Single Cell Metabolomics: A Future Tool to Unmask Cellular Heterogeneity and Virus-Host Interaction in Context of Emerging Viral Diseases

High-Throughput f-LAESI-IMS-MS for Mapping Biological Nitrogen Fixation One Cell at a Time

For the characterization of the metabolic heterogeneity of cell populations, high-throughput single-cell analysis platforms are needed. In this study, we utilized mass spectrometry (MS) enhanced with ion mobility separation (IMS) and coupled with an automated sampling platform, fiber-based laser ablation electrospray ionization (f-LAESI), for in situ high-throughput single-cell metabolomics in soybean (Glycine max) root nodules. By fully automating the in situ sampling platform, an overall sampling rate of 804 cells/h was achieved for high numbers (>500) of tissue-embedded plant cells. This is an improvement by a factor of 13 compared to the previous f-LAESI-MS configuration. By introducing IMS, the molecular coverage improved, and structural isomers were separated on a millisecond time scale. The enhanced f-LAESI-IMS-MS platform produced 259 sample-related peaks/cell, almost twice as much as the 131 sample-related peaks/cell produced by f-LAESI-MS without IMS. Using the upgraded system, two types of metabolic heterogeneity characterization methods became possible. For unimodal metabolite abundance distributions, the metabolic noise reported on the metabolite level variations within the cell population. For bimodal distributions, the presence of metabolically distinct subpopulations was established. Discovering these latent cellular phenotypes could be linked to the presence of different cell states, e.g., proliferating bacteria in partially occupied plant cells and quiescent bacteroids in fully occupied cells in biological nitrogen fixation, or spatial heterogeneity due to altered local environments.

Evidence of Cytolysin A nanopore incorporation in mammalian cells assessed by a graphical user interface

Technologies capable of assessing cellular metabolites with high precision and temporal resolution are currently limited. Recent developments in the field of nanopore sensors allow the non-stochastic quantification of metabolites, where a nanopore is acting as an electrical transducer for selective substrate binding proteins (SBPs). Here we show that incorporation of the pore-forming toxin Cytolysin A (ClyA) into the plasma membrane of Chinese hamster ovary cells (CHO-K1) results in the appearance of single-channel conductance amenable to multiplexed automated patch-clamp (APC) electrophysiology. In CHO-K1 cells, SBPs modify the ionic current flowing though ClyA nanopores, thus demonstrating its potential for metabolite sensing of living cells. Moreover, we developed a graphical user interface for the analysis of the complex signals resulting from multiplexed APC recordings. This system lays the foundation to bridge the gap between recent advances in the nanopore field (e.g., proteomic and transcriptomic) and potential cellular applications.

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.

Single-cell metabolomics by mass spectrometry: ready for primetime?

Single-cell metabolomics (SCMs) is a powerful tool for studying cellular heterogeneity by providing insight into the differences between individual cells. With the development of a set of promising SCMs pipelines, this maturing technology is expected to be widely used in biomedical research. However, before SCMs is ready for primetime, there are some challenges to overcome. In this review, we summarize the trends and challenges in the development of SCMs. We also highlight the latest methodologies, applications, and sketch the perspective for integration with other omics and imaging approaches.

Computational studies indicated the effectiveness of human metabolites against SARS-Cov-2 main protease

To fight against the devastating coronavirus disease 2019 (COVID-19), identifying robust anti-SARS-CoV-2 therapeutics from all possible directions is necessary. To contribute to this effort, we selected a human metabolites database containing waters and lipid-soluble metabolites to screen against the 3-chymotrypsin-like proteases (3CLpro) protein of SARS-CoV-2. The top 8 hits from virtual screening displayed a docking score varying between ~ - 11 and ~ - 14 kcal/mol. Molecular dynamics simulations complement the virtual screening study in conjunction with the molecular mechanics generalized Born surface area (MM/GBSA) scheme. Our analyses revealed that (HMDB0132640) has the best glide docking score, - 14.06 kcal/mol, and MM-GBSA binding free energy, - 18.08 kcal/mol. The other three lead molecules are also selected along with the top molecule through a critical inspection of their pharmacokinetic properties. HMDB0132640 displayed a better binding affinity than the other three compounds (HMDB0127868, HMDB0134119, and HMDB0125821) due to increased favorable contributions from the intermolecular electrostatic and van der Waals interactions. Further, we have investigated the ligand-induced structural dynamics of the main protease. Overall, we have identified new compounds that can serve as potential leads for developing novel antiviral drugs against SARS-CoV-2 and elucidated molecular mechanisms of their binding to the main protease. Identification of probable hits from human metabolites against SARS-CoV-2 using integrated computational approaches-Missed against MS.

Metabolomics meets systems immunology

Metabolic processes play a critical role in immune regulation. Metabolomics is the systematic analysis of small molecules (metabolites) in organisms or biological samples, providing an opportunity to comprehensively study interactions between metabolism and immunity in physiology and disease. Integrating metabolomics into systems immunology allows the exploration of the interactions of multilayered features in the biological system and the molecular regulatory mechanism of these features. Here, we provide an overview on recent technological developments of metabolomic applications in immunological research. To begin, two widely used metabolomics approaches are compared: targeted and untargeted metabolomics. Then, we provide a comprehensive overview of the analysis workflow and the computational tools available, including sample preparation, raw spectra data preprocessing, data processing, statistical analysis, and interpretation. Third, we describe how to integrate metabolomics with other omics approaches in immunological studies using available tools. Finally, we discuss new developments in metabolomics and its prospects for immunology research. This review provides guidance to researchers using metabolomics and multiomics in immunity research, thus facilitating the application of systems immunology to disease research.

Metabolomics Profiles Reveal New Insights of Herpes Simplex Virus Type 1 Infection

Herpes simplex virus type 1 (HSV-1) is a ubiquitous human pathogen that can cause significant morbidity, primarily facial cold sores and herpes simplex encephalitis. Previous studies have shown that a variety of viruses can reprogram the metabolic profiles of host cells to facilitate self-replication. In order to further elucidate the metabolic interactions between the host cell and HSV-1, we used liquid chromatography-tandem mass spectrometry (LC-MS/MS) to analyze the metabolic profiles in human lung fibroblasts KMB17 infected with HSV-1. The results showed that 654 and 474 differential metabolites were identified in positive and negative ion modes, respectively, and 169 and 114 metabolic pathways that might be altered were screened. These altered metabolites are mainly involved in central carbon metabolism, choline metabolism, amino acid metabolism, purine and pyrimidine metabolism, cholesterol metabolism, bile secretion, and prolactin signaling pathway. Further, we confirmed that the addition of tryptophan metabolite kynurenine promotes HSV-1 replication, and the addition of 25-Hydroxycholesterol inhibits viral replication. Significantly, HSV-1 replication was obviously enhanced in the ChOKα (a choline metabolic rate-limiting enzyme) deficient mouse macrophages. These results indicated that HSV-1 induces the metabolic reprogramming of host cells to promote or resist viral replication. Taken together, these observations highlighted the significance of host cell metabolism in HSV-1 replication, which would help to clarify the pathogenesis of HSV-1 and identify new anti-HSV-1 therapeutic targets.

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.

Postinfection Metabolic Reprogramming of the Murine Trigeminal Ganglion Limits Herpes Simplex Virus-1 Replication

Herpes simplex virus type-1 (HSV-1) infections are known to alter the host metabolism for efficient propagation in vitro. However, in vivo metabolic perturbations upon prolonged HSV-1 infection remain poorly understood. We used high-resolution liquid chromatography coupled with mass spectrometry (LC-MS) and functional assays to determine the state of the trigeminal ganglion (TG) tissue metabolism upon prolonged corneal HSV-1 infection in a murine model. The metabolomics data indicated significant alterations in the host metabolic profile. After HSV-1 infection, the TG microenvironment assumed downregulation of central carbon metabolism and nucleotide synthesis pathways. We validated our observations using in vitro and ex vivo models through targeted inhibition of crucial metabolic polyamine pathways identified in our metabolomics screen. Our findings collectively suggested that HSV-1 infection altered the host metabolic product regulations that limit the energy and macromolecular precursors required for viral replication.

Mass Spectrometry Imaging technology in metabolomics: a systematic review

Mass spectrometry imaging (MSI) is a powerful label-free analysis technique that can provide simultaneous spatial distribution of multiple compounds in a single experiment. By combining the sensitive and rapid screening of high-throughput mass spectrometry with spatial chemical information, metabolite analysis and morphological characteristics are presented in a single image. MSI can be used for qualitative and quantitative analysis of metabolic profiles and it can provide visual analysis of spatial distribution information of complex biological and microbial systems. Matrix assisted laser desorption ionization, laser ablation electrospray ionization and desorption electrospray ionization are commonly used in MSI. Here, we summarize and compare these three technologies, as well as the applications and prospects of MSI in metabolomics.

Advances in nano- and microscale NMR spectroscopy using diamond quantum sensors

Quantum technologies have seen a rapid developmental surge over the last couple of years. Though often overshadowed by quantum computation, quantum sensors show tremendous potential for widespread applications in chemistry and biology. One system stands out in particular: the nitrogen-vacancy (NV) center in diamond, an atomic-sized sensor allowing the detection of nuclear magnetic resonance (NMR) signals at unprecedented length scales down to a single proton. In this article, we review the fundamentals of NV center-based quantum sensing and its distinct impact on nano- and microscale NMR spectroscopy. Furthermore, we highlight possible future applications of this novel technology ranging from energy research, materials science, to single-cell biology, and discuss the associated challenges of these rapidly developing NMR sensors.

Single-Cell Mass Spectrometry Enables Insight into Heterogeneity in Infectious Disease

Cellular heterogeneity is generally overlooked in infectious diseases. In this study, we investigated host cell heterogeneity during infection with Trypanosoma cruzi (T. cruzi) parasites, causative agents of Chagas disease (CD). In chronic-stage CD, only a few host cells are infected with a large load of parasites and symptoms may appear at sites distal to parasite colonization. Furthermore, recent work has revealed T. cruzi heterogeneity with regard to replication rates and drug susceptibility. However, the role of cellular-level metabolic heterogeneity in these processes has yet to be assessed. To fill this knowledge gap, we developed a Single-probe SCMS (single-cell mass spectrometry) method compatible with biosafety protocols, to acquire metabolomics data from individual cells during T. cruzi infection. This study revealed heterogeneity in the metabolic response of the host cells to T. cruzi infection in vitro. Our results showed that parasite-infected cells possessed divergent metabolism compared to control cells. Strikingly, some uninfected cells adjacent to infected cells showed metabolic impacts as well. Specific metabolic changes include increases in glycerophospholipids with infection. These results provide novel insight into the pathogenesis of CD. Furthermore, they represent the first application of bioanalytical SCMS to the study of mammalian-infectious agents, with the potential for broad applications to study infectious diseases.

Biomarkers of mitochondrial origin: a futuristic cancer diagnostic

Cancer is a highly fatal disease without effective early-stage diagnosis and proper treatment. Along with the oncoproteins and oncometabolites, several organelles from cancerous cells are also emerging as potential biomarkers. Mitochondria isolated from cancer cells are one such biomarker candidates. Cancerous mitochondria exhibit different profiles compared with normal ones in morphology, genomic, transcriptomic, proteomic and metabolic landscape. Here, the possibilities of exploring such characteristics as potential biomarkers through single-cell omics and Artificial Intelligence (AI) are discussed. Furthermore, the prospects of exploiting the biomarker-based diagnosis and its futuristic utilization through circulatory tumor cell technology are analyzed. A successful alliance of circulatory tumor cell isolation protocols and a single-cell omics platform can emerge as a next-generation diagnosis and personalized treatment procedure.

Senecavirus A- and Non-Infected Cells at Early Stage of Infection: Comparative Metabolomic Profiles

Senecavirus A (SVA), classified into the genus Senecavirus in the family Picornaviridae, causes an infectious disease in pigs. This virus can efficiently replicate in some non-pig-derived cells, such as the BHK cell line and its derivative (BSR-T7/5 cell line). We had recovered a wild-type SVA from its cDNA clone previously, and then uncovered the proteomic profile of SVA-infected BSR-T7/5 cells at 12 h post inoculation (hpi). In order to explore the cellular metabolomics further, the SVA-inoculated BSR-T7/5 cell monolayer was collected at 12 hpi for assay via liquid chromatography-tandem mass spectrometry (LC-MS/MS). The resultant data set was comprehensively analyzed using bioinformatics tools. A total of 451 metabolites were identified using in-house and public databases. Out of these metabolites, sixty-one showed significantly differential values (p value < 0.05). The Kyoto Encyclopedia of Genes and Genomes (KEGG) database was used to analyze metabolic pathways of the significantly differential metabolites. There were eighty-one identified KEGG pathways, out of which twenty-seven showed their p values < 0.05. The pyrimidine metabolism revealed the minimum p value and the maximum number of significantly differential metabolites, implying the pyrimidine played a key role in cellular metabolism after SVA infection. SVA replication must rely on the cellular metabolism. The present study on metabolomics would shed light on impacts of SVA-induced multiple interactions among metabolites on cells or even on natural hosts.

A Compass to Guide Insights into TH17 Cellular Metabolism and Autoimmunity

T cells rapidly convert their cellular metabolic requirements upon activation, switching to a highly glycolytic program to satisfy their increasingly complex energy needs. Fundamental metabolic differences have been established for the development of Foxp3⁺ T regulatory (Treg) cells versus T_H17 cells, alterations of which can drive disease. T_H17 cell dysregulation is a driver of autoimmunity and chronic inflammation, contributing to pathogenesis in diseases such as multiple sclerosis. A recent paper published in Cell by Wagner, et al. combined scRNA-seq and metabolic mapping data to interrogate potential metabolic modulators of T_H17 cell pathogenicity. This Compass to T_H17 cell metabolism highlights the polyamine pathway as a critical regulator of T_H17/Treg cell function, signifying its potential as a therapeutic target.

Toward modeling metabolic state from single-cell transcriptomics

Background

Single-cell metabolic studies bring new insights into cellular function, which can often not be captured on other omics layers. Metabolic information has wide applicability, such as for the study of cellular heterogeneity or for the understanding of drug mechanisms and biomarker development. However, metabolic measurements on single-cell level are limited by insufficient scalability and sensitivity, as well as resource intensiveness, and are currently not possible in parallel with measuring transcript state, commonly used to identify cell types. Nevertheless, because omics layers are strongly intertwined, it is possible to make metabolic predictions based on measured data of more easily measurable omics layers together with prior metabolic network knowledge.

Scope of Review

We summarize the current state of single-cell metabolic measurement and modeling approaches, motivating the use of computational techniques. We review three main classes of computational methods used for prediction of single-cell metabolism: pathway-level analysis, constraint-based modeling, and kinetic modeling. We describe the unique challenges arising when transitioning from bulk to single-cell modeling. Finally, we propose potential model extensions and computational methods that could be leveraged to achieve these goals.

Major Conclusions

Single-cell metabolic modeling is a rising field that provides a new perspective for understanding cellular functions. The presented modeling approaches vary in terms of input requirements and assumptions, scalability, modeled metabolic layers, and newly gained insights. We believe that the use of prior metabolic knowledge will lead to more robust predictions and will pave the way for mechanistic and interpretable machine-learning models.

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Single-cell RNA sequencing and prior metabolic knowledge enable metabolic predictions.

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When compared to bulk, single-cell modeling is linked to unique insights and challenges.

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Computational modelling approaches differ in applicability and newly provided insights.

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The use of prior metabolic knowledge paves the way for mechanistic machine-learning.

Quantifying the propagation of parametric uncertainty on flux balance analysis

Flux balance analysis (FBA) and associated techniques operating on stoichiometric genome-scale metabolic models play a central role in quantifying metabolic flows and constraining feasible phenotypes. At the heart of these methods lie two important assumptions: (i) the biomass precursors and energy requirements neither change in response to growth conditions nor environmental/genetic perturbations, and (ii) metabolite production and consumption rates are equal at all times (i.e., steady-state). Despite the stringency of these two assumptions, FBA has been shown to be surprisingly robust at predicting cellular phenotypes. In this paper, we formally assess the impact of these two assumptions on FBA results by quantifying how uncertainty in biomass reaction coefficients, and departures from steady-state due to temporal fluctuations could propagate to FBA results. In the first case, conditional sampling of parameter space is required to re-weigh the biomass reaction so as the molecular weight remains equal to 1 g mmol^-1, and in the second case, metabolite (and elemental) pool conservation must be imposed under temporally varying conditions. Results confirm the importance of enforcing the aforementioned constraints and explain the robustness of FBA biomass yield predictions.

Single-Cell Microgels for Diagnostics and Therapeutics

Cell encapsulation within hydrogel droplets is transforming what is feasible in multiple fields of biomedical science such as tissue engineering and regenerative medicine, in vitro modeling, and cell-based therapies. Recent advances have allowed researchers to miniaturize material encapsulation complexes down to single-cell scales, where each complex, termed a single-cell microgel, contains only one cell surrounded by a hydrogel matrix while remaining <100 μm in size. With this achievement, studies requiring single-cell resolution are now possible, similar to those done using liquid droplet encapsulation. Of particular note, applications involving long-term in vitro cultures, modular bioinks, high-throughput screenings, and formation of 3D cellular microenvironments can be tuned independently to suit the needs of individual cells and experimental goals. In this progress report, an overview of established materials and techniques used to fabricate single-cell microgels, as well as insight into potential alternatives is provided. This focused review is concluded by discussing applications that have already benefited from single-cell microgel technologies, as well as prospective applications on the cusp of achieving important new capabilities.

Virus Infection Variability by Single-Cell Profiling

Cell-to-cell variability of infection has long been known, yet it has remained one of the least understood phenomena in infection research. It impacts on disease onset and development, yet only recently underlying mechanisms have been studied in clonal cell cultures by single-virion immunofluorescence microscopy and flow cytometry. In this review, we showcase how single-cell RNA sequencing (scRNA-seq), single-molecule RNA-fluorescence in situ hybridization (FISH), and copper(I)-catalyzed azide-alkyne cycloaddition (click) with alkynyl-tagged viral genomes dissect infection variability in human and mouse cells. We show how the combined use of scRNA-FISH and click-chemistry reveals highly variable onsets of adenoviral gene expression, and how single live cell plaques reveal lytic and nonlytic adenovirus transmissions. The review highlights how scRNA-seq profiling and scRNA-FISH of coxsackie, influenza, dengue, zika, and herpes simplex virus infections uncover transcriptional variability, and how the host interferon response tunes influenza and sendai virus infections. We introduce the concept of "cell state" in infection variability, and conclude with advances by single-cell simultaneous measurements of chromatin accessibility and mRNA counts at high-throughput. Such technology will further dissect the sequence of events in virus infection and pathology, and better characterize the genetic and genomic stability of viruses, cell autonomous innate immune responses, and mechanisms of tissue injury.

Mitochondrial Lipid Signaling and Adaptive Thermogenesis

Thermogenesis is an energy demanding process by which endotherms produce heat to maintain their body temperature in response to cold exposure. Mitochondria in the brown and beige adipocytes play a key role in thermogenesis, as the site for uncoupling protein 1 (UCP1), which allows for the diffusion of protons through the mitochondrial inner membrane to produce heat. To support this energy demanding process, the mitochondria in brown and beige adipocytes increase oxidation of glucose, amino acids, and lipids. This review article explores the various mitochondria-produced and processed lipids that regulate thermogenesis including cardiolipins, free fatty acids, and acylcarnitines. These lipids play a number of roles in thermogenic adipose tissue including structural support of UCP1, transcriptional regulation, fuel source, and activation of cell signaling cascades.

Immuno-metabolic interfaces in cardiac disease and failure

The interplay between the cardiovascular system, metabolism, and inflammation plays a central role in the pathophysiology of a wide spectrum of cardiovascular diseases, including heart failure. Here, we provide an overview of the fundamental aspects of the interrelation between inflammation and metabolism, ranging from the role of metabolism in immune cell function to the processes how inflammation modulates systemic and cardiac metabolism. Furthermore, we discuss how disruption of this immuno-metabolic interface is involved in the development and progression of cardiovascular disease, with a special focus on heart failure. Finally, we present new technologies and therapeutic approaches that have recently emerged and hold promise for the future of cardiovascular medicine.

Boolean Feedforward Neural Network Modeling of Molecular Regulatory Networks for Cellular State Conversion

The molecular regulatory network (MRN) within a cell determines cellular states and transitions between them. Thus, modeling of MRNs is crucial, but this usually requires extensive analysis of time-series measurements, which is extremely difficult to obtain from biological experiments. However, single-cell measurement data such as single-cell RNA-sequencing databases have recently provided a new insight into resolving this problem by ordering thousands of cells in pseudo-time according to their differential gene expressions. Neural network modeling can be employed by using temporal data as learning data. In contrast, Boolean network modeling of MRNs has a growing interest, as it is a parameter-free logical modeling and thereby robust to noisy data while still capturing essential dynamics of biological networks. In this study, we propose a Boolean feedforward neural network (FFN) modeling by combining neural network and Boolean network modeling approach to reconstruct a practical and useful MRN model from large temporal data. Furthermore, analyzing the reconstructed MRN model can enable us to identify control targets for potential cellular state conversion. Here, we show the usefulness of Boolean FFN modeling by demonstrating its applicability through a toy model and biological networks.

High-throughput metabolomic method based on liquid chromatography: high resolution mass spectrometry with chemometrics for metabolic biomarkers and pathway analysis to reveal the protective effects of baicalin on thyroid cancer

Cell metabonomics focuses on discovering metabolic biomarkers and pathway changes in cells from biological systems to obtain the cell properties and functional information under different conditions. Baicalin possesses various pharmacological activities, and plays a vital role in the oncology research field. However, the detailed mechanism of its action is still unclear. In this work, we employed ultra-high performance liquid chromatography-mass spectrometry (UPLC-MS) based non-targeted metabolomics method associated with chemometrics analysis to explore metabolic pathways and biomarkers for investigating the efficacy and pharmacological targets of baicalin against thyroid cancer cells. In addition, morphological observation, parameter calculation of cell proliferation and apoptosis were carried out, which assisted in elucidation of pharmacological activity of baicalin on the human thyroid cancer cells. The results showed that baicalin possesses an intense stimulative apoptosis and inhibits proliferation activity on SW579 human thyroid cancer cells, and partially reversed the cell metabolite abnormalities. A total of nineteen differentiated metabolites in SW579 cells were identified and deemed as potential biomarkers after the baicalin treatment, involving nine metabolic pathways, such as taurine and hypotaurine metabolism, pyrimidine metabolism, fructose and mannose metabolism, steroid hormone biosynthesis and sphingolipid metabolism. High-throughput non-targeted metabolomics provide an insight into specialized mechanism of baicalin against thyroid cancer and contributes to novel drug discovery and thyroid cancer management in clinical practice.