid parts of P. americana, based on quasimolecular ion AP-SMALDI MS images of sections from the whole body and dissected areas of the insect. Furthermore, the anti-inflammatory and vasorelaxant effects of isolated compounds were investigated. Compounds 8 and 9 presented significant and moderate anti-inflammatory potentials, respectively. Compounds 8, 10, 12 and 15 possess significant vasorelaxant potentials at concentrations correlated with EC50 values of 6.7-23.7 μM.

CONCLUSION

Fifteen NADA oligomers were isolated from P. americana. The distribution of these compounds was visualized by AP-SMALDI imaging experiments and NADA oligomers were mainly observed in peripheral parts. Bioassays showed that the tested compounds had anti-inflammatory and vasorelaxant activities, which indicated that NADA oligomers are active ingredients of this insect-based TCM and have potential for the treatment of cardiovascular disease.

Deciphering the metabolic heterogeneity of hematopoietic stem cells with single-cell resolution

Metabolic status is crucial for stem cell functions; however, the metabolic heterogeneity of endogenous stem cells has never been directly assessed. Here, we develop a platform for high-throughput single-cell metabolomics (hi-scMet) of hematopoietic stem cells (HSCs). By combining flow cytometric isolation and nanoparticle-enhanced laser desorption/ionization mass spectrometry, we routinely detected >100 features from single cells. We mapped the single-cell metabolomes of all hematopoietic cell populations and HSC subpopulations with different division times, detecting 33 features whose levels exhibited trending changes during HSC proliferation. We found progressive activation of the oxidative pentose phosphate pathway (OxiPPP) from dormant to active HSCs. Genetic or pharmacological interference with OxiPPP increased reactive oxygen species level in HSCs, reducing HSC self-renewal upon oxidative stress. Together, our work uncovers the metabolic dynamics during HSC proliferation, reveals a role of OxiPPP for HSC activation, and illustrates the utility of hi-scMet in dissecting metabolic heterogeneity of immunophenotypically defined cell populations.

Advanced progress of spatial metabolomics in head and neck cancer research

Head and neck cancer ranks as the sixth most prevalent malignancy, constituting 5 % of all cancer cases. Its inconspicuous onset often leads to advanced stage diagnoses, prompting the need for early detection to enhance patient prognosis. Currently, research into early diagnostic markers relies predominantly on genomics, proteomics, transcriptomics, and other methods, which, unfortunately, necessitate tumor tissue homogenization, resulting in the loss of temporal and spatial information. Emerging as a recent addition to the omics toolkit, spatial metabolomics stands out. This method conducts in situ mass spectrometry analyses on fresh tissue specimens while effectively preserving their spatiotemporal information. The utilization of spatial metabolomics in life science research offers distinct advantages. This article comprehensively reviews the progress of spatial metabolomics in head and neck cancer research, encompassing insights into cancer cell metabolic reprogramming. Various mass spectrometry imaging techniques, such as secondary ion mass spectrometry, stroma-assisted laser desorption/ionization, and desorption electrospray ionization, enable in situ metabolite analysis for head and neck cancer. Finally, significant emphasis is placed on the application of presently available techniques for early diagnosis, margin assessment, and prognosis of head and neck cancer.

Metabolic heterogeneity in cancer

Cancer cells rewire their metabolism to survive during cancer progression. In this context, tumour metabolic heterogeneity arises and develops in response to diverse environmental factors. This metabolic heterogeneity contributes to cancer aggressiveness and impacts therapeutic opportunities. In recent years, technical advances allowed direct characterisation of metabolic heterogeneity in tumours. In addition to the metabolic heterogeneity observed in primary tumours, metabolic heterogeneity temporally evolves along with tumour progression. In this Review, we summarize the mechanisms of environment-induced metabolic heterogeneity. In addition, we discuss how cancer metabolism and the key metabolites and enzymes temporally and functionally evolve during the metastatic cascade and treatment.

Recent developments and applications of ambient mass spectrometry imaging in pharmaceutical research: an overview

The application of ambient mass spectrometry imaging "MSI" is expanding in the areas of fundamental research on drug delivery and multiple phases of the process of identifying and developing drugs. Precise monitoring of a drug's pharmacological workflows, such as intake, distribution, metabolism, and discharge, is made easier by MSI's ability to determine the concentrations of the initiating drug and its metabolites across dosed samples without losing spatial data. Lipids, glycans, and proteins are just a few of the many phenotypes that MSI may be used to concurrently examine. Each of these substances has a particular distribution pattern and biological function throughout the body. MSI offers the perfect analytical tool for examining a drug's pharmacological features, especially in vitro and in vivo effectiveness, security, probable toxic effects, and putative molecular pathways, because of its high responsiveness in chemical and physical environments. The utilization of MSI in the field of pharmacy has further extended from the traditional tissue examination to the early stages of drug discovery and development, including examining the structure-function connection, high-throughput capabilities in vitro examination, and ex vivo research on individual cells or tumor spheroids. Additionally, an enormous array of endogenous substances that may function as tissue diagnostics can be scanned simultaneously, giving the specimen a highly thorough characterization. Ambient MSI techniques are soft enough to allow for easy examination of the native sample to gather data on exterior chemical compositions. This paper provides a scientific and methodological overview of ambient MSI utilization in research on pharmaceuticals.

Mobility-Modulated Sequential Dissociation Analysis Enables Structural Lipidomics in Mass Spectrometry Imaging

Spatial lipidomics based on mass spectrometry imaging (MSI) is a powerful tool for fundamental biology studies and biomarker discovery. But the structure-resolving capability of MSI is limited because of the lack of multiplexed tandem mass spectrometry (MS/MS) method, primarily due to the small sample amount available from each pixel and the poor ion usage in MS/MS analysis. Here, we report a mobility-modulated sequential dissociation (MMSD) strategy for multiplex MS/MS imaging of distinct lipids from biological tissues. With ion mobility-enabled data-independent acquisition and automated spectrum deconvolution, MS/MS spectra of a large number of lipid species from each tissue pixel are acquired, at no expense of imaging speed. MMSD imaging is highlighted by MS/MS imaging of 24 structurally distinct lipids in the mouse brain and the revealing of the correlation of a structurally distinct phosphatidylethanolamine isomer (PE 18 : 1_18 : 1) from a human hepatocellular carcinoma (HCC) tissue. Mapping of structurally distinct lipid isomers is now enabled and spatial lipidomics becomes feasible for MSI.

Single-cell spatial metabolomics with cell-type specific protein profiling for tissue systems biology

Metabolic reprogramming in cancer and immune cells occurs to support their increasing energy needs in biological tissues. Here we propose Single Cell Spatially resolved Metabolic (scSpaMet) framework for joint protein-metabolite profiling of single immune and cancer cells in male human tissues by incorporating untargeted spatial metabolomics and targeted multiplexed protein imaging in a single pipeline. We utilized the scSpaMet to profile cell types and spatial metabolomic maps of 19507, 31156, and 8215 single cells in human lung cancer, tonsil, and endometrium tissues, respectively. The scSpaMet analysis revealed cell type-dependent metabolite profiles and local metabolite competition of neighboring single cells in human tissues. Deep learning-based joint embedding revealed unique metabolite states within cell types. Trajectory inference showed metabolic patterns along cell differentiation paths. Here we show scSpaMet's ability to quantify and visualize the cell-type specific and spatially resolved metabolic-protein mapping as an emerging tool for systems-level understanding of tissue biology.

Recent developments in ionization techniques for single-cell mass spectrometry

The variation among individual cells plays a significant role in many biological functions. Single-cell analysis is advantageous for gaining insight into intricate biochemical mechanisms rarely accessible when studying tissues as a whole. However, measurement on a unicellular scale is still challenging due to unicellular complex composition, minute substance quantities, and considerable differences in compound concentrations. Mass spectrometry has recently gained extensive attention in unicellular analytical fields due to its exceptional sensitivity, throughput, and compound identification abilities. At present, single-cell mass spectrometry primarily concentrates on the enhancement of ionization methods. The principal ionization approaches encompass nanoelectrospray ionization (nano-ESI), laser desorption ionization (LDI), secondary ion mass spectrometry (SIMS), and inductively coupled plasma (ICP). This article summarizes the most recent advancements in ionization techniques and explores their potential directions within the field of single-cell mass spectrometry.

Nanoparticle-based applications by atmospheric pressure matrix assisted desorption/ionization mass spectrometry

Recently, the development of atmospheric pressure matrix assisted desorption/ionization mass spectrometry (AP MALDI MS) has made contributions not only to biomolecule analysis but also to spatial distribution. This has positioned AP MALDI as a powerful tool in multiple domains, thanks to its comprehensive advantages compared to conventional MALDI MS. These developments have addressed challenges associated with previous AP MALDI analysis systems, such as optimization of apparatus settings, synthesis of novel matrices, preconcentration and isolation strategies before analysis. Herein, applications in different fields using AP MALDI MS were described, including peptide and protein analysis, metabolite analysis, pharmaceutical analysis, and mass spectrometry imaging.

Decoding the tumor microenvironment with spatial technologies

Visualization of the cellular heterogeneity and spatial architecture of the tumor microenvironment (TME) is becoming increasingly important to understand mechanisms of disease progression and therapeutic response. This is particularly relevant in the era of cancer immunotherapy, in which the contexture of immune cell positioning within the tumor landscape has been proven to affect efficacy. Although single-cell technologies have mostly replaced conventional approaches to analyze specific cellular subsets within tumors, those that integrate a spatial dimension are now on the rise. In this Review, we assess the strengths and limitations of emerging spatial technologies with a focus on their applications in tumor immunology, as well as forthcoming opportunities for artificial intelligence (AI) and the value of integrating multiomics datasets to achieve a holistic picture of the TME.

FluoMALDI Microscopy: Matrix Co-Crystallization Simultaneously Enhances Fluorescence and MALDI Imaging

Here, the authors report that co-crystallization of fluorophores with matrix-assisted laser desorption/ionization (MALDI) imaging matrices significantly enhances fluorophore brightness up to 79-fold, enabling the amplification of innate tissue autofluorescence. This discovery facilitates FluoMALDI, the imaging of the same biological sample by both fluorescence microscopy and MALDI imaging. The approach combines the high spatial resolution and specific labeling capabilities of fluorescence microscopy with the inherently multiplexed, versatile imaging capabilities of MALDI imaging. This new paradigm simplifies registration by avoiding physical changes between fluorescence and MALDI imaging, allowing to image the exact same cells in tissues with both modalities. Matrix-fluorophore co-crystallization also facilitates applications with insufficient fluorescence brightness. The authors demonstrate  feasibility of FluoMALDI imaging with endogenous and exogenous fluorophores and autofluorescence-based FluoMALDI of brain and kidney tissue sections. FluoMALDI will advance structural-functional microscopic imaging in cell biology, biomedicine, and pathology.

Progress and challenges in exploring aquatic microbial communities using non-targeted metabolomics

Advances in bioanalytical technologies are constantly expanding our insights into complex ecosystems. Here, we highlight strategies and applications that make use of non-targeted metabolomics methods in aquatic chemical ecology research and discuss opportunities and remaining challenges of mass spectrometry-based methods to broaden our understanding of environmental systems.

MSIpixel: a fully automated pipeline for compound annotation and quantitation in mass spectrometry imaging experiments

Mass spectrometry imaging (MSI) is commonly used to map the spatial distribution of small molecules within complex biological matrices. One of the major challenges in imaging MS-based spatial metabolomics is molecular identification and metabolite annotation, to address this limitation, annotation is often complemented with parallel bulk LC-MS2-based metabolomics to confirm and validate identifications. Here we applied MSI method, utilizing data-dependent acquisition, to visualize and identify unknown molecules in a single instrument run. To reach this aim we developed MSIpixel, a fully automated pipeline for compound annotation and quantitation in MSI experiments. It overcomes challenges in molecular identification, and improving reliability and comprehensiveness in MSI-based spatial metabolomics.

Integrative Multiscale Biochemical Mapping of the Brain via Deep-Learning-Enhanced High-Throughput Mass Spectrometry

Elucidating the spatial-biochemical organization of the brain across different scales produces invaluable insight into the molecular intricacy of the brain. While mass spectrometry imaging (MSI) provides spatial localization of compounds, comprehensive chemical profiling at a brain-wide scale in three dimensions by MSI with single-cell resolution has not been achieved. We demonstrate complementary brain-wide and single-cell biochemical mapping via MEISTER, an integrative experimental and computational mass spectrometry framework. MEISTER integrates a deep-learning-based reconstruction that accelerates high-mass-resolving MS by 15-fold, multimodal registration creating 3D molecular distributions, and a data integration method fitting cell-specific mass spectra to 3D data sets. We imaged detailed lipid profiles in tissues with data sets containing millions of pixels, and in large single-cell populations acquired from the rat brain. We identified region-specific lipid contents, and cell-specific localizations of lipids depending on both cell subpopulations and anatomical origins of the cells. Our workflow establishes a blueprint for future developments of multiscale technologies for biochemical characterization of the brain.

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.

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.

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.

Advances in mass spectrometry-based multi-scale metabolomic methodologies and their applications in biological and clinical investigations

Metabolomics is a nascent field of inquiry that emerged in the late 20th century. It encompasses the comprehensive profiling of metabolites across a spectrum of organisms, ranging from bacteria and cells to tissues. The rapid evolution of analytical methods and data analysis has greatly accelerated progress in this dynamic discipline over recent decades. Sophisticated techniques such as liquid chromatograph mass spectrometry (MS), gas chromatograph MS, capillary electrophoresis MS, and nuclear magnetic resonance serve as the cornerstone of metabolomic analysis. Building upon these methods, a plethora of modifications and combinations have emerged to propel the advancement of metabolomics. Despite this progress, scrutinizing metabolism at the single-cell or single-organelle level remains an arduous task over the decades. Some of the most thrilling advancements, such as single-cell and single-organelle metabolic profiling techniques, offer profound insights into the intricate mechanisms within cells and organelles. This allows for a comprehensive study of metabolic heterogeneity and its pivotal role in multiple biological processes. The progress made in MS imaging has enabled high-resolution in situ metabolic profiling of tissue sections and even individual cells. Spatial reconstruction techniques enable the direct representation of metabolic distribution and alteration in three-dimensional space. The application of novel metabolomic techniques has led to significant breakthroughs in biological and clinical studies, including the discovery of novel metabolic pathways, determination of cell fate in differentiation, anti-aging intervention through modulating metabolism, metabolomics-based clinicopathologic analysis, and surgical decision-making based on on-site intraoperative metabolic analysis. This review presents a comprehensive overview of both conventional and innovative metabolomic techniques, highlighting their applications in groundbreaking biological and clinical studies.

Visualization of metabolites and microbes at high spatial resolution using MALDI mass spectrometry imaging and in situ fluorescence labeling

Label-free molecular imaging techniques such as matrix-assisted laser desorption ionization mass spectrometry imaging (MALDI-MSI) enable the direct and simultaneous mapping of hundreds of different metabolites in thin sections of biological tissues. However, in host-microbe interactions it remains challenging to localize microbes and to assign metabolites to the host versus members of the microbiome. We therefore developed a correlative imaging approach combining MALDI-MSI with fluorescence in situ hybridization (FISH) on the same section to identify and localize microbial cells. Here, we detail metaFISH as a robust and easy method for assigning the spatial distribution of metabolites to microbiome members based on imaging of nucleic acid probes, down to single-cell resolution. We describe the steps required for tissue preparation, on-tissue hybridization, fluorescence microscopy, data integration into a correlative image dataset, matrix application and MSI data acquisition. Using metaFISH, we map hundreds of metabolites and several microbial species to the micrometer scale on a single tissue section. For example, intra- and extracellular bacteria, host cells and their associated metabolites can be localized in animal tissues, revealing their complex metabolic interactions. We explain how we identify low-abundance bacterial infection sites as regions of interest for high-resolution MSI analysis, guiding the user to a trade-off between metabolite signal intensities and fluorescence signals. MetaFISH is suitable for a broad range of users from environmental microbiologists to clinical scientists. The protocol requires ~2 work days.

Genomic Mysteries of Giant Bacteria: Insights and Implications

Bacteria and Archaea are traditionally regarded as organisms with a simple morphology constrained to a size of 2-3 µm. Nevertheless, the history of microbial research is rich in the description of giant bacteria exceeding tens and even hundreds of micrometers in length or diameter already from its early days, for example, Beggiatoa spp., to the present, for example, Candidatus Thiomargarita magnifica. While some of these giants are still being studied, some were lost to science, with merely drawings and photomicrographs as evidence for their existence. The physiology and biogeochemical role of giant bacteria have been studied, with a large focus on those involved in the sulfur cycle. With the onset of the genomic era, no special emphasis has been given to this group, in an attempt to gain a novel, evolutionary, and molecular understanding of the phenomenon of bacterial gigantism. The few existing genomic studies reveal a mysterious world of hyperpolyploid bacteria with hundreds to hundreds of thousands of chromosomes that are, in some cases, identical and in others, extremely different. These studies on giant bacteria reveal novel organelles, cellular compartmentalization, and novel mechanisms to combat the accumulation of deleterious mutations in polyploid bacteria. In this perspective paper, we provide a brief overview of what is known about the genomics of giant bacteria and build on that to highlight a few burning questions that await to be addressed.

Single-cell lipidomics enabled by dual-polarity ionization and ion mobility-mass spectrometry imaging

Single-cell (SC) analysis provides unique insight into individual cell dynamics and cell-to-cell heterogeneity. Here, we utilize trapped ion mobility separation coupled with dual-polarity ionization mass spectrometry imaging (MSI) to enable high-throughput in situ profiling of the SC lipidome. Multimodal SC imaging, in which dual-polarity-mode MSI is used to perform serial data acquisition runs on individual cells, significantly enhanced SC lipidome coverage. High-spatial resolution SC-MSI identifies both inter- and intracellular lipid heterogeneity; this heterogeneity is further explicated by Uniform Manifold Approximation and Projection and machine learning-driven classifications. We characterize SC lipidome alteration in response to stearoyl-CoA desaturase 1 inhibition and, additionally, identify cell-layer specific lipid distribution patterns in mouse cerebellar cortex. This integrated multimodal SC-MSI technology enables high-resolution spatial mapping of intercellular and cell-to-cell lipidome heterogeneity, SC lipidome remodeling induced by pharmacological intervention, and region-specific lipid diversity within tissue.

Mass Spectral Imaging to Map Plant-Microbe Interactions

Plant-microbe interactions are of rising interest in plant sustainability, biomass production, plant biology, and systems biology. These interactions have been a challenge to detect until recent advancements in mass spectrometry imaging. Plants and microbes interact in four main regions within the plant, the rhizosphere, endosphere, phyllosphere, and spermosphere. This mini review covers the challenges within investigations of plant and microbe interactions. We highlight the importance of sample preparation and comparisons among time-of-flight secondary ion mass spectroscopy (ToF-SIMS), matrix-assisted laser desorption/ionization (MALDI), laser desorption ionization (LDI/LDPI), and desorption electrospray ionization (DESI) techniques used for the analysis of these interactions. Using mass spectral imaging (MSI) to study plants and microbes offers advantages in understanding microbe and host interactions at the molecular level with single-cell and community communication information. More research utilizing MSI has emerged in the past several years. We first introduce the principles of major MSI techniques that have been employed in the research of microorganisms. An overview of proper sample preparation methods is offered as a prerequisite for successful MSI analysis. Traditionally, dried or cryogenically prepared, frozen samples have been used; however, they do not provide a true representation of the bacterial biofilms compared to living cell analysis and chemical imaging. New developments such as microfluidic devices that can be used under a vacuum are highly desirable for the application of MSI techniques, such as ToF-SIMS, because they have a subcellular spatial resolution to map and image plant and microbe interactions, including the potential to elucidate metabolic pathways and cell-to-cell interactions. Promising results due to recent MSI advancements in the past five years are selected and highlighted. The latest developments utilizing machine learning are captured as an important outlook for maximal output using MSI to study microorganisms.