Single-cell metabolomics: where are we and where are we going?

Understanding metabolic plasticity at single cell resolution

It is increasingly clear that cellular metabolic function varies not just between cells of different tissues, but also within tissues and cell types. In this essay, we envision how differences in central carbon metabolism arise from multiple sources, including the cell cycle, circadian rhythms, intrinsic metabolic cycles, and others. We also discuss and compare methods that enable such variation to be detected, including single-cell metabolomics and RNA-sequencing. We pay particular attention to biosensors for AMPK and central carbon metabolites, which when used in combination with metabolic perturbations, provide clear evidence of cellular variance in metabolic function.

Recent Developments in Single-Cell Metabolomics by Mass Spectrometry─A Perspective

Recent advancements in single-cell (sc) resolution analyses, particularly in sc transcriptomics and sc proteomics, have revolutionized our ability to probe and understand cellular heterogeneity. The study of metabolism through small molecules, metabolomics, provides an additional level of information otherwise unattainable by transcriptomics or proteomics by shedding light on the metabolic pathways that translate gene expression into functional outcomes. Metabolic heterogeneity, critical in health and disease, impacts developmental outcomes, disease progression, and treatment responses. However, dedicated approaches probing the sc metabolome have not reached the maturity of other sc omics technologies. Over the past decade, innovations in sc metabolomics have addressed some of the practical limitations, including cell isolation, signal sensitivity, and throughput. To fully exploit their potential in biological research, however, remaining challenges must be thoroughly addressed. Additionally, integrating sc metabolomics with orthogonal sc techniques will be required to validate relevant results and gain systems-level understanding. This perspective offers a broad-stroke overview of recent mass spectrometry (MS)-based sc metabolomics advancements, focusing on ongoing challenges from a biologist's viewpoint, aimed at addressing pertinent and innovative biological questions. Additionally, we emphasize the use of orthogonal approaches and showcase biological systems that these sophisticated methodologies are apt to explore.

Advancements in Omics and Breakthrough Gene Therapies: A Glimpse into the Future of Peripheral Artery Disease

Peripheral artery disease (PAD), a highly prevalent global disease, associates with significant morbidity and mortality in affected patients. Despite progress in endovascular and open revascularization techniques for advanced PAD, these interventions grapple with elevated rates of arterial restenosis and vein graft failure attributed to intimal hyperplasia (IH). Novel multiomics technologies, coupled with sophisticated analyses tools recently powered by advances in artificial intelligence, have enabled the study of atherosclerosis and IH with unprecedented single-cell and spatial precision. Numerous studies have pinpointed gene hubs regulating pivotal atherogenic and atheroprotective signaling pathways as potential therapeutic candidates. Leveraging advancements in viral and nonviral gene therapy (GT) platforms, gene editing technologies, and cutting-edge biomaterial reservoirs for delivery uniquely positions us to develop safe, efficient, and targeted GTs for PAD-related diseases. Gene therapies appear particularly fitting for ex vivo genetic engineering of IH-resistant vein grafts. This manuscript highlights currently available state-of-the-art multiomics approaches, explores promising GT-based candidates, and details GT delivery modalities employed by our laboratory and others to thwart mid-term vein graft failure caused by IH, as well as other PAD-related conditions. The potential clinical translation of these targeted GTs holds the promise to revolutionize PAD treatment, thereby enhancing patients' quality of life and life expectancy.

Harnessing Multi-Omics Strategies and Bioinformatics Innovations for Advancing Soybean Improvement: A Comprehensive Review

Soybean improvement has entered a new era with the advent of multi-omics strategies and bioinformatics innovations, enabling more precise and efficient breeding practices. This comprehensive review examines the application of multi-omics approaches in soybean-encompassing genomics, transcriptomics, proteomics, metabolomics, epigenomics, and phenomics. We first explore pre-breeding and genomic selection as tools that have laid the groundwork for advanced trait improvement. Subsequently, we dig into the specific contributions of each -omics field, highlighting how bioinformatics tools and resources have facilitated the generation and integration of multifaceted data. The review emphasizes the power of integrating multi-omics datasets to elucidate complex traits and drive the development of superior soybean cultivars. Emerging trends, including novel computational techniques and high-throughput technologies, are discussed in the context of their potential to revolutionize soybean breeding. Finally, we address the challenges associated with multi-omics integration and propose future directions to overcome these hurdles, aiming to accelerate the pace of soybean improvement. This review serves as a crucial resource for researchers and breeders seeking to leverage multi-omics strategies for enhanced soybean productivity and resilience.

Polarization of Macrophages in Tumor Microenvironment Using High-Throughput Single-Cell Metabolomics

Macrophages consist of a heterogeneous population of functionally distinct cells that participate in many physiological and pathological processes. They exhibit prominent plasticity by changing their different functional phenotypes represented by proinflammatory (M1) and anti-inflammatory (M2) in response to different environmental stimuli. Emerging evidence illustrates the importance of intracellular metabolic pathways in macrophage polarizations and functions. In the tumor microenvironment (TME), macrophages tend to M2 polarization, which promotes tumor growth and leads to adverse physiological effects. Due to the lack of highly specific antigens in M1 and M2 macrophages, significant challenges present in isolating these subtypes from clinical samples or in vitro coculture models of tumor-immune cells. In reverse, the single-cell technique provides the possibility to investigate the factors influencing macrophage polarization in the TME. In this research, we employed inertial microfluidic chip-mass spectrometry (IMC-MS) to conduct single-cell metabolomics analysis of macrophages polarized into the two major phenotypes, respectively, and 213 metabolites were identified in total. Subsequently, differential metabolites between macrophage phenotypes were analyzed using volcano plots and binary logistic regression models. Glutamine was pinpointed as a key metabolite for the M1 and M2 phenotypes. Experimental results from both monoculture and coculture cell models demonstrated that M1 polarization is more reliant on the presence of glutamine in the culture environment than M2 polarization. Glutamine deficiency resulted in failed M1 polarization, while its absence had a less pronounced effect on M2 polarization. Replenishing an appropriate amount of glutamine during the intermediate stages of coculture models significantly enhanced the proportion of M1 polarization and suppressed the growth of tumor cells. This research elucidated glutamine as a key factor influencing macrophage polarization in the TME via single-cell metabolomics based on IMC-MS, offering promising insights and targets for tumor therapies.

Interactions of SARS-CoV-2 with Human Target Cells-A Metabolic View

Viruses are obligate intracellular parasites, and they exploit the cellular pathways and resources of their respective host cells to survive and successfully multiply. The strategies of viruses concerning how to take advantage of the metabolic capabilities of host cells for their own replication can vary considerably. The most common metabolic alterations triggered by viruses affect the central carbon metabolism of infected host cells, in particular glycolysis, the pentose phosphate pathway, and the tricarboxylic acid cycle. The upregulation of these processes is aimed to increase the supply of nucleotides, amino acids, and lipids since these metabolic products are crucial for efficient viral proliferation. In detail, however, this manipulation may affect multiple sites and regulatory mechanisms of host-cell metabolism, depending not only on the specific viruses but also on the type of infected host cells. In this review, we report metabolic situations and reprogramming in different human host cells, tissues, and organs that are favorable for acute and persistent SARS-CoV-2 infection. This knowledge may be fundamental for the development of host-directed therapies.

Single-Cell Time-Resolved Metabolomics and Lipidomics Reveal Apoptotic and Ferroptotic Heterogeneity during Foam Cell Formation

Macrophage-derived foam cells play a crucial role in plaque formation and rupture during the progression of atherosclerosis. Traditional studies have often overlooked the heterogeneity of foam cells, focusing instead on populations of cells. To address this, we have developed time-resolved, single-cell metabolomics and lipidomics approaches to explore the heterogeneity of macrophages during foam cell formation. Our dynamic metabolomic and lipidomic analyses revealed a dual regulatory axis involving inflammation and ferroptosis. Further, single-cell metabolomics and lipidomics have delineated a continuum of macrophage states, with varied susceptibilities to apoptosis and ferroptosis. Single-cell transcriptomic profiling confirmed these divergent fates, both in established cell lines and in macrophages derived from peripheral blood monocytes. This research has uncovered the complex molecular interactions that dictate these divergent cell fates, providing crucial insights into the pathogenesis of atherosclerosis.

A Single-Cell Metabolic Profiling Characterizes Human Aging via SlipChip-SERS

Metabolic dysregulation is a key driver of cellular senescence, contributing to the progression of systemic aging. The heterogeneity of senescent cells and their metabolic shifts are complex and unexplored. A microfluidic SlipChip integrated with surface-enhanced Raman spectroscopy (SERS), termed SlipChip-SERS, is developed for single-cell metabolism analysis. This SlipChip-SERS enables compartmentalization of single cells, parallel delivery of saponin and nanoparticles to release intracellular metabolites and to realize SERS detection with simple slipping operations. Analysis of different cancer cell lines using SlipChip-SERS demonstrated its capability for sensitive and multiplexed metabolic profiling of individual cells. When applied to human primary fibroblasts of different ages, it identified 12 differential metabolites, with spermine validated as a potent inducer of cellular senescence. Prolonged exposure to spermine can induce a classic senescence phenotype, such as increased senescence-associated β-glactosidase activity, elevated expression of senescence-related genes and reduced LMNB1 levels. Additionally, the senescence-inducing capacity of spermine in HUVECs and WRL-68 cells is confirmed, and exogenous spermine treatment increased the accumulation and release of H2O2. Overall, a novel SlipChip-SERS system is developed for single-cell metabolic analysis, revealing spermine as a potential inducer of senescence across multiple cell types, which may offer new strategies for addressing ageing and ageing-related diseases.

Emerging Functional Connections Between Metabolism and Epigenetic Remodeling in Neural Differentiation

Stem cells possess extraordinary capacities for self-renewal and differentiation, making them highly valuable in regenerative medicine. Among these, neural stem cells (NSCs) play a fundamental role in neural development and repair processes. NSC characteristics and fate are intricately regulated by the microenvironment and intracellular signaling. Interestingly, metabolism plays a pivotal role in orchestrating the epigenome dynamics during neural differentiation, facilitating the transition from undifferentiated NSC to specialized neuronal and glial cell types. This intricate interplay between metabolism and the epigenome is essential for precisely regulating gene expression patterns and ensuring proper neural development. This review highlights the mechanisms behind metabolic regulation of NSC fate and their connections with epigenetic regulation to shape transcriptional programs of stemness and neural differentiation. A comprehensive understanding of these molecular gears appears fundamental for translational applications in regenerative medicine and personalized therapies for neurological conditions.

Investigation of MSC potency metrics via integration of imaging modalities with lipidomic characterization

Mesenchymal stem/stromal cell (MSC) therapies have had limited success so far in clinical trials due in part to heterogeneity in immune-responsive phenotypes. Therefore, techniques to characterize these properties of MSCs are needed during biomanufacturing. Imaging cell shape, or morphology, has been found to be associated with MSC immune responsivity-but a direct relationship between single-cell morphology and function has not been established. We used label-free differential phase contrast imaging and matrix-assisted laser desorption/ionization mass spectrometry imaging (MALDI-MSI) to evaluate single-cell morphology and explore relationships with lipid metabolic immune response. In interferon gamma (IFN-γ)-stimulated MSCs, we found higher lipid abundances from the ceramide-1-phosphate (C1P), phosphatidylcholine (PC), LysoPC, and triglyceride (TAG) families that are involved in cell immune function. Furthermore, we identified differences in lipid signatures in morphologically defined MSC subpopulations. The use of single-cell optical imaging coupled with single-cell spatial lipidomics could assist in optimizing the MSC production process and improve mechanistic understanding of manufacturing process effects on MSC immune activity and heterogeneity.

Beyond the Heartbeat: Single-Cell Omics Redefining Cardiovascular Research

PURPOSE OF REVIEW

This review aims to explore recent advances in single-cell omics techniques as applied to various regions of the human heart, illuminating cellular diversity, regulatory networks, and disease mechanisms. We examine the contributions of single-cell transcriptomics, genomics, proteomics, epigenomics, and spatial transcriptomics in unraveling the complexity of cardiac tissues.

RECENT FINDINGS

Recent strides in single-cell omics technologies have revolutionized our understanding of the heart's cellular composition, cell type heterogeneity, and molecular dynamics. These advancements have elucidated pathological conditions as well as the cellular landscape in heart development. We highlight emerging applications of integrated single-cell omics, particularly for cardiac regeneration, disease modeling, and precision medicine, and emphasize the transformative potential of these technologies to advance cardiovascular research and clinical practice.

Single-Cell Analyses Reveal the Metabolic Heterogeneity and Plasticity of the Tumor Microenvironment during Head and Neck Squamous Cell Carcinoma Progression

Metabolic reprogramming is a hallmark of cancer. In addition to metabolic alterations in the tumor cells, multiple other metabolically active cell types in the tumor microenvironment (TME) contribute to the emergence of a tumor-specific metabolic milieu. Here, we defined the metabolic landscape of the TME during the progression of head and neck squamous cell carcinoma (HNSCC) by performing single-cell RNA sequencing on 26 human patient specimens, including normal tissue, precancerous lesions, early stage cancer, advanced-stage cancer, lymph node metastases, and recurrent tumors. The analysis revealed substantial heterogeneity at the transcriptional, developmental, metabolic, and functional levels in different cell types. SPP1+ macrophages were identified as a protumor and prometastatic macrophage subtype with high fructose and mannose metabolism, which was further substantiated by integrative analysis and validation experiments. An inhibitor of fructose metabolism reduced the proportion of SPP1+ macrophages, reshaped the immunosuppressive TME, and suppressed tumor growth. In conclusion, this work delineated the metabolic landscape of HNSCC at a single-cell resolution and identified fructose metabolism as a key metabolic feature of a protumor macrophage subpopulation. Significance: Fructose and mannose metabolism is a metabolic feature of a protumor and prometastasis macrophage subtype and can be targeted to reprogram macrophages and the microenvironment of head and neck squamous cell carcinoma.

Multiscale metabolomics techniques: Insights into neuroscience research

The field of metabolomics examines the overall composition and dynamic patterns of metabolites in living organisms. The primary methods used in metabolomics include liquid chromatography (LC), nuclear magnetic resonance (NMR), and mass spectrometry (MS) analysis. These methods enable the identification and examination of metabolite types and contents within organisms, as well as modifications to metabolic pathways and their connection to the emergence of diseases. Research in metabolomics has extensive value in basic and applied sciences. The field of metabolomics is growing quickly, with the majority of studies concentrating on biomedicine, particularly early disease diagnosis, therapeutic management of human diseases, and mechanistic knowledge of biochemical processes. Multiscale metabolomics is an approach that integrates metabolomics techniques at various scales, including the holistic, tissue, cellular, and organelle scales, to enable more thorough and in-depth studies of metabolic processes in organisms. Multiscale metabolomics can be combined with methods from systems biology and bioinformatics. In recent years, multiscale metabolomics approaches have become increasingly important in neuroscience research due to the nervous system's high metabolic demands. Multiscale metabolomics can offer novel concepts and approaches for the diagnosis, treatment, and development of medication for neurological illnesses in addition to a more thorough understanding of brain metabolism and nervous system function. In this review, we summarize the use of multiscale metabolomics techniques in neuroscience, address the promise and constraints of these techniques, and provide an overview of the metabolome and its applications in neuroscience.

Challenges of Spatially Resolved Metabolism in Cancer Research

Stable isotope-resolved metabolomics comprises a critical set of technologies that can be applied to a wide variety of systems, from isolated cells to whole organisms, to define metabolic pathway usage and responses to perturbations such as drugs or mutations, as well as providing the basis for flux analysis. As the diversity of stable isotope-enriched compounds is very high, and with newer approaches to multiplexing, the coverage of metabolism is now very extensive. However, as the complexity of the model increases, including more kinds of interacting cell types and interorgan communication, the analytical complexity also increases. Further, as studies move further into spatially resolved biology, new technical problems have to be overcome owing to the small number of analytes present in the confines of a single cell or cell compartment. Here, we review the overall goals and solutions made possible by stable isotope tracing and their applications to models of increasing complexity. Finally, we discuss progress and outstanding difficulties in high-resolution spatially resolved tracer-based metabolic studies.

Challenges and recent advances in quantitative mass spectrometry-based metabolomics

The field of metabolomics has gained tremendous interest in recent years. Whether the goal is to discover biomarkers related to certain pathologies or to better understand the impact of a drug or contaminant, numerous studies have demonstrated how crucial it is to understand variations in metabolism. Detailed knowledge of metabolic variabilities can lead to more effective treatments, as well as faster or less invasive diagnostics. Exploratory approaches are often employed in metabolomics, using relative quantitation to look at perturbations between groups of samples. Most metabolomics studies have been based on metabolite profiling using relative quantitation, with very few studies using an approach for absolute quantitation. Using accurate quantitation facilitates the comparison between different studies, as well as enabling longitudinal studies. In this review, we discuss the most widely used techniques for quantitative metabolomics using mass spectrometry (MS). Various aspects will be addressed, such as the use of external and/or internal standards, derivatization techniques, in vivo isotopic labelling, or quantitative MS imaging. The principles, as well as the associated limitations and challenges, will be described for each approach.

Single-Particle and Single-Molecule Characterization of Immobilized Enzymes: A Multiscale Path toward Optimizing Heterogeneous Biocatalysts

Heterogeneous biocatalysis is highly relevant in biotechnology as it offers several benefits and practical uses. To leverage the full potential of heterogeneous biocatalysts, the establishment of well-crafted protocols, and a deeper comprehension of enzyme immobilization on solid substrates are essential. These endeavors seek to optimize immobilized biocatalysts, ensuring maximal enzyme performance within confined spaces. For this aim, multidimensional characterization of heterogeneous biocatalysts is required. In this context, spectroscopic and microscopic methodologies conducted at different space and temporal scales can inform about the intraparticle enzyme kinetics, the enzyme spatial distribution, and the mass transport issues. In this Minireview, we identify enzyme immobilization, enzyme catalysis, and enzyme inactivation as the three main processes for which advanced characterization tools unveil fundamental information. Recent advances in operando characterization of immobilized enzymes at the single-particle (SP) and single-molecule (SM) levels inform about their functional properties, unlocking the full potential of heterogeneous biocatalysis toward biotechnological applications.

Hallmarks of cancer stemness

Cancer stemness is recognized as a key component of tumor development. Previously coined "cancer stem cells" (CSCs) and believed to be a rare population with rigid hierarchical organization, there is good evidence to suggest that these cells exhibit a plastic cellular state influenced by dynamic CSC-niche interplay. This revelation underscores the need to reevaluate the hallmarks of cancer stemness. Herein, we summarize the techniques used to identify and characterize the state of these cells and discuss their defining and emerging hallmarks, along with their enabling and associated features. We also highlight potential future directions in this field of research.

Spatial metabolomics and its application in the liver

Hepatocytes work in highly structured, repetitive hepatic lobules. Blood flow across the radial axis of the lobule generates oxygen, nutrient, and hormone gradients, which result in zoned spatial variability and functional diversity. This large heterogeneity suggests that hepatocytes in different lobule zones may have distinct gene expression profiles, metabolic features, regenerative capacity, and susceptibility to damage. Here, we describe the principles of liver zonation, introduce metabolomic approaches to study the spatial heterogeneity of the liver, and highlight the possibility of exploring the spatial metabolic profile, leading to a deeper understanding of the tissue metabolic organization. Spatial metabolomics can also reveal intercellular heterogeneity and its contribution to liver disease. These approaches facilitate the global characterization of liver metabolic function with high spatial resolution along physiological and pathological time scales. This review summarizes the state of the art for spatially resolved metabolomic analysis and the challenges that hinder the achievement of metabolome coverage at the single-cell level. We also discuss several major contributions to the understanding of liver spatial metabolism and conclude with our opinion on the future developments and applications of these exciting new technologies.

The rise of single-cell transcriptomics in yeast

The field of single-cell omics has transformed our understanding of biological processes and is constantly advancing both experimentally and computationally. One of the most significant developments is the ability to measure the transcriptome of individual cells by single-cell RNA-seq (scRNA-seq), which was pioneered in higher eukaryotes. While yeast has served as a powerful model organism in which to test and develop transcriptomic technologies, the implementation of scRNA-seq has been significantly delayed in this organism, mainly because of technical constraints associated with its intrinsic characteristics, namely the presence of a cell wall, a small cell size and little amounts of RNA. In this review, we examine the current technologies for scRNA-seq in yeast and highlight their strengths and weaknesses. Additionally, we explore opportunities for developing novel technologies and the potential outcomes of implementing single-cell transcriptomics and extension to other modalities. Undoubtedly, scRNA-seq will be invaluable for both basic and applied yeast research, providing unique insights into fundamental biological processes.

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

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

Transcriptomics, proteomics, and metabolomics interventions prompt crop improvement against metal(loid) toxicity

The escalating challenges posed by metal(loid) toxicity in agricultural ecosystems, exacerbated by rapid climate change and anthropogenic pressures, demand urgent attention. Soil contamination is a critical issue because it significantly impacts crop productivity. The widespread threat of metal(loid) toxicity can jeopardize global food security due to contaminated food supplies and pose environmental risks, contributing to soil and water pollution and thus impacting the whole ecosystem. In this context, plants have evolved complex mechanisms to combat metal(loid) stress. Amid the array of innovative approaches, omics, notably transcriptomics, proteomics, and metabolomics, have emerged as transformative tools, shedding light on the genes, proteins, and key metabolites involved in metal(loid) stress responses and tolerance mechanisms. These identified candidates hold promise for developing high-yielding crops with desirable agronomic traits. Computational biology tools like bioinformatics, biological databases, and analytical pipelines support these omics approaches by harnessing diverse information and facilitating the mapping of genotype-to-phenotype relationships under stress conditions. This review explores: (1) the multifaceted strategies that plants use to adapt to metal(loid) toxicity in their environment; (2) the latest findings in metal(loid)-mediated transcriptomics, proteomics, and metabolomics studies across various plant species; (3) the integration of omics data with artificial intelligence and high-throughput phenotyping; (4) the latest bioinformatics databases, tools and pipelines for single and/or multi-omics data integration; (5) the latest insights into stress adaptations and tolerance mechanisms for future outlooks; and (6) the capacity of omics advances for creating sustainable and resilient crop plants that can thrive in metal(loid)-contaminated environments.

Novel Insights into the Initiation, Evolution, and Progression of Multiple Myeloma by Multi-Omics Investigation

The evolutionary history of multiple myeloma (MM) includes malignant transformation, followed by progression to pre-malignant stages and overt malignancy, ultimately leading to more aggressive and resistant forms. Over the past decade, large effort has been made to identify the potential therapeutic targets in MM. However, MM remains largely incurable. Most patients experience multiple relapses and inevitably become refractory to treatment. Tumor-initiating cell populations are the postulated population, leading to the recurrent relapses in many hematological malignancies. Clonal evolution of tumor cells in MM has been identified along with the disease progression. As a consequence of different responses to the treatment of heterogeneous MM cell clones, the more aggressive populations survive and evolve. In addition, the tumor microenvironment is a complex ecosystem which plays multifaceted roles in supporting tumor cell evolution. Emerging multi-omics research at single-cell resolution permits an integrative and comprehensive profiling of the tumor cells and microenvironment, deepening the understanding of biological features of MM. In this review, we intend to discuss the novel insights into tumor cell initiation, clonal evolution, drug resistance, and tumor microenvironment in MM, as revealed by emerging multi-omics investigations. These data suggest a promising strategy to unravel the pivotal mechanisms of MM progression and enable the improvement in treatment, both holistically and precisely.

Mass Spectrometry-Based Proteogenomics: New Therapeutic Opportunities for Precision Medicine

Proteogenomics refers to the integration of comprehensive genomic, transcriptomic, and proteomic measurements from the same samples with the goal of fully understanding the regulatory processes converting genotypes to phenotypes, often with an emphasis on gaining a deeper understanding of disease processes. Although specific genetic mutations have long been known to drive the development of multiple cancers, gene mutations alone do not always predict prognosis or response to targeted therapy. The benefit of proteogenomics research is that information obtained from proteins and their corresponding pathways provides insight into therapeutic targets that can complement genomic information by providing an additional dimension regarding the underlying mechanisms and pathophysiology of tumors. This review describes the novel insights into tumor biology and drug resistance derived from proteogenomic analysis while highlighting the clinical potential of proteogenomic observations and advances in technique and analysis tools.

A Potential "Anti-Warburg Effect" in Circulating Tumor Cell-mediated Metastatic Progression?

Metabolic reprogramming is a defining hallmark of cancer metastasis, warranting thorough exploration. The tumor-promoting function of the "Warburg Effect", marked by escalated glycolysis and restrained mitochondrial activity, is widely acknowledged. Yet, the functional significance of mitochondria-mediated oxidative phosphorylation (OXPHOS) during metastasis remains controversial. Circulating tumor cells (CTCs) are considered metastatic precursors that detach from primary or secondary sites and harbor the potential to seed distant metastases through hematogenous dissemination. A comprehensive metabolic characterization of CTCs faces formidable obstacles, including the isolation of these rare cells from billions of blood cells, coupled with the complexities of ex vivo-culturing of CTC lines or the establishment of CTC-derived xenograft models (CDX). This review summarized the role of the "Warburg Effect" in both tumorigenesis and CTC-mediated metastasis. Intriguingly, bioinformatic analysis of single-CTC transcriptomic studies unveils a potential OXPHOS dominance over Glycolysis signature genes across several important cancer types. From these observations, we postulate a potential "Anti-Warburg Effect" (AWE) in CTCs-a metabolic shift bridging primary tumors and metastases. The observed AWE could be clinically important as they are significantly correlated with therapeutic response in melanoma and prostate patients. Thus, unraveling dynamic metabolic regulations within CTC populations might reveal an additional layer of regulatory complexities of cancer metastasis, providing an avenue for innovative anti-metastasis therapies.

Reducing Mass Confusion over the Microbiome

As genetic tools continue to emerge and mature, more information is revealed about the identity and diversity of microbial community members. Genetic tools can also be used to make predictions about the chemistry that bacteria and fungi produce to function and communicate with one another and the host. Ongoing efforts to identify these products and link genetic information to microbiome chemistry rely on analytical tools. This tutorial highlights recent advancements in microbiome studies driven by techniques in mass spectrometry.

Metabolic predictors of response to immune checkpoint blockade therapy

Metabolism of immune cells in the tumor microenvironment (TME) plays a critical role in cancer patient response to immune checkpoint inhibitors (ICI). Yet, a metabolic characterization of immune cells in the TME of patients treated with ICI is lacking. To bridge this gap we performed a semi-supervised analysis of ∼1700 metabolic genes using single-cell RNA-seq data of > 1 million immune cells from ∼230 samples of cancer patients treated with ICI. When clustering cells based on their metabolic gene expression, we found that similar immunological cellular states are found in different metabolic states. Most importantly, we found metabolic states that are significantly associated with patient response. We then built a metabolic predictor based on a dozen gene signature, which significantly differentiates between responding and non-responding patients across different cancer types (AUC = 0.8-0.92). Taken together, our results demonstrate the power of metabolism in predicting patient response to ICI.

Chemical tagging mass spectrometry: an approach for single-cell omics

Single-cell (SC) analysis offers new insights into the study of fundamental biological phenomena and cellular heterogeneity. The superior sensitivity, high throughput, and rich chemical information provided by mass spectrometry (MS) allow MS to emerge as a leading technology for molecular profiling of SC omics, including the SC metabolome, lipidome, and proteome. However, issues such as ionization suppression, low concentration, and huge span of dynamic concentrations of SC components lead to poor MS response for certain types of molecules. It is noted that chemical tagging/derivatization has been adopted in SCMS analysis, and this strategy has been proven an effective solution to circumvent these issues in SCMS analysis. Herein, we review the basic principle and general strategies of chemical tagging/derivatization in SCMS analysis, along with recent applications of chemical derivatization to single-cell metabolomics and multiplexed proteomics, as well as SCMS imaging. Furthermore, the challenges and opportunities for the improvement of chemical derivatization strategies in SCMS analysis are discussed.

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.

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.

Role of caveolin-1 in metabolic programming of fetal brain

Mice lacking caveolin-1 (Cav1), a key protein of plasma membrane, exhibit brain aging at an early adult stage. Here, integrative analyses of metabolomics, transcriptomics, epigenetics, and single-cell data were performed to test the hypothesis that metabolic deregulation of fetal brain due to the ablation of Cav1 is linked to brain aging in these mice. The results of this study show that lack of Cav1 caused deregulation in the lipid and amino acid metabolism in the fetal brain, and genes associated with these deregulated metabolites were significantly altered in the brain upon aging. Moreover, ablation of Cav1 deregulated several metabolic genes in specific cell types of the fetal brain and impacted DNA methylation of those genes in coordination with mouse epigenetic clock. The findings of this study suggest that the aging program of brain is confounded by metabolic abnormalities in the fetal stage due to the absence of Cav1.

Microbial single-cell mass spectrometry: status, challenges, and prospects

Single-cell analysis uncovers phenotypic differences between cells in a population and dissects their individual physiological states and differences on all omics levels from genome to phenome. Spectrometric observation allows label-free analysis of the metabolome and proteome of individual cells, but is still mainly limited to the analysis of mammalian single cells. Recent progress in mass spectrometry approaches now enables the analysis of microbial single cells - mainly by miniaturizing cell handling, incubation, and improving chip-coupling concepts for analyte ionization by interfacing microfluidic chips and mass spectrometers. This review aims at distilling the enabling principles behind microbial single-cell mass spectrometry and puts them into perspective for the future of the field.

Metabolic adaption of mucosal macrophages: Is metabolism a driver of persistence across tissues?

Macrophages play essential roles in tissue homeostasis, defense, and repair. Their functions are highly tissue-specific, and when damage and inflammation stimulate repopulation by circulating monocytes, the incoming monocytes rapidly acquire the same, tissue-specific functions as the previous, resident macrophages. Several environmental factors are thought to guide the functional differentiation of recruited monocytes, including metabolic pressures imposed by the fuel sources available in each tissue. Here we discuss whether such a model of metabolic determinism can be applied to macrophage differentiation across barrier sites, from the lung to the skin. We suggest an alternative model, in which metabolic phenotype is a consequence of macrophage longevity rather than an early driver of tissue-specific adaption.

Advances in single-cell metabolomics to unravel cellular heterogeneity in plant biology

Single-cell metabolomics is a powerful tool that can reveal cellular heterogeneity and can elucidate the mechanisms of biological phenomena in detail. It is a promising approach in studying plants, especially when cellular heterogeneity has an impact on different biological processes. In addition, metabolomics, which can be regarded as a detailed phenotypic analysis, is expected to answer previously unrequited questions which will lead to expansion of crop production, increased understanding of resistance to diseases, and in other applications as well. In this review, we will introduce the flow of sample acquisition and single-cell techniques to facilitate the adoption of single-cell metabolomics. Furthermore, the applications of single-cell metabolomics will be summarized and reviewed.

Recent Review on Selected Xenobiotics and Their Impacts on Gut Microbiome and Metabolome

As it is well known, the gut is one of the primary sites in any host for xenobiotics, and the many microbial metabolites responsible for the interactions between the gut microbiome and the host. However, there is a growing concern about the negative impacts on human health induced by toxic xenobiotics. Metabolomics, broadly including lipidomics, is an emerging approach to studying thousands of metabolites in parallel. In this review, we summarized recent advancements in mass spectrometry (MS) technologies in metabolomics. In addition, we reviewed recent applications of MS-based metabolomics for the investigation of toxic effects of xenobiotics on microbial and host metabolism. It was demonstrated that metabolomics, gut microbiome profiling, and their combination have a high potential to identify metabolic and microbial markers of xenobiotic exposure and determine its mechanism. Further, there is increasing evidence supporting that reprogramming the gut microbiome could be a promising approach to the intervention of xenobiotic toxicity.

Surviving the hunger games: Metabolic reprogramming in medulloblastoma

Medulloblastoma is a highly malignant pediatric brain tumor characterized by its aggressive nature and limited treatment options. Metabolic changes have recently emerged as key factors in the development, progression, and response to therapy in various types of cancer. Cancer cells exhibit remarkable adaptability by modulating glucose, lipids, amino acids, and nucleotide metabolism to survive in nutrient- and oxygen-deprived environments. Although medulloblastoma has been extensively studied from a genomic perspective, leading to the identification of four subgroups and their respective subcategories, the investigation of its metabolic phenotype has remained relatively understudied. This review focus on the available literature, aiming to summarize the current knowledge about the main metabolic pathways that are deregulated in medulloblastoma tumors, while emphasizing the controversial aspects and the progress that is yet to be made. Furthermore, we underscored the insights gained so far regarding the impact of metabolism on the development of drug resistance in medulloblastoma and the therapeutic strategies employed to target specific metabolic pathways.

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.

An epidemiological introduction to human metabolomic investigations

Metabolomics holds great promise for uncovering insights around biological processes impacting disease in human epidemiological studies. Metabolites can be measured across biological samples, including plasma, serum, saliva, urine, stool, and whole organs and tissues, offering a means to characterize metabolic processes relevant to disease etiology and traits of interest. Metabolomic epidemiology studies face unique challenges, such as identifying metabolites from targeted and untargeted assays, defining standards for quality control, harmonizing results across platforms that often capture different metabolites, and developing statistical methods for high-dimensional and correlated metabolomic data. In this review, we introduce metabolomic epidemiology to the broader scientific community, discuss opportunities and challenges presented by these studies, and highlight emerging innovations that hold promise to uncover new biological insights.

Metabolomics: A useful tool for ischemic stroke research

Ischemic stroke (IS) is a multifactorial and heterogeneous disease. Despite years of studies, effective strategies for the diagnosis, management and treatment of stroke are still lacking in clinical practice. Metabolomics is a growing field in systems biology. It is starting to show promise in the identification of biomarkers and in the use of pharmacometabolomics to help patients with certain disorders choose their course of treatment. The development of metabolomics has enabled further and more biological applications. Particularly, metabolomics is increasingly being used to diagnose diseases, discover new drug targets, elucidate mechanisms, and monitor therapeutic outcomes and its potential effect on precision medicine. In this review, we reviewed some recent advances in the study of metabolomics as well as how metabolomics might be used to identify novel biomarkers and understand the mechanisms of IS. Then, the use of metabolomics approaches to investigate the molecular processes and active ingredients of Chinese herbal formulations with anti-IS capabilities is summarized. We finally summarized recent developments in single cell metabolomics for exploring the metabolic profiles of single cells. Although the field is relatively young, the development of single cell metabolomics promises to provide a powerful tool for unraveling the pathogenesis of IS.

B cell metabolism in autoimmune diseases: signaling pathways and interventions

Autoimmune diseases are heterogeneous disorders believed to stem from the immune system's inability to distinguish between auto- and foreign- antigens. B lymphocytes serve a crucial role in humoral immunity as they generate antibodies and present antigens. Dysregulation of B cell function induce the onset of autoimmune disorders by generating autoantibodies and pro-inflammatory cytokines, resulting in an imbalance in immune regulation. New research in immunometabolism shows that cellular metabolism plays an essential role in controlling B lymphocytes immune reactions by providing the energy and substrates for B lymphocytes activation, differentiation, and function. However, dysregulated immunometabolism lead to autoimmune diseases by disrupting self-tolerance mechanisms. This review summarizes the latest research on metabolic reprogramming of B lymphocytes in autoimmune diseases, identifying crucial pathways and regulatory factors. Moreover, we consider the potential of metabolic interventions as a promising therapeutic strategy. Understanding the metabolic mechanisms of B cells brings us closer to developing novel therapies for autoimmune disorders.

Emerging therapies in cancer metabolism

Metabolic reprogramming in cancer is not only a biological hallmark but also reveals treatment vulnerabilities. Numerous metabolic molecules have shown promise as treatment targets to impede tumor progression in preclinical studies, with some advancing to clinical trials. However, the intricacy and adaptability of metabolic networks hinder the effectiveness of metabolic therapies. This review summarizes the metabolic targets for cancer treatment and provides an overview of the current status of clinical trials targeting cancer metabolism. Additionally, we decipher crucial factors that limit the efficacy of metabolism-based therapies and propose future directions. With advances in integrating multi-omics, single-cell, and spatial technologies, as well as the ability to track metabolic adaptation more precisely and dynamically, clinicians can personalize metabolic therapies for improved cancer treatment.

Computational approaches in rheumatic diseases - Deciphering complex spatio-temporal cell interactions

Inflammatory arthritis, including rheumatoid (RA), and psoriatic (PsA) arthritis, are clinically and immunologically heterogeneous diseases with no identified cure. Chronic inflammation of the synovial tissue ushers loss of function of the joint that severely impacts the patient's quality of life, eventually leading to disability and life-threatening comorbidities. The pathogenesis of synovial inflammation is the consequence of compounded immune and stromal cell interactions influenced by genetic and environmental factors. Deciphering the complexity of the synovial cellular landscape has accelerated primarily due to the utilisation of bulk and single cell RNA sequencing. Particularly the capacity to generate cell-cell interaction networks could reveal evidence of previously unappreciated processes leading to disease. However, there is currently a lack of universal nomenclature as a result of varied experimental and technological approaches that discombobulates the study of synovial inflammation. While spatial transcriptomic analysis that combines anatomical information with transcriptomic data of synovial tissue biopsies promises to provide more insights into disease pathogenesis, in vitro functional assays with single-cell resolution will be required to validate current bioinformatic applications. In order to provide a comprehensive approach and translate experimental data to clinical practice, a combination of clinical and molecular data with machine learning has the potential to enhance patient stratification and identify individuals at risk of arthritis that would benefit from early therapeutic intervention. This review aims to provide a comprehensive understanding of the effect of computational approaches in deciphering synovial inflammation pathogenesis and discuss the impact that further experimental and novel computational tools may have on therapeutic target identification and drug development.

Metabolomics in drug research and development: The recent advances in technologies and applications

Emerging evidence has demonstrated the vital role of metabolism in various diseases or disorders. Metabolomics provides a comprehensive understanding of metabolism in biological systems. With advanced analytical techniques, metabolomics exhibits unprecedented significant value in basic drug research, including understanding disease mechanisms, identifying drug targets, and elucidating the mode of action of drugs. More importantly, metabolomics greatly accelerates the drug development process by predicting pharmacokinetics, pharmacodynamics, and drug response. In addition, metabolomics facilitates the exploration of drug repurposing and drug-drug interactions, as well as the development of personalized treatment strategies. Here, we briefly review the recent advances in technologies in metabolomics and update our knowledge of the applications of metabolomics in drug research and development.

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.

The Emerging Role of Raman Spectroscopy as an Omics Approach for Metabolic Profiling and Biomarker Detection toward Precision Medicine

Omics technologies have rapidly evolved with the unprecedented potential to shape precision medicine. Novel omics approaches are imperative toallow rapid and accurate data collection and integration with clinical information and enable a new era of healthcare. In this comprehensive review, we highlight the utility of Raman spectroscopy (RS) as an emerging omics technology for clinically relevant applications using clinically significant samples and models. We discuss the use of RS both as a label-free approach for probing the intrinsic metabolites of biological materials, and as a labeled approach where signal from Raman reporters conjugated to nanoparticles (NPs) serve as an indirect measure for tracking protein biomarkers in vivo and for high throughout proteomics. We summarize the use of machine learning algorithms for processing RS data to allow accurate detection and evaluation of treatment response specifically focusing on cancer, cardiac, gastrointestinal, and neurodegenerative diseases. We also highlight the integration of RS with established omics approaches for holistic diagnostic information. Further, we elaborate on metal-free NPs that leverage the biological Raman-silent region overcoming the challenges of traditional metal NPs. We conclude the review with an outlook on future directions that will ultimately allow the adaptation of RS as a clinical approach and revolutionize precision medicine.

Lactate as Key Metabolite in Prostate Cancer Progression: What Are the Clinical Implications?

Advanced prostate cancer represents the fifth leading cause of cancer death in men worldwide. Although androgen-receptor signaling is the major driver of the disease, evidence is accumulating that disease progression is supported by substantial metabolic changes. Alterations in de novo lipogenesis and fatty acid catabolism are consistently reported during prostate cancer development and progression in association with androgen-receptor signaling. Therefore, the term "lipogenic phenotype" is frequently used to describe the complex metabolic rewiring that occurs in prostate cancer. However, a new scenario has emerged in which lactate may play a major role. Alterations in oncogenes/tumor suppressors, androgen signaling, hypoxic conditions, and cells in the tumor microenvironment can promote aerobic glycolysis in prostate cancer cells and the release of lactate in the tumor microenvironment, favoring immune evasion and metastasis. As prostate cancer is composed of metabolically heterogenous cells, glycolytic prostate cancer cells or cancer-associated fibroblasts can also secrete lactate and create "symbiotic" interactions with oxidative prostate cancer cells via lactate shuttling to sustain disease progression. Here, we discuss the multifaceted role of lactate in prostate cancer progression, taking into account the influence of the systemic metabolic and gut microbiota. We call special attention to the clinical opportunities of imaging lactate accumulation for patient stratification and targeting lactate metabolism.

Pyrophosphate as allosteric regulator of ATP-phosphofructokinase in Clostridium thermocellum and other bacteria with ATP- and PPi-phosphofructokinases

The phosphofructokinase (Pfk) reaction represents one of the key regulatory points in glycolysis. While most organisms encode for Pfks that use ATP as phosphoryl donor, some organisms also encode for PP_i-dependent Pfks. Despite this central role, the biochemical characteristics as well as the physiological role of both Pfks is often not known. Clostridium thermocellum is an example of a microorganism that encodes for both Pfks, however, only PP_i-Pfk activity has been detected in cell-free extracts and little is known about the regulation and function of both enzymes. In this study, the ATP- and PP_i-Pfk of C. thermocellum were purified and biochemically characterized. No allosteric regulators were found for PP_i-Pfk amongst common effectors. With fructose-6-P, PP_i, fructose-1,6-bisP, and P_i PP_i-Pfk showed high specificity (K_M < 0.62 mM) and maximum activity (V_max > 156 U mg^-1). In contrast, ATP-Pfk showed much lower affinity (K_0.5 of 9.26 mM) and maximum activity (14.5 U mg^-1) with fructose-6-P. In addition to ATP, also GTP, UTP and ITP could be used as phosphoryl donors. The catalytic efficiency with GTP was 7-fold higher than with ATP, suggesting that GTP is the preferred substrate. The enzyme was activated by NH₄⁺, and pronounced inhibition was observed with GDP, FBP, PEP, and especially with PP_i (K_i of 0.007 mM). Characterization of purified ATP-Pfks originating from eleven different bacteria, encoding for only ATP-Pfk or for both ATP- and PP_i-Pfk, identified that PP_i inhibition of ATP-Pfks could be a common phenomenon for organisms with a PP_i-dependent glycolysis.

Spatial single cell metabolomics: Current challenges and future developments

Single cell metabolomics is a rapidly advancing field of bio-analytical chemistry which aims to observe cellular biology with the greatest detail possible. Mass spectrometry imaging and selective cell sampling (e.g. using nanocapillaries) are two common approaches within the field. Recent achievements such as observation of cell-cell interactions, lipids determining cell states and rapid phenotypic identification demonstrate the efficacy of these approaches and the momentum of the field. However, single cell metabolomics can only continue with the same impetus if the universal challenges to the field are met, such as the lack of strategies for standardisation and quantification, and lack of specificity/sensitivity. Mass spectrometry imaging and selective cell sampling come with unique advantages and challenges which, in many cases are complementary to each other. We propose here that the challenges specific to each approach could be ameliorated with collaboration between the two communities driving these approaches.

The toxicity of nanoparticles and their interaction with cells: an in vitro metabolomic perspective

Nowadays, nanomaterials (NMs) are widely present in daily life due to their significant benefits, as demonstrated by their application in many fields such as biomedicine, engineering, food, cosmetics, sensing, and energy. However, the increasing production of NMs multiplies the chances of their release into the surrounding environment, making human exposure to NMs inevitable. Currently, nanotoxicology is a crucial field, which focuses on studying the toxicity of NMs. The toxicity or effects of nanoparticles (NPs) on the environment and humans can be preliminary assessed in vitro using cell models. However, the conventional cytotoxicity assays, such as the MTT assay, have some drawbacks including the possibility of interference with the studied NPs. Therefore, it is necessary to employ more advanced techniques that provide high throughput analysis and avoid interferences. In this case, metabolomics is one of the most powerful bioanalytical strategies to assess the toxicity of different materials. By measuring the metabolic change upon the introduction of a stimulus, this technique can reveal the molecular information of the toxicity induced by NPs. This provides the opportunity to design novel and efficient nanodrugs and minimizes the risks of NPs used in industry and other fields. Initially, this review summarizes the ways that NPs and cells interact and the NP parameters that play a role in this interaction, and then the assessment of these interactions using conventional assays and the challenges encountered are discussed. Subsequently, in the main part, we introduce the recent studies employing metabolomics for the assessment of these interactions in vitro.

A contribution of metabolic engineering to addressing medical problems: Metabolic flux analysis

Metabolic engineering has served as a systematic discipline for industrial biotechnology as it has offered systematic tools and methods for strain development and bioprocess optimization. Because these metabolic engineering tools and methods are concerned with the biological network of a cell with emphasis on metabolic network, they have also been applied to a range of medical problems where better understanding of metabolism has also been perceived to be important. Metabolic flux analysis (MFA) is a unique systematic approach initially developed in the metabolic engineering community, and has proved its usefulness and potential when addressing a range of medical problems. In this regard, this review discusses the contribution of MFA to addressing medical problems. For this, we i) provide overview of the milestones of MFA, ii) define two main branches of MFA, namely constraint-based reconstruction and analysis (COBRA) and isotope-based MFA (iMFA), and iii) present successful examples of their medical applications, including characterizing the metabolism of diseased cells and pathogens, and identifying effective drug targets. Finally, synergistic interactions between metabolic engineering and biomedical sciences are discussed with respect to MFA.

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.