Elemental metabolomics

Elemental metabolomics Abstract Elemental metabolomics is quantification and characterization of total concentration of chemical elements in biological samples and monitoring of their changes. Recent advances in inductively coupled plasma mass spectrometry have enabled simultaneous measurement of concentrations of > 70 elements in biological samples. In living organisms, elements interact and compete with each other for absorption and molecular interactions. They also interact with proteins and nucleotide sequences. These interactions modulate enzymatic activities and are critical for many molecular and cellular functions. Testing for concentration of > 40 elements in blood, other bodily fluids and tissues is now in routine use in advanced medical laboratories. In this article, we define the basic concepts of elemental metabolomics, summarize standards and workflows, and propose minimum information for reporting the results of an elemental metabolomics experiment. Major statistical and informatics tools for elemental metabolomics are reviewed, and examples of applications are discussed. Elemental metabolomics is emerging as an important new technology with applications in medical diagnostics, nutrition, agriculture, food science, environmental science and multiplicity of other areas. biomonitoring, chemometrics, clinical laboratory testing, ionomics, metallomics, mineralomics Introduction A metabolite is a product of chemical processes in an organism or a cell. The concepts of metabolic patterns, individual metabolic patterns and ‘hypothetical average individual’ were promoted in 1940s [1]. Thanks to the improvements in analytical methods, instrumentation and analytical capabilities, quantitative metabolic profiling of biomedical specimens using mass spectrometry became possible in 1970s [2]. Improvements in instrumentation, molecular methods, cheminformatics and bioinformatics, over the following 30 years, have lead to high-throughput measurement of multiple metabolites in human and plant specimens [3, 4]. The ability to simultaneously measure hundreds of metabolites, with high accuracy, at high speed and at affordable cost enabled the definition of metabolome and metabolomics. Elemental metabolomics focuses on comprehensive analysis of >70 chemical elements in biological samples that represent various biological conditions and states including genetic perturbations, nutritional intervention, disease and disease progression, drug effects or environmental influences. Key definitions used in metabolomics [5–8] and new definitions in elemental metabolomics are shown in Table 1. Metabolomics is the end point of the Omics cascade (genomics → transcriptomics → proteomics → metabolomics). Omics focuses on high-throughput measurements of molecular data and their interpretations that connect organism’s genetic code with the characteristics of its phenotype defining a key component of systems biology [9]. Elemental metabolomics is an interdisciplinary field at the intersection of chemistry, physics, biology, mathematics and data sciences. The workflow for elemental metabolomics is shown in Figure 1. The design of this workflows was informed by multiple inputs including proposed metabolomics workflows [10, 11], standard operating procedures (SOPs) for handling blood and urine for metabolomics studies [12, 13], imaging and speciation analysis methods [14], minimum information reporting standard (MIRS) for results of metabolomics experiments [15], formal determination of reference values of elements [16] and various statistical and machine learning methods used in metabolite screening [10, 11, 17]. Table 1. Definitions of key concepts and approaches in metabolomics [5–9] and elemental metabolomics Term  Description  Metabolite  An intermediate or final product of chemical reactions in an organism or a cell  Metabolome  A complete set of small molecules (molecular weight <1500 Da) that exist in a given biological tissues or sample  Metabolomics  Quantification of metabolome in target samples, monitoring their change and characterization of phenotypes over time or in response to various stimuli  Elemental metabolomics  Quantification and characterization of total concentration of chemical elements in biological samples and monitoring their changes  Elemental profiling  Quantification of chemical elements in a given sample or specimen  Elemental fingerprinting  Classification of biological samples using their elemental profiles  Elemental signature  Elemental composition derived from a representative selection of samples that represents some condition or status (e.g. geographic origin, genetic origin or health status)  Term  Description  Metabolite  An intermediate or final product of chemical reactions in an organism or a cell  Metabolome  A complete set of small molecules (molecular weight <1500 Da) that exist in a given biological tissues or sample  Metabolomics  Quantification of metabolome in target samples, monitoring their change and characterization of phenotypes over time or in response to various stimuli  Elemental metabolomics  Quantification and characterization of total concentration of chemical elements in biological samples and monitoring their changes  Elemental profiling  Quantification of chemical elements in a given sample or specimen  Elemental fingerprinting  Classification of biological samples using their elemental profiles  Elemental signature  Elemental composition derived from a representative selection of samples that represents some condition or status (e.g. geographic origin, genetic origin or health status)  Table 1. Definitions of key concepts and approaches in metabolomics [5–9] and elemental metabolomics Term  Description  Metabolite  An intermediate or final product of chemical reactions in an organism or a cell  Metabolome  A complete set of small molecules (molecular weight <1500 Da) that exist in a given biological tissues or sample  Metabolomics  Quantification of metabolome in target samples, monitoring their change and characterization of phenotypes over time or in response to various stimuli  Elemental metabolomics  Quantification and characterization of total concentration of chemical elements in biological samples and monitoring their changes  Elemental profiling  Quantification of chemical elements in a given sample or specimen  Elemental fingerprinting  Classification of biological samples using their elemental profiles  Elemental signature  Elemental composition derived from a representative selection of samples that represents some condition or status (e.g. geographic origin, genetic origin or health status)  Term  Description  Metabolite  An intermediate or final product of chemical reactions in an organism or a cell  Metabolome  A complete set of small molecules (molecular weight <1500 Da) that exist in a given biological tissues or sample  Metabolomics  Quantification of metabolome in target samples, monitoring their change and characterization of phenotypes over time or in response to various stimuli  Elemental metabolomics  Quantification and characterization of total concentration of chemical elements in biological samples and monitoring their changes  Elemental profiling  Quantification of chemical elements in a given sample or specimen  Elemental fingerprinting  Classification of biological samples using their elemental profiles  Elemental signature  Elemental composition derived from a representative selection of samples that represents some condition or status (e.g. geographic origin, genetic origin or health status)  Figure 1. View largeDownload slide Data workflow within an elemental metabolomics experiment is shown. Each of these steps needs to be properly documented in reports to ensure proper evaluation of results, comparability and replication of any experiment and ultimately pooling together data from multiple experiments for large-scale analyses. The overall workflow can be divided into six steps representing horizontal flow: design → sampling → analytical chemistry → data analytics → interpretation/modeling → further use. There is a terminology overlap where some words may have different meaning in chemistry and bioinformatics. Additional details about these steps are available in Tables 2 and 3 and throughout the main text. Figure 1. View largeDownload slide Data workflow within an elemental metabolomics experiment is shown. Each of these steps needs to be properly documented in reports to ensure proper evaluation of results, comparability and replication of any experiment and ultimately pooling together data from multiple experiments for large-scale analyses. The overall workflow can be divided into six steps representing horizontal flow: design → sampling → analytical chemistry → data analytics → interpretation/modeling → further use. There is a terminology overlap where some words may have different meaning in chemistry and bioinformatics. Additional details about these steps are available in Tables 2 and 3 and throughout the main text. The size of actual metabolome for any given organism is unknown and difficult to estimate [18]. As of September 2016, the Human Metabolome Database [19] has some 42 000 metabolite entries. It has been estimated that >200 000 metabolites are present in the plant kingdom [6]. Current state of the art of mass spectrometry and separation methods enables routine analysis of up to 1000 metabolites with throughput of 1000 samples per day [20]. Metabolomics produces mega-variate data creating situations where the number of variables easily exceeds the number of samples used in a research study resulting in the curse of dimensionality [21]. Such data are difficult to analyze, as false correlations and overfitting of statistical models are common, while the artefacts of experimental design are amplified. This makes high-throughput metabolomics studies difficult to interpret, and there is a need for methodology that can simplify and streamline metabolomics data analysis. The measurement of chemical elements is a logical first step in metabolomics studies that determines the total amount of each element in the sample. These measurements have direct diagnostic value in medicine and can also be used for food authentication and determination of food origin. Furthermore, knowing the total amount of each element in the sample provides useful information for the next metabolomics step that involves screening of thousands of metabolites. This article provides an overview of elemental metabolomics, a neglected subfield of metabolomics. In the following sections, we will describe the basic concept of elemental metabolomics, standards used to ensure accurate and reproducible measurements, methods for data capture and processing and bioinformatics methods for data analysis and interpretation. We have also reviewed applications related to elemental metabolomics and provided brief comments about bioinformatics tools for these applications. Elemental metabolomics Approximately 96% of elemental composition of human body, by mass, consists of four elements (C, H, N and O), and we term them ‘mega elements’. Another 3.25% consists of macro elements (Ca, Cl, K, Mg, Na, P and S), while the remaining elements are called trace (Al, As, B, Ba, Br, Cd, Ce, Co, Cs, Cu, Cr, F, Fe, Ge, Hg, I, Li, Mn, Mo, Ni, Pb, Rb, Se, Si, Sn, Sr, Ti and Zn) or ultratrace (Ag, Au, Be, Bi, Ga, Hf, In, Ir, Nb, Os, Pd, Pt, Re, Rh, Ru, Sb, Sc, Ta, Tc, Te, Th, Tl, U, V, W, Y, Zr and the 14 lanthanides). Ultratrace elements are present in concentration of roughly 0.1–10 µg/kg of sample. In practical terms, a human body of 75 kg would have ∼72.5 kg of four mega elements, 2.5 kg of seven macro elements (Ca and P make ∼1.9 kg), 15 g of trace elements (total of 28) and 1 mg of ultratrace (total of 41) elements [22]. It is not surprising that elemental metabolomics studies are few—until recently, the accuracy of measurement and the cost were not at the level that would support its routine use. Our ability to accurately and reproducibly measure ultratrace elements has realized only in the past 10–15 years. It is only over the past few years that elemental metabolomics studies became affordable. Advances in inductively coupled plasma mass spectrometry (ICP-MS) enabled accurate and affordable measurement of >70 elements at reasonable cost [23] (Figure 2). ICP-MS is suitable for elemental metabolomics measurement, given its performance characteristics including detection limits, concentration working range, sample throughput capacity and methods for compensation of interferences [24]. Sophisticated methods including matrix removal, mathematical correction equations and collision/reaction cell ICP-MS have been developed to minimize or nullify the measurement interferences. Interested readers can find more details about ICP-MS use in life sciences in [25]. Figure 2. View largeDownload slide Common elemental analytes detectable and measurable by ICP-MS are shown. Groupings into essential, beneficial, common without clearly defined function, and toxic elements are somewhat arbitrary and need to be taken within a specific biological context. Elements labeled as either essential or beneficial are either the components of major structural components in the body (Ca, P, S); responsible for maintenance of ionic equilibria, activation, or signaling (Ca, K, Mg, Na); components of enzymes or hormones (Co, Cr, Cu, Fe, I, Mn, Mo, Ni, Se, Sn, V, Zn). Essential and beneficial (suggested that may be essential) are designated as given in [127]. For example, several elements display essentiality-toxicity duality. In chromium, this duality is related to speciation - trivalent Cr(III) is an essential nutrient considered non-toxic, while hexavalent Cr(VI) is considered toxic and carcinogenic. Elements indicated as toxic risk are those reported as high toxic risk [128], implicated as neurotoxic [129], or are radioactive. Figure 2. View largeDownload slide Common elemental analytes detectable and measurable by ICP-MS are shown. Groupings into essential, beneficial, common without clearly defined function, and toxic elements are somewhat arbitrary and need to be taken within a specific biological context. Elements labeled as either essential or beneficial are either the components of major structural components in the body (Ca, P, S); responsible for maintenance of ionic equilibria, activation, or signaling (Ca, K, Mg, Na); components of enzymes or hormones (Co, Cr, Cu, Fe, I, Mn, Mo, Ni, Se, Sn, V, Zn). Essential and beneficial (suggested that may be essential) are designated as given in [127]. For example, several elements display essentiality-toxicity duality. In chromium, this duality is related to speciation - trivalent Cr(III) is an essential nutrient considered non-toxic, while hexavalent Cr(VI) is considered toxic and carcinogenic. Elements indicated as toxic risk are those reported as high toxic risk [128], implicated as neurotoxic [129], or are radioactive. Large-scale high-throughput elemental profiling studies have been known as ionomics [26, 27], mineralomics [28, 29], metallomics [30, 31] and elementomics [32]. These high-throughput approaches study the relationships between elemental minerals, metals or nonmetals measured in their elemental form (as total element quantity) in biological samples, and correlate them to physiological states, biological processes and phenotypes. They refer essentially to the same concept—the role of individual elements in physiology and functioning of an organism. Elemental metabolomics, therefore, describes studies of metabolic role of the comprehensive matrix of multiple elements. Elemental metabolomics shares the scope with metallomics that studies metals, metalloids and trace elements—‘the metallome’—of biological systems and the interaction of the metallome with other ‘omes’ in a given organism. Elemental metabolomics, however, provides broader and a more formal framework, as it promotes: formalization of standards and procedures including standardized workflows and SOPs developed specifically for elemental metabolomics for different kinds of biological materials; the use of sample-matched reference materials defined for >70 elements; completeness of screening that includes all measurable elements (currently >70); use of sophisticated statistical and mathematical modeling tools to extract maximum knowledge from elemental profiles; concurrent measurement of bioavailability and transfer of elements between the environment, food and organisms and elemental accumulation in various tissues. Individual elements interact at multiple levels in living organisms including competitive absorption in roots of plants [33] and through mucosal surfaces in animals [34]. They may act as antagonists, synergists or catalysts [35], can replace each other from their molecular binding sites [36] and interact with various targets to enhance biological effects [37]. The essentiality and toxicity of elements depend on chemical speciation [14, 38]. Chemical speciation is of interest in toxicology and nutrition (As, Ba, Ca, Cd, Co, Cu, Pb, Se, Sr and Zn), environmental exposure (As, Cd, Cr, Fe, Ge, Hg, Pb, Sb, Se and Sn), industrial exposure (As, Fe, Ga, Hg, Ni, Rh, Ru and V) and medically related exposure (Al, Co, Cr, Cu, Fe, Mo, Ni, Pt, Ru, Ti and Zn), where toxicity and biological effects depend on chemical species [38]. Differences in toxic effects of elements depend on their chemical forms that exhibit differences in solubility, transport, reactivity, absorbability and the chemical complexes they form within tissues and cells [39]. For example, chromium is an essential element that comes mainly in form of Cr(III) that is of low toxicity and Cr(VI) that is highly toxic [39]. Most forms of mercury (Hg) are highly toxic to humans, particularly to the nervous system. Elemental and inorganic ionic forms of Hg are poorly absorbed from the gut, but organic forms such as methylmercury (MeHg) are readily absorbed from the gastrointestinal tract and can also readily cross the blood–brain barrier. Industrial pollution commonly discharges Hg in ionic or elemental form. Aquatic organisms convert elemental and ionic Hg into MeHg increasing bioavailability to humans mainly through fish and other seafood [40]. In addition to the concerns about arsenic and cadmium in rice, it was suggested that rice may be another significant source of dietary MeHg [41]. Toxic element exposure, therefore, requires elemental assessment in complex matrices spanning multiple nutritional to various environmental or occupational sources. Isotopes represent forms of a given chemical element. Isotopes of an element have an identical number of protons, but differ in the number of neutrons in each atom. The natural abundance of isotopes at a given point of time is relatively stable, but small variations may characterize the source, e.g. oceans versus soils, or different geographic locations [42, 43]. Isotopic abundance patterns are used for mega elements (C, H, N and O) and also Br, F, P, S and Si [44]. Isotope screening is used for identification of food origin, including geographic region and production system (e.g. organic versus conventional) [45]. Stable isotope analysis refers to the determination of isotopic abundances. Isotopic compositions of samples reflect their biological or physical provenance and history and, thus, provide additional discriminating tool for samples that have similar elemental signatures. Current practice does not define criteria for preference of elemental signatures over isotopic profiles or the reverse. We anticipate that in future, more comprehensive measurements including isotopes will be performed; currently, these measurements are limited by the cost of high-resolution mass spectrometry. The list of stable isotopes is provided in Supplementary Table S1 [46]. Despite that only 73 elements (Figure 2) and low-abundance isotopes of C, H, N and O can be measured (Figure 2), taking into account isotopes, speciation and mutual interactions of elements, variation of elemental metabolomics is of high combinatorial complexity. Furthermore, elemental metabolomics is emerging as an important methodology for molecular biology, medical diagnosis, prognosis and monitoring, toxicology, environmental studies, food safety and traceability, nutrition and other fields such as forensics and archeology. Variation of biologically relevant samples is huge—it is affected by the source, origin, season, geographic location, health status and other variables. Individual studies having tens of thousands of samples will soon become the norm. Statistical and bioinformatics methods are essential for elemental metabolomics. Some of these methods can be adapted from metabolomics, laboratory science and food science, while new methods are needed to deal with specific needs of elemental metabolomics. Standards and minimum information for elemental metabolomics To ensure validity, analytical measurements using biological matrices must adhere to SOP in both pre-analytical and analytical steps. A particular problem is presented with ultratrace elements. Elements that are used or are present in equipment and materials that are used in pre-analytical and analytical steps represent major potential sources of contamination. Pre-analytical SOPs ensure that sample collection and handling preserve the integrity and the content of the samples before the analytical step is performed [47]. Analytical SOP must ensure that elemental content during preparation and measurement steps is not compromised. SOP prescribes the use of reference standards including blanks, instrument calibration standards and internal standards and ensure the selectivity, accuracy, precision, integrity and stability of the measurement method [48, 49]. Blood collection and handling require appropriate accessories and procedures (flasks, tubes, needles and reagents) to prevent contamination, and SOPs in this area are well defined [50]. SOPs for other areas of biomonitoring, such as food, do exist [51, 52]. Given the wide variety of biological samples, it is necessary that SOPs are developed for each type of biological sample. Prevention of sample contamination with trace and ultratrace elements requires clean handling and clean laboratory environment. Ideally, an ISO Class 5 (ISO Guide 14644, equivalent to FED STD 209E Class 100) laboratory should be used for the analysis of trace elements [52], and possible sources of contamination should be minimized or eliminated. For example, it was observed that chromium contamination increases 10-fold when frozen muscle samples are cut by surgical blade as compared with fresh tissue cutting by surgical blade [53]. Similarly, mills, grinders and laboratory wares are common sources of trace element contamination. Extensive recommendations on prevention and remediation of laboratory contamination are given in [54]. Usefulness of elemental metabolomics will depend on large data sets where elemental profiles for a variety of samples are available and on ability to compare results from different studies. In addition to SOP, standard reference materials (SRMs) and reference materials are needed for comparability of results. Reference materials are homogeneous, stable and well-defined sources with known trace element concentrations. Well-characterized SRMs and certified reference materials are critical for instrument to ensure the accuracy of measurements. There are three categories of assigned values of trace element concentrations in SRMs (in declining order of confidence): certified values, reference values and ‘for information’ values [55]. Reference materials for trace elements exist, but they rarely have reported values for >30 elements. Furthermore, SRMs may consist of material that has very different concentration of elements than the sample increasing the possibility of measurement error of low-concentration elements close to detection limits. For example, the SRM for lanthanides from mussel tissue (BCR-668) and aquatic plant tissue (BCR-670) have concentrations of individual lanthanides that can be 2–3 orders of magnitude higher than some target materials such as meat [56]. More details about SRM and complexities of establishing them can be found in [57]. Comprehensive metabolomics studies may include the screening of a variety of samples—from environment, soil, food and human samples to track elements and their movements through the chain to study their effect on human health. Because of potentially large differences in concentration of certain elements, multiple calibration standards need to be used to ensure that all elemental measurements will be within linear calibration ranges. To assure high accuracy of measurements, comprehensive sample-matched reference materials need to be developed and used. Environmental disasters and accidents may result in unpredictably elevated environmental exposure to a range of elements making SRMs unsuitable, even when multiple reference materials will be available. Standard analytical practices—dilution in combination with internal standards—are expected to be adequate in disaster and accidents monitoring. Elemental metabolomics studies need to share MIRSs to enable comparability between studies and provide means to understand, evaluate, repeat and reinvestigate these studies. MIRS relevant to elemental metabolomics includes those used in chemical analyses [15] and metabolomics experiments [58–60]. Based on these recommendations and our experience with elemental screening of concurrent measurement of large number of elements [17, 61], we have compiled a tentative MIRS for elemental metabolomics (Table 2). Examples of studies that have well-reported data can be found in [54, 62, 63]. For publications, it is recommended that the detailed reports and individual sample measurements are provided as supplementary materials. These standards transcend and unify all subfields of elemental metabolomics (ionomics, metalomics, minerallomics and elementomics) [26–32]. Table 2. Tentative minimal information reporting standards (MIRS) for elemental metabolomics studies Metadata  Reporting items  Sampling process and protocol  Replicate sampling and analysis Tissue harvesting method Biofluid harvesting and collection method Tissue processing method (pre-analytic) Storage conditions Transport and shipping of samples Methods for prevention of contamination  Sample preparation for elemental analysis  Chemicals and their descriptions Internal standards, if any Microwave oven program for digestion Digestate handling/storage Transport and shipping of digestates  Sample preparation for speciation  Chemicals/solvents and their description Internal standards Extracts handling/storage Transport and shipping of extracts  ICP-MS  Instrument description Sample introduction and delivery Ionization source Mass analyzer description Data acquisition parameters SRMs used Targeted stable isotopes  Separation technique coupled to ICP-MS for speciation  Instrument description and setup Interface to ICP-MS Separation technique description  Instrumental checks and performance  Calibration and accuracy/precision assessment Quality control samples Replicates Blanks Spiked samples SRMs Limits of detection (LOD) values Limits of quantitation (LOQ) values List of acceptance criteria  Data preprocessing  Data file format and conversion methods Data pre-processing Calibration curves Background subtraction Noise reduction Interference correction measures Dealing with LOD and LOQ   Data reporting  Elemental matrices and concentrations Ranges, medians and averages Reference intervals Units (consistent with SI nomenclature [64]) Use µg/kg for solid, or µg/l for liquid samples ppm (parts per million) and related terms (ppb, ppt) should not be used Different scale units (such as mg/kg and µg/kg) should not be used in the same report mol/kg or mol/l measures can be used if conversion factors to µg/kg and µg/l are provided   Biological experiment  IUPAC nomenclature Use recognized ontologies whenever possible Sample description Organism, type, subtype and genotype Sample composition and sources Sample type (phenotype, weight, age, sex, characteristics and tissue) Other relevant details Environment and conditions descriptions Control samples, disease/disorder, pollution, type of food taken, toxicity, occupational hazard, geographic location, time, production system, maintenance procedure and parameters, etc.   Metadata  Reporting items  Sampling process and protocol  Replicate sampling and analysis Tissue harvesting method Biofluid harvesting and collection method Tissue processing method (pre-analytic) Storage conditions Transport and shipping of samples Methods for prevention of contamination  Sample preparation for elemental analysis  Chemicals and their descriptions Internal standards, if any Microwave oven program for digestion Digestate handling/storage Transport and shipping of digestates  Sample preparation for speciation  Chemicals/solvents and their description Internal standards Extracts handling/storage Transport and shipping of extracts  ICP-MS  Instrument description Sample introduction and delivery Ionization source Mass analyzer description Data acquisition parameters SRMs used Targeted stable isotopes  Separation technique coupled to ICP-MS for speciation  Instrument description and setup Interface to ICP-MS Separation technique description  Instrumental checks and performance  Calibration and accuracy/precision assessment Quality control samples Replicates Blanks Spiked samples SRMs Limits of detection (LOD) values Limits of quantitation (LOQ) values List of acceptance criteria  Data preprocessing  Data file format and conversion methods Data pre-processing Calibration curves Background subtraction Noise reduction Interference correction measures Dealing with LOD and LOQ   Data reporting  Elemental matrices and concentrations Ranges, medians and averages Reference intervals Units (consistent with SI nomenclature [64]) Use µg/kg for solid, or µg/l for liquid samples ppm (parts per million) and related terms (ppb, ppt) should not be used Different scale units (such as mg/kg and µg/kg) should not be used in the same report mol/kg or mol/l measures can be used if conversion factors to µg/kg and µg/l are provided   Biological experiment  IUPAC nomenclature Use recognized ontologies whenever possible Sample description Organism, type, subtype and genotype Sample composition and sources Sample type (phenotype, weight, age, sex, characteristics and tissue) Other relevant details Environment and conditions descriptions Control samples, disease/disorder, pollution, type of food taken, toxicity, occupational hazard, geographic location, time, production system, maintenance procedure and parameters, etc.   Note. More details are available from [15, 58, 59]. Proper MIRS should be established and agreed by the professional communities, and this MIRS framework represents an informed starting point. The concentration of elements is expressed as w/w (µg/kg) or w/v (µg/l) because they are widely used, well-understood and are consistent with the international system of units (SI) nomenclature and guidelines [64]. For sample description, European Food Safety Authority has provided a comprehensive set of guidelines [65]. Table 2. Tentative minimal information reporting standards (MIRS) for elemental metabolomics studies Metadata  Reporting items  Sampling process and protocol  Replicate sampling and analysis Tissue harvesting method Biofluid harvesting and collection method Tissue processing method (pre-analytic) Storage conditions Transport and shipping of samples Methods for prevention of contamination  Sample preparation for elemental analysis  Chemicals and their descriptions Internal standards, if any Microwave oven program for digestion Digestate handling/storage Transport and shipping of digestates  Sample preparation for speciation  Chemicals/solvents and their description Internal standards Extracts handling/storage Transport and shipping of extracts  ICP-MS  Instrument description Sample introduction and delivery Ionization source Mass analyzer description Data acquisition parameters SRMs used Targeted stable isotopes  Separation technique coupled to ICP-MS for speciation  Instrument description and setup Interface to ICP-MS Separation technique description  Instrumental checks and performance  Calibration and accuracy/precision assessment Quality control samples Replicates Blanks Spiked samples SRMs Limits of detection (LOD) values Limits of quantitation (LOQ) values List of acceptance criteria  Data preprocessing  Data file format and conversion methods Data pre-processing Calibration curves Background subtraction Noise reduction Interference correction measures Dealing with LOD and LOQ   Data reporting  Elemental matrices and concentrations Ranges, medians and averages Reference intervals Units (consistent with SI nomenclature [64]) Use µg/kg for solid, or µg/l for liquid samples ppm (parts per million) and related terms (ppb, ppt) should not be used Different scale units (such as mg/kg and µg/kg) should not be used in the same report mol/kg or mol/l measures can be used if conversion factors to µg/kg and µg/l are provided   Biological experiment  IUPAC nomenclature Use recognized ontologies whenever possible Sample description Organism, type, subtype and genotype Sample composition and sources Sample type (phenotype, weight, age, sex, characteristics and tissue) Other relevant details Environment and conditions descriptions Control samples, disease/disorder, pollution, type of food taken, toxicity, occupational hazard, geographic location, time, production system, maintenance procedure and parameters, etc.   Metadata  Reporting items  Sampling process and protocol  Replicate sampling and analysis Tissue harvesting method Biofluid harvesting and collection method Tissue processing method (pre-analytic) Storage conditions Transport and shipping of samples Methods for prevention of contamination  Sample preparation for elemental analysis  Chemicals and their descriptions Internal standards, if any Microwave oven program for digestion Digestate handling/storage Transport and shipping of digestates  Sample preparation for speciation  Chemicals/solvents and their description Internal standards Extracts handling/storage Transport and shipping of extracts  ICP-MS  Instrument description Sample introduction and delivery Ionization source Mass analyzer description Data acquisition parameters SRMs used Targeted stable isotopes  Separation technique coupled to ICP-MS for speciation  Instrument description and setup Interface to ICP-MS Separation technique description  Instrumental checks and performance  Calibration and accuracy/precision assessment Quality control samples Replicates Blanks Spiked samples SRMs Limits of detection (LOD) values Limits of quantitation (LOQ) values List of acceptance criteria  Data preprocessing  Data file format and conversion methods Data pre-processing Calibration curves Background subtraction Noise reduction Interference correction measures Dealing with LOD and LOQ   Data reporting  Elemental matrices and concentrations Ranges, medians and averages Reference intervals Units (consistent with SI nomenclature [64]) Use µg/kg for solid, or µg/l for liquid samples ppm (parts per million) and related terms (ppb, ppt) should not be used Different scale units (such as mg/kg and µg/kg) should not be used in the same report mol/kg or mol/l measures can be used if conversion factors to µg/kg and µg/l are provided   Biological experiment  IUPAC nomenclature Use recognized ontologies whenever possible Sample description Organism, type, subtype and genotype Sample composition and sources Sample type (phenotype, weight, age, sex, characteristics and tissue) Other relevant details Environment and conditions descriptions Control samples, disease/disorder, pollution, type of food taken, toxicity, occupational hazard, geographic location, time, production system, maintenance procedure and parameters, etc.   Note. More details are available from [15, 58, 59]. Proper MIRS should be established and agreed by the professional communities, and this MIRS framework represents an informed starting point. The concentration of elements is expressed as w/w (µg/kg) or w/v (µg/l) because they are widely used, well-understood and are consistent with the international system of units (SI) nomenclature and guidelines [64]. For sample description, European Food Safety Authority has provided a comprehensive set of guidelines [65]. Statistics and bioinformatics in elemental metabolomics Elemental matrices measured in biological samples are information-rich and show big variation of the values across different materials. Proper statistical and computational modeling techniques, ranging from descriptive statistics to the use of complex models, are essential for getting the value out of data. Descriptive statistics is commonly used to provide an overview of elemental profiles. Descriptive statistics of elemental profiling studies provides meaningful summaries. Typical summaries report the number of samples, central tendency spread (minimum, maximum, average or median values along with standard deviation and quantiles) and shape of the distributions (coefficients of skewness and kurtosis or histograms) [66–68]. Descriptive statistics enables identification of simple patterns and identification of dependencies in data sets. We can use descriptive univariate statistics for identification of outliers, possible associations, for example association between elemental levels in blood and mother’s milk [67], and possible agonist and antagonist relations between individual elements [69]. Correlation tests are frequently used to test possible association between two variables. Linear correlation analysis identified pairs of elements that show strong positive correlation in human scalp hair samples and in fingernail samples, and also identified elements that showed strong positive correlation between their concentrations in hair and nail [70, 71]. Reference Value Advisor program [72] is useful for univariate analysis of elemental matrices. It enables calculation of reference intervals along with their 90% confidence intervals for each element. These values are calculated using appropriate statistics and transformations (nonparametric, parametric and robust Box–Cox transformed methods) in accordance to international recommendations [73]. In addition, it tests the normality of distributions and displays Q–Q plots, identifies possible outliers and displays the distribution plots and histograms. Knowledge of reference values and their statistical properties allows comparison of results measured by different methods, or comparison of samples that represent different conditions or their variations. Hypothesis testing is a common goal of exploratory data analysis such as univariate analysis and data agreement testing [74]. Selection of an appropriate method depends on the research questions and the types of data collected [75]. One of the most used test, analysis of variance (ANOVA), tests the differences of means between two or more groups. In [76], ANOVA was used to determine significant variation of Cr, Mn, Sr, Pb and V between cow milk samples from different farms in South Africa. These results suggested that milk samples from different geographic regions may have different elemental compositions, thus having different nutritional profiles and different toxic content. More complex interactions, such as exposure to multiple elements and their dependencies, can be studied using multiple regression. Regression analysis can generate a mathematical equation that can predict the dependent variable from values of independent variables. Linear regression analysis of elemental profiles in human hair revealed possible dependencies between groups of elements [71]—it indicated that the concentration of Al in hair can be expressed as a function of concentrations of U, P and Mn. The same study indicated strong interrelationships of industrially related elements that contribute to environmental pollution in hair samples from exposed population. Synergistic and antagonistic relationships between elements have been observed—the combined effect, such as toxicity, of two elements is higher than individual effects when both elements are present (synergy), or combined effect might be lower than individual effects (antagonism) [77]. Nonlinear multiple regression was used to study occupational hazard related to eight metals in welders [78]. This study showed that welders have higher concentration of metals in blood and urine, and that they have higher rate of DNA damage than controls. Logistic regression model estimates the probability of categorical dependent variable using data from elemental matrices. In elemental metabolomics type of study, logistic regression was used to predict physiological status of plant from leaf elemental profile (five elements) [79], to study possible associations of autism with hair concentration of trace elements (17 elements) [80] and to evaluate possible interactions in metal ions as risk factors for prostate cancer (four elements) [81]. A major deficiency in majority of these studies is that most of them involved screening of relatively small number of elements as compared with >70 that are currently measurable by ICP-MS. Exploratory analysis of high-dimensional data sets and hypothesis generation is supported by a variety of machine learning approaches that perform regression analysis, clustering, classification and data visualizations. Methods that are commonly used for elemental data analysis are principal component analysis (PCA), linear discriminant analysis (LDA), classification and regression trees, k-means clustering and hierarchical clustering with dendograms [61, 82]. PCA applies orthogonal transformation to a set of measurements to produce linearly uncorrelated variables or ‘principal components’. PCA is commonly used for reduction of dimensionality of elemental profiles and is used in screening plant, food and clinical samples [83–85]. Hierarchical clustering partitions set into groups of similar objects or ‘clusters’ and build a dendogram of the hierarchy of the clusters. This allows us to see how the samples group together based on similarity of elemental concentrations. It is widely used in study of food, water and medical samples [86–88]. Predictive models including network models and decision trees have been explored in the study of elemental profiles in food science and health. For example, elemental analysis for identification of the island of origin of wine from Canary Islands showed that LDA and artificial neural networks (ANN) were highly accurate with ANN outperforming LDA [89]. ANN, support vector machines (SVM) and decision trees were applied for classification of rice by geographic origin [90, 91]. Comparative analysis of ANN, SVM and decision tree classifiers were reported for classification of grape juice [92]. These studies are useful, as they describe basic usage of advanced classification methods using elemental profiles, but need to be taken with caution, as they were developed using a small number of samples. With the number of reported samples growing and application of common standards across studies, the advanced classification methods will gain prominence. Elemental profiles combined with mutual information analysis was used to study associations between ion modules and networks with obesity, metabolic syndrome and type 2 diabetes in Chinese adults [93]. The results were used to construct ‘disease-associated ion networks’. A study of illicit drugs classification by origin using elemental profiles determined that PCA and hierarchical clustering were inadequate, while ANN-based classification showed 96–99% correct classification of ecstasy tablets by the law enforcement seizure [94]. Elemental profiling, thus can be used to trace illicit drugs to distribution networks, and ultimately the origin of drugs. To build a predictive model from multidimensional data, it is often needed to reduce dimensionality by selection of critical variables and then use these variables to build classification models. In an investigation of chemical signatures (anionic, elemental and isotopic profiles) of chemical threat agents, a comprehensive analysis was performed to enable tracing the source of cyanides [95]. Multiple approaches for selection of variables and for building classification models were assessed and compared. Dimensionality reduction was performed using hierarchical cluster analysis, PCA, Fisher ratio from ANOVA, interval partial least squares (PLS) regression and genetic algorithm-based PLS regression. Classification models in [95] were built using PLS discriminant, K-nearest neighbor clustering and SVM. This study is interesting because it has demonstrated the use of several methods, showed how results may differ between methods and showed how multiple methods can be combined for interpretation of results. We have summarized a selection of methods commonly used in elemental metabolomics in Table 3. Table 3. List of types of data analysis and a list of methods commonly used in elemental metabolomics studies Type of analysis  Examples of methods and tools  Used for  Descriptive statistics  Plots, charts and histograms Mean, median, range and quantiles Variance and standard deviation Skewness and kurtosis Correlation and covariance  Summarizing samples Assessing central tendency and dispersion Assessing shapes of distribution Measuring dependencies  Data cleaning  Clustering algorithms Data imputation methods Outlier detection Filtering algorithms Aggregation and normalization algorithms  Data correction Data preprocessing Data harmonization Data standardization Combining or grouping data  Exploratory data analysis  Principal component analysis Clustering methods Regression methods Partial least squares discriminant analysis Classification and regression trees Analysis of variance Linear discriminant analysis Visualization techniques  Assessment of assumptions Statistical inference Dimensionality reduction Feature selection and model selection Formulating and testing hypotheses Pattern recognition Design of further analysis Design of further experiments  Predictive modeling and simulation  Regression models Probabilistic models Decision trees Cluster analysis Markov chains Neural networks Support vector machines Hybrid models Ensemble models  Statistical inference Pattern recognition Classification and prediction Survival analysis Decision-making Data mining Big data analytics  Type of analysis  Examples of methods and tools  Used for  Descriptive statistics  Plots, charts and histograms Mean, median, range and quantiles Variance and standard deviation Skewness and kurtosis Correlation and covariance  Summarizing samples Assessing central tendency and dispersion Assessing shapes of distribution Measuring dependencies  Data cleaning  Clustering algorithms Data imputation methods Outlier detection Filtering algorithms Aggregation and normalization algorithms  Data correction Data preprocessing Data harmonization Data standardization Combining or grouping data  Exploratory data analysis  Principal component analysis Clustering methods Regression methods Partial least squares discriminant analysis Classification and regression trees Analysis of variance Linear discriminant analysis Visualization techniques  Assessment of assumptions Statistical inference Dimensionality reduction Feature selection and model selection Formulating and testing hypotheses Pattern recognition Design of further analysis Design of further experiments  Predictive modeling and simulation  Regression models Probabilistic models Decision trees Cluster analysis Markov chains Neural networks Support vector machines Hybrid models Ensemble models  Statistical inference Pattern recognition Classification and prediction Survival analysis Decision-making Data mining Big data analytics  Note. This list is not exhaustive, but it is intended to provide a brief overview of statistical and mathematical tools that support elemental metabolomics data workflow shown in Figure 1B. A comprehensive study involves types of analysis done sequentially: descriptive statistics → data cleaning → exploratory data analysis → predictive modeling. Table 3. List of types of data analysis and a list of methods commonly used in elemental metabolomics studies Type of analysis  Examples of methods and tools  Used for  Descriptive statistics  Plots, charts and histograms Mean, median, range and quantiles Variance and standard deviation Skewness and kurtosis Correlation and covariance  Summarizing samples Assessing central tendency and dispersion Assessing shapes of distribution Measuring dependencies  Data cleaning  Clustering algorithms Data imputation methods Outlier detection Filtering algorithms Aggregation and normalization algorithms  Data correction Data preprocessing Data harmonization Data standardization Combining or grouping data  Exploratory data analysis  Principal component analysis Clustering methods Regression methods Partial least squares discriminant analysis Classification and regression trees Analysis of variance Linear discriminant analysis Visualization techniques  Assessment of assumptions Statistical inference Dimensionality reduction Feature selection and model selection Formulating and testing hypotheses Pattern recognition Design of further analysis Design of further experiments  Predictive modeling and simulation  Regression models Probabilistic models Decision trees Cluster analysis Markov chains Neural networks Support vector machines Hybrid models Ensemble models  Statistical inference Pattern recognition Classification and prediction Survival analysis Decision-making Data mining Big data analytics  Type of analysis  Examples of methods and tools  Used for  Descriptive statistics  Plots, charts and histograms Mean, median, range and quantiles Variance and standard deviation Skewness and kurtosis Correlation and covariance  Summarizing samples Assessing central tendency and dispersion Assessing shapes of distribution Measuring dependencies  Data cleaning  Clustering algorithms Data imputation methods Outlier detection Filtering algorithms Aggregation and normalization algorithms  Data correction Data preprocessing Data harmonization Data standardization Combining or grouping data  Exploratory data analysis  Principal component analysis Clustering methods Regression methods Partial least squares discriminant analysis Classification and regression trees Analysis of variance Linear discriminant analysis Visualization techniques  Assessment of assumptions Statistical inference Dimensionality reduction Feature selection and model selection Formulating and testing hypotheses Pattern recognition Design of further analysis Design of further experiments  Predictive modeling and simulation  Regression models Probabilistic models Decision trees Cluster analysis Markov chains Neural networks Support vector machines Hybrid models Ensemble models  Statistical inference Pattern recognition Classification and prediction Survival analysis Decision-making Data mining Big data analytics  Note. This list is not exhaustive, but it is intended to provide a brief overview of statistical and mathematical tools that support elemental metabolomics data workflow shown in Figure 1B. A comprehensive study involves types of analysis done sequentially: descriptive statistics → data cleaning → exploratory data analysis → predictive modeling. Complex elemental interactions are often difficult to analyze and interpret. The interpretation of these results needs to be taken with caution because a causal relationship cannot be inferred from these results. Causation can be inferred in a randomized study that takes into account multiple factors that could be associated. For elemental studies of human samples, these factors include, among others, diet, nutritional supplementation, lifestyle, environmental and occupational exposure, age, sex, genetic predisposition, use of cosmetics and medication. This makes human studies much more complex than studies with laboratory animals, farm animals or plants, where many of the factors can be controlled. Conclusion and discussion Important applications of elemental metabolomics used for biomonitoring are emerging in environmental science, agriculture, food science, nutrition, pharmacology and medicine [96]. ICP-MS combined with microwave digestion has been validated for simultaneous determination of multiple elements in various foodstuffs [97–99] including crops [100, 101], livestock [63, 102] and fish [103]. Important applications include food quality control [104], food authentication [17] and food safety and control [105]. ICP-MS is suitable for biomonitoring of human samples including blood, urine and hair [106–108]. ICP-MS is used in environmental research [109], infant and adult nutrition [67, 110, 111], as well as the analysis of drugs and pharmaceuticals, medicinal plants and supplements [96]. Furthermore, ICP-MS-based multielemental monitoring has applications in medicine including toxicology [112] and occupational health [77], and is being developed for clinical diagnostics and monitoring [113, 114]. Currently, there are >40 clinical laboratory blood tests for individual elements in human blood and other fluids, and most of them use ICP-MS. These tests are performed individually for each element. Multielement clinical laboratory testing offers an enormous opportunity for the improvement of diagnostics, as it will enable understanding of elemental interactions, and more detailed understanding of the exposure patterns to elements and their combinations. It was shown that a large number of elements can be screened simultaneously, for example 60 elements were screened for their concentration in human blood [115]. However, only in the past few years, we acquired the ability to make these screenings sufficiently accurate and cheap for routine use in wide range of applications. Development of tests for simultaneous multielement screening will require improved SRMs, well-defined reference values, databases that store values of elemental profiles representative of the conditions of interest and specialized computational algorithms for data comparison and analysis. New SRMs that can be used for screening of >70 elements are needed. For specific applications, SRMs should have elemental concentration ranges similar to the concentration ranges in target samples—to ensure good matching of linear ranges of measurements. They should ideally be derived from materials similar to target samples. Elemental reference values—concentrations of elements expected to be found in control populations—have been proposed [106, 116]. These reference values are crude, as they were done on small sample and do not provide detailed description of target population (by age, geographic origin, occupation, etc.). There is a need for systematic screening of population for establishing baseline values that take into account various confounding variables. The interpretation of elemental profile measurements requires comparison with data from reference databases of elemental profiles that represent a broad variety of biological samples and various conditions and geographic origins. Such databases have been proposed in various meetings, but to our knowledge, they either do not exist or they are not publicly available. Along with the emergence of these databases, analysis tools for comparison of samples and analysis will be developed. The main obstacles for advancing the field of elemental metabolomics are the incomplete screening and small number of samples that are used in a today’s typical study. Recent technological advancements in mass spectrometry including triple-quadrupole ICP-MS configurations offer unprecedented measurement capabilities and increased productivity. Improvements available within several configurations include increased matrix tolerance and reduced measurement drift because of clogging, higher sensitivity, improved measurement for problematic analytes such as S or Si and dynamic range of up to 11 orders of magnitude [117]. Laser ablation-coupled ICP-MS (LA-ICP-MS) enables screening of tiny samples such as nanoparticles, single cells or single strands of hair as well as variation in distribution of elements within the sample on a nanometer scale [101, 118, 119] opening possibilities for the development of new diagnostic methods. Current technologies demonstrated the ability for profiling of 13C, 23Na, 24Mg, 31P, 39K, 56Fe, 63Cu, 65Cu and 64Zn from a single cell from cultured neurons [120]. Although ICP-MS is a destructive method, the minute quantities of sample can be used, thus enabling needle biopsies, colonoscopy, hair analysis and other minimally invasive methods to be deployed. The analysis of a hair using LA-ICP-MS has demonstrated that exposure change because of nutrition [121] or geographic relocation [119] can be reproducibly measured. Such unprecedented ability for accurate measurement of elements from tiny samples will enable comprehensive analysis of elemental profiles including elements that are present in ultralow concentrations. Many potential applications are emerging because we can monitor trace element homeostasis using systems approach rather than traditional single-element analysis [122, 123]. Understanding of multielement homeostasis is necessary for understanding the mechanistic basis of toxicity because of chronic exposure, and related cell and organ toxicity and related health consequences. Elemental toxicity is complex and may be because of increased intake (environmental, occupational, nutritional and medicinal), increased permeability (e.g. gut, dermal or vascular), excretory deficiency (e.g. organ damage and genetic impairments), malnutrition or chronic homeostatic imbalances of elements such as Ca, Cu, Fe and Zn [34, 124–126]. Chronic toxicity, therefore, is a multifactorial process, and its study involves complex system analysis. Elemental metabolomics involves the study of bioavailability and transfer of elements between the environment, food chain and various subsystems of plant, animal and human organisms. Highly accurate profiling of trace and ultratrace elements enables insight into elemental homeostasis and genomic, proteomic and metabolomics analysis of tissues and cell types. In total, >20 elements are considered as toxic risk (Ag, As, Au, Bi, Cd, Ce, Ch, Co, Cu, Fe, Ga, Hg, Mn, Ni, Pb, Pt, Sb, Sn, Te, Th, U, V and Zn) and seven of them are considered as high-risk (As, Al, Cd, Cr, Fe, Hg and Pb) [127]. Neurotoxicity of As, Cd, Cu, Fe, Hg, Mn, Pb and Zn has been well-known, but the toxic dose was difficult to establish because co-exposure to multiple metals can result in toxicity at individually sub-toxic doses. New methods for establishing toxicity are needed, and we anticipate that multielement signatures that correspond to toxicity will be defined through use of elemental metabolomics and appropriate statistical modeling. The ability to concurrently measure bioavailability of elements from environment, food and various tissues enables elemental metabolomics to be a comprehensive medical diagnostic tool [125] and provides basis for design of medical and nutritional intervention. For example, elemental metabolomics can be used to decipher the mystery of neurotoxic damage such as in autism spectrum disorder, by combining elemental screening of a variety of biological samples at multiple times during pregnancy, infancy and early development. Exposure to toxic metals and homeostatic perturbations are associated with autism either through mother exposure during pregnancy or exposure during early development [126–128]. Known gestational exposures associated to autism include As, Cd, Hg and Pb and possibly Al, Cr, F, Mn, Ni, Sn and U [128]. Exposure to heavy metals was reported to be higher in infants (0–3 years of age) than in older children [127]. Furthermore, Mg and Zn deficiency and lower levels of Ca, Fe, Mn and Se and increased level of Cu and other toxic metals have been observed, and these differences have been more pronounced in autistic children than in controls [122, 126–128]. Elemental metabolomics enables screening of environmental, nutritional and medical (blood, hair and nails) samples where comprehensive elemental signatures that confer high or low risk of autism can be identified and stored in databases. These signatures promise to be better risk and exposure biomarkers than individual elements/toxins because effects of toxic chemicals are synergistic and cumulative. Biomonitoring of elemental complete elemental profiles can provide early risk assessment and diagnosis and subsequent early intervention (Figure 3). Figure 3. View largeDownload slide A model of elemental metabolomics screening of external (environment, nutrition, medicine/cosmetics and occupational) and internal (excess, deficiency, tissue distribution and homeostatic disbalance) factors. Elemental profiling provides exposure signatures and diagnostic signatures for estimation of toxicity, diagnosis and design of intervention—nutritional, medical or preventative. Resistance to toxicity can be studied by the analysis of genetic variants of susceptibility or protection specific for an individual or considering antagonism or synergy of elements. This model is suitable for the study of complex disorders such as autism and other disorders such as metabolic disorders or organ damage. A convenient starting point of this model is the measurement of external factors, indicated as the shaded box. Figure 3. View largeDownload slide A model of elemental metabolomics screening of external (environment, nutrition, medicine/cosmetics and occupational) and internal (excess, deficiency, tissue distribution and homeostatic disbalance) factors. Elemental profiling provides exposure signatures and diagnostic signatures for estimation of toxicity, diagnosis and design of intervention—nutritional, medical or preventative. Resistance to toxicity can be studied by the analysis of genetic variants of susceptibility or protection specific for an individual or considering antagonism or synergy of elements. This model is suitable for the study of complex disorders such as autism and other disorders such as metabolic disorders or organ damage. A convenient starting point of this model is the measurement of external factors, indicated as the shaded box. ICP-MS alone can measure only the total concentration of each element and their isotopes in the sample. This information alone is often insufficient for understanding the inorganic metabolome effects. For example, the risk of toxicity of trace metals such as arsenic, mercury, chromium or selenium is dependent on bioavailability related to elemental species [39, 130]. Multiple inorganic and organic forms are available for these metals, and they differ in toxicity, absorption and concentration in different biological samples. Basic elemental metabolomics represent an important baseline and can determine overall subtoxic levels of these elements. Separation techniques [38] are often coupled with ICP-MS; they discriminate different species of elements and provide for deep insight about the concentration of each species. Elemental metabolomics combined with speciation analysis provides a link between inorganic and organic metabolites and detailed profiling of toxic element compounds in both elemental and organic form. We estimate that speciation analysis will introduce additional 50–100 inorganic species and 200–300 organic species of interest to a complete complement of elemental metabolomics targets. Continuous improvement of analytical capabilities and detection limits will see the increase of these numbers. Elemental metabolomics focuses on comprehensive measurement of total elemental concentrations and is, by its nature, targeted screening. Speciation analysis provides finer granularity of targeted screening by providing concentrations of elemental species. Furthermore, speciation analysis provides a link between inorganic and organic metabolites, as large number of organic metabolites can be measured directly as elemental species after the separation step. We foresee a significant growth of the field of elemental metabolomics, as it connects multiple fields that affect quality of life, health and the economy. The new development will include creation of several types of databases of comprehensive elemental profiles for plants, animals, human and environment. These databases will focus on physiology, food and nutrition and health. They will be populated from large-scale projects that will generate tens of thousands of individual fingerprints. These fingerprints will be used to define elemental signatures that will characterize origin of samples and that will be characteristic of various functional states. Comparison of new samples with the elemental signatures will enable characterization of these samples. These databases will be combined with advanced data analytical tools that will inform traditional metabolomics studies about total content and the distributions of elements in the samples. They will also enable investigations such as distinguishing various disease and healthy states, origin of food and detection of adulteration and enhancement of proteomics and genomics studies. The applications of elemental metabolomics will be numerous, examples including the design of new diagnostic and prognostic tools for health, improved occupational health, improvements of agricultural practices and quality of food and better protection of environment. Multielement analysis by ICP-MS is increasingly being used in biotechnology because of ultrahigh sensitivity and selectivity, high-throughput multielement measuring capability, accurate absolute quantification in complex matrices, easy combination with chromatographic separation methods, its complementarity with organic mass spectrometry and isotope measuring ability [130]. About 30% of proteins in human body is metalloproteins. Clinical testing often involves quantification of specific proteins that serve as disease markers, but reliable, reproducible and traceable measurements are currently lacking. Metrology of metalloproteins recognized ICP-MS elemental measurement combined with separation techniques as a valid methodology with traceability because of availability of SRMs [131]. Metalloproteins provide a direct link between elemental metabolomics and proteomics, and we expect that an increasing number of metabolite measurements will be done by direct measurement of elements. Furthermore, an increasing number of specialized multielement clinical testings have been validated [132, 133], and ICP-MS is used routinely for a large number of laboratory tests for total elements in biological samples (Ag, Al, As, Ba, Be, Bi, Cd, Cr, Co, Cu, Fe, Gd, Hg, I, Mg, Mn, Mo, Ni, Pb, Pt, Se, Sb, Sn, Th, Ti, U, V and Zn), including several metal panels, in reputable clinical laboratories [134]. Elemental metabolomics offers possibilities of improving our understanding of environment, nutrition and health. Elemental metabolomics will inform traditional metabolomics through reduction of complexity of metabolic experiments, better design of metabolomics studies and improved ability for interpretation of results. Ultimately, elemental metabolomics will enable better understanding of biological functioning of organisms and the role of environmental influences, nutrition and chronic exposure to various elements. Key Points Elemental metabolomics is simultaneous quantification and characterization of total concentration of chemical elements in biological samples and monitoring their changes. ICP-MS is increasingly being used for multielement screening because of its ultrahigh sensitivity and selectivity, high-throughput multielement measuring capability, accurate absolute quantification in complex matrices, easy combination with chromatographic separation methods, its complementarity with organic mass spectrometry and isotope measuring ability. Elemental metabolomics is emerging as an important methodology for molecular biology, medical diagnosis, prognosis and monitoring, toxicology, environmental studies, food safety and traceability, nutrition and other fields such as forensics and archeology. We have provided a template workflow for elemental metabolomics experiment and a corresponding data workflow along with the discussion of the standards for the field, including SOPs, the use of reference materials and minimum information for reporting results of elemental metabolomics experiments. Supplementary data Supplementary data are available online at http://bib.oxfordjournals.org/. Funding This work was supported by Menzies Health Institute Queensland, Agricultural University of Athens, and Nazarbayev University. Ping Zhang is a Research Fellow at Menzies Health Institute Queensland, Griffith University Australia. Her research interests focus on bioinformatics and health informatics. She develops and applies techniques for pattern recognition, machine learning and statistical analysis in biomedicine. Constantinos A. Georgiou is a Professor of Analytical Chemistry in the Department of Food Science and human nutrition at the Agricultural University of Athens, Greece. 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Elemental metabolomics

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Abstract

Abstract Elemental metabolomics is quantification and characterization of total concentration of chemical elements in biological samples and monitoring of their changes. Recent advances in inductively coupled plasma mass spectrometry have enabled simultaneous measurement of concentrations of > 70 elements in biological samples. In living organisms, elements interact and compete with each other for absorption and molecular interactions. They also interact with proteins and nucleotide sequences. These interactions modulate enzymatic activities and are critical for many molecular and cellular functions. Testing for concentration of > 40 elements in blood, other bodily fluids and tissues is now in routine use in advanced medical laboratories. In this article, we define the basic concepts of elemental metabolomics, summarize standards and workflows, and propose minimum information for reporting the results of an elemental metabolomics experiment. Major statistical and informatics tools for elemental metabolomics are reviewed, and examples of applications are discussed. Elemental metabolomics is emerging as an important new technology with applications in medical diagnostics, nutrition, agriculture, food science, environmental science and multiplicity of other areas. biomonitoring, chemometrics, clinical laboratory testing, ionomics, metallomics, mineralomics Introduction A metabolite is a product of chemical processes in an organism or a cell. The concepts of metabolic patterns, individual metabolic patterns and ‘hypothetical average individual’ were promoted in 1940s [1]. Thanks to the improvements in analytical methods, instrumentation and analytical capabilities, quantitative metabolic profiling of biomedical specimens using mass spectrometry became possible in 1970s [2]. Improvements in instrumentation, molecular methods, cheminformatics and bioinformatics, over the following 30 years, have lead to high-throughput measurement of multiple metabolites in human and plant specimens [3, 4]. The ability to simultaneously measure hundreds of metabolites, with high accuracy, at high speed and at affordable cost enabled the definition of metabolome and metabolomics. Elemental metabolomics focuses on comprehensive analysis of >70 chemical elements in biological samples that represent various biological conditions and states including genetic perturbations, nutritional intervention, disease and disease progression, drug effects or environmental influences. Key definitions used in metabolomics [5–8] and new definitions in elemental metabolomics are shown in Table 1. Metabolomics is the end point of the Omics cascade (genomics → transcriptomics → proteomics → metabolomics). Omics focuses on high-throughput measurements of molecular data and their interpretations that connect organism’s genetic code with the characteristics of its phenotype defining a key component of systems biology [9]. Elemental metabolomics is an interdisciplinary field at the intersection of chemistry, physics, biology, mathematics and data sciences. The workflow for elemental metabolomics is shown in Figure 1. The design of this workflows was informed by multiple inputs including proposed metabolomics workflows [10, 11], standard operating procedures (SOPs) for handling blood and urine for metabolomics studies [12, 13], imaging and speciation analysis methods [14], minimum information reporting standard (MIRS) for results of metabolomics experiments [15], formal determination of reference values of elements [16] and various statistical and machine learning methods used in metabolite screening [10, 11, 17]. Table 1. Definitions of key concepts and approaches in metabolomics [5–9] and elemental metabolomics Term  Description  Metabolite  An intermediate or final product of chemical reactions in an organism or a cell  Metabolome  A complete set of small molecules (molecular weight <1500 Da) that exist in a given biological tissues or sample  Metabolomics  Quantification of metabolome in target samples, monitoring their change and characterization of phenotypes over time or in response to various stimuli  Elemental metabolomics  Quantification and characterization of total concentration of chemical elements in biological samples and monitoring their changes  Elemental profiling  Quantification of chemical elements in a given sample or specimen  Elemental fingerprinting  Classification of biological samples using their elemental profiles  Elemental signature  Elemental composition derived from a representative selection of samples that represents some condition or status (e.g. geographic origin, genetic origin or health status)  Term  Description  Metabolite  An intermediate or final product of chemical reactions in an organism or a cell  Metabolome  A complete set of small molecules (molecular weight <1500 Da) that exist in a given biological tissues or sample  Metabolomics  Quantification of metabolome in target samples, monitoring their change and characterization of phenotypes over time or in response to various stimuli  Elemental metabolomics  Quantification and characterization of total concentration of chemical elements in biological samples and monitoring their changes  Elemental profiling  Quantification of chemical elements in a given sample or specimen  Elemental fingerprinting  Classification of biological samples using their elemental profiles  Elemental signature  Elemental composition derived from a representative selection of samples that represents some condition or status (e.g. geographic origin, genetic origin or health status)  Table 1. Definitions of key concepts and approaches in metabolomics [5–9] and elemental metabolomics Term  Description  Metabolite  An intermediate or final product of chemical reactions in an organism or a cell  Metabolome  A complete set of small molecules (molecular weight <1500 Da) that exist in a given biological tissues or sample  Metabolomics  Quantification of metabolome in target samples, monitoring their change and characterization of phenotypes over time or in response to various stimuli  Elemental metabolomics  Quantification and characterization of total concentration of chemical elements in biological samples and monitoring their changes  Elemental profiling  Quantification of chemical elements in a given sample or specimen  Elemental fingerprinting  Classification of biological samples using their elemental profiles  Elemental signature  Elemental composition derived from a representative selection of samples that represents some condition or status (e.g. geographic origin, genetic origin or health status)  Term  Description  Metabolite  An intermediate or final product of chemical reactions in an organism or a cell  Metabolome  A complete set of small molecules (molecular weight <1500 Da) that exist in a given biological tissues or sample  Metabolomics  Quantification of metabolome in target samples, monitoring their change and characterization of phenotypes over time or in response to various stimuli  Elemental metabolomics  Quantification and characterization of total concentration of chemical elements in biological samples and monitoring their changes  Elemental profiling  Quantification of chemical elements in a given sample or specimen  Elemental fingerprinting  Classification of biological samples using their elemental profiles  Elemental signature  Elemental composition derived from a representative selection of samples that represents some condition or status (e.g. geographic origin, genetic origin or health status)  Figure 1. View largeDownload slide Data workflow within an elemental metabolomics experiment is shown. Each of these steps needs to be properly documented in reports to ensure proper evaluation of results, comparability and replication of any experiment and ultimately pooling together data from multiple experiments for large-scale analyses. The overall workflow can be divided into six steps representing horizontal flow: design → sampling → analytical chemistry → data analytics → interpretation/modeling → further use. There is a terminology overlap where some words may have different meaning in chemistry and bioinformatics. Additional details about these steps are available in Tables 2 and 3 and throughout the main text. Figure 1. View largeDownload slide Data workflow within an elemental metabolomics experiment is shown. Each of these steps needs to be properly documented in reports to ensure proper evaluation of results, comparability and replication of any experiment and ultimately pooling together data from multiple experiments for large-scale analyses. The overall workflow can be divided into six steps representing horizontal flow: design → sampling → analytical chemistry → data analytics → interpretation/modeling → further use. There is a terminology overlap where some words may have different meaning in chemistry and bioinformatics. Additional details about these steps are available in Tables 2 and 3 and throughout the main text. The size of actual metabolome for any given organism is unknown and difficult to estimate [18]. As of September 2016, the Human Metabolome Database [19] has some 42 000 metabolite entries. It has been estimated that >200 000 metabolites are present in the plant kingdom [6]. Current state of the art of mass spectrometry and separation methods enables routine analysis of up to 1000 metabolites with throughput of 1000 samples per day [20]. Metabolomics produces mega-variate data creating situations where the number of variables easily exceeds the number of samples used in a research study resulting in the curse of dimensionality [21]. Such data are difficult to analyze, as false correlations and overfitting of statistical models are common, while the artefacts of experimental design are amplified. This makes high-throughput metabolomics studies difficult to interpret, and there is a need for methodology that can simplify and streamline metabolomics data analysis. The measurement of chemical elements is a logical first step in metabolomics studies that determines the total amount of each element in the sample. These measurements have direct diagnostic value in medicine and can also be used for food authentication and determination of food origin. Furthermore, knowing the total amount of each element in the sample provides useful information for the next metabolomics step that involves screening of thousands of metabolites. This article provides an overview of elemental metabolomics, a neglected subfield of metabolomics. In the following sections, we will describe the basic concept of elemental metabolomics, standards used to ensure accurate and reproducible measurements, methods for data capture and processing and bioinformatics methods for data analysis and interpretation. We have also reviewed applications related to elemental metabolomics and provided brief comments about bioinformatics tools for these applications. Elemental metabolomics Approximately 96% of elemental composition of human body, by mass, consists of four elements (C, H, N and O), and we term them ‘mega elements’. Another 3.25% consists of macro elements (Ca, Cl, K, Mg, Na, P and S), while the remaining elements are called trace (Al, As, B, Ba, Br, Cd, Ce, Co, Cs, Cu, Cr, F, Fe, Ge, Hg, I, Li, Mn, Mo, Ni, Pb, Rb, Se, Si, Sn, Sr, Ti and Zn) or ultratrace (Ag, Au, Be, Bi, Ga, Hf, In, Ir, Nb, Os, Pd, Pt, Re, Rh, Ru, Sb, Sc, Ta, Tc, Te, Th, Tl, U, V, W, Y, Zr and the 14 lanthanides). Ultratrace elements are present in concentration of roughly 0.1–10 µg/kg of sample. In practical terms, a human body of 75 kg would have ∼72.5 kg of four mega elements, 2.5 kg of seven macro elements (Ca and P make ∼1.9 kg), 15 g of trace elements (total of 28) and 1 mg of ultratrace (total of 41) elements [22]. It is not surprising that elemental metabolomics studies are few—until recently, the accuracy of measurement and the cost were not at the level that would support its routine use. Our ability to accurately and reproducibly measure ultratrace elements has realized only in the past 10–15 years. It is only over the past few years that elemental metabolomics studies became affordable. Advances in inductively coupled plasma mass spectrometry (ICP-MS) enabled accurate and affordable measurement of >70 elements at reasonable cost [23] (Figure 2). ICP-MS is suitable for elemental metabolomics measurement, given its performance characteristics including detection limits, concentration working range, sample throughput capacity and methods for compensation of interferences [24]. Sophisticated methods including matrix removal, mathematical correction equations and collision/reaction cell ICP-MS have been developed to minimize or nullify the measurement interferences. Interested readers can find more details about ICP-MS use in life sciences in [25]. Figure 2. View largeDownload slide Common elemental analytes detectable and measurable by ICP-MS are shown. Groupings into essential, beneficial, common without clearly defined function, and toxic elements are somewhat arbitrary and need to be taken within a specific biological context. Elements labeled as either essential or beneficial are either the components of major structural components in the body (Ca, P, S); responsible for maintenance of ionic equilibria, activation, or signaling (Ca, K, Mg, Na); components of enzymes or hormones (Co, Cr, Cu, Fe, I, Mn, Mo, Ni, Se, Sn, V, Zn). Essential and beneficial (suggested that may be essential) are designated as given in [127]. For example, several elements display essentiality-toxicity duality. In chromium, this duality is related to speciation - trivalent Cr(III) is an essential nutrient considered non-toxic, while hexavalent Cr(VI) is considered toxic and carcinogenic. Elements indicated as toxic risk are those reported as high toxic risk [128], implicated as neurotoxic [129], or are radioactive. Figure 2. View largeDownload slide Common elemental analytes detectable and measurable by ICP-MS are shown. Groupings into essential, beneficial, common without clearly defined function, and toxic elements are somewhat arbitrary and need to be taken within a specific biological context. Elements labeled as either essential or beneficial are either the components of major structural components in the body (Ca, P, S); responsible for maintenance of ionic equilibria, activation, or signaling (Ca, K, Mg, Na); components of enzymes or hormones (Co, Cr, Cu, Fe, I, Mn, Mo, Ni, Se, Sn, V, Zn). Essential and beneficial (suggested that may be essential) are designated as given in [127]. For example, several elements display essentiality-toxicity duality. In chromium, this duality is related to speciation - trivalent Cr(III) is an essential nutrient considered non-toxic, while hexavalent Cr(VI) is considered toxic and carcinogenic. Elements indicated as toxic risk are those reported as high toxic risk [128], implicated as neurotoxic [129], or are radioactive. Large-scale high-throughput elemental profiling studies have been known as ionomics [26, 27], mineralomics [28, 29], metallomics [30, 31] and elementomics [32]. These high-throughput approaches study the relationships between elemental minerals, metals or nonmetals measured in their elemental form (as total element quantity) in biological samples, and correlate them to physiological states, biological processes and phenotypes. They refer essentially to the same concept—the role of individual elements in physiology and functioning of an organism. Elemental metabolomics, therefore, describes studies of metabolic role of the comprehensive matrix of multiple elements. Elemental metabolomics shares the scope with metallomics that studies metals, metalloids and trace elements—‘the metallome’—of biological systems and the interaction of the metallome with other ‘omes’ in a given organism. Elemental metabolomics, however, provides broader and a more formal framework, as it promotes: formalization of standards and procedures including standardized workflows and SOPs developed specifically for elemental metabolomics for different kinds of biological materials; the use of sample-matched reference materials defined for >70 elements; completeness of screening that includes all measurable elements (currently >70); use of sophisticated statistical and mathematical modeling tools to extract maximum knowledge from elemental profiles; concurrent measurement of bioavailability and transfer of elements between the environment, food and organisms and elemental accumulation in various tissues. Individual elements interact at multiple levels in living organisms including competitive absorption in roots of plants [33] and through mucosal surfaces in animals [34]. They may act as antagonists, synergists or catalysts [35], can replace each other from their molecular binding sites [36] and interact with various targets to enhance biological effects [37]. The essentiality and toxicity of elements depend on chemical speciation [14, 38]. Chemical speciation is of interest in toxicology and nutrition (As, Ba, Ca, Cd, Co, Cu, Pb, Se, Sr and Zn), environmental exposure (As, Cd, Cr, Fe, Ge, Hg, Pb, Sb, Se and Sn), industrial exposure (As, Fe, Ga, Hg, Ni, Rh, Ru and V) and medically related exposure (Al, Co, Cr, Cu, Fe, Mo, Ni, Pt, Ru, Ti and Zn), where toxicity and biological effects depend on chemical species [38]. Differences in toxic effects of elements depend on their chemical forms that exhibit differences in solubility, transport, reactivity, absorbability and the chemical complexes they form within tissues and cells [39]. For example, chromium is an essential element that comes mainly in form of Cr(III) that is of low toxicity and Cr(VI) that is highly toxic [39]. Most forms of mercury (Hg) are highly toxic to humans, particularly to the nervous system. Elemental and inorganic ionic forms of Hg are poorly absorbed from the gut, but organic forms such as methylmercury (MeHg) are readily absorbed from the gastrointestinal tract and can also readily cross the blood–brain barrier. Industrial pollution commonly discharges Hg in ionic or elemental form. Aquatic organisms convert elemental and ionic Hg into MeHg increasing bioavailability to humans mainly through fish and other seafood [40]. In addition to the concerns about arsenic and cadmium in rice, it was suggested that rice may be another significant source of dietary MeHg [41]. Toxic element exposure, therefore, requires elemental assessment in complex matrices spanning multiple nutritional to various environmental or occupational sources. Isotopes represent forms of a given chemical element. Isotopes of an element have an identical number of protons, but differ in the number of neutrons in each atom. The natural abundance of isotopes at a given point of time is relatively stable, but small variations may characterize the source, e.g. oceans versus soils, or different geographic locations [42, 43]. Isotopic abundance patterns are used for mega elements (C, H, N and O) and also Br, F, P, S and Si [44]. Isotope screening is used for identification of food origin, including geographic region and production system (e.g. organic versus conventional) [45]. Stable isotope analysis refers to the determination of isotopic abundances. Isotopic compositions of samples reflect their biological or physical provenance and history and, thus, provide additional discriminating tool for samples that have similar elemental signatures. Current practice does not define criteria for preference of elemental signatures over isotopic profiles or the reverse. We anticipate that in future, more comprehensive measurements including isotopes will be performed; currently, these measurements are limited by the cost of high-resolution mass spectrometry. The list of stable isotopes is provided in Supplementary Table S1 [46]. Despite that only 73 elements (Figure 2) and low-abundance isotopes of C, H, N and O can be measured (Figure 2), taking into account isotopes, speciation and mutual interactions of elements, variation of elemental metabolomics is of high combinatorial complexity. Furthermore, elemental metabolomics is emerging as an important methodology for molecular biology, medical diagnosis, prognosis and monitoring, toxicology, environmental studies, food safety and traceability, nutrition and other fields such as forensics and archeology. Variation of biologically relevant samples is huge—it is affected by the source, origin, season, geographic location, health status and other variables. Individual studies having tens of thousands of samples will soon become the norm. Statistical and bioinformatics methods are essential for elemental metabolomics. Some of these methods can be adapted from metabolomics, laboratory science and food science, while new methods are needed to deal with specific needs of elemental metabolomics. Standards and minimum information for elemental metabolomics To ensure validity, analytical measurements using biological matrices must adhere to SOP in both pre-analytical and analytical steps. A particular problem is presented with ultratrace elements. Elements that are used or are present in equipment and materials that are used in pre-analytical and analytical steps represent major potential sources of contamination. Pre-analytical SOPs ensure that sample collection and handling preserve the integrity and the content of the samples before the analytical step is performed [47]. Analytical SOP must ensure that elemental content during preparation and measurement steps is not compromised. SOP prescribes the use of reference standards including blanks, instrument calibration standards and internal standards and ensure the selectivity, accuracy, precision, integrity and stability of the measurement method [48, 49]. Blood collection and handling require appropriate accessories and procedures (flasks, tubes, needles and reagents) to prevent contamination, and SOPs in this area are well defined [50]. SOPs for other areas of biomonitoring, such as food, do exist [51, 52]. Given the wide variety of biological samples, it is necessary that SOPs are developed for each type of biological sample. Prevention of sample contamination with trace and ultratrace elements requires clean handling and clean laboratory environment. Ideally, an ISO Class 5 (ISO Guide 14644, equivalent to FED STD 209E Class 100) laboratory should be used for the analysis of trace elements [52], and possible sources of contamination should be minimized or eliminated. For example, it was observed that chromium contamination increases 10-fold when frozen muscle samples are cut by surgical blade as compared with fresh tissue cutting by surgical blade [53]. Similarly, mills, grinders and laboratory wares are common sources of trace element contamination. Extensive recommendations on prevention and remediation of laboratory contamination are given in [54]. Usefulness of elemental metabolomics will depend on large data sets where elemental profiles for a variety of samples are available and on ability to compare results from different studies. In addition to SOP, standard reference materials (SRMs) and reference materials are needed for comparability of results. Reference materials are homogeneous, stable and well-defined sources with known trace element concentrations. Well-characterized SRMs and certified reference materials are critical for instrument to ensure the accuracy of measurements. There are three categories of assigned values of trace element concentrations in SRMs (in declining order of confidence): certified values, reference values and ‘for information’ values [55]. Reference materials for trace elements exist, but they rarely have reported values for >30 elements. Furthermore, SRMs may consist of material that has very different concentration of elements than the sample increasing the possibility of measurement error of low-concentration elements close to detection limits. For example, the SRM for lanthanides from mussel tissue (BCR-668) and aquatic plant tissue (BCR-670) have concentrations of individual lanthanides that can be 2–3 orders of magnitude higher than some target materials such as meat [56]. More details about SRM and complexities of establishing them can be found in [57]. Comprehensive metabolomics studies may include the screening of a variety of samples—from environment, soil, food and human samples to track elements and their movements through the chain to study their effect on human health. Because of potentially large differences in concentration of certain elements, multiple calibration standards need to be used to ensure that all elemental measurements will be within linear calibration ranges. To assure high accuracy of measurements, comprehensive sample-matched reference materials need to be developed and used. Environmental disasters and accidents may result in unpredictably elevated environmental exposure to a range of elements making SRMs unsuitable, even when multiple reference materials will be available. Standard analytical practices—dilution in combination with internal standards—are expected to be adequate in disaster and accidents monitoring. Elemental metabolomics studies need to share MIRSs to enable comparability between studies and provide means to understand, evaluate, repeat and reinvestigate these studies. MIRS relevant to elemental metabolomics includes those used in chemical analyses [15] and metabolomics experiments [58–60]. Based on these recommendations and our experience with elemental screening of concurrent measurement of large number of elements [17, 61], we have compiled a tentative MIRS for elemental metabolomics (Table 2). Examples of studies that have well-reported data can be found in [54, 62, 63]. For publications, it is recommended that the detailed reports and individual sample measurements are provided as supplementary materials. These standards transcend and unify all subfields of elemental metabolomics (ionomics, metalomics, minerallomics and elementomics) [26–32]. Table 2. Tentative minimal information reporting standards (MIRS) for elemental metabolomics studies Metadata  Reporting items  Sampling process and protocol  Replicate sampling and analysis Tissue harvesting method Biofluid harvesting and collection method Tissue processing method (pre-analytic) Storage conditions Transport and shipping of samples Methods for prevention of contamination  Sample preparation for elemental analysis  Chemicals and their descriptions Internal standards, if any Microwave oven program for digestion Digestate handling/storage Transport and shipping of digestates  Sample preparation for speciation  Chemicals/solvents and their description Internal standards Extracts handling/storage Transport and shipping of extracts  ICP-MS  Instrument description Sample introduction and delivery Ionization source Mass analyzer description Data acquisition parameters SRMs used Targeted stable isotopes  Separation technique coupled to ICP-MS for speciation  Instrument description and setup Interface to ICP-MS Separation technique description  Instrumental checks and performance  Calibration and accuracy/precision assessment Quality control samples Replicates Blanks Spiked samples SRMs Limits of detection (LOD) values Limits of quantitation (LOQ) values List of acceptance criteria  Data preprocessing  Data file format and conversion methods Data pre-processing Calibration curves Background subtraction Noise reduction Interference correction measures Dealing with LOD and LOQ   Data reporting  Elemental matrices and concentrations Ranges, medians and averages Reference intervals Units (consistent with SI nomenclature [64]) Use µg/kg for solid, or µg/l for liquid samples ppm (parts per million) and related terms (ppb, ppt) should not be used Different scale units (such as mg/kg and µg/kg) should not be used in the same report mol/kg or mol/l measures can be used if conversion factors to µg/kg and µg/l are provided   Biological experiment  IUPAC nomenclature Use recognized ontologies whenever possible Sample description Organism, type, subtype and genotype Sample composition and sources Sample type (phenotype, weight, age, sex, characteristics and tissue) Other relevant details Environment and conditions descriptions Control samples, disease/disorder, pollution, type of food taken, toxicity, occupational hazard, geographic location, time, production system, maintenance procedure and parameters, etc.   Metadata  Reporting items  Sampling process and protocol  Replicate sampling and analysis Tissue harvesting method Biofluid harvesting and collection method Tissue processing method (pre-analytic) Storage conditions Transport and shipping of samples Methods for prevention of contamination  Sample preparation for elemental analysis  Chemicals and their descriptions Internal standards, if any Microwave oven program for digestion Digestate handling/storage Transport and shipping of digestates  Sample preparation for speciation  Chemicals/solvents and their description Internal standards Extracts handling/storage Transport and shipping of extracts  ICP-MS  Instrument description Sample introduction and delivery Ionization source Mass analyzer description Data acquisition parameters SRMs used Targeted stable isotopes  Separation technique coupled to ICP-MS for speciation  Instrument description and setup Interface to ICP-MS Separation technique description  Instrumental checks and performance  Calibration and accuracy/precision assessment Quality control samples Replicates Blanks Spiked samples SRMs Limits of detection (LOD) values Limits of quantitation (LOQ) values List of acceptance criteria  Data preprocessing  Data file format and conversion methods Data pre-processing Calibration curves Background subtraction Noise reduction Interference correction measures Dealing with LOD and LOQ   Data reporting  Elemental matrices and concentrations Ranges, medians and averages Reference intervals Units (consistent with SI nomenclature [64]) Use µg/kg for solid, or µg/l for liquid samples ppm (parts per million) and related terms (ppb, ppt) should not be used Different scale units (such as mg/kg and µg/kg) should not be used in the same report mol/kg or mol/l measures can be used if conversion factors to µg/kg and µg/l are provided   Biological experiment  IUPAC nomenclature Use recognized ontologies whenever possible Sample description Organism, type, subtype and genotype Sample composition and sources Sample type (phenotype, weight, age, sex, characteristics and tissue) Other relevant details Environment and conditions descriptions Control samples, disease/disorder, pollution, type of food taken, toxicity, occupational hazard, geographic location, time, production system, maintenance procedure and parameters, etc.   Note. More details are available from [15, 58, 59]. Proper MIRS should be established and agreed by the professional communities, and this MIRS framework represents an informed starting point. The concentration of elements is expressed as w/w (µg/kg) or w/v (µg/l) because they are widely used, well-understood and are consistent with the international system of units (SI) nomenclature and guidelines [64]. For sample description, European Food Safety Authority has provided a comprehensive set of guidelines [65]. Table 2. Tentative minimal information reporting standards (MIRS) for elemental metabolomics studies Metadata  Reporting items  Sampling process and protocol  Replicate sampling and analysis Tissue harvesting method Biofluid harvesting and collection method Tissue processing method (pre-analytic) Storage conditions Transport and shipping of samples Methods for prevention of contamination  Sample preparation for elemental analysis  Chemicals and their descriptions Internal standards, if any Microwave oven program for digestion Digestate handling/storage Transport and shipping of digestates  Sample preparation for speciation  Chemicals/solvents and their description Internal standards Extracts handling/storage Transport and shipping of extracts  ICP-MS  Instrument description Sample introduction and delivery Ionization source Mass analyzer description Data acquisition parameters SRMs used Targeted stable isotopes  Separation technique coupled to ICP-MS for speciation  Instrument description and setup Interface to ICP-MS Separation technique description  Instrumental checks and performance  Calibration and accuracy/precision assessment Quality control samples Replicates Blanks Spiked samples SRMs Limits of detection (LOD) values Limits of quantitation (LOQ) values List of acceptance criteria  Data preprocessing  Data file format and conversion methods Data pre-processing Calibration curves Background subtraction Noise reduction Interference correction measures Dealing with LOD and LOQ   Data reporting  Elemental matrices and concentrations Ranges, medians and averages Reference intervals Units (consistent with SI nomenclature [64]) Use µg/kg for solid, or µg/l for liquid samples ppm (parts per million) and related terms (ppb, ppt) should not be used Different scale units (such as mg/kg and µg/kg) should not be used in the same report mol/kg or mol/l measures can be used if conversion factors to µg/kg and µg/l are provided   Biological experiment  IUPAC nomenclature Use recognized ontologies whenever possible Sample description Organism, type, subtype and genotype Sample composition and sources Sample type (phenotype, weight, age, sex, characteristics and tissue) Other relevant details Environment and conditions descriptions Control samples, disease/disorder, pollution, type of food taken, toxicity, occupational hazard, geographic location, time, production system, maintenance procedure and parameters, etc.   Metadata  Reporting items  Sampling process and protocol  Replicate sampling and analysis Tissue harvesting method Biofluid harvesting and collection method Tissue processing method (pre-analytic) Storage conditions Transport and shipping of samples Methods for prevention of contamination  Sample preparation for elemental analysis  Chemicals and their descriptions Internal standards, if any Microwave oven program for digestion Digestate handling/storage Transport and shipping of digestates  Sample preparation for speciation  Chemicals/solvents and their description Internal standards Extracts handling/storage Transport and shipping of extracts  ICP-MS  Instrument description Sample introduction and delivery Ionization source Mass analyzer description Data acquisition parameters SRMs used Targeted stable isotopes  Separation technique coupled to ICP-MS for speciation  Instrument description and setup Interface to ICP-MS Separation technique description  Instrumental checks and performance  Calibration and accuracy/precision assessment Quality control samples Replicates Blanks Spiked samples SRMs Limits of detection (LOD) values Limits of quantitation (LOQ) values List of acceptance criteria  Data preprocessing  Data file format and conversion methods Data pre-processing Calibration curves Background subtraction Noise reduction Interference correction measures Dealing with LOD and LOQ   Data reporting  Elemental matrices and concentrations Ranges, medians and averages Reference intervals Units (consistent with SI nomenclature [64]) Use µg/kg for solid, or µg/l for liquid samples ppm (parts per million) and related terms (ppb, ppt) should not be used Different scale units (such as mg/kg and µg/kg) should not be used in the same report mol/kg or mol/l measures can be used if conversion factors to µg/kg and µg/l are provided   Biological experiment  IUPAC nomenclature Use recognized ontologies whenever possible Sample description Organism, type, subtype and genotype Sample composition and sources Sample type (phenotype, weight, age, sex, characteristics and tissue) Other relevant details Environment and conditions descriptions Control samples, disease/disorder, pollution, type of food taken, toxicity, occupational hazard, geographic location, time, production system, maintenance procedure and parameters, etc.   Note. More details are available from [15, 58, 59]. Proper MIRS should be established and agreed by the professional communities, and this MIRS framework represents an informed starting point. The concentration of elements is expressed as w/w (µg/kg) or w/v (µg/l) because they are widely used, well-understood and are consistent with the international system of units (SI) nomenclature and guidelines [64]. For sample description, European Food Safety Authority has provided a comprehensive set of guidelines [65]. Statistics and bioinformatics in elemental metabolomics Elemental matrices measured in biological samples are information-rich and show big variation of the values across different materials. Proper statistical and computational modeling techniques, ranging from descriptive statistics to the use of complex models, are essential for getting the value out of data. Descriptive statistics is commonly used to provide an overview of elemental profiles. Descriptive statistics of elemental profiling studies provides meaningful summaries. Typical summaries report the number of samples, central tendency spread (minimum, maximum, average or median values along with standard deviation and quantiles) and shape of the distributions (coefficients of skewness and kurtosis or histograms) [66–68]. Descriptive statistics enables identification of simple patterns and identification of dependencies in data sets. We can use descriptive univariate statistics for identification of outliers, possible associations, for example association between elemental levels in blood and mother’s milk [67], and possible agonist and antagonist relations between individual elements [69]. Correlation tests are frequently used to test possible association between two variables. Linear correlation analysis identified pairs of elements that show strong positive correlation in human scalp hair samples and in fingernail samples, and also identified elements that showed strong positive correlation between their concentrations in hair and nail [70, 71]. Reference Value Advisor program [72] is useful for univariate analysis of elemental matrices. It enables calculation of reference intervals along with their 90% confidence intervals for each element. These values are calculated using appropriate statistics and transformations (nonparametric, parametric and robust Box–Cox transformed methods) in accordance to international recommendations [73]. In addition, it tests the normality of distributions and displays Q–Q plots, identifies possible outliers and displays the distribution plots and histograms. Knowledge of reference values and their statistical properties allows comparison of results measured by different methods, or comparison of samples that represent different conditions or their variations. Hypothesis testing is a common goal of exploratory data analysis such as univariate analysis and data agreement testing [74]. Selection of an appropriate method depends on the research questions and the types of data collected [75]. One of the most used test, analysis of variance (ANOVA), tests the differences of means between two or more groups. In [76], ANOVA was used to determine significant variation of Cr, Mn, Sr, Pb and V between cow milk samples from different farms in South Africa. These results suggested that milk samples from different geographic regions may have different elemental compositions, thus having different nutritional profiles and different toxic content. More complex interactions, such as exposure to multiple elements and their dependencies, can be studied using multiple regression. Regression analysis can generate a mathematical equation that can predict the dependent variable from values of independent variables. Linear regression analysis of elemental profiles in human hair revealed possible dependencies between groups of elements [71]—it indicated that the concentration of Al in hair can be expressed as a function of concentrations of U, P and Mn. The same study indicated strong interrelationships of industrially related elements that contribute to environmental pollution in hair samples from exposed population. Synergistic and antagonistic relationships between elements have been observed—the combined effect, such as toxicity, of two elements is higher than individual effects when both elements are present (synergy), or combined effect might be lower than individual effects (antagonism) [77]. Nonlinear multiple regression was used to study occupational hazard related to eight metals in welders [78]. This study showed that welders have higher concentration of metals in blood and urine, and that they have higher rate of DNA damage than controls. Logistic regression model estimates the probability of categorical dependent variable using data from elemental matrices. In elemental metabolomics type of study, logistic regression was used to predict physiological status of plant from leaf elemental profile (five elements) [79], to study possible associations of autism with hair concentration of trace elements (17 elements) [80] and to evaluate possible interactions in metal ions as risk factors for prostate cancer (four elements) [81]. A major deficiency in majority of these studies is that most of them involved screening of relatively small number of elements as compared with >70 that are currently measurable by ICP-MS. Exploratory analysis of high-dimensional data sets and hypothesis generation is supported by a variety of machine learning approaches that perform regression analysis, clustering, classification and data visualizations. Methods that are commonly used for elemental data analysis are principal component analysis (PCA), linear discriminant analysis (LDA), classification and regression trees, k-means clustering and hierarchical clustering with dendograms [61, 82]. PCA applies orthogonal transformation to a set of measurements to produce linearly uncorrelated variables or ‘principal components’. PCA is commonly used for reduction of dimensionality of elemental profiles and is used in screening plant, food and clinical samples [83–85]. Hierarchical clustering partitions set into groups of similar objects or ‘clusters’ and build a dendogram of the hierarchy of the clusters. This allows us to see how the samples group together based on similarity of elemental concentrations. It is widely used in study of food, water and medical samples [86–88]. Predictive models including network models and decision trees have been explored in the study of elemental profiles in food science and health. For example, elemental analysis for identification of the island of origin of wine from Canary Islands showed that LDA and artificial neural networks (ANN) were highly accurate with ANN outperforming LDA [89]. ANN, support vector machines (SVM) and decision trees were applied for classification of rice by geographic origin [90, 91]. Comparative analysis of ANN, SVM and decision tree classifiers were reported for classification of grape juice [92]. These studies are useful, as they describe basic usage of advanced classification methods using elemental profiles, but need to be taken with caution, as they were developed using a small number of samples. With the number of reported samples growing and application of common standards across studies, the advanced classification methods will gain prominence. Elemental profiles combined with mutual information analysis was used to study associations between ion modules and networks with obesity, metabolic syndrome and type 2 diabetes in Chinese adults [93]. The results were used to construct ‘disease-associated ion networks’. A study of illicit drugs classification by origin using elemental profiles determined that PCA and hierarchical clustering were inadequate, while ANN-based classification showed 96–99% correct classification of ecstasy tablets by the law enforcement seizure [94]. Elemental profiling, thus can be used to trace illicit drugs to distribution networks, and ultimately the origin of drugs. To build a predictive model from multidimensional data, it is often needed to reduce dimensionality by selection of critical variables and then use these variables to build classification models. In an investigation of chemical signatures (anionic, elemental and isotopic profiles) of chemical threat agents, a comprehensive analysis was performed to enable tracing the source of cyanides [95]. Multiple approaches for selection of variables and for building classification models were assessed and compared. Dimensionality reduction was performed using hierarchical cluster analysis, PCA, Fisher ratio from ANOVA, interval partial least squares (PLS) regression and genetic algorithm-based PLS regression. Classification models in [95] were built using PLS discriminant, K-nearest neighbor clustering and SVM. This study is interesting because it has demonstrated the use of several methods, showed how results may differ between methods and showed how multiple methods can be combined for interpretation of results. We have summarized a selection of methods commonly used in elemental metabolomics in Table 3. Table 3. List of types of data analysis and a list of methods commonly used in elemental metabolomics studies Type of analysis  Examples of methods and tools  Used for  Descriptive statistics  Plots, charts and histograms Mean, median, range and quantiles Variance and standard deviation Skewness and kurtosis Correlation and covariance  Summarizing samples Assessing central tendency and dispersion Assessing shapes of distribution Measuring dependencies  Data cleaning  Clustering algorithms Data imputation methods Outlier detection Filtering algorithms Aggregation and normalization algorithms  Data correction Data preprocessing Data harmonization Data standardization Combining or grouping data  Exploratory data analysis  Principal component analysis Clustering methods Regression methods Partial least squares discriminant analysis Classification and regression trees Analysis of variance Linear discriminant analysis Visualization techniques  Assessment of assumptions Statistical inference Dimensionality reduction Feature selection and model selection Formulating and testing hypotheses Pattern recognition Design of further analysis Design of further experiments  Predictive modeling and simulation  Regression models Probabilistic models Decision trees Cluster analysis Markov chains Neural networks Support vector machines Hybrid models Ensemble models  Statistical inference Pattern recognition Classification and prediction Survival analysis Decision-making Data mining Big data analytics  Type of analysis  Examples of methods and tools  Used for  Descriptive statistics  Plots, charts and histograms Mean, median, range and quantiles Variance and standard deviation Skewness and kurtosis Correlation and covariance  Summarizing samples Assessing central tendency and dispersion Assessing shapes of distribution Measuring dependencies  Data cleaning  Clustering algorithms Data imputation methods Outlier detection Filtering algorithms Aggregation and normalization algorithms  Data correction Data preprocessing Data harmonization Data standardization Combining or grouping data  Exploratory data analysis  Principal component analysis Clustering methods Regression methods Partial least squares discriminant analysis Classification and regression trees Analysis of variance Linear discriminant analysis Visualization techniques  Assessment of assumptions Statistical inference Dimensionality reduction Feature selection and model selection Formulating and testing hypotheses Pattern recognition Design of further analysis Design of further experiments  Predictive modeling and simulation  Regression models Probabilistic models Decision trees Cluster analysis Markov chains Neural networks Support vector machines Hybrid models Ensemble models  Statistical inference Pattern recognition Classification and prediction Survival analysis Decision-making Data mining Big data analytics  Note. This list is not exhaustive, but it is intended to provide a brief overview of statistical and mathematical tools that support elemental metabolomics data workflow shown in Figure 1B. A comprehensive study involves types of analysis done sequentially: descriptive statistics → data cleaning → exploratory data analysis → predictive modeling. Table 3. List of types of data analysis and a list of methods commonly used in elemental metabolomics studies Type of analysis  Examples of methods and tools  Used for  Descriptive statistics  Plots, charts and histograms Mean, median, range and quantiles Variance and standard deviation Skewness and kurtosis Correlation and covariance  Summarizing samples Assessing central tendency and dispersion Assessing shapes of distribution Measuring dependencies  Data cleaning  Clustering algorithms Data imputation methods Outlier detection Filtering algorithms Aggregation and normalization algorithms  Data correction Data preprocessing Data harmonization Data standardization Combining or grouping data  Exploratory data analysis  Principal component analysis Clustering methods Regression methods Partial least squares discriminant analysis Classification and regression trees Analysis of variance Linear discriminant analysis Visualization techniques  Assessment of assumptions Statistical inference Dimensionality reduction Feature selection and model selection Formulating and testing hypotheses Pattern recognition Design of further analysis Design of further experiments  Predictive modeling and simulation  Regression models Probabilistic models Decision trees Cluster analysis Markov chains Neural networks Support vector machines Hybrid models Ensemble models  Statistical inference Pattern recognition Classification and prediction Survival analysis Decision-making Data mining Big data analytics  Type of analysis  Examples of methods and tools  Used for  Descriptive statistics  Plots, charts and histograms Mean, median, range and quantiles Variance and standard deviation Skewness and kurtosis Correlation and covariance  Summarizing samples Assessing central tendency and dispersion Assessing shapes of distribution Measuring dependencies  Data cleaning  Clustering algorithms Data imputation methods Outlier detection Filtering algorithms Aggregation and normalization algorithms  Data correction Data preprocessing Data harmonization Data standardization Combining or grouping data  Exploratory data analysis  Principal component analysis Clustering methods Regression methods Partial least squares discriminant analysis Classification and regression trees Analysis of variance Linear discriminant analysis Visualization techniques  Assessment of assumptions Statistical inference Dimensionality reduction Feature selection and model selection Formulating and testing hypotheses Pattern recognition Design of further analysis Design of further experiments  Predictive modeling and simulation  Regression models Probabilistic models Decision trees Cluster analysis Markov chains Neural networks Support vector machines Hybrid models Ensemble models  Statistical inference Pattern recognition Classification and prediction Survival analysis Decision-making Data mining Big data analytics  Note. This list is not exhaustive, but it is intended to provide a brief overview of statistical and mathematical tools that support elemental metabolomics data workflow shown in Figure 1B. A comprehensive study involves types of analysis done sequentially: descriptive statistics → data cleaning → exploratory data analysis → predictive modeling. Complex elemental interactions are often difficult to analyze and interpret. The interpretation of these results needs to be taken with caution because a causal relationship cannot be inferred from these results. Causation can be inferred in a randomized study that takes into account multiple factors that could be associated. For elemental studies of human samples, these factors include, among others, diet, nutritional supplementation, lifestyle, environmental and occupational exposure, age, sex, genetic predisposition, use of cosmetics and medication. This makes human studies much more complex than studies with laboratory animals, farm animals or plants, where many of the factors can be controlled. Conclusion and discussion Important applications of elemental metabolomics used for biomonitoring are emerging in environmental science, agriculture, food science, nutrition, pharmacology and medicine [96]. ICP-MS combined with microwave digestion has been validated for simultaneous determination of multiple elements in various foodstuffs [97–99] including crops [100, 101], livestock [63, 102] and fish [103]. Important applications include food quality control [104], food authentication [17] and food safety and control [105]. ICP-MS is suitable for biomonitoring of human samples including blood, urine and hair [106–108]. ICP-MS is used in environmental research [109], infant and adult nutrition [67, 110, 111], as well as the analysis of drugs and pharmaceuticals, medicinal plants and supplements [96]. Furthermore, ICP-MS-based multielemental monitoring has applications in medicine including toxicology [112] and occupational health [77], and is being developed for clinical diagnostics and monitoring [113, 114]. Currently, there are >40 clinical laboratory blood tests for individual elements in human blood and other fluids, and most of them use ICP-MS. These tests are performed individually for each element. Multielement clinical laboratory testing offers an enormous opportunity for the improvement of diagnostics, as it will enable understanding of elemental interactions, and more detailed understanding of the exposure patterns to elements and their combinations. It was shown that a large number of elements can be screened simultaneously, for example 60 elements were screened for their concentration in human blood [115]. However, only in the past few years, we acquired the ability to make these screenings sufficiently accurate and cheap for routine use in wide range of applications. Development of tests for simultaneous multielement screening will require improved SRMs, well-defined reference values, databases that store values of elemental profiles representative of the conditions of interest and specialized computational algorithms for data comparison and analysis. New SRMs that can be used for screening of >70 elements are needed. For specific applications, SRMs should have elemental concentration ranges similar to the concentration ranges in target samples—to ensure good matching of linear ranges of measurements. They should ideally be derived from materials similar to target samples. Elemental reference values—concentrations of elements expected to be found in control populations—have been proposed [106, 116]. These reference values are crude, as they were done on small sample and do not provide detailed description of target population (by age, geographic origin, occupation, etc.). There is a need for systematic screening of population for establishing baseline values that take into account various confounding variables. The interpretation of elemental profile measurements requires comparison with data from reference databases of elemental profiles that represent a broad variety of biological samples and various conditions and geographic origins. Such databases have been proposed in various meetings, but to our knowledge, they either do not exist or they are not publicly available. Along with the emergence of these databases, analysis tools for comparison of samples and analysis will be developed. The main obstacles for advancing the field of elemental metabolomics are the incomplete screening and small number of samples that are used in a today’s typical study. Recent technological advancements in mass spectrometry including triple-quadrupole ICP-MS configurations offer unprecedented measurement capabilities and increased productivity. Improvements available within several configurations include increased matrix tolerance and reduced measurement drift because of clogging, higher sensitivity, improved measurement for problematic analytes such as S or Si and dynamic range of up to 11 orders of magnitude [117]. Laser ablation-coupled ICP-MS (LA-ICP-MS) enables screening of tiny samples such as nanoparticles, single cells or single strands of hair as well as variation in distribution of elements within the sample on a nanometer scale [101, 118, 119] opening possibilities for the development of new diagnostic methods. Current technologies demonstrated the ability for profiling of 13C, 23Na, 24Mg, 31P, 39K, 56Fe, 63Cu, 65Cu and 64Zn from a single cell from cultured neurons [120]. Although ICP-MS is a destructive method, the minute quantities of sample can be used, thus enabling needle biopsies, colonoscopy, hair analysis and other minimally invasive methods to be deployed. The analysis of a hair using LA-ICP-MS has demonstrated that exposure change because of nutrition [121] or geographic relocation [119] can be reproducibly measured. Such unprecedented ability for accurate measurement of elements from tiny samples will enable comprehensive analysis of elemental profiles including elements that are present in ultralow concentrations. Many potential applications are emerging because we can monitor trace element homeostasis using systems approach rather than traditional single-element analysis [122, 123]. Understanding of multielement homeostasis is necessary for understanding the mechanistic basis of toxicity because of chronic exposure, and related cell and organ toxicity and related health consequences. Elemental toxicity is complex and may be because of increased intake (environmental, occupational, nutritional and medicinal), increased permeability (e.g. gut, dermal or vascular), excretory deficiency (e.g. organ damage and genetic impairments), malnutrition or chronic homeostatic imbalances of elements such as Ca, Cu, Fe and Zn [34, 124–126]. Chronic toxicity, therefore, is a multifactorial process, and its study involves complex system analysis. Elemental metabolomics involves the study of bioavailability and transfer of elements between the environment, food chain and various subsystems of plant, animal and human organisms. Highly accurate profiling of trace and ultratrace elements enables insight into elemental homeostasis and genomic, proteomic and metabolomics analysis of tissues and cell types. In total, >20 elements are considered as toxic risk (Ag, As, Au, Bi, Cd, Ce, Ch, Co, Cu, Fe, Ga, Hg, Mn, Ni, Pb, Pt, Sb, Sn, Te, Th, U, V and Zn) and seven of them are considered as high-risk (As, Al, Cd, Cr, Fe, Hg and Pb) [127]. Neurotoxicity of As, Cd, Cu, Fe, Hg, Mn, Pb and Zn has been well-known, but the toxic dose was difficult to establish because co-exposure to multiple metals can result in toxicity at individually sub-toxic doses. New methods for establishing toxicity are needed, and we anticipate that multielement signatures that correspond to toxicity will be defined through use of elemental metabolomics and appropriate statistical modeling. The ability to concurrently measure bioavailability of elements from environment, food and various tissues enables elemental metabolomics to be a comprehensive medical diagnostic tool [125] and provides basis for design of medical and nutritional intervention. For example, elemental metabolomics can be used to decipher the mystery of neurotoxic damage such as in autism spectrum disorder, by combining elemental screening of a variety of biological samples at multiple times during pregnancy, infancy and early development. Exposure to toxic metals and homeostatic perturbations are associated with autism either through mother exposure during pregnancy or exposure during early development [126–128]. Known gestational exposures associated to autism include As, Cd, Hg and Pb and possibly Al, Cr, F, Mn, Ni, Sn and U [128]. Exposure to heavy metals was reported to be higher in infants (0–3 years of age) than in older children [127]. Furthermore, Mg and Zn deficiency and lower levels of Ca, Fe, Mn and Se and increased level of Cu and other toxic metals have been observed, and these differences have been more pronounced in autistic children than in controls [122, 126–128]. Elemental metabolomics enables screening of environmental, nutritional and medical (blood, hair and nails) samples where comprehensive elemental signatures that confer high or low risk of autism can be identified and stored in databases. These signatures promise to be better risk and exposure biomarkers than individual elements/toxins because effects of toxic chemicals are synergistic and cumulative. Biomonitoring of elemental complete elemental profiles can provide early risk assessment and diagnosis and subsequent early intervention (Figure 3). Figure 3. View largeDownload slide A model of elemental metabolomics screening of external (environment, nutrition, medicine/cosmetics and occupational) and internal (excess, deficiency, tissue distribution and homeostatic disbalance) factors. Elemental profiling provides exposure signatures and diagnostic signatures for estimation of toxicity, diagnosis and design of intervention—nutritional, medical or preventative. Resistance to toxicity can be studied by the analysis of genetic variants of susceptibility or protection specific for an individual or considering antagonism or synergy of elements. This model is suitable for the study of complex disorders such as autism and other disorders such as metabolic disorders or organ damage. A convenient starting point of this model is the measurement of external factors, indicated as the shaded box. Figure 3. View largeDownload slide A model of elemental metabolomics screening of external (environment, nutrition, medicine/cosmetics and occupational) and internal (excess, deficiency, tissue distribution and homeostatic disbalance) factors. Elemental profiling provides exposure signatures and diagnostic signatures for estimation of toxicity, diagnosis and design of intervention—nutritional, medical or preventative. Resistance to toxicity can be studied by the analysis of genetic variants of susceptibility or protection specific for an individual or considering antagonism or synergy of elements. This model is suitable for the study of complex disorders such as autism and other disorders such as metabolic disorders or organ damage. A convenient starting point of this model is the measurement of external factors, indicated as the shaded box. ICP-MS alone can measure only the total concentration of each element and their isotopes in the sample. This information alone is often insufficient for understanding the inorganic metabolome effects. For example, the risk of toxicity of trace metals such as arsenic, mercury, chromium or selenium is dependent on bioavailability related to elemental species [39, 130]. Multiple inorganic and organic forms are available for these metals, and they differ in toxicity, absorption and concentration in different biological samples. Basic elemental metabolomics represent an important baseline and can determine overall subtoxic levels of these elements. Separation techniques [38] are often coupled with ICP-MS; they discriminate different species of elements and provide for deep insight about the concentration of each species. Elemental metabolomics combined with speciation analysis provides a link between inorganic and organic metabolites and detailed profiling of toxic element compounds in both elemental and organic form. We estimate that speciation analysis will introduce additional 50–100 inorganic species and 200–300 organic species of interest to a complete complement of elemental metabolomics targets. Continuous improvement of analytical capabilities and detection limits will see the increase of these numbers. Elemental metabolomics focuses on comprehensive measurement of total elemental concentrations and is, by its nature, targeted screening. Speciation analysis provides finer granularity of targeted screening by providing concentrations of elemental species. Furthermore, speciation analysis provides a link between inorganic and organic metabolites, as large number of organic metabolites can be measured directly as elemental species after the separation step. We foresee a significant growth of the field of elemental metabolomics, as it connects multiple fields that affect quality of life, health and the economy. The new development will include creation of several types of databases of comprehensive elemental profiles for plants, animals, human and environment. These databases will focus on physiology, food and nutrition and health. They will be populated from large-scale projects that will generate tens of thousands of individual fingerprints. These fingerprints will be used to define elemental signatures that will characterize origin of samples and that will be characteristic of various functional states. Comparison of new samples with the elemental signatures will enable characterization of these samples. These databases will be combined with advanced data analytical tools that will inform traditional metabolomics studies about total content and the distributions of elements in the samples. They will also enable investigations such as distinguishing various disease and healthy states, origin of food and detection of adulteration and enhancement of proteomics and genomics studies. The applications of elemental metabolomics will be numerous, examples including the design of new diagnostic and prognostic tools for health, improved occupational health, improvements of agricultural practices and quality of food and better protection of environment. Multielement analysis by ICP-MS is increasingly being used in biotechnology because of ultrahigh sensitivity and selectivity, high-throughput multielement measuring capability, accurate absolute quantification in complex matrices, easy combination with chromatographic separation methods, its complementarity with organic mass spectrometry and isotope measuring ability [130]. About 30% of proteins in human body is metalloproteins. Clinical testing often involves quantification of specific proteins that serve as disease markers, but reliable, reproducible and traceable measurements are currently lacking. Metrology of metalloproteins recognized ICP-MS elemental measurement combined with separation techniques as a valid methodology with traceability because of availability of SRMs [131]. Metalloproteins provide a direct link between elemental metabolomics and proteomics, and we expect that an increasing number of metabolite measurements will be done by direct measurement of elements. Furthermore, an increasing number of specialized multielement clinical testings have been validated [132, 133], and ICP-MS is used routinely for a large number of laboratory tests for total elements in biological samples (Ag, Al, As, Ba, Be, Bi, Cd, Cr, Co, Cu, Fe, Gd, Hg, I, Mg, Mn, Mo, Ni, Pb, Pt, Se, Sb, Sn, Th, Ti, U, V and Zn), including several metal panels, in reputable clinical laboratories [134]. Elemental metabolomics offers possibilities of improving our understanding of environment, nutrition and health. Elemental metabolomics will inform traditional metabolomics through reduction of complexity of metabolic experiments, better design of metabolomics studies and improved ability for interpretation of results. Ultimately, elemental metabolomics will enable better understanding of biological functioning of organisms and the role of environmental influences, nutrition and chronic exposure to various elements. Key Points Elemental metabolomics is simultaneous quantification and characterization of total concentration of chemical elements in biological samples and monitoring their changes. ICP-MS is increasingly being used for multielement screening because of its ultrahigh sensitivity and selectivity, high-throughput multielement measuring capability, accurate absolute quantification in complex matrices, easy combination with chromatographic separation methods, its complementarity with organic mass spectrometry and isotope measuring ability. Elemental metabolomics is emerging as an important methodology for molecular biology, medical diagnosis, prognosis and monitoring, toxicology, environmental studies, food safety and traceability, nutrition and other fields such as forensics and archeology. We have provided a template workflow for elemental metabolomics experiment and a corresponding data workflow along with the discussion of the standards for the field, including SOPs, the use of reference materials and minimum information for reporting results of elemental metabolomics experiments. Supplementary data Supplementary data are available online at http://bib.oxfordjournals.org/. Funding This work was supported by Menzies Health Institute Queensland, Agricultural University of Athens, and Nazarbayev University. Ping Zhang is a Research Fellow at Menzies Health Institute Queensland, Griffith University Australia. Her research interests focus on bioinformatics and health informatics. She develops and applies techniques for pattern recognition, machine learning and statistical analysis in biomedicine. Constantinos A. Georgiou is a Professor of Analytical Chemistry in the Department of Food Science and human nutrition at the Agricultural University of Athens, Greece. 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Briefings in BioinformaticsOxford University Press

Published: Jan 10, 2017

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