Single-Cell Metabolomics by Mass Spectrometry

Single-cell metabolomics in rare disease: From technology to disease

With the development of clinical experience and technology, rare diseases (RDs) are gradually coming into the limelight. As they often lead to poor prognosis, it is urgent to promote the accuracy and rapidity of diagnosis and promote the development of therapeutic drugs. In recent years, with the rapid improvement of single-cell sequencing technology, the advantages of multi-omics combined application in diseases have been continuously explored. Single-cell metabolomics represents a powerful tool for advancing our understanding of rare diseases, particularly metabolic RDs, and transforming clinical practice. By unraveling the intricacies of cellular metabolism at a single-cell resolution, this innovative approach holds the potential to revolutionize diagnosis, treatment, and management strategies, ultimately improving outcomes for RDs patients. Continued research and technological advancements in single-cell metabolomics are essential for realizing its full potential in the field of RDs diagnosis and therapeutics. It is expected that single-cell metabolomics can be better applied to RDs research in the future, for the benefit of patients and society.

High-throughput single-cell mass spectrometry enables metabolic network analysis by resolving phospholipid C[double bond, length as m-dash]C isomers

Single-cell mass spectrometry (MS) is an essential technology for sensitive and multiplexed analysis of metabolites and lipids for cell phenotyping and pathway studies. However, the structural elucidation of lipids from single cells remains a challenge, especially in the high-throughput scenario. Technically, there is a contradiction between the inadequate sample amount (i.e. a single cell, 0.5-20 pL) for replicate or multiple analysis, on the one hand, and the high metabolite coverage and multidimensional structure analysis that needs to be performed for each single cell, on the other hand. Here, we have developed a high-throughput single-cell MS platform that can perform both lipid profiling and lipid carbon-carbon double bond (C[double bond, length as m-dash]C) location isomer resolution analysis, aided by C[double bond, length as m-dash]C activation in unsaturated lipids by the Paternò-Büchi (PB) reaction and tandem MS, termed single-cell structural lipidomics analysis. The method can achieve a single-cell analysis throughput of 51 cells per minute. A total of 145 lipids were structurally characterized at the subclass level, of which the relative abundance of 17 isomeric lipids differing in the location of C[double bond, length as m-dash]C from 5 lipid precursors was determined. While cell-to-cell variations in MS1-based lipid profiling can be large, an advantage of quantifying lipid C[double bond, length as m-dash]C location isomers is the significantly improved quantitation accuracy. For example, the relative standard deviations (RSDs) of the relative amounts of PC 34:1 C[double bond, length as m-dash]C position isomers in MDA-MB-468 cells are half smaller than those measured for PC 34:1 as a whole by MS1 abundance profiling. Taken together, the developed method can be effectively used for in-depth structural lipid metabolism network analysis by high-throughput analysis of 142 MDA-MB-468 human breast cancer cells.

Gastruloids - a minimalistic model to study complex developmental metabolism

Metabolic networks are well placed to orchestrate the coordination of multiple cellular processes associated with embryonic development such as cell growth, proliferation, differentiation and cell movement. Here, we discuss the advantages that gastruloids, aggregates of mammalian embryonic stem cells that self-assemble a rudimentary body plan, have for uncovering the instructive role of metabolic pathways play in directing developmental processes. We emphasise the importance of using such reductionist systems to link specific pathways to defined events of early mammalian development and their utility for obtaining enough material for metabolomic studies. Finally, we review the ways in which the basic gastruloid protocol can be adapted to obtain specific models of embryonic cell types, tissues and regions. Together, we propose that gastruloids are an ideal system to rapidly uncover new mechanistic links between developmental signalling pathways and metabolic networks, which can then inform precise in vivo studies to confirm their function in the embryo.

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.

Omics approaches for the assessment of biological responses to nanoparticles

Nanotechnology has enabled the development of innovative therapeutics, diagnostics, and drug delivery systems. Nanoparticles (NPs) can influence gene expression, protein synthesis, cell cycle, metabolism, and other subcellular processes. While conventional methods have limitations in characterizing responses to NPs, omics approaches can analyze complete sets of molecular entities that change upon exposure to NPs. This review discusses key omics approaches, namely transcriptomics, proteomics, metabolomics, lipidomics and multi-omics, applied to the assessment of biological responses to NPs. Fundamental concepts and analytical methods used for each approach are presented, as well as good practices for omics experiments. Bioinformatics tools are essential to analyze, interpret and visualize large omics data, and to correlate observations in different molecular layers. The authors envision that conducting interdisciplinary multi-omics analyses in future nanomedicine studies will reveal integrated cell responses to NPs at different omics levels, and the incorporation of omics into the evaluation of targeted delivery, efficacy, and safety will improve the development of nanomedicine therapies.

Integration of UHPLC/Q-OrbitrapMS-based metabolomics and activities evaluation to rapidly explore the anti-inflammatory components from lasianthus

Lasianthus, belonging to Rubiaceae, has been verified to improve clinical syndrome in immune diseases (e.g., hepatitis, nephritis, and rheumatoid arthritis). Both the anti-inflammatory function and chemical composition of Lasianthus vary considerably between different species but few studies focus. So essential it is to explore lasianthus and further search for anti-inflammatory substances. The target of this artical is to analyze the anti-inflammatory activity and chemical composition of lasianthus of different species. And the subsequent active compounds were explored. Primary, the anti-inflammatory activity among seven species of lasianthus (e.g., L. fordii., L. wallichii., L. hookeri C., L. verticillatus., L. sikkimensis., L. appressihirtus., and L. hookeri var) were evaluated by vitro experiments (RAW 264.7 cells). Next, UHPLC/Q-Orbitrap-MS-based metabolomics and the mass defect filter (MDF) algorithm were performed to explore metabolites. In addition, principal component analysis (PCA) was to screen out differential compounds in seven species. Finally, the correlation analysis between activities and composition to rapidly discover the active compounds (compounds were verified pharmacologically). Among the 7 species of lasianthus, the L. fordii. and L. hookeri C indicated the best anti-inflammatory activity. Untargeted metabolomics and MDF show 112 compounds, classified into six dominant types (e.g., flavonoids, phenolic acids, alkaloids, iridoids, coumarins, and anthraquinones). Furthermore, 33 differential metabolites were confirmed by PCA. Then according to correlation analysis and pharmacological validation, 7 compounds IC₅₀＜100 (e.g., scopoletin, asperulosidic acid, chlorogenic acid, ferulic acid, betaine, syringic acid, and emodin) were verified as anti-inflammatory compounds and conduct quantitative analysis. Metabolomics integrated with activities evaluation might be a rapid and effective strategy to explore the active compounds from natural products.

Spatial metabolomics reveals glycogen as an actionable target for pulmonary fibrosis

Matrix assisted laser desorption/ionization imaging has greatly improved our understanding of spatial biology, however a robust bioinformatic pipeline for data analysis is lacking. Here, we demonstrate the application of high-dimensionality reduction/spatial clustering and histopathological annotation of matrix assisted laser desorption/ionization imaging datasets to assess tissue metabolic heterogeneity in human lung diseases. Using metabolic features identified from this pipeline, we hypothesize that metabolic channeling between glycogen and N-linked glycans is a critical metabolic process favoring pulmonary fibrosis progression. To test our hypothesis, we induced pulmonary fibrosis in two different mouse models with lysosomal glycogen utilization deficiency. Both mouse models displayed blunted N-linked glycan levels and nearly 90% reduction in endpoint fibrosis when compared to WT animals. Collectively, we provide conclusive evidence that lysosomal utilization of glycogen is required for pulmonary fibrosis progression. In summary, our study provides a roadmap to leverage spatial metabolomics to understand foundational biology in pulmonary diseases.

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.

Crosstalk between COVID-19 Infection and Kidney Diseases: A Review on the Metabolomic Approaches

The novel severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) causes COVID-19, a respiratory disorder. Various organ injuries have been reported in response to this virus, including kidney injury and, in particular, kidney tubular injury. It has been discovered that infection with the virus does not only cause new kidney disease but also increases treatment difficulty and mortality rates in people with kidney diseases. In individuals hospitalized with COVID-19, urinary metabolites from several metabolic pathways are used to distinguish between patients with acute kidney injury (AKI) and those without. This review summarizes the pathogenesis, pathophysiology, treatment strategies, and role of metabolomics in relation to AKI in COVID-19 patients. Metabolomics is likely to play a greater role in predicting outcomes for patients with kidney disease and COVID-19 with varying levels of severity in the near future as data on metabolic profiles expand rapidly. Here, we also discuss the correlation between COVID-19 and kidney diseases and the available metabolomics approaches.

Heat Stress During Gametogenesis Irreversibly Damages Female Reproductive Organ in Rice

Heat stress during gametogenesis leads to spikelet sterility. To ascertain the role of female reproductive organ (pistil), two rice genotypes N22 and IR64 with contrasting heat stress responses were exposed to control (30 °C) and heat stress (38 °C and 40 °C) during megasporogenesis. Anatomical observations of ovule revealed greater disappearance of megaspore mother cell and nuclei at early stages, and during later stages mature embryo sac without female germ unit, improper positioning of nuclei, and shrunken embryo sac was observed in the sensitive IR64. Under heat stress, a decrease in sugar and starch, increase in H₂O₂ and malondialdehyde with lower antioxidant enzyme activities were recorded in pistils of both N22 and IR64. Lower accumulation of TCA cycle metabolites and amino acids were noticed in IR64 pistils under heat stress at gametogenesis, whereas N22 exhibited favorable metabolite profiles. At heading, however, N22 pistils had higher carbohydrate accumulation and better ROS homeostasis, suggesting higher recovery after heat stress exposure. In summary, the results indicate that heat stress during megasporogenesis leads to irreversible anatomical and physiological changes in pistil and alters metabolic signatures leading to increased spikelet sterility in rice. Mechanisms identified for enhanced heat tolerance in pistil can help in developing rice varieties that are better adapted to future hotter climate.

Metabolomics combined with network pharmacology to explore the mechanisms of modified Guishen pill to ameliorate polycystic ovary syndrome

BACKGROUND

The modified Guishen pill (MGP) has a prominent therapeutic effect on polycystic ovary syndrome (PCOS). However, its mechanism is still unclear. This study aimed to uncover the mechanism of MGP for PCOS treatment through a comprehensive strategy integrating metabolomics and network pharmacology.

METHODS

A letrozole-induced PCOS model was used to evaluate ovarian function in rats. Plasma metabolomics was used to authenticate differential metabolites and enriched related pathways using the MetaboAnalyst platform. Network pharmacology was utilized to explore the endogenous targets of MGP treatment for PCOS. Finally, the potential targets and related biological functions were verified experimentally.

RESULTS

MGP improved PCOS symptoms by regulating abnormal levels of sex hormones and alleviating ovarian pathological changes in rats; fifty-four potential differential metabolites involved in MGP treatment for PCOS, and the hub genes derived from network pharmacology were consistent with the metabolomic analysis results to varying degrees. The comprehensive analysis identified that a key novel target for endothelial nitric oxide synthase (eNOS/NOS3), five key metabolites (ornithine, citrulline, l-glutamic acid, acetylornithine, and hydroxyproline), and one pathway (arginine and proline metabolism) were related to the therapy of PCOS with MGP. Subsequently, we verified the localization and expression of eNOS in the ovaries, and it significantly improved insulin resistance, apoptosis, and oxidative stress in letrozole-induced PCOS rats.

CONCLUSION

Our work reveals the complex mechanism of MGP therapy for PCOS. This study is a successful paradigm for elucidating the pharmacological mechanism of the traditional Chinese medicine compound.

Revealing the metabolic mechanism of dandelion extract against A549 cells using UPLC-QTOF MS

Dandelion extract shows potential anticancer activity and is expected to be a new type of natural anti-cancer drug. However, the effect mechanism of dandelion extract to lung cancer cells is still unclear. Here, untargeted metabolomics approach based on liquid chromatography-mass spectrograph (LC-MS) was used to characterize the metabolic responses of A549 cell to dandelion extract exposure, to provide new clues for the anti-tumor mechanism of dandelion extract from the perspective of metabolomics. A total of 16 differentially expressed and time-related metabolites were identified between dandelion extract exposure and control groups. The perturbed metabolic pathways of A549 cells after dandelion extract exposure mainly include the glycerophospholipid metabolism and purine metabolism. These results concluded that dandelion extract may exert anticancer activity by affecting the malignant proliferation, disturbing the stability of cell membrane structure, reducing the adhesion of tumor cells to extracellular matrix and fibronectin and finally inducing tumor cell death.

Multi-Omics Approaches to Define Calcific Aortic Valve Disease Pathogenesis

Calcific aortic valve disease sits at the confluence of multiple world-wide epidemics of aging, obesity, diabetes, and renal dysfunction, and its prevalence is expected to nearly triple over the next 3 decades. This is of particularly dire clinical relevance, as calcific aortic valve disease can progress rapidly to aortic stenosis, heart failure, and eventually premature death. Unlike in atherosclerosis, and despite the heavy clinical toll, to date, no pharmacotherapy has proven effective to halt calcific aortic valve disease progression, with invasive and costly aortic valve replacement representing the only treatment option currently available. This substantial gap in care is largely because of our still-limited understanding of both normal aortic valve biology and the key regulatory mechanisms that drive disease initiation and progression. Drug discovery is further hampered by the inherent intricacy of the valvular microenvironment: a unique anatomic structure, a complex mixture of dynamic biomechanical forces, and diverse and multipotent cell populations collectively contributing to this currently intractable problem. One promising and rapidly evolving tactic is the application of multiomics approaches to fully define disease pathogenesis. Herein, we summarize the application of (epi)genomics, transcriptomics, proteomics, and metabolomics to the study of valvular heart disease. We also discuss recent forays toward the omics-based characterization of valvular (patho)biology at single-cell resolution; these efforts promise to shed new light on cellular heterogeneity in healthy and diseased valvular tissues and represent the potential to efficaciously target and treat key cell subpopulations. Last, we discuss systems biology- and network medicine-based strategies to extract meaning, mechanisms, and prioritized drug targets from multiomics datasets.

Chemical Tagging Assisted Mass Spectrometry Analysis Enables Sensitive Determination of Phosphorylated Compounds in a Single Cell

Polar phosphorylated metabolites are involved in a variety of biological processes and play vital roles in energetic metabolism, cofactor regeneration, and nucleic acid synthesis. However, it is often challenging to interrogate polar phosphorylated metabolites and compounds from biological samples. Liquid chromatography-mass spectrometry (LC/MS) now plays a central role in metabolomic studies. However, LC/MS-based approaches have been hampered by the issues of the low ionization efficiencies, low in vivo concentrations, and less chemical stability of polar phosphorylated metabolites. In this work, we synthesized paired reagents of light and heavy isotopomers, 2-(diazomethyl)phenyl)(9-methyl-1,3,4,9-tetrahydro-2H-pyrido[3,4-b]indol-2-yl)methanone (DMPI) and d₃-(2-(diazomethyl)phenyl)(9-methyl-1,3,4,9-tetrahydro-2H-pyrido[3,4-b]indol-2-yl)methanone (d₃-DMPI). The paired reagents of DMPI and d₃-DMPI carry diazo groups that can efficiently and selectively react with the phosphate group on polar phosphorylated metabolites under mild conditions. As a proof of concept, we found that the transfer of the indole heterocycle group from DMPI/d₃-DMPI to ribonucleotides led to the significant increase of ionization efficiencies of ribonucleotides during LC/MS analysis. The detection sensitivities of these ribonucleotides increased by 25-1137-fold upon DMPI tagging with the limits of detection (LODs) being between 7 and 150 amol. With the developed method, we achieved the determination of all the 12 ribonucleotides from a single mammalian cell and from a single stamen of Arabidopsis thaliana. The method provides a valuable tool to investigate the dynamic changes of polar phosphorylated metabolites in a single cell under particular conditions.

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.

Metabolomics: A Tool to Understand the Impact of Genetic Mutations in Amyotrophic Lateral Sclerosis

Metabolomics studies performed in patients with amyotrophic lateral sclerosis (ALS) reveal a set of distinct metabolites that can shed light on the pathological alterations taking place in each individual. Metabolites levels are influenced by disease status, and genetics play an important role both in familial and sporadic ALS cases. Metabolomics analysis helps to unravel the differential impact of the most common ALS-linked genetic mutations (as C9ORF72, SOD1, TARDBP, and FUS) in specific signaling pathways. Further, studies performed in genetic models of ALS reinforce the role of TDP-43 pathology in the vast majority of ALS cases. Studies performed in differentiated cells from ALS-iPSC (induced Pluripotent Stem Cells) reveal alterations in the cell metabolism that are also found in ALS models and ultimately in ALS patients. The development of metabolomics approaches in iPSC derived from ALS patients allow addressing and ultimately understanding the pathological mechanisms taking place in any patient. Lately, the creation of a "patient in a dish" will help to identify patients that may benefit from specific treatments and allow the implementation of personalized medicine.