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

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

Nanoneedle-Based Materials for Intracellular Studies

Nanoneedles, defined as high aspect ratio structures with tip diameters of 5 to approximately 500 nm, are uniquely able to interface with the interior of living cells. Their nanoscale dimensions mean that they are able to penetrate the plasma membrane with minimal disruption of normal cellular functions, allowing researchers to probe the intracellular space and deliver or extract material from individual cells. In the last decade, a variety of strategies have been developed using nanoneedles, either singly or as arrays, to investigate the biology of cancer cells in vitro and in vivo. These include hollow nanoneedles for soluble probe delivery, nanocapillaries for single-cell biopsy, nano-AFM for direct physical measurements of cytosolic proteins, and a wide range of fluorescent and electrochemical nanosensors for analyte detection. Nanofabrication has improved to the point that nanobiosensors can detect individual vesicles inside the cytoplasm, delineate tumor margins based on intracellular enzyme activity, and measure changes in cell metabolism almost in real time. While most of these applications are currently in the proof-of-concept stage, nanoneedle technology is poised to offer cancer biologists a powerful new set of tools for probing cells with unprecedented spatial and temporal resolution.

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.

Immunometabolism in the Single-Cell Era

Emerging research has identified metabolic pathways that are crucial for the proper regulation of immune cells and how, when deranged, they can cause immune dysfunction and disease progression. However, due to technical limitations such insights have relied heavily on bulk measurements in immune cells, often activated in vitro. But with the emergence of single-cell applications, researchers can now estimate the metabolic state of individual immune cells in clinical samples. Here, we review these single-cell techniques and their ability to validate common principles in immunometabolism, while also revealing context-dependent metabolic heterogeneity within the immune cell compartment. We also discuss current gaps and limitations, as well as identify future opportunities to move the field forward toward the development of therapeutic targets and improved diagnostic capabilities.

Developmental scRNAseq Trajectories in Gene- and Cell-State Space-The Flatworm Example

Single-cell RNA sequencing has become a standard technique to characterize tissue development. Hereby, cross-sectional snapshots of the diversity of cell transcriptomes were transformed into (pseudo-) longitudinal trajectories of cell differentiation using computational methods, which are based on similarity measures distinguishing cell phenotypes. Cell development is driven by alterations of transcriptional programs e.g., by differentiation from stem cells into various tissues or by adapting to micro-environmental requirements. We here complement developmental trajectories in cell-state space by trajectories in gene-state space to more clearly address this latter aspect. Such trajectories can be generated using self-organizing maps machine learning. The method transforms multidimensional gene expression patterns into two dimensional data landscapes, which resemble the metaphoric Waddington epigenetic landscape. Trajectories in this landscape visualize transcriptional programs passed by cells along their developmental paths from stem cells to differentiated tissues. In addition, we generated developmental "vector fields" using RNA-velocities to forecast changes of RNA abundance in the expression landscapes. We applied the method to tissue development of planarian as an illustrative example. Gene-state space trajectories complement our data portrayal approach by (pseudo-)temporal information about changing transcriptional programs of the cells. Future applications can be seen in the fields of tissue and cell differentiation, ageing and tumor progression and also, using other data types such as genome, methylome, and also clinical and epidemiological phenotype data.

Emerging Roles of Single-Cell Multi-Omics in Studying Developmental Temporal Patterning

The complexity of brain structure and function is rooted in the precise spatial and temporal regulation of selective developmental events. During neurogenesis, both vertebrates and invertebrates generate a wide variety of specialized cell types through the expansion and specification of a restricted set of neuronal progenitors. Temporal patterning of neural progenitors rests on fine regulation between cell-intrinsic and cell-extrinsic mechanisms. The rapid emergence of high-throughput single-cell technologies combined with elaborate computational analysis has started to provide us with unprecedented biological insights related to temporal patterning in the developing central nervous system (CNS). Here, we present an overview of recent advances in Drosophila and vertebrates, focusing both on cell-intrinsic mechanisms and environmental influences. We then describe the various multi-omics approaches that have strongly contributed to our current understanding and discuss perspectives on the various -omics approaches that hold great potential for the future of temporal patterning research.

Raman-guided subcellular pharmaco-metabolomics for metastatic melanoma cells

Non-invasively probing metabolites within single live cells is highly desired but challenging. Here we utilize Raman spectro-microscopy for spatial mapping of metabolites within single cells, with the specific goal of identifying druggable metabolic susceptibilities from a series of patient-derived melanoma cell lines. Each cell line represents a different characteristic level of cancer cell de-differentiation. First, with Raman spectroscopy, followed by stimulated Raman scattering (SRS) microscopy and transcriptomics analysis, we identify the fatty acid synthesis pathway as a druggable susceptibility for differentiated melanocytic cells. We then utilize hyperspectral-SRS imaging of intracellular lipid droplets to identify a previously unknown susceptibility of lipid mono-unsaturation within de-differentiated mesenchymal cells with innate resistance to BRAF inhibition. Drugging this target leads to cellular apoptosis accompanied by the formation of phase-separated intracellular membrane domains. The integration of subcellular Raman spectro-microscopy with lipidomics and transcriptomics suggests possible lipid regulatory mechanisms underlying this pharmacological treatment. Our method should provide a general approach in spatially-resolved single cell metabolomics studies.

Single-cell metabolomics can offer deep insights into the metabolic reprogramming that accompanies disease states. Here, the authors use Raman spectro-microscopy for non-invasive metabolite analysis and identification of druggable metabolic susceptibilities in single live melanoma cells.

Next-generation metabolic engineering approaches towards development of plant cell suspension cultures as specialized metabolite producing biofactories

Plant cell suspension culture (PCSC) has emerged as a viable technology to produce plant specialized metabolites (PSM). While Taxol® and ginsenoside are two examples of successfully commercialized PCSC-derived PSM, widespread utilization of the PCSC platform has yet to be realized primarily due to a lack of understanding of the molecular genetics of PSM biosynthesis. Recent advances in computational, molecular and synthetic biology tools provide the opportunity to rapidly characterize and harness the specialized metabolic potential of plants. Here, we discuss the prospects of integrating computational modeling, artificial intelligence, and precision genome editing (CRISPR/Cas and its variants) toolboxes to discover the genetic regulators of PSM. We also explore how synthetic biology can be applied to develop metabolically optimized PSM-producing native and heterologous PCSC systems. Taken together, this review provides an interdisciplinary approach to realize and link the potential of next-generation computational and molecular tools to convert PCSC into commercially viable PSM-producing biofactories.

Combining Mass Spectrometry with Paternò-Büchi Reaction to Determine Double-Bond Positions in Lipids at the Single-Cell Level

Single cell MS (SCMS) techniques are under rapid development for molecular analysis of individual cells among heterogeneous populations. Lipids are basic cellular constituents playing essential functions in energy storage and the cellular signaling processes of cells. Unsaturated lipids are characterized with one or multiple carbon-carbon double (C═C) bonds, and they are critical for cell functions and human diseases. Characterizing unsaturated lipids in single cells allows for better understanding of metabolomic biomarkers and therapeutic targets of rare cells (e.g., cancer stem cells); however, these studies remain challenging. We developed a new technique using a micropipette needle, in which Paternò-Büchi (PB) reactions at C═C bond can be induced, to determine locations of C═C bonds in unsaturated lipids at the single-cell level. The micropipette needle is produced by combining a pulled glass capillary needle with a fused silica capillary. Cell lysis solvent and PB reagent (acetone or benzophenone) are delivered into the micropipette needle (tip size ≈ 15 um) through a fused silica capillary. The capillary needle plays multiple functions (i.e., single cell sampling probe, cell lysis container, microreactor, and nano-ESI emitter) in the experiments. Both regular (no reaction) and reactive (with PB reaction) SCMS analyses of the same cell can be achieved. C═C bond locations were determined from MS scan and MS/MS of PB products assisted by Python programs. This technique can potentially be used for other reactive SCMS studies to enhance molecular analysis for broad ranges of single cells.

Do Mixed-Species Biofilms Dominate in Chronic Infections?-Need for in situ Visualization of Bacterial Organization

Chronic infections present a serious economic burden to health-care systems. The severity and prevalence of chronic infections are continuously increasing due to an aging population and an elevated number of lifestyle related diseases such as diabetes. Treatment of chronic infections has proven difficult, mainly due to the presence of biofilms that render bacteria more tolerant toward antimicrobials and the host immune response. Chronic infections have been described to harbor several different bacterial species and it has been hypothesized that microscale interactions and mixed-species consortia are present as described for most natural occurring biofilms i.e., aquatic systems and industrial settings, but also for some commensal human biofilms i.e., the mouth microbiota. However, the presence of mixed-species biofilms in chronic infections is most often an assumption based on culture-based methods and/or by means of molecular approaches, such as PCR and sequencing performed from homogenized bulk tissue samples. These methods disregard the spatial organization of the bacterial community and thus valuable information on biofilm aggregate composition, spatial organization, and possible interactions between different species is lost. Hitherto, only few studies have made visual in situ presentations of mixed-species biofilms in chronic infections, which is pivotal for the description of bacterial composition, spatial distribution, and interspecies interaction on the microscale. In order for bacteria to interact (synergism, commensalism, mutualism, competition, etc.) they need to be in close proximity to each other on the scale where they can affect e.g., solute concentrations. We argue that visual proof of mixed species biofilms in chronic infections is scarce compared to what is seen in e.g., environmental biofilms and call for a debate on the importance of mixed-species biofilm in chronic infections.

Metabolomics in acute myeloid leukemia

Acute myeloid leukemia (AML) is a complex, heterogenous hematological malignancy caused by mutations in myeloid differentiation and proliferation. Response to therapy and long-term outcomes vary widely based on chromosomal and molecular aberrations. Many platforms have been used to characterize and stratify AML. Metabolomics, the global profiling of small molecules in a biological sample, has emerged in the last decade as an important tool for studying the metabolic dependency of cancer cells. Metabolic reprogramming is not only an important manifestation of AML but clinically relevant for diagnosis, risk stratification and targeted drug development. In this review, we discuss notable metabolic studies of the last decade and their application to novel therapies.

Advances and challenges in epigenomic single-cell sequencing applications

Understanding multicellular physiology and pathobiology requires analysis of the relationship between genotype, chromatin organisation and phenotype. In the multi-omics era, many methods exist to investigate biological processes across the genome, transcriptome, epigenome, proteome and metabolome. Until recently, this was only possible for populations of cells or complex tissues, creating an averaging effect that may obscure direct correlations between multiple layers of data. Single-cell sequencing methods have removed this averaging effect, but computational integration after profiling distinct modalities separately may still not completely reflect underlying biology. Multiplexed assays resolving multiple modalities in the same cell are required to overcome these shortcomings and have the potential to deliver unprecedented understanding of biology and disease.

Use of Single-Cell -Omic Technologies to Study the Gastrointestinal Tract and Diseases, From Single Cell Identities to Patient Features

Single cells are the building blocks of tissue systems that determine organ phenotypes, behaviors, and functions. Understanding the differences between cell types and their activities might provide us with insights into normal tissue physiology, development of disease, and new therapeutic strategies. Although -omic level single-cell technologies are a relatively recent development that have been used only in research settings, these approaches might eventually be used in the clinic. We review the prospects of applying single-cell genome, transcriptome, epigenome, proteome, and metabolome analyses to gastroenterology and hepatology research. Combining data from multi-omic platforms coupled to rapid technological development could lead to new diagnostic, prognostic, and therapeutic approaches.

Metabolic Modelling as a Framework for Metabolomics Data Integration and Analysis

Metabolic networks are regulated to ensure the dynamic adaptation of biochemical reaction fluxes to maintain cell homeostasis and optimal metabolic fitness in response to endogenous and exogenous perturbations. To this end, metabolism is tightly controlled by dynamic and intricate regulatory mechanisms involving allostery, enzyme abundance and post-translational modifications. The study of the molecular entities involved in these complex mechanisms has been boosted by the advent of high-throughput technologies. The so-called omics enable the quantification of the different molecular entities at different system layers, connecting the genotype with the phenotype. Therefore, the study of the overall behavior of a metabolic network and the omics data integration and analysis must be approached from a holistic perspective. Due to the close relationship between metabolism and cellular phenotype, metabolic modelling has emerged as a valuable tool to decipher the underlying mechanisms governing cell phenotype. Constraint-based modelling and kinetic modelling are among the most widely used methods to study cell metabolism at different scales, ranging from cells to tissues and organisms. These approaches enable integrating metabolomic data, among others, to enhance model predictive capabilities. In this review, we describe the current state of the art in metabolic modelling and discuss future perspectives and current challenges in the field.

Lineage tracing meets single-cell omics: opportunities and challenges

A fundamental goal of developmental and stem cell biology is to map the developmental history (ontogeny) of differentiated cell types. Recent advances in high-throughput single-cell sequencing technologies have enabled the construction of comprehensive transcriptional atlases of adult tissues and of developing embryos from measurements of up to millions of individual cells. Parallel advances in sequencing-based lineage-tracing methods now facilitate the mapping of clonal relationships onto these landscapes and enable detailed comparisons between molecular and mitotic histories. Here we review recent progress and challenges, as well as the opportunities that emerge when these two complementary representations of cellular history are synthesized into integrated models of cell differentiation.

Links between Nutrition, Infectious Diseases, and Microbiota: Emerging Technologies and Opportunities for Human-Focused Research

The interaction between nutrition and human infectious diseases has always been recognized. With the emergence of molecular tools and post-genomics, high-resolution sequencing technologies, the gut microbiota has been emerging as a key moderator in the complex interplay between nutrients, human body, and infections. Much of the host-microbial and nutrition research is currently based on animals or simplistic in vitro models. Although traditional in vivo and in vitro models have helped to develop mechanistic hypotheses and assess the causality of the host-microbiota interactions, they often fail to faithfully recapitulate the complexity of the human nutrient-microbiome axis in gastrointestinal homeostasis and infections. Over the last decade, remarkable progress in tissue engineering, stem cell biology, microfluidics, sequencing technologies, and computing power has taken place, which has produced a new generation of human-focused, relevant, and predictive tools. These tools, which include patient-derived organoids, organs-on-a-chip, computational analyses, and models, together with multi-omics readouts, represent novel and exciting equipment to advance the research into microbiota, infectious diseases, and nutrition from a human-biology-based perspective. After considering some limitations of the conventional in vivo and in vitro approaches, in this review, we present the main novel available and emerging tools that are suitable for designing human-oriented research.

Circles of Life: linking metabolic and epigenetic cycles to immunity

Metabolites are the essential substrates for epigenetic modification enzymes to write or erase the epigenetic blueprint in cells. Hence, the availability of nutrients and activity of metabolic pathways strongly influence the enzymatic function. Recent studies have shed light on the choreography between metabolome and epigenome in the control of immune cell differentiation and function, with a major focus on histone modifications. Yet, despite its importance in gene regulation, DNA methylation and its relationship with metabolism is relatively unclear. In this review, we will describe how the metabolic flux can influence epigenetic networks in innate and adaptive immune cells, with a focus on the DNA methylation cycle and the metabolites S-adenosylmethionine and α-ketoglutarate. Future directions will be discussed for this rapidly emerging field.

A High-Resolution Mass Spectrometry-Based Quantitative Metabolomic Workflow Highlights Defects in 5-Fluorouracil Metabolism in Cancer Cells with Acquired Chemoresistance

Currently, 5-fluorouracil (5-FU)-based combination chemotherapy is the mainstay in the treatment of metastatic colorectal cancer (CRC), which benefits approximately 50% of the patients. However, these tumors inevitably acquire chemoresistance resulting in treatment failure. The molecular mechanisms driving acquired chemotherapeutic drug resistance in CRC is fundamental for the development of novel strategies for circumventing resistance. However, the specific phenomenon that drives the cancer cells to acquire resistance is poorly understood. Understanding the molecular mechanisms that regulate chemoresistance will uncover new avenues for the treatment of CRC. Among the various mechanisms of acquired chemoresistance, defects in the drug metabolism pathways could play a major role. In the case of 5-FU, it gets converted into various active metabolites, which, directly or indirectly, interferes with the replication and transcription of dividing cells causing DNA and RNA damage. In this project, we developed a high-resolution mass spectrometry-based method to effectively extract and quantify levels of the 5-FU metabolites in cell lysates and media of parental and 5-FU resistant LIM1215 CRC cells. The analysis highlighted that the levels of 5-FU metabolites are significantly reduced in 5-FU resistant cells. Specifically, the level of the nucleotide fluorodeoxyuridine monophosphate (FdUMP) is reduced with treatment of 5-FU clarifying the compromised 5-FU metabolism in resistant cells. Corroborating the metabolomic analysis, treatment of the resistant cells with FdUMP, an active metabolite of 5-FU, resulted in effective killing of the resistant cells. Overall, in this study, an effective protocol was developed for comparative quantitation of polar metabolites and nucleotide analogues from the adherent cells efficiently. Furthermore, the utility of FdUMP as an alternative for CRC therapy is highlighted.

Cell-intrinsic metabolic regulation of mononuclear phagocyte activation: Findings from the tip of the iceberg

We have only recently started to appreciate the extent to which immune cell activation involves significant changes in cellular metabolism. We are now beginning to understand how commitment to specific metabolic pathways influences aspects of cellular biology that are the more usual focus of immunological studies, such as activation-induced changes in gene transcription, post-transcriptional regulation of transcription, post-translational modifications of proteins, cytokine secretion, etc. Here, we focus on metabolic reprogramming in mononuclear phagocytes downstream of stimulation with inflammatory signals (such as LPS and IFNγ) vs alternative activation signals (IL-4), with an emphasis on work on dendritic cells and macrophages from our laboratory, and related studies from others. We cover aspects of glycolysis and its branching pathways (glycogen synthesis, pentose phosphate, serine synthesis, hexose synthesis, and glycerol 3 phosphate shuttle), the tricarboxylic acid pathway, fatty acid synthesis and oxidation, and mitochondrial biology. Although our understanding of the metabolism of mononuclear phagocytes has progressed significantly over the last 10 years, major challenges remain, including understanding the effects of tissue residence on metabolic programming related to cellular activation, and the translatability of findings from mouse to human biology.

Modeling Plant Metabolism: From Network Reconstruction to Mechanistic Models

Mathematical modeling of plant metabolism enables the plant science community to understand the organization of plant metabolism, obtain quantitative insights into metabolic functions, and derive engineering strategies for manipulation of metabolism. Among the various modeling approaches, metabolic pathway analysis can dissect the basic functional modes of subsections of core metabolism, such as photorespiration, and reveal how classical definitions of metabolic pathways have overlapping functionality. In the many studies using constraint-based modeling in plants, numerous computational tools are currently available to analyze large-scale and genome-scale metabolic networks. For ¹³C-metabolic flux analysis, principles of isotopic steady state have been used to study heterotrophic plant tissues, while nonstationary isotope labeling approaches are amenable to the study of photoautotrophic and secondary metabolism. Enzyme kinetic models explore pathways in mechanistic detail, and we discuss different approaches to determine or estimate kinetic parameters. In this review, we describe recent advances and challenges in modeling plant metabolism.

Analytical techniques for metabolomic studies: a review

Metabolomics is the comprehensive study of small-molecule metabolites. Obtaining a wide coverage of the metabolome is challenging because of the broad range of physicochemical properties of the small molecules. To study the compounds of interest spectroscopic (NMR), spectrometric (MS) and separation techniques (LC, GC, supercritical fluid chromatography, CE) are used. The choice for a given technique is influenced by the sample matrix, the concentration and properties of the metabolites, and the amount of sample. This review discusses the most commonly used analytical techniques for metabolomic studies, including their advantages, drawbacks and some applications.

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

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

Systems metabolomics: from metabolomic snapshots to design principles

Metabolomics is a rapidly expanding technology that finds increasing application in a variety of fields, form metabolic disorders to cancer, from nutrition and wellness to design and optimization of cell factories. The integration of metabolic snapshots with metabolic fluxes, physiological readouts, metabolic models, and knowledge-informed Artificial Intelligence tools, is required to obtain a system-level understanding of metabolism. The emerging power of multi-omic approaches and the development of integrated experimental and computational tools, able to dissect metabolic features at cellular and subcellular resolution, provide unprecedented opportunities for understanding design principles of metabolic (dis)regulation and for the development of precision therapies in multifactorial diseases, such as cancer and neurodegenerative diseases.

Single-cell omics in ageing: a young and growing field

Organismal ageing results from interlinked molecular changes in multiple organs over time. The study of ageing at the molecular level is complicated by varying decay characteristics and kinetics-both between and within organs-driven by intrinsic and extracellular factors. Emerging single-cell omics methods allow for molecular and spatial profiling of cells, and probing of regulatory states and cell-fate determination, thus providing promising tools for unravelling the heterogeneous process of ageing and making it amenable to intervention. These new strategies are enabled by advances in genomic, epigenomic and transcriptomic technologies. Combined with methods for proteome and metabolome analysis, single-cell techniques provide multidimensional, integrated data with unprecedented detail and throughput. Here, we provide an overview of the current state, and perspectives on the future, of this emerging field. We discuss how single-cell approaches can advance understanding of mechanisms underlying organismal ageing and aid in the development of interventions for ageing and ageing-associated diseases.

Turning Cold into Hot: Firing up the Tumor Microenvironment

Cancers develop within complex tissue environments consisting of diverse innate and adaptive immune cells, along with stromal cells, vascular networks, and many other cellular and noncellular components. The high heterogeneity within the tumor microenvironment (TME) remains a key obstacle in understanding and treating cancer. Understanding the dynamic functional interplay within this intricate ecosystem will provide important insights into the design of effective combinatorial strategies against cancer. Here, we present recent technical advances to explore the complexity of the TME. Then, we discuss how innate immune sensing machinery, genetic alterations of oncogenic signaling, cellular metabolism, and epigenetic factors are involved in modulating the TME. Finally, we summarize the potential strategies to boost antitumor immunity by therapeutically exploiting the TME.

Prospects and challenges of multi-omics data integration in toxicology

Exposure of cells or organisms to chemicals can trigger a series of effects at the regulatory pathway level, which involve changes of levels, interactions, and feedback loops of biomolecules of different types. A single-omics technique, e.g., transcriptomics, will detect biomolecules of one type and thus can only capture changes in a small subset of the biological cascade. Therefore, although applying single-omics analyses can lead to the identification of biomarkers for certain exposures, they cannot provide a systemic understanding of toxicity pathways or adverse outcome pathways. Integration of multiple omics data sets promises a substantial improvement in detecting this pathway response to a toxicant, by an increase of information as such and especially by a systemic understanding. Here, we report the findings of a thorough evaluation of the prospects and challenges of multi-omics data integration in toxicological research. We review the availability of such data, discuss options for experimental design, evaluate methods for integration and analysis of multi-omics data, discuss best practices, and identify knowledge gaps. Re-analyzing published data, we demonstrate that multi-omics data integration can considerably improve the confidence in detecting a pathway response. Finally, we argue that more data need to be generated from studies with a multi-omics-focused design, to define which omics layers contribute most to the identification of a pathway response to a toxicant.

Metabolomic tools used in marine natural product drug discovery

Introduction: The marine environment is a very promising resource for natural product research, with many of these reaching the market as new drugs, especially in the field of cancer therapy as well as the drug discovery pipeline for new antimicrobials. Exploitation for bioactive marine compounds with unique structures and novel bioactivity such as the isoquinoline alkaloid; trabectedin, the polyether macrolide; halichondrin B, and the peptide; dolastatin 10, requires the use of analytical techniques, which can generate unbiased, quantitative, and qualitative data to benefit the biodiscovery process. Metabolomics has shown to bridge this understanding and facilitate the development of new potential drugs from marine sources and particularly their microbial symbionts.Areas covered: In this review, articles on applied secondary metabolomics ranging from 1990-2018 as well as to the last quarter of 2019 were probed to investigate the impact of metabolomics on drug discovery for new antibiotics and cancer treatment.Expert opinion: The current literature review highlighted the effectiveness of metabolomics in the study of targeting biologically active secondary metabolites from marine sources for optimized discovery of potential new natural products to be made accessible to a R&D pipeline.

The Progress of Multi-Omics Technologies: Determining Function in Lactic Acid Bacteria Using a Systems Level Approach

Lactic Acid Bacteria (LAB) have long been recognized as having a significant impact ranging from commercial to health domains. A vast amount of research has been carried out on these microbes, deciphering many of the pathways and components responsible for these desirable effects. However, a large proportion of this functional information has been derived from a reductionist approach working with pure culture strains. This provides limited insight into understanding the impact of LAB within intricate systems such as the gut microbiome or multi strain starter cultures. Whole genome sequencing of strains and shotgun metagenomics of entire systems are powerful techniques that are currently widely used to decipher function in microbes, but they also have their limitations. An available genome or metagenome can provide an image of what a strain or microbiome, respectively, is potentially capable of and the functions that they may carry out. A top-down, multi-omics approach has the power to resolve the functional potential of an ecosystem into an image of what is being expressed, translated and produced. With this image, it is possible to see the real functions that members of a system are performing and allow more accurate and impactful predictions of the effects of these microorganisms. This review will discuss how technological advances have the potential to increase the yield of information from genomics, transcriptomics, proteomics and metabolomics. The potential for integrated omics to resolve the role of LAB in complex systems will also be assessed. Finally, the current software approaches for managing these omics data sets will be discussed.

Recent Advances in In Vivo SPME Sampling

In vivo solid-phase microextraction (SPME) has been recently proposed for the extraction, clean-up and preconcentration of analytes of biological and clinical concern. Bioanalysis can be performed by sampling exo- or endogenous compounds directly in living organisms with minimum invasiveness. In this context, innovative and miniaturized devices characterized by both commercial and lab-made coatings for in vivo SPME tissue sampling have been proposed, thus assessing the feasibility of this technique for biomarker discovery, metabolomics studies or for evaluating the environmental conditions to which organisms can be exposed. Finally, the possibility of directly interfacing SPME to mass spectrometers represents a valuable tool for the rapid quali- and quantitative analysis of complex matrices. This review article provides a survey of in vivo SPME applications focusing on the extraction of tissues, cells and simple organisms. This survey will attempt to cover the state-of- the-art from 2014 up to 2019.

Integrative omics approaches provide biological and clinical insights: examples from mitochondrial diseases

High-throughput technologies for genomics, transcriptomics, proteomics, and metabolomics, and integrative analysis of these data, enable new, systems-level insights into disease pathogenesis. Mitochondrial diseases are an excellent target for hypothesis-generating omics approaches, as the disease group is mechanistically exceptionally complex. Although the genetic background in mitochondrial diseases is in either the nuclear or the mitochondrial genome, the typical downstream effect is dysfunction of the mitochondrial respiratory chain. However, the clinical manifestations show unprecedented variability, including either systemic or tissue-specific effects across multiple organ systems, with mild to severe symptoms, and occurring at any age. So far, the omics approaches have provided mechanistic understanding of tissue-specificity and potential treatment options for mitochondrial diseases, such as metabolome remodeling. However, no curative treatments exist, suggesting that novel approaches are needed. In this Review, we discuss omics approaches and discoveries with the potential to elucidate mechanisms of and therapies for mitochondrial diseases.

Metabolic Control of Treg Cell Stability, Plasticity, and Tissue-Specific Heterogeneity

Regulatory T (Treg) cells are crucial for peripheral immune tolerance and prevention of autoimmunity and tissue damage. Treg cells are inherently defined by the expression of the transcription factor Foxp3, which enforces lineage development and immune suppressive function of these cells. Under various conditions as observed in autoimmunity, cancer and non-lymphoid tissues, a proportion of Treg cells respond to specific environmental signals and display altered stability, plasticity and tissue-specific heterogeneity, which further shape their context-dependent suppressive functions. Recent studies have revealed that metabolic programs play pivotal roles in controlling these processes in Treg cells, thereby considerably expanding our understanding of Treg cell biology. Here we summarize these recent advances that highlight how cell-extrinsic factors, such as nutrients, vitamins and metabolites, and cell-intrinsic metabolic programs, orchestrate Treg cell stability, plasticity, and tissue-specific heterogeneity. Understanding metabolic regulation of Treg cells should provide new insight into immune homeostasis and disease, with important therapeutic implications for autoimmunity, cancer, and other immune-mediated disorders.

Redesigning the T-probe for mass spectrometry analysis of online lysis of non-adherent single cells

Single cell mass spectrometry (SCMS) allows for molecular analysis of individual cells while avoiding the inevitable drawbacks of using cell lysate prepared from populations of cells. Based on our previous design of the T-probe, a microscale sampling and ionization device for SCMS analysis, we further developed the device to perform online, and real time lysis of non-adherent live single cells for mass spectrometry (MS) analysis at ambient conditions. This redesigned T-probe includes three parts: a sampling probe with a small tip to withdraw a whole cell, a solvent-providing capillary to deliver lysis solution (i.e., acetonitrile), and a nano-ESI emitter in which rapid cell lysis and ionization occur followed by MS analysis. These three components are embedded between two polycarbonate slides and are jointed through a T-junction to form an integrated device. Colon cancer cells (HCT-116) under control and treatment (using anticancer drug irinotecan) conditions were analyzed. We detected a variety of intracellular species, and structural identification of selected ions was conducted using tandem MS (MS2). We further conducted statistical analysis (e.g., PLS-DA and t-test) to gain biological insights of cellular metabolism. Our results indicate that the influence of anticancer drugs on cellular metabolism of live non-adherent cells can be obtained using the SCMS experiments combined with statistical data analysis.

Metabolomics for Investigating Physiological and Pathophysiological Processes

Metabolomics uses advanced analytical chemistry techniques to enable the high-throughput characterization of metabolites from cells, organs, tissues, or biofluids. The rapid growth in metabolomics is leading to a renewed interest in metabolism and the role that small molecule metabolites play in many biological processes. As a result, traditional views of metabolites as being simply the "bricks and mortar" of cells or just the fuel for cellular energetics are being upended. Indeed, metabolites appear to have much more varied and far more important roles as signaling molecules, immune modulators, endogenous toxins, and environmental sensors. This review explores how metabolomics is yielding important new insights into a number of important biological and physiological processes. In particular, a major focus is on illustrating how metabolomics and discoveries made through metabolomics are improving our understanding of both normal physiology and the pathophysiology of many diseases. These discoveries are yielding new insights into how metabolites influence organ function, immune function, nutrient sensing, and gut physiology. Collectively, this work is leading to a much more unified and system-wide perspective of biology wherein metabolites, proteins, and genes are understood to interact synergistically to modify the actions and functions of organelles, organs, and organisms.

Feasibility of Localized Metabolomics in the Study of Pancreatic Islets and Diabetes

(1) Background: Disruption of insulin production by native or transplanted pancreatic islets caused by auto/allo-immunity leads to hyperglycemia, a serious health condition and important therapeutic challenge due to the lifelong need for exogeneous insulin administration. Early metabolic biomarkers can prompt timely interventions to preserve islet function, but reliable biomarkers are currently lacking. We explored the feasibility of "localized metabolomics" where initial biomarker discovery is made in aqueous humor samples for further validation in the circulation. (2) Methods: We conducted non-targeted metabolomic studies in parallel aqueous humor and plasma samples from diabetic and nondiabetic mice. Metabolite levels and associated pathways were compared in both compartments as well as to an earlier longitudinal dataset in hyperglycemia-progressor versus non-progressor non-obese diabetic (NOD) mice. (3) Results: We confirmed that aqueous humor samples can be used to assess metabolite levels. About half of the identified metabolites had well-correlated levels in the aqueous humor and plasma. Several plasma metabolites were significantly different between diabetic and nondiabetic animals and between males and females, and many of them were correlated with the aqueous humor. (4) Conclusions: This study provides proof-of-concept evidence that aqueous humor samples enriched with islet-related metabolites and representative of the immediate islet microenvironment following intraocular islet transplant can be used to assess metabolic changes that could otherwise be overlooked in the general circulation. The findings support localized metabolomics, with and without intraocular islet transplant, to identify biomarkers associated with diabetes and islet allograft rejection.

Emerging role of glycosylation in the polarization of tumor-associated macrophages

Tumors are formed by several cell types interacting in a complex environment of soluble and matrix molecules. The crosstalk between the cells and extracellular components control tumor fate. Macrophages are highly plastic and diverse immune cells that are known to be key regulators of this complex network, which is mostly because they can adjust their metabolism and reprogram their phenotype and effector function. Here, we review the studies that disclose the central role of metabolism and tumor microenvironment in shaping the phenotype and function of macrophages, highlighting the importance of the hexosamine biosynthetic pathway. We further discuss growing evidence of nutrient-sensitive protein modifications such as O-GlcNAcylation and extracellular glycosylation in the function and polarization of tumor-associated macrophages.

Cellular Metabolic Heterogeneity In Vivo Is Recapitulated in Tumor Organoids

Heterogeneous populations within a tumor have varying metabolic profiles, which can muddle the interpretation of bulk tumor imaging studies of treatment response. Although methods to study tumor metabolism at the cellular level are emerging, these methods provide a single time point “snapshot” of tumor metabolism and require a significant time and animal burden while failing to capture the longitudinal metabolic response of a single tumor to treatment. Here, we investigated a novel method for longitudinal, single-cell tracking of metabolism across heterogeneous tumor cell populations using optical metabolic imaging (OMI), which measures autofluorescence of metabolic coenzymes as a report of metabolic activity. We also investigated whether in vivo cellular metabolic heterogeneity can be accurately captured using tumor-derived three-dimensional organoids in a genetically engineered mouse model of breast cancer. OMI measurements of response to paclitaxel and the phosphatidylinositol-3-kinase inhibitor XL147 in tumors and organoids taken at single cell resolution revealed parallel shifts in metaboltruic heterogeneity. Interestingly, these previously unappreciated heterogeneous metabolic responses in tumors and organoids could not be attributed to tumor cell fate or varying leukocyte content within the microenvironment, suggesting that heightened metabolic heterogeneity upon treatment is largely due to heterogeneous metabolic shifts within tumor cells. Together, these studies show that OMI revealed remarkable heterogeneity in response to treatment, which could provide a novel approach to predict the presence of potentially unresponsive tumor cell subpopulations lurking within a largely responsive bulk tumor population, which might otherwise be overlooked by traditional measurements.

Individualized metabolomics: opportunities and challenges

The goal of advancing science in health care is to provide high quality treatment and therapeutic opportunities to patients in need. This is especially true in precision medicine, wherein the ultimate goal is to link disease phenotypes to targeted treatments and novel therapeutics at the scale of an individual. With the advent of -omics technologies, such as genomics, proteomics, microbiome, among others, the metabolome is of wider and immediate interest for its important role in metabolic regulation. The metabolome, of course, comes with its own questions regarding technological challenges. In this opinion article, I attempt to interrogate some of the main challenges associated with individualized metabolomics, and available opportunities in the context of its clinical application. Some questions this article addresses and attempts to find answers for are: Can a personal metabolome (n = 1) be inexpensive, affordable and informative enough (i.e. provide predictive yet validated biomarkers) to represent the entirety of a population? How can a personal metabolome complement advances in other -omics areas and the use of monitoring devices, which occupy our personal space?

Sensitive analysis of multiple low-molecular-weight thiols in a single human cervical cancer cell by chemical derivatization-liquid chromatography-mass spectrometry

Simultaneous quantification of multiple low-molecular-weight thiols from a single HeLa cell was realized by chemical derivatization assisted LC-MS method.