Single-cell metabolomics by mass spectrometry: Advances, challenges, and future applications

The isolation strategy and chemical analysis of oil cells from Asari Radix et Rhizoma

BACKGROUND

Single-cell analysis, a rapidly evolving field, encounters significant challenges in detecting individual cells within complex plant tissues, particularly oil cells (OCs). The intricate process of single-cell isolation, coupled with the inherent chemical volatility of oil cells, necessitates a comprehensive methodology.

RESULTS

This study presents a method for obtaining intact OC from Asari Radix et Rhizoma (ARR), a traditional herbal medicine. The developed approach facilitates both qualitative and quantitative analysis of diverse OCs. To determine the most reliable approach, four practical methods-laser capture microdissection, micromanipulation capturing, micromanipulation piping, and cell picking-were systematically compared and evaluated, unequivocally establishing cell picking as the most effective method for OC isolation and chemical analysis. Microscopic observations showed that OCs predominantly distribute in the cortex of adventitious and fibrous roots, as well as the pith and cortex of the rhizome, with distinct morphologies-oblong in roots and circular in rhizomes. Sixty-three volatile constituents were identified in OCs, with eighteen compounds exhibiting significant differences. Safrole, methyleugenol, and asaricin emerged as the most abundant constituents in OCs. Notably, cis-4-thujanol and tetramethylpyrazine were exclusive to rhizome OCs, while isoeugenol methyl ether was specific to fibrous root OCs based on the detections. ARR roots and rhizomes displayed marked disparities in OC distribution, morphology, and constituents.

CONCLUSION

The study highlights the efficacy of cell picking coupled with HS-SPME-GC-MS as a flexible, reliable, and sensitive method for OC isolation and chemical analysis, providing a robust methodology for future endeavors in single-cell analyses.

Quantification of MicroRNA in a Single Living Cell via Ionic Current Rectification-Based Nanopore for Triple Negative Breast Cancer Diagnosis

Accurate analysis of microRNAs (miRNAs) at the single-cell level is extremely important for deeply understanding their multiple and intricate biological functions. Despite some advancements in analyzing single-cell miRNAs, challenges such as intracellular interferences and insufficient detection limits still remain. In this work, an ultrasensitive nanopore sensor for quantitative single-cell miRNA-155 detection is constructed based on ionic current rectification (ICR) coupled with enzyme-free catalytic hairpin assembly (CHA). Benefiting from the enzyme-free CHA amplification strategy, the detection limit of the nanopore sensor for miRNA-155 reaches 10 fM and the nanopore sensor is more adaptable to complex intracellular environments. With the nanopore sensor, the concentration of miRNA-155 in living single cells is quantified to realize the early diagnosis of triple-negative breast cancer (TNBC). Furthermore, the nanopore sensor can be applied in screening anticancer drugs by tracking the expression level of miRNA-155. This work provides an adaptive and universal method for quantitatively analyzing intracellular miRNAs, which will greatly improve our understanding of cell heterogeneity and provide a more reliable scientific basis for exploring major diseases at the single-cell level.

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.

Global and Spatial Metabolomics of Individual Cells Using a Tapered Pneumatically Assisted nano-DESI Probe

Single-cell metabolomics has the potential to reveal unique insights into intracellular mechanisms and biological processes. However, the detection of metabolites from individual cells is challenging due to their versatile chemical properties and concentrations. Here, we demonstrate a tapered probe for pneumatically assisted nanospray desorption electrospray ionization (PA nano-DESI) mass spectrometry that enables both chemical imaging of larger cells and global metabolomics of smaller 15 μm cells. Additionally, by depositing cells in predefined arrays, we show successful metabolomics from three individual INS-1 cells per minute, which enabled the acquisition of data from 479 individual cells. Several cells were used to optimize analytical conditions, and 93 or 97 cells were used to monitor metabolome alterations in INS-1 cells after exposure to a low or high glucose concentration, respectively. Our analytical approach offers insights into cellular heterogeneity and provides valuable information about cellular processes and responses in individual cells.

Can single-cell and spatial omics unravel the pathophysiology of pre-eclampsia?

Pre-eclampsia is a leading cause of maternal and fetal morbidity and mortality. Characterised by the onset of hypertension and proteinuria in the second half of pregnancy, it can lead to maternal end-organ injury such as cerebral ischemia and oedema, pulmonary oedema and renal failure, and potentially fatal outcomes for both mother and fetus. The causes of the different maternal end-organ phenotypes of pre-eclampsia and why some women develop pre-eclampsia condition early in pregnancy have yet to be elucidated. Omics methods include proteomics, genomics, metabolomics, transcriptomics. These omics techniques, previously mostly used on bulk tissue and individually, are increasingly available at a single cellular level and can be combined with each other. Multi-omics techniques on a single-cell or spatial level provide us with a powerful tool to understand the pathophysiology of pre-eclampsia. This review will explore the status of omics methods and how they can and could contribute to understanding the pathophysiology of pre-eclampsia.

The endohyphal microbiome: current progress and challenges for scaling down integrative multi-omic microbiome research

As microbiome research has progressed, it has become clear that most, if not all, eukaryotic organisms are hosts to microbiomes composed of prokaryotes, other eukaryotes, and viruses. Fungi have only recently been considered holobionts with their own microbiomes, as filamentous fungi have been found to harbor bacteria (including cyanobacteria), mycoviruses, other fungi, and whole algal cells within their hyphae. Constituents of this complex endohyphal microbiome have been interrogated using multi-omic approaches. However, a lack of tools, techniques, and standardization for integrative multi-omics for small-scale microbiomes (e.g., intracellular microbiomes) has limited progress towards investigating and understanding the total diversity of the endohyphal microbiome and its functional impacts on fungal hosts. Understanding microbiome impacts on fungal hosts will advance explorations of how "microbiomes within microbiomes" affect broader microbial community dynamics and ecological functions. Progress to date as well as ongoing challenges of performing integrative multi-omics on the endohyphal microbiome is discussed herein. Addressing the challenges associated with the sample extraction, sample preparation, multi-omic data generation, and multi-omic data analysis and integration will help advance current knowledge of the endohyphal microbiome and provide a road map for shrinking microbiome investigations to smaller scales. Video Abstract.

The Neurovasculome: Key Roles in Brain Health and Cognitive Impairment: A Scientific Statement From the American Heart Association/American Stroke Association

BACKGROUND

Preservation of brain health has emerged as a leading public health priority for the aging world population. Advances in neurovascular biology have revealed an intricate relationship among brain cells, meninges, and the hematic and lymphatic vasculature (the neurovasculome) that is highly relevant to the maintenance of cognitive function. In this scientific statement, a multidisciplinary team of experts examines these advances, assesses their relevance to brain health and disease, identifies knowledge gaps, and provides future directions.

METHODS

Authors with relevant expertise were selected in accordance with the American Heart Association conflict-of-interest management policy. They were assigned topics pertaining to their areas of expertise, reviewed the literature, and summarized the available data.

RESULTS

The neurovasculome, composed of extracranial, intracranial, and meningeal vessels, as well as lymphatics and associated cells, subserves critical homeostatic functions vital for brain health. These include delivering O2 and nutrients through blood flow and regulating immune trafficking, as well as clearing pathogenic proteins through perivascular spaces and dural lymphatics. Single-cell omics technologies have unveiled an unprecedented molecular heterogeneity in the cellular components of the neurovasculome and have identified novel reciprocal interactions with brain cells. The evidence suggests a previously unappreciated diversity of the pathogenic mechanisms by which disruption of the neurovasculome contributes to cognitive dysfunction in neurovascular and neurodegenerative diseases, providing new opportunities for the prevention, recognition, and treatment of these conditions.

CONCLUSIONS

These advances shed new light on the symbiotic relationship between the brain and its vessels and promise to provide new diagnostic and therapeutic approaches for brain disorders associated with cognitive dysfunction.

Coupling of Solid-Phase Microextraction Directly to Mass Spectrometry via an Improved Microfluidic Open Interface to Facilitate High-Throughput Determinations

Mass spectrometry analysis can be performed by introducing samples directly to mass spectrometry, allowing the increase of the analysis throughput; however, some disadvantages of direct-to-mass spectrometry analysis include susceptibility to matrix effects and risk of instrument contamination from inadequate sample preparation. Solid-phase microextraction is one of the most suitable sample preparation methods for direct-to-mass spectrometry analysis, as it offers matrix-compatible coatings which ensure analyte enrichment with minimal or no interference from matrix. One of the ways solid-phase microextraction can be coupled directly to mass spectrometry is via a microfluidic open interface. This manuscript reports improvements made to the initial microfluidic open interface design, where the system components have been simplified to mostly commercially available materials. In addition, the analysis of samples has been automated by implementing software that fully controls the analysis workflow, where the washing procedure is optimized to completely reduce the carryover. Herein, the extraction and desorption time profiles from thin and thick SPME devices was studied where the overall workflow consisted of high-throughput sample preparation of 1.3 min per 96 samples and <1 min per sample instrumental analysis.

Small molecule metabolites: discovery of biomarkers and therapeutic targets

Metabolic abnormalities lead to the dysfunction of metabolic pathways and metabolite accumulation or deficiency which is well-recognized hallmarks of diseases. Metabolite signatures that have close proximity to subject's phenotypic informative dimension, are useful for predicting diagnosis and prognosis of diseases as well as monitoring treatments. The lack of early biomarkers could lead to poor diagnosis and serious outcomes. Therefore, noninvasive diagnosis and monitoring methods with high specificity and selectivity are desperately needed. Small molecule metabolites-based metabolomics has become a specialized tool for metabolic biomarker and pathway analysis, for revealing possible mechanisms of human various diseases and deciphering therapeutic potentials. It could help identify functional biomarkers related to phenotypic variation and delineate biochemical pathways changes as early indicators of pathological dysfunction and damage prior to disease development. Recently, scientists have established a large number of metabolic profiles to reveal the underlying mechanisms and metabolic networks for therapeutic target exploration in biomedicine. This review summarized the metabolic analysis on the potential value of small-molecule candidate metabolites as biomarkers with clinical events, which may lead to better diagnosis, prognosis, drug screening and treatment. We also discuss challenges that need to be addressed to fuel the next wave of breakthroughs.

Genomics, Proteomics, and Metabolomics Approaches to Improve Abiotic Stress Tolerance in Tomato Plant

To explore changes in proteins and metabolites under stress circumstances, genomics, proteomics, and metabolomics methods are used. In-depth research over the previous ten years has gradually revealed the fundamental processes of plants' responses to environmental stress. Abiotic stresses, which include temperature extremes, water scarcity, and metal toxicity brought on by human activity and urbanization, are a major cause for concern, since they can result in unsustainable warming trends and drastically lower crop yields. Furthermore, there is an emerging reliance on agrochemicals. Stress is responsible for physiological transformations such as the formation of reactive oxygen, stomatal opening and closure, cytosolic calcium ion concentrations, metabolite profiles and their dynamic changes, expression of stress-responsive genes, activation of potassium channels, etc. Research regarding abiotic stresses is lacking because defense feedbacks to abiotic factors necessitate regulating the changes that activate multiple genes and pathways that are not properly explored. It is clear from the involvement of these genes that plant stress response and adaptation are complicated processes. Targeting the multigenicity of plant abiotic stress responses caused by genomic sequences, transcripts, protein organization and interactions, stress-specific and cellular transcriptome collections, and mutant screens can be the first step in an integrative approach. Therefore, in this review, we focused on the genomes, proteomics, and metabolomics of tomatoes under abiotic stress.

Applications of Mass Spectrometry in Dentistry

Mass Spectrometry (MS) is one of the fastest-developing methods in analytical instrumentation. As a highly sensitive, universal detector, it can identify known and unknown compounds, which can indeed be found in a minimal concentration. This review aims to highlight the significant milestones in MS applications in dentistry during recent decades. MS can be applied in three different fields of dentistry: (1) in research of dental materials and chemical agents, (2) in laboratory analysis of biospecimens, and (3) as a real-time diagnostic tool in service of oral surgery and pathology. MS applications on materials and agents may focus on numerous aspects, such as their clinical behavior, possible toxicity, or antimicrobial properties. MS is also a valuable, non-invasive tool for biomarkers' detection in saliva and has found great application in -omics technologies as it achieves efficient structure-finding in metabolites. As metabolites are located beyond the central dogma, this technique can provide a complete understanding of cellular functions. Thus, it is possible to determine the biological profile in normal and pathological conditions, detect various oral or systematic diseases and conditions, and predict their course. Lastly, some promising advances concerning the surgical approach to potentially oral malignant or malignant disorders exist. This breakthrough method provides a comprehensive approach to dental materials research and biomarker discovery in dental and craniofacial tissues. The current availability of various 'OMIC' approaches paves the way for individualized dentistry and provides suggestions for clinical applications in the point-of-care hubs.

Defining atherosclerotic plaque biology by mass spectrometry-based omics approaches

Atherosclerosis is the principal cause of vascular diseases and one of the leading causes of worldwide death. Even though several insights into its natural course, risk factors and interventions have been identified, it is still an ongoing global pandemic. Since the structure and biochemical composition of the plaques show high heterogeneity, a comprehensive understanding of the intraplaque composition, its microenvironment, and the mechanisms of the progression and instability across different vascular beds at their progression stages is crucial for better risk stratification and treatment modalities. Even though several cell-based studies, animal studies, and extensive multicentric population studies have been conducted concerning cardiovascular diseases for assessing the risk factors and plaque biology, the studies on human clinical samples are very limited. New novel approaches utilize samples from percutaneous coronary interventions, which could possibly gain more access to clinical samples at different stages of the diseases without complex invasive resections. As an emerging technological platform in disease discovery research, mass spectrometry-based omics technologies offer capabilities for a comprehensive understanding of the mechanisms linked to several vascular diseases. Here, we discuss the cellular and molecular processes of atherosclerosis, different mass spectrometry-based omics approaches, and the studies mostly done on clinical samples of atheroma plaque using mass spectrometry-based proteomics, metabolomics and lipidomics approaches.

Advancements in Pulsed Stable Isotope-Resolved Metabolomics

The understanding of biochemical processes of metabolism is gained through the measurement of the concentration of intermediates and the rate of metabolite conversion. However, the measurement of metabolite concentrations does not give a full representation of this dynamic system. To understand the kinetics of metabolism, the system must be described and quantified in terms of metabolite flow as a function of time. In order to measure the metabolite flow, or more precisely the metabolic flux through a biological system, substrates of the cell are labelled with stable isotopes. The usage of these substrates by the cell leads to the incorporation of the isotopes into downstream intermediates.The most important metabolic pathways are encompassed in the central carbon metabolism (CCM). According to the Kyoto Encyclopedia of Genes and Genomes (KEGG), the central carbon metabolism "is the most basic aspect of life". It includes all metabolites and enzymatic reactions within: glycolysis and gluconeogenesis, pentose phosphate pathway (PPP), tricarboxylic acid (TCA) cycle, oxidative phosphorylation (OXPHOS), amino acids and nucleotide metabolic pathways. Some molecules are at the crossroad of metabolic pathways, interconnecting diverse metabolic and therefore functional outcomes. Labelling these nodal metabolites and analysing their isotopic composition allows the precise determination of the metabolic flow within the biochemical networks that they are in.Application of stable isotope labelled substrates allows the measurement of metabolic flux through a biochemical pathway. The rapid turnover of metabolites in pathways requires pulse-feeding cells with a labelled substrate. This method allows for the determination of different cell states. For example, the action of a drug from immediate impact until the compensatory response of the metabolic system (cell, organs, organisms). Pulsed labelling is an elegant way to analyse the action of small molecules and drugs and enables the analysis of regulatory metabolic processes in short time scales.

Mass spectrometry-based strategies for single-cell metabolomics

Single cell analysis has drawn increasing interest from the research community due to its capability to interrogate cellular heterogeneity, allowing refined tissue classification and facilitating novel biomarker discovery. With the advancement of relevant instruments and techniques, it is now possible to perform multiple omics including genomics, transcriptomics, metabolomics or even proteomics at single cell level. In comparison with other omics studies, single-cell metabolomics (SCM) represents a significant challenge since it involves many types of dynamically changing compounds with a wide range of concentrations. In addition, metabolites cannot be amplified. Although difficult, considerable progress has been made over the past decade in mass spectrometry (MS)-based SCM in terms of processing technologies and biochemical applications. In this review, we will summarize recent progress in the development of promising MS platforms, sample preparation methods and SCM analysis of various cell types (including plant cell, cancer cell, neuron, embryo cell, and yeast cell). Current limitations and future research directions in the field of SCM will also be discussed.

Recent progress in mass spectrometry for single-cell metabolomics

Metabolites are the end products of cellular vital activities and can reflect the state of cellular to a certain extent. Rapid change of metabolites and the low abundance of signature metabolites cause difficulties in single-cell detection, which is a great challenge in single-cell metabolomics analysis. Mass spectrometry (MS) is a powerful tool that uniquely suited to detect intracellular small-molecule metabolites and has shown good application in single-cell metabolite analysis. In this mini-review, we describe three types of emerging technologies for MS-based single-cell metabolic analysis in recent years, including nano-ESI-MS based single-cell metabolomics analysis, high-throughput analysis via flow cytometry, and cellular metabolic imaging analysis. These techniques provide a large amount of single-cell metabolic data, allowing the potential of MS in single-cell metabolic analysis is gradually being explored and is of great importance in disease and life science research.

Single-cell mass spectrometry

Owing to recent advances in mass spectrometry (MS), tens to hundreds of proteins, lipids, and small molecules can be measured in single cells. The ability to characterize the molecular heterogeneity of individual cells is necessary to define the full assortment of cell subtypes and identify their function. We review single-cell MS including high-throughput, targeted, mass cytometry-based approaches and antibody-free methods for broad profiling of the proteome and metabolome of single cells. The advantages and disadvantages of different methods are discussed, as well as the challenges and opportunities for further improvements in single-cell MS. These methods is being used in biomedicine in several applications including revealing tumor heterogeneity and high-content drug screening.

Advances in measuring cancer cell metabolism with subcellular resolution

Characterizing metabolism in cancer is crucial for understanding tumor biology and for developing potential therapies. Although most metabolic investigations analyze averaged metabolite levels from all cell compartments, subcellular metabolomics can provide more detailed insight into the biochemical processes associated with the disease. Methodological limitations have historically prevented the wider application of subcellular metabolomics in cancer research. Recently, however, ways to distinguish and identify metabolic pathways within organelles have been developed, including state-of-the-art methods to monitor metabolism in situ (such as mass spectrometry-based imaging, Raman spectroscopy and fluorescence microscopy), to isolate key organelles via new approaches and to use tailored isotope-tracing strategies. Herein, we examine the advantages and limitations of these developments and look to the future of this field of research.

Single-Cell Metabolomics in Hematopoiesis and Hematological Malignancies

Aberrant metabolism contributes to tumor initiation, progression, metastasis, and drug resistance. Metabolic dysregulation has emerged as a hallmark of several hematologic malignancies. Decoding the molecular mechanism underlying metabolic rewiring in hematological malignancies would provide promising avenues for novel therapeutic interventions. Single-cell metabolic analysis can directly offer a meaningful readout of the cellular phenotype, allowing us to comprehensively dissect cellular states and access biological information unobtainable from bulk analysis. In this review, we first highlight the unique metabolic properties of hematologic malignancies and underscore potential metabolic vulnerabilities. We then emphasize the emerging single-cell metabolomics techniques, aiming to provide a guide to interrogating metabolism at single-cell resolution. Furthermore, we summarize recent studies demonstrating the power of single-cell metabolomics to uncover the roles of metabolic rewiring in tumor biology, cellular heterogeneity, immunometabolism, and therapeutic resistance. Meanwhile, we describe a practical view of the potential applications of single-cell metabolomics in hematopoiesis and hematological malignancies. Finally, we present the challenges and perspectives of single-cell metabolomics development.

Nanotechnology Tools Enabling Biological Discovery

Over the years, the engineering aspect of nanotechnology has been significantly exploited. Medical intervention strategies have been developed by leveraging existing molecular biology knowledge and combining it with nanotechnology tools to improve outcomes. However, little attention has been paid to harnessing the strengths of nanotechnology as a biological discovery tool. Fundamental understanding of controlling dynamic biological processes at the subcellular level is key to developing personalized therapeutic and diagnostic interventions. Single-cell analyses using intravital microscopy, expansion microscopy, and microfluidic-based platforms have been helping to better understand cell heterogeneity in healthy and diseased cells, a major challenge in oncology. Also, single-cell analysis has revealed critical signaling pathways and biological intracellular components with key biological functions. The physical manipulation enabled by nanotools can allow real-time monitoring of biological changes at a single-cell level by sampling intracellular fluid from the same cell. The formation of intercellular highways by nanotube-like structures has important clinical implications such as metastasis development. The integration of nanomaterials into optical and molecular imaging techniques has rendered valuable morphological, structural, and biological information. Nanoscale imaging unravels mechanisms of temporality by enabling the visualization of nanoscale dynamics never observed or measured between individual cells with standard biological techniques. The exceptional sensitivity of nanozymes, artificial enzymes, make them perfect components of the next-generation mobile diagnostics devices. Here, we highlight these impactful cancer-related biological discoveries enabled by nanotechnology and producing a paradigm shift in cancer research and oncology.

Capillary microsampling-based single-cell metabolomics by mass spectrometry and its applications in medicine and drug discovery

Characterization of cellular metabolic states is a technical challenge in biomedicine. Cellular heterogeneity caused by inherent diversity in expression of metabolic enzymes or due to sensitivity of metabolic reactions to perturbations, necessitates single cell analysis of metabolism. Heterogeneity is typically seen in cancer and thus, single-cell metabolomics is expectedly useful in studying cancer progression, metastasis, and variations in cancer drug response. However, low sample volumes and analyte concentrations limit detection of critically important metabolites. Capillary microsampling-based mass spectrometry approaches are emerging as a promising solution for achieving single-cell omics. Herein, we focus on the recent advances in capillary microsampling-based mass spectrometry techniques for single-cell metabolomics. We discuss recent technical developments and applications to cancer medicine and drug discovery.

Unraveling antimicrobial resistance using metabolomics

The emergence of antimicrobial resistance (AMR) in bacterial pathogens represents a global health threat. The metabolic state of bacteria is associated with a range of genetic and phenotypic resistance mechanisms. This review provides an overview of the roles of metabolic processes that are associated with AMR mechanisms, including energy production, cell wall synthesis, cell-cell communication, and bacterial growth. These metabolic processes can be targeted with the aim of re-sensitizing resistant pathogens to antibiotic treatments. We discuss how state-of-the-art metabolomics approaches can be used for comprehensive analysis of microbial AMR-related metabolism, which may facilitate the discovery of novel drug targets and treatment strategies. TEASER: Novel treatment strategies are needed to address the emerging threat of antimicrobial resistance (AMR) in bacterial pathogens. Metabolomics approaches may help to unravel the biochemical underpinnings of AMR, thereby facilitating the discovery of metabolism-associated drug targets and treatment strategies.

Advances in MALDI Mass Spectrometry Imaging Single Cell and Tissues

Compared with conventional optical microscopy techniques, mass spectrometry imaging (MSI) or imaging mass spectrometry (IMS) is a powerful, label-free analytical technique, which can sensitively and simultaneously detect, quantify, and map hundreds of biomolecules, such as peptides, proteins, lipid, and other organic compounds in cells and tissues. So far, although several soft ionization techniques, such as desorption electrospray ionization (DESI) and secondary ion mass spectrometry (SIMS) have been used for imaging biomolecules, matrix-assisted laser desorption/ionization (MALDI) is still the most widespread MSI scanning method. Here, we aim to provide a comprehensive review of MALDI-MSI with an emphasis on its advances of the instrumentation, methods, application, and future directions in single cell and biological tissues.

Single Cell Technologies to Dissect Heterogenous Immune Cell Therapy Products

Single cell tools have dramatically transformed the life sciences; concurrently, autologous and allogeneic immune cell therapies have recently entered the clinic. Here we discuss methods, applications, and considerations for single cell technologies in the context of immune cell manufacturing. Molecular heterogeneity can be profiled at the level of the genome, epigenome, transcriptome, proteome, metabolome, and antigen receptor repertoire, in isolation or in tandem through multi-omic approaches. Such data inform heterogeneity within cell products and can be linked to potency readouts and clinical data, with the ultimate goal of identifying Critical Quality Attributes to predict patient outcomes. Non-destructive approaches hold promise for monitoring cell state and analyzing the impacts of gene edits within engineered products. Destructive omics approaches could be combined with non-destructive technologies to predict therapeutic potency. These technologies are poised to redefine cell manufacturing toward rapid, cost-effective, and high-throughput methods to detect and respond to dynamic cell states.

Mass Spectrometry Imaging of Arabidopsis thaliana Leaves at the Single-Cell Level by Infrared Laser Ablation Atmospheric Pressure Photoionization (LAAPPI)

In this study, we show that infrared laser ablation atmospheric pressure photoionization mass spectrometry (LAAPPI-MS) imaging with 70 μm lateral resolution allows for the analysis of Arabidopsis thaliana (A. thaliana) leaf substructures ranging from single-cell trichomes and the interveinal leaf lamina to primary, secondary, and tertiary veins. The method also showed its potential for depth profiling analysis for the first time by mapping analytes at the different depths of the leaf and spatially resolving the topmost trichomes and cuticular wax layer from the underlying tissues. Negative ion LAAPPI-MS detected many different flavonol glycosides, fatty acids, fatty acid esters, galactolipids, and glycosphingolipids, whose distributions varied significantly between the different substructures of A. thaliana leaves. The results show that LAAPPI-MS provides a highly promising new tool to study the role of metabolites in plants.

A graph neural network model to estimate cell-wise metabolic flux using single-cell RNA-seq data

The metabolic heterogeneity and metabolic interplay between cells are known as significant contributors to disease treatment resistance. However, with the lack of a mature high-throughput single-cell metabolomics technology, we are yet to establish systematic understanding of the intra-tissue metabolic heterogeneity and cooperative mechanisms. To mitigate this knowledge gap, we developed a novel computational method, namely, single-cell flux estimation analysis (scFEA), to infer the cell-wise fluxome from single-cell RNA-sequencing (scRNA-seq) data. scFEA is empowered by a systematically reconstructed human metabolic map as a factor graph, a novel probabilistic model to leverage the flux balance constraints on scRNA-seq data, and a novel graph neural network-based optimization solver. The intricate information cascade from transcriptome to metabolome was captured using multilayer neural networks to capitulate the nonlinear dependency between enzymatic gene expressions and reaction rates. We experimentally validated scFEA by generating an scRNA-seq data set with matched metabolomics data on cells of perturbed oxygen and genetic conditions. Application of scFEA on this data set showed the consistency between predicted flux and the observed variation of metabolite abundance in the matched metabolomics data. We also applied scFEA on five publicly available scRNA-seq and spatial transcriptomics data sets and identified context- and cell group-specific metabolic variations. The cell-wise fluxome predicted by scFEA empowers a series of downstream analyses including identification of metabolic modules or cell groups that share common metabolic variations, sensitivity evaluation of enzymes with regards to their impact on the whole metabolic flux, and inference of cell-tissue and cell-cell metabolic communications.

Towards high throughput and high information coverage: advanced single-cell mass spectrometric techniques

Mass spectrometry (MS) is attractive for single-cell analysis because of its high sensitivity, rich information, and large dynamic ranges, especially for the single-cell metabolome and proteome analysis. Efforts have been made to deal with the throughput and information coverage problems in typical manual single-cell MS techniques. In this review, advanced techniques to improve the automation and throughput for single-cell sampling and single-cell metabolome and proteome MS detection have been discussed. Furthermore, representative MS-based strategies that can increase the in-depth cellular information coverage and achieve the more comprehensive single-cell multiomics information during high throughput detection have been highlighted, providing an ongoing perspective of the MS performance for the single-cell research.

Microfluidics-Based Single-Cell Research for Intercellular Interaction

Intercellular interaction between cell-cell and cell-ECM is critical to numerous biology and medical studies, such as stem cell differentiation, immunotherapy and tissue engineering. Traditional methods employed for delving into intercellular interaction are limited by expensive equipment and sophisticated procedures. Microfluidics technique is considered as one of the powerful measures capable of precisely capturing and manipulating cells and achieving low reagent consumption and high throughput with decidedly integrated functional components. Over the past few years, microfluidics-based systems for intercellular interaction study at a single-cell level have become frequently adopted. This review focuses on microfluidic single-cell studies for intercellular interaction in a 2D or 3D environment with a variety of cell manipulating techniques and applications. The challenges to be overcome are highlighted.

The limitless applications of single-cell metabolomics

Single-cell metabolomics (SCM) is currently one of the most powerful tools for performing high-throughput metabolic analysis at the cellular level. The power of single-cell metabolomics to determine the metabolic profiles of individual cells makes it very suitable for decoding cell heterogeneity. SCM bears great potential in cell type identification and differentiation within cell colonies. With the development of various equipment and techniques, SCM analysis has become possible for a wide range of biological samples. Many fields have incorporated this cutting-edge analytic tool to generate fruitful findings. This review article pays close attention to the prevalent techniques utilized in SCM and the exciting new findings and applications developed by studies in phytology, neurology, and oncology using SCM.

SpaceM reveals metabolic states of single cells

A growing appreciation of the importance of cellular metabolism and revelations concerning the extent of cell-cell heterogeneity demand metabolic characterization of individual cells. We present SpaceM, an open-source method for in situ single-cell metabolomics that detects >100 metabolites from >1,000 individual cells per hour, together with a fluorescence-based readout and retention of morpho-spatial features. We validated SpaceM by predicting the cell types of cocultured human epithelial cells and mouse fibroblasts. We used SpaceM to show that stimulating human hepatocytes with fatty acids leads to the emergence of two coexisting subpopulations outlined by distinct cellular metabolic states. Inducing inflammation with the cytokine interleukin-17A perturbs the balance of these states in a process dependent on NF-κB signaling. The metabolic state markers were reproduced in a murine model of nonalcoholic steatohepatitis. We anticipate SpaceM to be broadly applicable for investigations of diverse cellular models and to democratize single-cell metabolomics.

In Situ Single-Cell Stimulation and Real-Time Electrochemical Detection of Lactate Response Using a Microfluidic Probe

Metabolism of a single cell, even within the same organization, differs from other cells by orders of magnitude. Single-cell analysis provides key information for early diagnosis of cancer as well as drug screening. Any slight change in the microenvironment may affect the state of a single cell. Timely and effective cell monitoring is conducive to better understand the behavior of single cells. The immediate response of a single cell described in this study is a liquid transfer-based approach for real-time electrochemical detection. The cell was in situ stimulated by continuous flow with glucose, and lactate secreted from the cell would diffuse into the microflow. The microflow was aspirated into the detection channel where lactate was then decomposed by coupled enzyme reactions and detected by an electrode. This work provides a novel approach for detecting lactate response from a single cell by noninvasive measurements, and the position resolution of the microfluidic probe reaches the level of a single cell and permits individual heterogeneity in cells to be explored in the diagnosis and treatment of cancer as well as in many other situations.

Challenges in Studying Stem Cell Metabolism

The expanding field of stem cell metabolism has been supported by technical advances in metabolite profiling and novel functional analyses. While use of these methodologies has been fruitful, many challenges are posed by the intricacies of culturing stem cells in vitro, along with the distinctive scarcity of adult tissue stem cells and the complexities of their niches in vivo. This review provides an examination of the methodologies used to characterize stem cell metabolism, highlighting their utility while placing a sharper focus on their limitations and hurdles the field needs to overcome for the optimal study of stem cell metabolic networks.

Quantitative Approach for Protein Analysis in Small Cell Ensembles by an Integrated Microfluidic Chip with MALDI Mass Spectrometry

Increasing evidence has demonstrated that cells are individually heterogeneous. Advancing the technologies for single-cell analysis will improve our ability to characterize cells, study cell biology, design and screen drugs, and aid cancer diagnosis and treatment. Most current single-cell protein analysis approaches are based on fluorescent antibody-binding technology. However, this technology is limited by high background and cross-talk of multiple tags introduced by fluorescent labels. Stable isotope labels used in mass cytometry can overcome the spectral overlap of fluorophores. Nevertheless, the specificity of each antibody and heavy-metal-tagged antibody combination must be carefully validated to ensure detection of the intended target. Thus, novel single-cell protein analysis methods without using labels are urgently needed. Moreover, the labeling approach targets already known motifs, hampering the discovery of new biomarkers relevant to single-cell population variation. Here, we report a combined microfluidic and matrix-assisted laser desorption and ionization (MALDI) mass spectrometric approach for the analysis of protein biomarkers suitable for small cell ensembles. All necessary steps for cell analysis including cell lysis, protein capture, and digestion as well as MALDI matrix deposition are integrated on a microfluidic chip prior to the downstream MALDI-time-of-flight (TOF) detection. For proof of principle, this combined method is used to assess the amount of Bcl-2, an apoptosis regulator, in metastatic breast cancer cells (MCF-7) by using an isotope-labeled peptide as an internal standard. The proposed approach will eventually provide a new means for proteome studies in small cell ensembles with the potential for single-cell analysis and improve our ability in disease diagnosis, drug discovery, and personalized therapy.

The role of morphological changes in algae adaptation to nutrient stress at the single-cell level

Individual cell heterogeneity within a population can be critical to its peculiar function and fate. Conventional algal cell-based assays mainly analyze the average responses from a population of algal cells. Therefore, the mechanisms through which changes in population characteristics are driven by the behavior of single algal cells are still not well understood. Algal cells may modulate their physiology and metabolism by changing their morphology in response to environmental stress. In this study, an algal single-cell culture and analysis system was developed to investigate the potential role of morphological changes by algal cells during adaptation to nutrient stress based on a microwell array chip. The surface-to-volume ratio of Microcystis aeruginosa (M. aeruginosa) and the volume of Scenedesmus obliquus (S. obliquus) significantly increased with increasing culture time under nutrient stress. The eccentricity of M. aeruginosa and S. obliquus gradually increased and decreased, respectively, with increasing culture time, indicating that the morphology of M. aeruginosa and S. obliquus became increasingly irregular and regular, respectively, under nutrient stress. There were significant correlations between the morphological characteristics and physiological characteristics of M. aeruginosa and S. obliquus under nutrient stress. In M. aeruginosa, an increased surface-to-volume ratio facilitated a high specific fluorescence intensity, specific Raman intensity, and maximum electron transport rate. In S. obliquus, increased cell volume enhanced nutrient absorption, which facilitated a higher specific growth rate. M. aeruginosa and S. obliquus adopted different adaptation strategies in response to nutrient stress based on morphological changes. These findings facilitate the development of management strategies for controlling harmful cyanobacterial blooms.

Single cell metabolomics using mass spectrometry: Techniques and data analysis

Mass spectrometry (MS) based techniques are gaining popularity for metabolomics research due to their high sensitivity, wide detection range, and capability of molecular identification. Utilizing such powerful technique to explore the cellular metabolism at the single cell level not only appreciates the subtle cell-to-cell difference (i.e., cell heterogeneity), but also gains biological merits corresponding to individual cells or small cell subpopulations. In this review article, we first briefly summarize recent advances in single cell MS experimental techniques, and then emphasize on the single cell metabolomics data analysis approaches. Through implementation of statistical analysis and more advanced data analysis methods, single cell metabolomics is expected to find more potential applications in the translational and clinical fields in the future.

Metabolomic profiling of rare cell populations isolated by flow cytometry from tissues

Little is known about the metabolic regulation of rare cell populations because most metabolites are hard to detect in small numbers of cells. We previously described a method for metabolomic profiling of flow cytometrically isolated hematopoietic stem cells (HSCs) that detects 60 metabolites in 10,000 cells (Agathocleous et al., 2017). Here we describe a new method involving hydrophilic liquid interaction chromatography and high-sensitivity orbitrap mass spectrometry that detected 160 metabolites in 10,000 HSCs, including many more glycolytic and lipid intermediates. We improved chromatographic separation, increased mass resolution, minimized ion suppression, and eliminated sample drying. Most metabolite levels did not significantly change during cell isolation. Mouse HSCs exhibited increased glycerophospholipids relative to bone marrow cells and methotrexate treatment altered purine biosynthesis. Circulating human melanoma cells were depleted for purine intermediates relative to subcutaneous tumors, suggesting decreased purine synthesis during metastasis. These methods facilitate the routine metabolomic analysis of rare cells from tissues.

Single-cell omics analyses with single molecular detection: challenges and perspectives

The ultimate goal of single-cell analyses is to obtain the biomolecular content for each cell in unicellular and multicellular organisms at different points of their life cycle under variable environmental conditions. These require an assessment of: a) the total number of cells, b) the total number of cell types, and c) the complete and quantitative single molecular detection and identification for all classes of biopolymers, and organic and inorganic compounds, in each individual cell. For proteins, glycans, lipids, and metabolites, whose sequences cannot be amplified by copying as in the case of nucleic acids, the detection limit by mass spectrometry is about 10⁵ molecules. Therefore, proteomic, glycomic, lipidomic, and metabolomic analyses do not yet permit the assembly of the complete single-cell omes. The construction of novel nanoelectrophoretic arrays and nano in microarrays on a single 1-cm-diameter chip has shown proof of concept for a high throughput platform for parallel processing of thousands of individual cells. Combined with dynamic secondary ion mass spectrometry, with 3D scanning capability and lateral resolution of 50 nm, the sensitivity of single molecular quantification and identification for all classes of biomolecules could be reached. Further development and routine application of such technological and instrumentation solution would allow assembly of complete omes with a quantitative assessment of structural and functional cellular diversity at the molecular level.

Midbrain Dopaminergic Neuron Development at the Single Cell Level: In vivo and in Stem Cells

Parkinson's disease (PD) is a progressive neurodegenerative disorder that predominantly affects dopaminergic (DA) neurons of the substantia nigra. Current treatment options for PD are symptomatic and typically involve the replacement of DA neurotransmission by DA drugs, which relieve the patients of some of their motor symptoms. However, by the time of diagnosis, patients have already lost about 70% of their substantia nigra DA neurons and these drugs offer only temporary relief. Therefore, cell replacement therapy has garnered much interest as a potential treatment option for PD. Early studies using human fetal tissue for transplantation in PD patients provided proof of principle for cell replacement therapy, but they also highlighted the ethical and practical difficulties associated with using human fetal tissue as a cell source. In recent years, advancements in stem cell research have made human pluripotent stem cells (hPSCs) an attractive source of material for cell replacement therapy. Studies on how DA neurons are specified and differentiated in the developing mouse midbrain have allowed us to recapitulate many of the positional and temporal cues needed to generate DA neurons in vitro. However, little is known about the developmental programs that govern human DA neuron development. With the advent of single-cell RNA sequencing (scRNA-seq) and bioinformatics, it has become possible to analyze precious human samples with unprecedented detail and extract valuable high-quality information from large data sets. This technology has allowed the systematic classification of cell types present in the human developing midbrain along with their gene expression patterns. By studying human development in such an unbiased manner, we can begin to elucidate human DA neuron development and determine how much it differs from our knowledge of the rodent brain. Importantly, this molecular description of the function of human cells has become and will increasingly be a reference to define, evaluate, and engineer cell types for PD cell replacement therapy and disease modeling.

Resolving Metabolic Heterogeneity in Experimental Models of the Tumor Microenvironment from a Stable Isotope Resolved Metabolomics Perspective

The tumor microenvironment (TME) comprises complex interactions of multiple cell types that determines cell behavior and metabolism such as nutrient competition and immune suppression. We discuss the various types of heterogeneity that exist in solid tumors, and the complications this invokes for studies of TME. As human subjects and in vivo model systems are complex and difficult to manipulate, simpler 3D model systems that are compatible with flexible experimental control are necessary for studying metabolic regulation in TME. Stable Isotope Resolved Metabolomics (SIRM) is a valuable tool for tracing metabolic networks in complex systems, but at present does not directly address heterogeneous metabolism at the individual cell level. We compare the advantages and disadvantages of different model systems for SIRM experiments, with a focus on lung cancer cells, their interactions with macrophages and T cells, and their response to modulators in the immune microenvironment. We describe the experimental set up, illustrate results from 3D cultures and co-cultures of lung cancer cells with human macrophages, and outline strategies to address the heterogeneous TME.

Signaling lipids as diagnostic biomarkers for ocular surface cicatrizing conjunctivitis

Abstract

Metabolomics has been applied to diagnose diseases, predict disease progression, and design therapeutic strategies in various areas of medicine. However, it remains to be applied to the ocular surface diseases, where biological samples are often of limited quantities. We successfully performed proof-of-concept metabolomics assessment of volume-limited cytology samples from a clinical form of chronic inflammatory cicatrizing conjunctivitis, i.e., ocular MMP and discovered metabolic changes of signaling lipid mediators upon disease onset and progression. The metabolomics assessment revealed active oxylipins, lysophospholipids, fatty acids, and endocannabinoids alterations, from which potential biomarkers linked to inflammatory processes were identified. Possible underlying mechanisms such as dysregulated enzyme activities (e.g., lipoxygenases, cytochrome P450, and phospholipases) were suggested which may be considered as potential therapeutic targets in future studies.

Key messages

Metabolic profile of the ocular surface can be measured using impression cytology samples.

Metabolomics analysis of ocular pemphigoid is presented for the first time.

The metabolomics assessment of OCP patients revealed active oxylipins, lysophospholipids, fatty acids, and endocannabinoids alterations.

Several oxylipins are identified as diagnostic biomarkers for OCP.

Spatial Metabolomics and Imaging Mass Spectrometry in the Age of Artificial Intelligence

Spatial metabolomics is an emerging field of omics research that has enabled localizing metabolites, lipids, and drugs in tissue sections, a feat considered impossible just two decades ago. Spatial metabolomics and its enabling technology-imaging mass spectrometry-generate big hyper-spectral imaging data that have motivated the development of tailored computational methods at the intersection of computational metabolomics and image analysis. Experimental and computational developments have recently opened doors to applications of spatial metabolomics in life sciences and biomedicine. At the same time, these advances have coincided with a rapid evolution in machine learning, deep learning, and artificial intelligence, which are transforming our everyday life and promise to revolutionize biology and healthcare. Here, we introduce spatial metabolomics through the eyes of a computational scientist, review the outstanding challenges, provide a look into the future, and discuss opportunities granted by the ongoing convergence of human and artificial intelligence.