Single-Cell Metabolomics

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

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

Opportunities and challenges for sample preparation and enrichment in mass spectrometry for single-cell metabolomics

Single-cell heterogeneity in metabolism, drug resistance and disease type poses the need for analytical techniques for single-cell analysis. As the metabolome provides the closest view of the status quo in the cell, studying the metabolome at single-cell resolution may unravel said heterogeneity. A challenge in single-cell metabolome analysis is that metabolites cannot be amplified, so one needs to deal with picolitre volumes and a wide range of analyte concentrations. Due to high sensitivity and resolution, MS is preferred in single-cell metabolomics. Large numbers of cells need to be analysed for proper statistics; this requires high-throughput analysis, and hence automation of the analytical workflow. Significant advances in (micro)sampling methods, CE and ion mobility spectrometry have been made, some of which have been applied in high-throughput analyses. Microfluidics has enabled an automation of cell picking and metabolite extraction; image recognition has enabled automated cell identification. Many techniques have been used for data analysis, varying from conventional techniques to novel combinations of advanced chemometric approaches. Steps have been set in making data more findable, accessible, interoperable and reusable, but significant opportunities for improvement remain. Herein, advances in single-cell analysis workflows and data analysis are discussed, and recommendations are made based on the experimental goal.

Integrating (Nutri-)Metabolomics into the One Health Tendency-The Key for Personalized Medicine Advancement

Metabolomics is an advanced technology, still under development, with multiple research applications, especially in the field of health. Individual metabolic profiles, the functionality of the body, as well as its interaction with the environment, can be established using this technology. The body's response to various external factors, including the food consumed and the nutrients it contains, has increased researchers' interest in nutrimetabolomics. Establishing correlations between diet and the occurrence of various diseases, or even the development of personalized nutrition plans, could contribute to advances in precision medicine. The interdependence between humans, animals, and the environment is of particular importance today, with the dramatic emergence and spread of zoonotic diseases, food, water and soil contamination, and the degradation of resources and habitats. All these events have led to an increase in risk factors for functional diseases, burdening global health. Thus, this study aimed to highlight the importance of metabolomics, and, in particular, nutrimetabolomics, as a technical solution for a holistic, collaborative, and precise approach for the advancement of the One Health strategy.

Approaches towards Elucidating the Metabolic Program of Hematopoietic Stem/Progenitor Cells

Hematopoietic stem cells (HSCs) in bone marrow continuously supply a large number of blood cells throughout life in collaboration with hematopoietic progenitor cells (HPCs). HSCs and HPCs are thought to regulate and utilize intracellular metabolic programs to obtain metabolites, such as adenosine triphosphate (ATP), which is necessary for various cellular functions. Metabolites not only provide stem/progenitor cells with nutrients for ATP and building block generation but are also utilized for protein modification and epigenetic regulation to maintain cellular characteristics. In recent years, the metabolic programs of tissue stem/progenitor cells and their underlying molecular mechanisms have been elucidated using a variety of metabolic analysis methods. In this review, we first present the advantages and disadvantages of the current approaches applicable to the metabolic analysis of tissue stem/progenitor cells, including HSCs and HPCs. In the second half, we discuss the characteristics and regulatory mechanisms of HSC metabolism, including the decoupling of ATP production by glycolysis and mitochondria. These technologies and findings have the potential to advance stem cell biology and engineering from a metabolic perspective and to establish therapeutic approaches.

Next-Generation Personalized Medicine: Implementation of Variability Patterns for Overcoming Drug Resistance in Chronic Diseases

Chronic diseases are a significant healthcare problem. Partial or complete non-responsiveness to chronic therapies is a significant obstacle to maintaining the long-term effect of drugs in these patients. A high degree of intra- and inter-patient variability defines pharmacodynamics, drug metabolism, and medication response. This variability is associated with partial or complete loss of drug effectiveness. Regular drug dosing schedules do not comply with physiological variability and contribute to resistance to chronic therapies. In this review, we describe a three-phase platform for overcoming drug resistance: introducing irregularity for improving drug response; establishing a deep learning, closed-loop algorithm for generating a personalized pattern of irregularity for overcoming drug resistance; and upscaling the algorithm by implementing quantified personal variability patterns along with other individualized genetic and proteomic-based ways. The closed-loop, dynamic, subject-tailored variability-based machinery can improve the efficacy of existing therapies in patients with chronic diseases.

Single-Cell Multiomics Analysis for Drug Discovery

Given the heterogeneity seen in cell populations within biological systems, analysis of single cells is necessary for studying mechanisms that cannot be identified on a bulk population level. There are significant variations in the biological and physiological function of cell populations due to the functional differences within, as well as between, single species as a result of the specific proteome, transcriptome, and metabolome that are unique to each individual cell. Single-cell analysis proves crucial in providing a comprehensive understanding of the biological and physiological properties underlying human health and disease. Omics technologies can help to examine proteins (proteomics), RNA molecules (transcriptomics), and the chemical processes involving metabolites (metabolomics) in cells, in addition to genomes. In this review, we discuss the value of multiomics in drug discovery and the importance of single-cell multiomics measurements. We will provide examples of the benefits of applying single-cell omics technologies in drug discovery and development. Moreover, we intend to show how multiomics offers the opportunity to understand the detailed events which produce or prevent disease, and ways in which the separate omics disciplines complement each other to build a broader, deeper knowledge base.

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

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

A new glance at the chemosphere of macroalgal-bacterial interactions: In situ profiling of metabolites in symbiosis by mass spectrometry

Symbiosis is a dominant form of life that has been observed numerous times in marine ecosystems. For example, macroalgae coexist with bacteria that produce factors that promote algal growth and morphogenesis. The green macroalga Ulva mutabilis (Chlorophyta) develops into a callus-like phenotype in the absence of its essential bacterial symbionts Roseovarius sp. MS2 and Maribacter sp. MS6. Spatially resolved studies are required to understand symbiont interactions at the microscale level. Therefore, we used mass spectrometry profiling and imaging techniques with high spatial resolution and sensitivity to gain a new perspective on the mutualistic interactions between bacteria and macroalgae. Using atmospheric pressure scanning microprobe matrix-assisted laser desorption/ionisation high-resolution mass spectrometry (AP-SMALDI-HRMS), low-molecular-weight polar compounds were identified by comparative metabolomics in the chemosphere of Ulva. Choline (2-hydroxy-N,N,N-trimethylethan-1-aminium) was only determined in the alga grown under axenic conditions, whereas ectoine (1,4,5,6-tetrahydro-2-methyl-4-pyrimidinecarboxylic acid) was found in bacterial presence. Ectoine was used as a metabolic marker for localisation studies of Roseovarius sp. within the tripartite community because it was produced exclusively by these bacteria. By combining confocal laser scanning microscopy (cLSM) and AP-SMALDI-HRMS, we proved that Roseovarius sp. MS2 settled mainly in the rhizoidal zone (holdfast) of U. mutabilis. Our findings provide the fundament to decipher bacterial symbioses with multicellular hosts in aquatic ecosystems in an ecologically relevant context. As a versatile tool for microbiome research, the combined AP-SMALDI and cLSM imaging analysis with a resolution to level of a single bacterial cell can be easily applied to other microbial consortia and their hosts. The novelty of this contribution is the use of an in situ setup designed to avoid all types of external contamination and interferences while resolving spatial distributions of metabolites and identifying specific symbiotic bacteria.

Cellular aging beyond cellular senescence: Markers of senescence prior to cell cycle arrest in vitro and in vivo

The field of research on cellular senescence experienced a rapid expansion from being primarily focused on in vitro aspects of aging to the vast territories of animal and clinical research. Cellular senescence is defined by a set of markers, many of which are present and accumulate in a gradual manner prior to senescence induction or are found outside of the context of cellular senescence. These markers are now used to measure the impact of cellular senescence on aging and disease as well as outcomes of anti-senescence interventions, many of which are at the stage of clinical trials. It is thus of primary importance to discuss their specificity as well as their role in the establishment of senescence. Here, the presence and role of senescence markers are described in cells prior to cell cycle arrest, especially in the context of replicative aging and in vivo conditions. Specifically, this review article seeks to describe the process of "cellular aging": the progression of internal changes occurring in primary cells leading to the induction of cellular senescence and culminating in cell death. Phenotypic changes associated with aging prior to senescence induction will be characterized, as well as their effect on the induction of cell senescence and the final fate of cells reviewed. Using published datasets on assessments of senescence markers in vivo, it will be described how disparities between quantifications can be explained by the concept of cellular aging. Finally, throughout the article the applicational value of broadening cellular senescence paradigm will be discussed.

Stem Cell Impairment at the Host-Microbiota Interface in Colorectal Cancer

Colorectal cancer (CRC) initiation is believed to result from the conversion of normal intestinal stem cells (ISCs) into cancer stem cells (CSCs), also known as tumor-initiating cells (TICs). Hence, CRC evolves through the multiple acquisition of well-established genetic and epigenetic alterations with an adenoma-carcinoma sequence progression. Unlike other stem cells elsewhere in the body, ISCs cohabit with the intestinal microbiota, which consists of a diverse community of microorganisms, including bacteria, fungi, and viruses. The gut microbiota communicates closely with ISCs and mounting evidence suggests that there is significant crosstalk between host and microbiota at the ISC niche level. Metagenomic analyses have demonstrated that the host-microbiota mutually beneficial symbiosis existing under physiologic conditions is lost during a state of pathological microbial imbalance due to the alteration of microbiota composition (dysbiosis) and/or the genetic susceptibility of the host. The complex interaction between CRC and microbiota is at the forefront of the current CRC research, and there is growing attention on a possible role of the gut microbiome in the pathogenesis of CRC through ISC niche impairment. Here we primarily review the most recent findings on the molecular mechanism underlying the complex interplay between gut microbiota and ISCs, revealing a possible key role of microbiota in the aberrant reprogramming of CSCs in the initiation of CRC. We also discuss recent advances in OMICS approaches and single-cell analyses to explore the relationship between gut microbiota and ISC/CSC niche biology leading to a desirable implementation of the current precision medicine approaches.

Unveil early-stage nanocytotoxicity by a label-free single cell pH nanoprobe

Single-cell analysis is an emerging research area that aims to reveal delicate cellular status and underlying mechanisms by conquering the intercellular heterogeneity. Current single-cell research methods, however, are highly dependent on cell-destructive protocols and cannot sequentially display the progress of cellular events. A recently developed pH nanoprobe in our lab conceptually showed its ability to detect intracellular pH (pHi) without cell labeling or disruption. In the present study, we took the cytotoxicity of nanoparticles (NPs) as a typical example of cell heterogeneity, to testify the practicality of the pH nanoprobe in interpreting cell status. Three types of NPs (CeO2, TiO2, and SiO2) were employed to generate varied toxic effects. Results showed that the traditional assays - including cell viability, intracellular ROS generation, and mitochondrial inner membrane depolarization - not only failed to report the nanotoxicity accurately and timely, but also drew confusing or misleading conclusions. The pH nanoprobe revealed explicit pHi changes induced by the NPs, which corresponded well with the cell damages found by the transmission electron microscopic (TEM) imaging. Besides, our results unveiled an unexpectedly devastating effect of SiO2 NPs on cells during the early stage NP-cell interaction. The developed novel pH nanoprobe demonstrated a rapid sensing capability at single-cell resolution with minimum invasiveness. Therefore, it may become a promising alternative for a wide range of applications in areas such as single-cell research and precision medicine.

Intestinal Organoids: A Tool for Modelling Diet-Microbiome-Host Interactions

Dietary patterns, microbiome dysbiosis, and gut microbial metabolites (GMMs) have a pivotal role in the homeostasis of intestinal epithelial cells and in disease progression, such as that of colorectal cancer (CRC). Although GMMs and microorganisms have crucial roles in many biological activities, models for deciphering diet-microbiome-host relationships are largely limited to animal models. Thus, intestinal organoids (IOs) have provided unprecedented opportunities for the generation of in vitro platforms with the sufficient level of complexity to model physiological and pathological diet-microbiome-host conditions. Overall, IO responses to GMM metabolites and microorganisms can provide new insights into the mechanisms by which those agents may prevent or trigger diseases, significantly extending our knowledge of diet-microbiome-host interactions.

Circulating Metabolites as Potential Biomarkers for Neurological Disorders-Metabolites in Neurological Disorders

There are, still, limitations to predicting the occurrence and prognosis of neurological disorders. Biomarkers are molecules that can change in different conditions, a feature that makes them potential tools to improve the diagnosis of disease, establish a prognosis, and monitor treatments. Metabolites can be used as biomarkers, and are small molecules derived from the metabolic process found in different biological media, such as tissue samples, cells, or biofluids. They can be identified using various strategies, targeted or untargeted experiments, and by different techniques, such as high-performance liquid chromatography, mass spectrometry, or nuclear magnetic resonance. In this review, we aim to discuss the current knowledge about metabolites as biomarkers for neurological disorders. We will present recent developments that show the need and the feasibility of identifying such biomarkers in different neurological disorders, as well as discuss relevant research findings in the field of metabolomics that are helping to unravel the mechanisms underlying neurological disorders. Although several relevant results have been reported in metabolomic studies in patients with neurological diseases, there is still a long way to go for the clinical use of metabolites as potential biomarkers in these disorders, and more research in the field is needed.

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

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

Existing and Emerging Metabolomic Tools for ALS Research

Growing evidence suggests that aberrant energy metabolism could play an important role in the pathogenesis of amyotrophic lateral sclerosis (ALS). Despite this, studies applying advanced technologies to investigate energy metabolism in ALS remain scarce. The rapidly growing field of metabolomics offers exciting new possibilities for ALS research. Here, we review existing and emerging metabolomic tools that could be used to further investigate the role of metabolism in ALS. A better understanding of the metabolic state of motor neurons and their surrounding cells could hopefully result in novel therapeutic strategies.

Deciphering Metabolic Heterogeneity by Single-Cell Analysis

Single-cell analysis provides insights into cellular heterogeneity and dynamics of individual cells. This Feature highlights recent developments in key analytical techniques suited for single-cell metabolic analysis with a special focus on mass spectrometry-based analytical platforms and RNA-seq as well as imaging techniques that reveal stochasticity in metabolism.

Direct nano-electrospray ionization tandem mass spectrometry for the quantification and identification of metronidazole in its dosage form and human urine

A rapid, sensitive and direct nano-electrospray ionization-tandem mass spectrometry (NS-ESI-MS/MS) method, using an offline nanospray (NS) capillary, has been developed and validated for the analysis of metronidazole (MTZ). A mixture of 2 µl MTZ sample solution prepared in an ionization solvent consisting of methanol : water : formic acid in a ratio of 80 : 20 : 0.3, together with 2 µl of an internal standard (IS), 1,3,6-polytyrosine, is loaded into the back of the NS capillary. The NS capillary was fitted into the ion source at a distance of 3 mm between the NS tip and MS orifice. The sample is then analysed and acquired a sustainable signal that allowed for data compilation across various data points for MTZ identification and quantification. The quantification relied on the ratio of the [M + H]+ peaks of MTZ and IS with m/z values of 172.0717 and 182.0812, respectively, while the identification relied on the MS/MS of the precursor ions [M + H]+ of both compounds and their fragments at 128.05 for MTZ and 165.1 and 136.07 for the IS. The NS-ESI-MS/MS method was accurate and precise for the quantification of MTZ over the concentration range from 2.5 to 25 000 ng ml-1. The applicability of the method was confirmed by MTZ analysis in its pharmaceutical dosage form and detection of the analyte in clinical human urine samples without any sample treatment procedure.

Ultrastructural morphology is distinct among primary progenitor cell isolates from normal, inflamed, and cryopreserved equine hoof tissue and CD105+K14+ progenitor cells

The equine hoof dermal-epidermal interface requires progenitor cells with distinct characteristics. This study was designed to provide accurate ultrastructural depictions of progenitor cells isolated from inflamed tissue and normal tissue before and after cryopreservation and following selection of cells expressing both keratin (K) 14 (ectodermal) and cluster of differentiation (CD) 105 (mesodermal). Passage 3 cell ultrastructure was assessed following 2D culture and after 3D culture on decellularized hoof tissue scaffolds. Outcome measures included light, transmission electron, and scanning electron microscopy, immunocytochemistry, and CD105⁺K14⁺ cell trilineage plasticity. Cells from normal tissue had typical progenitor cell characteristics. Those from inflamed tissue had organelles and morphology consistent with catabolic activities including lysosomes, irregular rough endoplasmic reticulum, and fewer vacuoles and early endosomes than those from normal tissue. Cryopreserved tissue cells appeared apoptotic with an irregular cell membrane covered by cytoplasmic protrusions closely associated with endocytic and exocytic vesicles, chromatin aggregated on the nuclear envelop, abundant, poorly organized rough endoplasmic reticulum, and plentiful lysosomes. Cells that were CD105⁺K14⁺ were distinguishable from heterogenous cells by infrequent microvilli on the cell surface, sparse endosomes and vesicles, and desmosomes between cells. Cells expressed ectodermal (K15) and mesodermal (CD105) proteins in 2D and 3D cultures. Inflamed and cryopreserved tissue isolates attached poorly to tissue scaffold while normal tissue cells attached well, but only CD105⁺K14⁺ cells produced extracellular matrix after 4 d. The CD105⁺K14⁺ cells exhibited osteoblastic, adipocytic, and neurocytic differentiation. Ultrastructural information provided by this study contributes to understanding of equine hoof progenitor cells to predict their potential contributions to tissue maintenance, healing, and damage as well post-implantation behavior.

Single cell immune profiling in transplantation research

Recently developed single-cell profiling technologies hold promise to provide new insights including analysis of population heterogeneity and linkage of antigen receptors with gene expression. These technologies produce complex data sets that require knowledge of bioinformatics for appropriate analysis. In this minireview, we discuss several single-cell immune profiling technologies for gene and protein expression, including cytometry by time-of-flight, RNA sequencing, and antigen receptor sequencing, as well as key considerations for analysis that apply to each. Because of the critical importance of data analysis for high parameter single cell analysis, we discuss essential factors in analysis of these data, including quality control, quantification, examples of methods for high dimensional analysis, immune repertoire analysis, and preparation of analysis pipelines. We provide examples of, and suggestions for, application of these innovative methods to transplantation research.

Embracing Systems Toxicology at Single-Cell Resolution

As systems biology expands its multi-omic spectrum to increasing resolutions, distinguishing cells based on single-cell profiles becomes feasible. Unlike traditional bulk assays that average cellular responses and blur the distinct identities of responsive cells, single-cell technologies enable sensitive detection of small cellular changes and precise identification of those cells perturbed by toxicants. Among the suite of omic technologies that continue to expand and become affordable, single-cell RNA sequencing (scRNA-seq) is at the cutting edge and leading the way to transform systems toxicology. Single-cell systems toxicology can provide a wealth of information to elucidate cell-specific alterations and response trajectories, detect points-of-departure, map and develop dynamical models of toxicity pathways.

Single-Cell Omics Analyses Enabled by Microchip Technologies

Single-cell omics studies provide unique information regarding cellular heterogeneity at various levels of the molecular biology central dogma. This knowledge facilitates a deeper understanding of how underlying molecular and architectural changes alter cell behavior, development, and disease processes. The emerging microchip-based tools for single-cell omics analysis are enabling the evaluation of cellular omics with high throughput, improved sensitivity, and reduced cost. We review state-of-the-art microchip platforms for profiling genomics, epigenomics, transcriptomics, proteomics, metabolomics, and multi-omics at single-cell resolution. We also discuss the background of and challenges in the analysis of each molecular layer and integration of multiple levels of omics data, as well as how microchip-based methodologies benefit these fields. Additionally, we examine the advantages and limitations of these approaches. Looking forward, we describe additional challenges and future opportunities that will facilitate the improvement and broad adoption of single-cell omics in life science and medicine.

Nano-Sampling and Reporter Tools to Study Metabolic Regulation in Zebrafish

In the past years, evidence has emerged that hallmarks of human metabolic disorders can be recapitulated in zebrafish using genetic, pharmacological or dietary interventions. An advantage of modeling metabolic diseases in zebrafish compared to other "lower organisms" is the presence of a vertebrate body plan providing the possibility to study the tissue-intrinsic processes preceding the loss of metabolic homeostasis. While the small size of zebrafish is advantageous in many aspects, it also has shortcomings such as the difficulty to obtain sufficient amounts for biochemical analyses in response to metabolic challenges. A workshop at the European Zebrafish Principal Investigator meeting in Trento, Italy, was dedicated to discuss the advantages and disadvantages of zebrafish to study metabolic disorders. This perspective article by the participants highlights strategies to achieve improved tissue-resolution for read-outs using "nano-sampling" approaches for metabolomics as well as live imaging of zebrafish expressing fluorescent reporter tools that inform on cellular or subcellular metabolic processes. We provide several examples, including the use of reporter tools to study the heterogeneity of pancreatic beta-cells within their tissue environment. While limitations exist, we believe that with the advent of new technologies and more labs developing methods that can be applied to minimal amounts of tissue or single cells, zebrafish will further increase their utility to study energy metabolism.

New frontiers in precision medicine for sepsis-induced immunoparalysis

INTRODUCTION

In the last decade, the sepsis research field has shifted focus from targeting hyperinflammation to reversing sepsis-induced immunoparalysis. Sepsis-induced immunoparalysis is very heterogeneous: the magnitude and the nature of the underlying immune defects differ considerably between patients, but also within individuals over time. Therefore, a 'one-treatment-fits-all' strategy for sepsis-induced immunoparalysis is bound to fail, and an individualized 'precision medicine' approach is required. Such a strategy is nevertheless hampered by the unsuitability of the currently available markers to identify the many immune defects that can manifest in individual patients. Areas covered: We describe the currently available markers for sepsis-induced immunoparalysis and limitations pertaining to their use. Furthermore, future prospects and caveats are discussed, focusing on 'omics' approaches: genomics, transcriptomics, epigenomics, and metabolomics. Finally, we present a contemporary overview of adjuvant immunostimulatory therapies. Expert opinion: The integration of multiple omics techniques offers a systems biology approach which can yield biomarker profiles that accurately and comprehensively gauge the extent and nature of sepsis-induced immunoparalysis. We expect this development to be instrumental in facilitating precision medicine for sepsis-induced immunoparalysis, consisting of the application of targeted immunostimulatory therapies and follow-up measurements to monitor the response to treatment and to titrate or adjust medication.

Advances in mass spectrometry based single-cell metabolomics

Single cell metabolomics using mass spectrometry can contribute to understanding biological activities in health and disease.

Expression of the Alternative Oxidase Influences Jun N-Terminal Kinase Signaling and Cell Migration

Downregulation of Jun N-terminal kinase (JNK) signaling inhibits cell migration in diverse model systems. In Drosophila pupal development, attenuated JNK signaling in the thoracic dorsal epithelium leads to defective midline closure, resulting in cleft thorax. Here we report that concomitant expression of the Ciona intestinalis alternative oxidase (AOX) was able to compensate for JNK pathway downregulation, substantially correcting the cleft thorax phenotype. AOX expression also promoted wound-healing behavior and single-cell migration in immortalized mouse embryonic fibroblasts (iMEFs), counteracting the effect of JNK pathway inhibition. However, AOX was not able to rescue developmental phenotypes resulting from knockdown of the AP-1 transcription factor, the canonical target of JNK, nor its targets and had no effect on AP-1-dependent transcription. The migration of AOX-expressing iMEFs in the wound-healing assay was differentially stimulated by antimycin A, which redirects respiratory electron flow through AOX, altering the balance between mitochondrial ATP and heat production. Since other treatments affecting mitochondrial ATP did not stimulate wound healing, we propose increased mitochondrial heat production as the most likely primary mechanism of action of AOX in promoting cell migration in these various contexts.

Intratumoral Heterogeneity in Ductal Carcinoma In Situ: Chaos and Consequence

Ductal carcinoma in situ (DCIS) is a non-invasive proliferative growth in the breast that serves as a non-obligate precursor to invasive ductal carcinoma. The widespread adoption of screening mammography has led to a steep increase in the detection of DCIS, which now comprises approximately 20% of new breast cancer diagnoses in the United States. Interestingly, the intratumoral heterogeneity (ITH) that has been observed in invasive breast cancers may have been established early in tumorigenesis, given the vast and varied ITH that has been detected in DCIS. This review will discuss the intratumoral heterogeneity of DCIS, focusing on the phenotypic and genomic heterogeneity of tumor cells, as well as the compositional heterogeneity of the tumor microenvironment. In addition, we will assess the spatial heterogeneity that is now being appreciated in these lesions, and summarize new approaches to evaluate heterogeneity of tumor and stromal cells in the context of their spatial organization. Importantly, we will discuss how a growing understanding of ITH has led to a more holistic appreciation of the complex biology of DCIS, specifically its evolution and natural history. Finally, we will consider ways in which our knowledge of DCIS ITH might be translated in the future to guide clinical care for DCIS patients.

Single-Cell (Multi)omics Technologies

Single-cell multiomics technologies typically measure multiple types of molecule from the same individual cell, enabling more profound biological insight than can be inferred by analyzing each molecular layer from separate cells. These single-cell multiomics technologies can reveal cellular heterogeneity at multiple molecular layers within a population of cells and reveal how this variation is coupled or uncoupled between the captured omic layers. The data sets generated by these techniques have the potential to enable a deeper understanding of the key biological processes and mechanisms driving cellular heterogeneity and how they are linked with normal development and aging as well as disease etiology. This review details both established and novel single-cell mono- and multiomics technologies and considers their limitations, applications, and likely future developments.

Metabolomics: State-of-the-Art Technologies and Applications on Drosophila melanogaster

Metabolomics is one of the latest "omics" technology concerned with the high-throughput identification and quantification of metabolites, the final products of cellular processes. The revealed data provide an instantaneous snapshot of an organism's metabolic pathways, which can be used to explain its phenotype or physiology. On the other hand, Drosophila has shown its power in studying metabolism and related diseases. At this stage, we have the state-of-the-art knowledge in place: a potential candidate to study cellular metabolism (Drosophila melanogaster) and a powerful methodology for metabolic network decipherer (metabolomics). Yet missing is advanced metabolomics technologies like isotope-assisted metabolomics optimized for Drosophila. In this chapter, we will discuss on the current status and future perspectives in technologies and applications of Drosophila metabolomics.

Microdosing, isotopic labeling, radiotracers and metabolomics: relevance in drug discovery, development and safety

This review discusses the use of stable (¹³C, ²D) or radioactive isotopes (¹⁴C, ¹¹C, ¹⁸F, ¹³¹I, ⁶⁴Cu, ⁶⁸Ga) incorporated into the molecular structure of new drug entities for the purpose of pharmacokinetic or -dynamic studies. Metabolite in safety testing requires the administration of pharmacologically active doses. In such studies, radiotracers find application mainly in preclinical animal investigations, whereby LC-MS/MS is used to identify metabolite structure and drug-related effects. In contrast, first-in-human metabolite studies have to be carried out at nonpharmacological doses not exceeding 100 μg (microdose), which is generally too low for metabolite detection by LC-MS/MS. This short-coming can be overcome by specific radio- or isotopic labeling of the drug of interest and measurements using accelerator mass spectroscopy, single-photon emission computed tomography and positron emission tomography. Such combined radioisotope-based approaches permit Phase 0, first-in-human metabolite study.