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.     

Metabolomics and its application to studying metal toxicity

Here we explain the omics approach of metabolomics and how it can be applied to study a physiological response to toxic metal exposure. This review aims to educate the metallomics field to the tool of metabolomics. Metabolomics is becoming an increasingly used tool to compare natural and challenged states of various organisms, from disease states in humans to toxin exposure to environmental systems. This approach is key to understanding and identifying the cellular or biochemical targets of metals and the underlying physiological response. Metabolomics steps are described and overviews of its application to metal toxicity to organisms are given. As this approach is very new there are yet only a small number of total studies and therefore only a brief overview of some metal metabolomics studies is described. A frank critical evaluation of the approach is given to provide newcomers to the method a clear idea of the challenges and the rewards of applying metabolomics to their research.     

Metabolomics

Metabolomics is the systematic identification and quantitation of all metabolites in a given organism or biological sample. The enhanced resolution provided by nuclear magnetic resonance (NMR) spectroscopy and mass spectrometry (MS), along with powerful chemometric software, allows the simultaneous determination and comparison of thousands of chemical entities, which has lead to an expansion of small molecule biochemistry studies in bacteria, plants, and mammals. Continued development of these analytical platforms will accelerate the widespread use of metabolomics and allow further integration of small molecules into systems biology. Here, recent studies using metabolomics in xenobiotic metabolism and genetically modified mice are highlighted.     

Quantitative metabolomics based on gas chromatography mass spectrometry: status and perspectives

Metabolomics involves the unbiased quantitative and qualitative analysis of the complete set of metabolites present in cells, body fluids and tissues (the metabolome). By analyzing differences between metabolomes using biostatistics (multivariate data analysis; pattern recognition), metabolites relevant to a specific phenotypic characteristic can be identified. However, the reliability of the analytical data is a prerequisite for correct biological interpretation in metabolomics analysis. In this review the challenges in quantitative metabolomics analysis with regards to analytical as well as data preprocessing steps are discussed. Recommendations are given on how to optimize and validate comprehensive silylation-based methods from sample extraction and derivatization up to data preprocessing and how to perform quality control during metabolomics studies. The current state of method validation and data preprocessing methods used in published literature are discussed and a perspective on the future research necessary to obtain accurate quantitative data from comprehensive GC-MS data is provided.     

p53 functions and cell lines: Have we learned the lessons from the past?

p53 has a determinant role in cancer prevention and is among the most studied proteins in the world. The majority of studies devoted to this protein are carried out in cell lines because they are easy to use and have naturally emerged as the main research tool in laboratories. However, the p53 pathway is commonly deregulated in cancer cells, from which the experimental cell lines are generally derived. The fact that the pathway is deregulated challenges the relevance of using cancer‐derived cell lines to study wild‐type p53 activities, or, in a broader sense, to study any normal cellular process. In the present article, we identify and discuss a number of limitations of cell lines using examples related to p53. Finally, we point out the general limitations of cell lines and propose solutions as alternatives to these cells.     

Optimal molecular profiling of tissue and tissue components

Isolation of well-preserved pure cell populations is a prerequisite for sound studies of the molecular basis of any tissue-based biological phenomenon. This article reviews current methods for obtaining anatomically specific signals from molecules isolated from tissues, a basic requirement for productive linking of phenotype and genotype. The quality of samples isolated from tissue and used for molecular analysis is often glossed over or omitted from publications, making interpretation and replication of data difficult or impossible. Fortunately, recently developed techniques allow life scientists to better document and control the quality of samples used for a given assay, creating a foundation for improvement in this area. Tissue processing for molecular studies usually involves some or all of the following steps: tissue collection, gross dissection/identification, fixation, processing/embedding, storage/archiving, sectioning, staining, microdissection/annotation, and pure analyte labeling/identification and quantification. We provide a detailed comparison of some current tissue microdissection technologies, and provide detailed example protocols for tissue component handling upstream and downstream from microdissection. We also discuss some of the physical and chemical issues related to optimal tissue processing, and include methods specific to cytology specimens. We encourage each laboratory to use these as a starting point for optimization of their overall process of moving from collected tissue to high quality, appropriately anatomically tagged scientific results. In optimized protocols is a source of inefficiency in current life science research. Improvement in this area will significantly increase life science quality and productivity. The article is divided into introduction, materials, protocols, and notes sections. Because many protocols are covered in each of these sections, information relating to a single protocol is not contiguous. To get the greatest benefit from this article, readers are advised to read through the entire article first, identify protocols appropriate to their laboratory for each step in their workflow, and then reread entries in each section pertaining to each of these single protocols.     

磷蛋白组的研究技术及其进展

真核细胞中蛋白质磷酸化是一个重要事件。真核细胞利用可逆的蛋白磷酸化来控制许多细胞过程包括信号转换、基因表达、细胞周期等。磷蛋白组的研究涉及磷蛋白的分离和鉴定 ,磷酸化残基定位和定量分析。由于蛋白质磷酸化是一个动态过程 ,在细胞中磷蛋白含量低 ,磷酸化位点可变 ,且磷酸肽的质谱信号常常会受到抑制 ,所以磷蛋白的分析存在更多的困难。本文介绍了国内外在磷酸蛋白的分离鉴定及定量分析方面的研究技术以及进展情况。目前 ,质谱仍然是核心的鉴定技术 ,寻找更好富集方法是最大的挑战。定量蛋白组学是对蛋白质的差异表达进行精确的定量分析。目前还不存在一种独立的方法可以完成磷蛋白的分离、鉴定 ,以及磷酸位点的定位和定量分析。随着样品分离技术和相关仪器的发展 ,磷酸蛋白快速、准确、全面分析鉴定将能够实现。     

Cellular metabolomics of Escherchia coli

Escherichia coli is among the simplest and best-understood free-living organisms. It has served as a valuable model for numerous biological processes, including cellular metabolism. Just as E. coli stood at the front of the genomic revolution, it is playing a leading role in the development of cellular metabolomics: the study of the complete metabolic contents of cells, including their dynamic concentration changes and fluxes. This review briefly describes the essentials of cellular metabolomics and its fundamental differentiation from biomarker metabolomics and lipidomics. Key technologies for metabolite quantitation from E. coli are described, with a focus on those involving mass spectrometry. In particular emphasis is given to the cell handling and sample preparation steps required for collecting data of high biological reliability, such as fast metabolome quenching. Future challenges, both in terms of data collection and application of the data to obtain a comprehensive understanding of metabolic dynamics, are discussed.     

Mealtime,Temporal, and Daily Variability of the Human Urinary and Plasma Metabolomes in a Tightly Controlled Environment

While metabolomics has tremendous potential for diagnostic biomarker and therapeutic target discovery, its utility may be diminished by the variability that occurs due to environmental exposures including diet and the influences of the human circadian rhythm. For successful translation of metabolomics findings into the clinical setting, it is necessary to exhaustively define the sources of metabolome variation. To address these issues and to measure the variability of urinary and plasma metabolomes throughout the day, we have undertaken a comprehensive inpatient study in which we have performed non-targeted metabolomics analysis of blood and urine in 26 volunteers (13 healthy subjects with no known disease and 13 healthy subjects with autosomal dominant polycystic kidney disease not taking medication). These individuals were evaluated in a clinical research facility on two separate occasions, over three days, while on a standardized, weight-based diet. Subjects provided pre- and post-prandial blood and urine samples at the same time of day, and all samples were analyzed by “fast lane” LC-MS-based global metabolomics. The largest source of variability in blood and urine metabolomes was attributable to technical issues such as sample preparation and analysis, and less variability was due to biological variables, meals, and time of day. Higher metabolome variability was observed after the morning as compared to the evening meal, yet day-to-day variability was minimal and urine metabolome variability was greater than that of blood. Thus we suggest that blood and urine are suitable biofluids for metabolomics studies, though nontargeted mass spectrometry alone may not offer sufficient precision to reveal subtle changes in the metabolome. Additional targeted analyses may be needed to support the data from nontargeted mass spectrometric analyses. In light of these findings, future metabolomics studies should consider these sources of variability to allow for appropriate metabolomics testing and reliable clinical translation of metabolomics data.     

Oncometabolomics in cancer research

Cancer is not only a complex genetic disease, but also a disease of dysregulated bioenergetic metabolism. With improved technological advancements, the focus has shifted from changes in an individual biochemical pathway or metabolite toward changes in the context of the global network of metabolic pathways in a cell, tissue or organism. This global approach allows identifying changes in the pattern of metabolite expression in addition to changes in individual metabolite or pathway. Such a metabolomics approach promises a better understanding of tumor biology and identification of potential biomarkers with applications as diagnostic, prognostic and therapeutic targets. In this review, we discuss various techniques used in metabolomics and analysis of the data generated and its specific uses in cancer research including novel biomarker identification, development of more sensitive and specific diagnostic methods, monitoring of currently used cancer therapeutics to evaluate the prognostic outcome with a given therapy and evaluating novel therapeutic strategies.     

Epigenome-Wide Association Studies (EWAS) in Cancer

After completion of the human genome, genome-wide association studies were conducted to identify single nucleotide polymorphisms (SNPs) associated with cancer initiation and progression. Most of the studies identified SNPs that were located outside the coding region, and the odds ratios were too low to implement in clinical practice. Although the genome gives information about genome sequence and structure, the human epigenome provides functional aspects of the genome. Epigenome-wide association studies (EWAS) provide an opportunity to identify genome-wide epigenetic variants that are associated with cancer. However, there are problems and issues in implementing EWAS to establish an association between epigenetic profiles and cancer. Few challenges include selection and handling of samples, choice of population and sample size, accurate measurement of exposure, integrating data, and insufficient information about the role of repeat sequences. The current status of EWAS, challenges in the field, and their potential solutions are discussed in this article.     

Illuminating the dark metabolome to advance the molecular characterisation of biological systems

Background

The latest version of the Human Metabolome Database (v4.0) lists 114,100 individual entries. Typically, however, metabolomics studies identify only around 100 compounds and many features identified in mass spectra are listed only as ‘unknown compounds’. The lack of ability to detect all metabolites present, and fully identify all metabolites detected (the dark metabolome) means that, despite the great contribution of metabolomics to a range of areas in the last decade, a significant amount of useful information from publically funded studies is being lost or unused each year. This loss of data limits our potential gain in knowledge and understanding of important research areas such as cell biology, environmental pollution, plant science, food chemistry and health and biomedical research. Metabolomics therefore needs to develop new tools and methods for metabolite identification to advance as a field.

Aim of review

In this critical review, some potential issues with metabolite identification are identified and discussed. New and novel emerging technologies and tools which may contribute to expanding the number of compounds identified in metabolomics studies (thus illuminating the dark metabolome) are reviewed. The aim is to stimulate debate and research in the molecular characterisation of biological systems to drive forward metabolomic research.

Key scientific concepts of review

The work specifically discusses dynamic nuclear polarisation nuclear magnetic resonance spectroscopy (DNP-NMR), non-proton NMR active nuclei, two-dimensional liquid chromatography (2DLC) and Raman spectroscopy (RS). It is suggested that developing new methods for metabolomics with these techniques could lead to advances in the field and better characterisation of biological systems.

     

Pluripotent Stem Cells Models for Huntington＇s Disease： Prospects and Challenges

Pluripotent cellular models have shown great promise in the study of a number of neurological disorders.Several advantages of using a stem cell model include the potential for cells to derive disease relevant neuronal cell types,providing a system for researchers to monitor disease progression during neurogenesis,along with serving as a platform for drug discovery.A number of stem cell derived models have been employed to establish in vitro research models of Huntington’s disease that can be used to investigate cellular pathology and screen for drug and cell-based therapies.Although some progress has been made,there are a number of challenges and limitations that must be overcome before the true potential of this research strategy is achieved.In this article we review current stem cell models that have been reported,as well as discuss the issues that impair these studies.We also highlight the prospective application of Huntington’s disease stem cell models in the development of novel therapeutic strategies and advancement of personalized medicine.     

Difficulties and special issues associated with field research in behavioral neuroendocrinology

Classical behavioral neuroendocrinology has focused on a limited number of domestic mammals and birds. The model systems used in these studies represent a very small proportion of the diversity of hormone-behavior interactions found in nature. In the last three decades, an increasing number of researchers have concentrated their efforts on studying behavioral neuroendocrinology of wild animals. Field behavioral neuroendocrinology presents a series of challenges ranging from the design of the experiments to sample preservation and transportation. The constraints of field conditions limit the number of factors that can be controlled for and the questions that can be addressed. On the other side, many behaviors can be studied only in the field, and only a few species can be kept in captivity. Thus, field studies are necessary to understand the complexity and variety of interactions between hormones, brain, and behavior. In this article, we will review some of the peculiarities and challenges of field behavioral neuroendocrinology, including solutions for some of the most commonly encountered technical issues.     

The Drosophila ovary: an active stem cell community

Only a small number of cells in adult tissues (the stem cells) possess the ability to self-renew at every cell division,while producing differentiating daughter cells to maintain tissue homeostasis for an organism's lifetime.The Drosophilaovary harbors three different types of stem cell populations (germline stem cell (GSC),somatic stem cell (SSC) andescort stem cell (ESC)) located in a simple anatomical structure known as germarium,rendering it one of the best modelsystems for studying stem cell biology due to reliable stem cell identification and available sophisticated genetic toolsfor manipulating gene functions.Particularly,the niche for the GSC is among the first and best studied ones,and studieson the GSC and its niche have made many unique contributions to a better understanding of relationships between stemcells and their niche.So far,both the GSC and the SSC have been shown to be regulated by extrinsic factors originatingfrom their niche and intrinsic factors functioning within.Multiple signaling pathways are required for controlling GSCand SSC self-renewal and differentiation,which provide unique opportunities to investigate how multiple signals fromthe niche are interpreted in the stem cell.Since the Drosophila ovary contains three types of stem cells,it also providesoutstanding opportunities to study how multiple stem cells in a given tissue work collaboratively to contribute to tissuefunction and maintenance.This review highlights recent major advances in studying Drosophila ovarian stem cells andalso discusses future directions and challenges.     

‘Why do tumour cells glycolyse?’: From glycolysis through citrate to lipogenesis

The re-emergence of interest in intermediary metabolism and the development of metabolomics in relation to cancer and other diseases provide a timely reason to revisit issues of tumour cell metabolism. In this review, we address the issue of the role of high aerobic glycolysis, which is commonly associated with the metabolism of many tumour cells. The concept presented emphasises the importance of the glycolysis-citrate-lipogenesis pathway in providing the synthetic and bioenergetic requirements that are essential for the growth and proliferation of tumour cells. We hope that our discussion will be informative and instructive, and will stimulate interest and research regarding the intermediary metabolism and its regulation in tumour cells. We express our appreciation to the many pioneering and contemporary researchers whose studies provide much of the basis for this presentation. (Mol Cell Biochem xxx: 1–8, 2005)     

A systematic review on metabolomics-based diagnostic biomarker discovery and validation in pancreatic cancer

Introduction

Metabolomics is an emerging approach for early detection of cancer. Along with the development of metabolomics, high-throughput technologies and statistical learning, the integration of multiple biomarkers has significantly improved clinical diagnosis and management for patients.

Objectives

In this study, we conducted a systematic review to examine recent advancements in the oncometabolomics-based diagnostic biomarker discovery and validation in pancreatic cancer.

Methods

PubMed, Scopus, and Web of Science were searched for relevant studies published before September 2017. We examined the study designs, the metabolomics approaches, and the reporting methodological quality following PRISMA statement.

Results and Conclusion

The included 25 studies primarily focused on the identification rather than the validation of predictive capacity of potential biomarkers. The sample size ranged from 10 to 8760. External validation of the biomarker panels was observed in nine studies. The diagnostic area under the curve ranged from 0.68 to 1.00 (sensitivity: 0.43–1.00, specificity: 0.73–1.00). The effects of patients’ bio-parameters on metabolome alterations in a context-dependent manner have not been thoroughly elucidated. The most reported candidates were glutamic acid and histidine in seven studies, and glutamine and isoleucine in five studies, leading to the predominant enrichment of amino acid-related pathways. Notably, 46 metabolites were estimated in at least two studies. Specific challenges and potential pitfalls to provide better insights into future research directions were thoroughly discussed. Our investigation suggests that metabolomics is a robust approach that will improve the diagnostic assessment of pancreatic cancer. Further studies are warranted to validate their validity in multi-clinical settings.

     

Separating the wheat from the chaff: a prioritisation pipeline for the analysis of metabolomics datasets

Liquid Chromatography Mass Spectrometry (LC-MS) is a powerful and widely applied method for the study of biological systems, biomarker discovery and pharmacological interventions. LC-MS measurements are, however, significantly complicated by several technical challenges, including: (1) ionisation suppression/enhancement, disturbing the correct quantification of analytes, and (2) the detection of large amounts of separate derivative ions, increasing the complexity of the spectra, but not their information content. Here we introduce an experimental and analytical strategy that leads to robust metabolome profiles in the face of these challenges. Our method is based on rigorous filtering of the measured signals based on a series of sample dilutions. Such data sets have the additional characteristic that they allow a more robust assessment of detection signal quality for each metabolite. Using our method, almost 80% of the recorded signals can be discarded as uninformative, while important information is retained. As a consequence, we obtain a broader understanding of the information content of our analyses and a better assessment of the metabolites detected in the analyzed data sets. We illustrate the applicability of this method using standard mixtures, as well as cell extracts from bacterial samples. It is evident that this method can be applied in many types of LC-MS analyses and more specifically in untargeted metabolomics.