Abstract
Environmental health science aims to link environmental pollution sources to adverse health outcomes to develop effective exposure intervention strategies that reduce long-term disease risks. Over the past few decades, the public health community recognized that health risk is driven by interaction between the human genome and external environment. Now that the human genetic code has been sequenced, establishing this “G × E” (gene–environment) interaction requires a similar effort to decode the human exposome, which is the accumulation of an individual’s environmental exposures and metabolic responses throughout the person’s lifetime. The exposome is composed of endogenous and exogenous chemicals, many of which are measurable as biomarkers in blood, breath, and urine. Exposure to pollutants is assessed by analyzing biofluids for the pollutant itself or its metabolic products. New methods are being developed to use a subset of biomarkers, termed bioindicators, to demonstrate biological changes indicative of future adverse health effects. Typically, environmental biomarkers are assessed using noninvasive (excreted) media, such as breath and urine. Blood is often avoided for biomonitoring due to practical reasons such as medical personnel, infectious waste, or clinical setting, despite the fact that blood represents the central compartment that interacts with every living cell and is the most relevant biofluid for certain applications and analyses. The aims of this study were to (1) review the current use of blood samples in environmental health research, (2) briefly contrast blood with other biological media, and (3) propose additional applications for blood analysis in human exposure research.
Overview
Environmental health sciences employ various tools for linking environmental chemicals and other stressors to preclinical and ultimately adverse outcomes in humans. These include the traditional studies of chemicals from external exposures found in media, including air, water, food, dust, consumer products, and other sources from commerce, transportation, agriculture, and manufacturing (Araoud et al. 2016; Arayasiri et al. 2010; Duarte, Pena, and Lino 2011; Freire, Koifman, and Koifman 2015; Funk et al. 2014; Ghio, Sangani, and Roggli 2014; Käfferlein et al. 2005; Little et al. 2012; Liu and Pleil 2002; Marion et al. 2014; Mo et al. 2009; Pleil, Funk, and Rappaport 2006). In the past several decades, the focus has broadened to include analysis of human biological media, primarily blood, breath, and urine, to represent the cumulative dose integration of inhalation, der mal, and ingestion exposure pathways (Amann et al. 2014; _Pleil, Stiegel, and Sobus 2011a; Rappaport 2012a; 2012b; Roos and Jakubowski 2010). These chemicals measured in biological media are broadly termed biomarkers (or bioindicators) in the literature and comprise the human exposome (Burger and Gochfeld 2001; LaKind et al. 2014; Pleil 2012). Although a somewhat fluid distinction, the term bioindicators is used more specifically here as representing that subset of all biomarkers that is more closely related to indicating toward an adverse outcome. In addition to in vivo biomonitoring, contemporary exposure science has embraced suites of in vitro tests for metabolomic activity and toxicity testing based on static designs using specific adverse outcome pathways (AOP) and dynamic/kinetic designs using cell bioreactors (Angrish, Madden, and Pleil 2015; Baranska et al. 2015; Latterman and Buchs 2015; Pleil 2016; 2015; Pleil, Williams, and Sobus 2012; Tarrant 2010; Valcke and Haddad 2015).
In the diagnostic medical and clinical sciences, blood analysis has always been the “gold standard” with urine and breath measurements as important but secondary media. In public health, however, blood sampling is more difficult to implement because study participants, in contrast with medical patients, are generally representative of a healthy population and are therefore less likely to submit to an invasive procedure involving needles (Holland et al. 2003). Further, population-based studies require large numbers of subjects; thus, sample collection generally does not occur in a clinical setting and is more amenable to noninvasive techniques (sampling breath and urine). Nevertheless, similar to medicine, blood is an invaluable biofluid for public health assessment because it directly interacts with every living cell and is representative of all the chemicals circulating in the body (Barr et al. 2002; Levitt 2004).
The aims of this investigation were to (1) contrast the relative value of blood as a diagnostic biofluid with respect to breath and urine, (2) examine existing environmental science applications of blood analysis, and (3) propose future research and development for incorporating blood into routine public health and environmental exposure assessments. The purpose of blood (and other biomarker media) analyses is to develop a series of measurable chemicals that inform the progression from environmental exposure to internal dose to chemical metabolism and then to eventual health outcome (Pleil 2009; Valcke and Haddad 2015). The ultimate goal is then to use empirical measurements to explore the current state of the human exposome and deduce how perturbations from “normal” might interact with the genome to affect health state. Although blood biomarkers may also be useful for diagnosing short-term health effects for individuals, the general focus of environmental health science is to understand and mitigate long-term population-based disease.
Blood versus other biofluids:
As noted earlier, blood sampling is invasive for human subjects. In contrast with breath and urine collection, blood sampling tends to be more expensive, requires certified health professionals, creates more infectious wastes (e.g., needles, swabs, bandages), increases risk of infection, and needs to be performed in a controlled clinical environment (Holland et al. 2003; Vaught 2006). Further, blood samples need to be processed rapidly (before freezing) if analytical protocols require separation of plasma/serum from red blood cells or isolation of genetic material (Holland et al. 2005; Holland et al. 2003; Li, Todor, and Luo 2016). Finally, the amount and number of samples allowed for in environmental studies are limited by institutional review board (IRB) rules. These logistical factors tend to restrict the number of subjects and frequency of sample collection.
Existing blood applications:
Some blood-borne biomarker measurements for exposure/dose assessment are used to validate the interpretation of routine biomarker data from less invasive matrices, such as breath and urine. This is especially true for semivolatile and nonvolatile organic compounds from the environment, such as pesticides, polyaromatic hydrocarbons (PAH), and a variety of manufactured chemicals used in consumer products and industry (Hoet et al. 2009; Pleil 2009). The more prevalent applications of blood measurements for environmental health assess downstream biomarkers, including adducts (e.g., protein or DNA) and conjugates that are unique to blood, which reflect exposures over longer time periods (Johnson et al. 2010; Rappaport et al. 2012).
Future research for incorporating blood into exposure science:
There are four related research paths for expanding blood biomarker applications in the environmental health arena. The first is the continuous development of novel biomarkers that might bridge the gap between exposure and health outcome, including epigenetic markers (e.g., miRNA), ultra-trace-level adducts on specific protein peptides, and endogenous response markers (e.g., plasmaand cell-derived mediators, such as cytokines and chemokines). The second is developing postprocessing methods for efficiently extracting specific blood components from previously frozen samples, especially human serum albumin, hemoglobin, and DNA. The third path is developing less intrusive sampling methods that are more amenable to population-based studies outside of the clinical environment, including micropuncture capillaries, real-time measurements, and sample stabilization via dried blood spots. The fourth is incorporating highthroughput techniques, such as “omics” technologies that are capable of assaying thousands of proteins or metabolites simultaneously to identify differences in expression levels and patterns.
Method Issues for Using Blood
While there are many advantages to using blood for biomarker analysis, several methodological issues serve as drawbacks. One issue is the need for a medical team for sample collection, which increases the expense of the analysis and makes it difficult, if not impossible, to collect blood samples in remote field locations (Vaught 2006). Blood analysis also creates biohazardous waste that needs to be properly disposed of. All blood samples need to be considered infectious and handled with caution (Holland et al. 2005). Another major disadvantage to blood analysis is that subjects are more reticent to provide blood samples than breath or urine (Benyshek 2010; Holland et al. 2005). In a study of childhood leukemia, only 50% of the subjects provided blood samples while 95% gave buccal-cell samples (Holland et al. 2003). Blood sampling is more invasive and may cause brief pain. Some individuals, such as children, the elderly, the chronically ill, and pregnant women, are not included in blood analyses for ethical reasons, while others think it is simply unnecessary and choose not to participate. Blood might also have lower analyte concentrations compared with other matrices. Urine typically has concentrations in the part per billion (ppb) range, while blood concentrations tend to be part per trillion (ppt) (Barr et al. 2002). For some compounds, such as phthalate esters (PE), urine is the preferred matrix because metabolites levels are 10-fold higher in urine than serum (Alves et al. 2014). However, as analytical methods continue to improve and become more sensitive, the concentration issue is becoming less challenging.
Exposure Science Framework
Exposure science has traditionally focused on characterizing how an individual’s interaction with his or her environment leads from stressors (chemical or otherwise) to adverse health impacts (Cohen Hubal 2009; Lioy 2010; Lioy and Smith 2013). The manner in which an individual interacts with his environment then needs to be analyzed to determine the risk of exposure (duration and frequency of contact, activity level, and pathway of exposure) (Klepeis et al. 2001). Chemical-specific biomarkers and their mixtures are then used to quantify the exposure, demonstrating how much of the stressor entered the body and tracking its movement to different biological compartments (Sarigiannis and Hansen 2012). This information is then passed on to toxicologists in order to assess potential health effects based on levels of chemicals partitioned throughout the body (Calafat et al. 2005). Exposure science relies strongly on experimental data; however, studies are increasingly using predictive models in an attempt to characterize potential exposures in a more expeditious manner (http://www.epa.gov/tsca-screening-tools; Kavlock et al. 2008). Biomarkers are known to link exposure science and toxicology research to explain the entire dose-tooutcome continuum, including both exposure information (absorption, distribution, metabolism, and elimination [ADME]) and indications of biological effects (Pleil et al. 2007; Teeguarden et al. 2016) (Figure 1).
Figure 1.
The role of environmental and biomarker measurements in explaining the exposure source to health outcome continuum. Environmental measurements from water, air, smoke, dust and food are linked to biomarker measurements from blood, breath, and urine and are interpreted using different types of observational and mathematical tools, such as heat maps, bar graphs, and charts.
Biomarker Categorization
Linking exposure to potential adverse health effects requires analyzing multiple biomarkers that demonstrate an exposure has occurred and that show a change in the biological system that is indicative of an adverse effect (Pleil and Sheldon 2011). Biomarkers have typically been categorized broadly by function as biomarkers of exposure, effect, and susceptibility (Boffetta 2010; Chen et al. 2005). For the purposes of this review, the exposure/effect/susceptibility paradigm is used as follows:
Biomarkers of exposure are chemical-specific (the stressor itself or a chemical metabolite). These biomarkers are considered exogenous in origin and used to assess the quantity of the chemical within the biological system.
Biomarkers of effect are not necessarily structurally similar to the stressor; they are considered to be of endogenous origin, resulting from interactions with the environment and generally linked to an AOP in human systems biology.
Biomarkers of susceptibility demonstrate an individual propensity for a sensitized reaction to a stressor of interest. These biomarkers are typically related to genetic, epigenetic, or enzymatically induced traits and describe the likelihood that an environmental interaction might trigger a cascade leading to adverse effects.
Certain biomarkers from each of these categories may be more amenable to analysis in one biofluid versus another, while other biomarkers may be equally well determined in multiple biofluids. In this review, the focus is on the different categories of biomarkers that can be analyzed in blood.
Classes of Blood Biomarkers
There are four main types of biomarkers in addition to exogenous chemicals that are commonly found in blood: endogenous chemicals, inflammatory, molecular modification, and damage bioindicators.
Endogenous biomarkers reflect the earliest effects of external perturbations to the human organism. Typically, these compounds represent housekeeping chemicals from hydrocarbon and oxygen metabolism, as well as volatile oxygenated species, including alcohols, aldehydes, ketones, organic acids and proteins. The probative aspect is not their presence but a shift away from what is considered a normal (or unremarkable) pattern (Pleil 2012; Pleil, Stiegel, and Risby 2013). For example, increased levels of reactive oxygen species (ROS) are indicative of oxidative stress (Amann et al. 2014), which might be assessed by monitoring levels of glutathione (GSH) and glutathionine disulfide (GSSG) in blood (Dalle-Donne et al. 2006). Stress genes are often induced after exposure to an environmental stressor and may indicate toxicity. Heat-shock proteins, particularly Hsp70, are often expressed as a result of exposure to heavy metals or pesticides, and these exposures were found to produce recognizable heat shock protein expression patterns (Gupta et al. 2010). Clinical markers, including cholesterol, triglycerides, and liver enzymes in blood samples, may also be considered endogenous biomarkers and indicate adverse health risks when abnormal levels are observed (Pagana and Pagana 2009; Watkins and German 2002). Circulating mitochondrial DNA and microRNA (miRNA) were also identified as biomarkers of environmental exposure to pesticides and indicators of early cancer development (Budnik et al. 2013; Liu et al. 2012).
Inflammatory bioindicators represent the first level of biologically relevant parameters that react to an external stressor, which is often the first step in an AOP. The inflammatory response serves as a recruitment mediator for repair function stimulated by external stressors, such as air pollution, and generates macrophages to release chemokines and proinflammatory cytokines, which might be employed to track the biological response to the stressor. In some scenarios, anti-inflammatory messengers are also released, which reflects a depression of repair function and may be considered adverse. Cytokines were identified as biomarkers of exposure to traffic emissions and smoke, which have been linked to cardiovascular diseases (Brugge et al. 2013; Channell et al. 2012; Ghio et al. 2012; Kendzia et al. 2012; Stiegel et al. 2015; Zuurbier et al. 2011). The cytokine interleukin-6 (IL-6) stimulates chemokine expression as well as C-reactive protein synthesis and secretion from liver in response to inflammation. C-reactive protein is a serum biomarker of acute-phase response and an indicator of cardiovascular diseases (Devaraj, Xu, and Jialal 2003; Meier-Ewert et al. 2001; Yudkin et al. 1999). While these may be useful biomarkers that some exposures have occurred recently, cytokines (and other protein markers) are nonspecific, tend to have a short serum half-life, and are difficult to detect. In fact, the acute response may be delayed by a few (or more) hours and may recover within the same time frame (Angrish et al. 2016; Nelin et al. 2012). However, these endogenous markers are useful to assess health state and indicate an overall level of stress on the biological system. Inflammatory biomarkers also lack tissue specificity and recognizable toxin-specific expression patterns and are therefore difficult to interpret when more than one toxicity mechanism is possible (Tarrant 2010).
Molecular modification bioindicators are typically defined as large molecule adducts and conjugates. These include protein adducts, especially those on hemoglobin (Hb) and human serum albumin (HSA) proteins, which together comprise approximately 92% of all blood-borne proteins (Funk et al. 2008). Protein adducts are considered long-term biomarkers: Hb has a half-life of approximately 60 d, while HSA has a half-life of 30 d (Rappaport et al. 2012). HSA functions as a carrier for proteins and fatty acids (Fanali et al. 2012), while the primary function of Hb is to transport oxygen from lungs to tissues (Schechter 2008). However, exogenous chemicals and their metabolites might attach to the active sites of these proteins, forming adducts that interfere with their normal functions (Boysen and Hecht 2003). Similarly, exogenous compounds, such as PAHs, form adducts with cellular DNA, inducing DNA mutations that affect reproductive functions (Kwack et al. 2014). DNA adducts reveal how much of a carcinogen has been adsorbed, metabolized, and bound to DNA but not repaired (Gallo et al. 2008; Porru et al. 2014). Other stressors may affect DNA through epigenetic attachments, often in the form of methylation (Ouyang et al. 2013).
Damage bioindicators comprise a wide range of organic molecules and cellular fragments from adverse biological effects. The most common example of clinical damage markers is liver enzymes, which are essentially leakage proteins from injured cells. Two liver enzymes, aspartate aminotransferase (AST) and alanine aminotransferase (ALT), are released into blood circulation upon hepatocellular damage or cell death. Blood tests are performed to assess the potential for liver disease and other viral infections due to high AST or ALT levels (Kew 2000; Kim et al. 2008). Circulating blood cells provide a convenient source of DNA to assess DNA-specific damage indicators. Cytogenetic damage indicators include chromosome aberrations (CA), sister chromatid exchanges (SCE), and micronuclei (MN). CA are characterized by chromosome breaks and rearrangements greater than normal 1% occurrence. In SCE, entire DNA strands are separated and exchanged between two sister chromatids, while MN are entire chromosomes with centromeres left outside of the main nuclease due to structural damage or abnormal mitosis (Holland, Zhang, and Smith 2000; Watson and Mutti 2004). DNA singlestrand breaks (SSB) are another type of damage bioindicator that may be detected using a comet assay. Other indicators of DNA damage include physiological cell death, termed apoptosis, and accidental cell death, known as necrosis. Apoptosis suppression is a common biomarker of cancer (Ward et al. 2008), while necrosis is the result of severe injury and is characterized by cytoplasmic and mitochondrial swelling prior to membrane breakage (Kroemer, Dallaporta, and Resche-Rigon 1998). Cell count assays might be utilized to assess apoptosis and necrosis and determine the consequences of toxicity and damage (Ortiz et al. 2000). DNA damage bioindicators were shown to vary based on environmental exposures, as these often include fragments or whole molecular structures of the exogenous chemical (Gallo et al. 2008; Poirier 2004).
Environmental Exposure Continuum
The human exposome describes the counterpart to the human genome that represents chemicals, both exogenous and endogenous, that interact with human systems biology (Pleil and Sheldon 2011; Wild 2005). This concept was further elucidated to begin to explain the gene–environment interaction that appears to drive health outcomes from autoimmune diseases, cancer, and chronic conditions, including cardiopulmonary disease, neurological disorders, and asthma (Pleil 2012; Rappaport 2011; 2012b; Rappaport and Smith 2010; Schetter and Harris 2012; Smith et al. 2011). Circulating blood contains essentially all of the chemicals that comprise human systems biology and thus also represents the human exposome (Barr et al. 2002; Levitt 2004; Rappaport 2011). Other biological media may also reflect portions of the exposome, especially for those compounds eliminated via breath and urine or those sequestered in adipose tissue (Amann et al. 2014; Barr and Angerer 2006; Lehr, Hartwig, and Sell 2012; Pleil, Stiegel, and Sobus 2011a; Roos and Jakubowski 2010; Trayhurn 2005). However, blood is truly the central compartment for human metabolism. Figure 2 demonstrates how exposure to the health effect continuum is interpreted via biological measurements and the relationships among the major biomarkers and bioindicators and biological fluids within which these are found. The less invasive media, breath and urine, might certainly overlap in some suites of biomarker compounds and generally display higher concentrations, but with more sensitive contemporary instrumentation, the lower levels in circulating blood are now becoming accessible (Pasikanti, Ho, and Chan 2008; _Zhang et al. 2013b).
Figure 2.
The center sequence from environmental exposure through health effects is represented by the biological progression of biomarkers and responses of increasing complexity that are assessed in human biological samples. The upper entries represent bioindicators of effect (or at least preclinical effects) generally found in circulating blood. The lower entries represent biomarkers of recovery and repair functions; those labeled passive and active elimination are typically found in breath and urine or sequestered in adipose tissues. DNA repair and cell turnover are not generally directly observable as biomarkers but are inferred from a reduction in damage biomarkers.
As shown in the conceptual model proposed in Figure 2, markers representing effects pathways (top progression) are expressed in a series of groups depending upon their source along the internal exposure to effects progression (middle progression). All bioindicators are present in blood; some specific classes may also be measured in other biological media with emerging methodologies. For example, endogenous markers of carbohydrate metabolism, such as ketones and aldehydes, are detected in exhaled breath (Ligor et al. 2008), markers of systemic inflammation, such as cytokines are found in urine (Stiegel et al. 2015), and markers of DNA damage, such as DNA SSBs were identified in oral buccal swabs (Basu et al. 2005; Holland et al. 2008).
The primary advantage for using blood as the medium, however, is that all of the different bioindicator classes are available in one medium and are measured by applying the appropriate processing and analytical methodologies. Further, there is another class of biomarkers specific to circulating blood that are not on the effects pathway (upper) nor the recovery pathway (lower). These are the native compounds of exposure that are found in the “body burden” box and are measured prior to biological activation or removal (Pleil 2009). In the following sections, the typical blood collection and processing procedures are described, an assessment of the currently utilized blood biomarkers of exposure, effect, and susceptibility is provided, and also some of method issues for using blood as a biological medium are discussed.
Blood Biomarkers: Environmental Applications
Blood Composition, Collection, Transportation, and Storage
Blood is composed of many different cell types, including lymphocytes, granulocytes, and red blood cells (RBC) as well as serum and plasma (Holland et al. 2005). Blood lymphocytes are typically utilized to analyze CA and SCE (Holland et al. 2003). Neutrophils, a type of granulocyte, may be isolated for DNA analysis, while RBC are a good source for studying Hb adducts (Vaught 2006). Whole blood may be advantageous for evaluating certain biomarkers. For example, heavy metals, such as manganese (Mn), lead (Pb), and cadmium (Cd) are typically measured in whole blood due to their low levels in serum and plasma as well as a lack of epidemiological data from exposure assessments of these biomarkers in serum or plasma (Kim et al. 2015; Sommar et al. 2014). Different blood cell types and liquids require distinct sample preparation and storage conditions, which may need to be optimized prior to collecting samples for the study (Holland et al. 2003). Recently, a multicolor flow cytometry assay was developed that is capable of distinguishing major leukocyte subsets in human blood, bronchoalveolar lavage, and lung tissue, and this technique may be used to visualize the proportions of different blood cell types without requiring prior separation. This flow cytometry technique may be utilized in exposure science to compare the proportions of cell types present in samples from exposed and unexposed individuals, as demonstrated for smokers versus nonsmokers (Yu et al. 2016).
Blood sample collection for environmental applications is identical to methods used for clinical evaluation. Typically, a phlebotomist or other trained personnel is needed to draw blood samples, especially when samples are being collected from children (Holland et al. 2003; Vaught 2006). Blood is drawn from the antecubital vein (Levitt 2004) in volumes of 20 ml or less (Polkowska et al. 2004). It is vital that blood is collected in a sterile environment to avoid contamination and fungal or bacterial infections. In addition, the procedures utilized for blood sampling need to be standardized for all participants to ensure that differences in sample collection do not influence the results of the study (Holland et al. 2003). Samples for case and control subjects need to be drawn at the same time and in the same manner for consistency (Holland et al. 2003; Holland et al. 2005). Sterile evacuated plastic tubes are typically used for sample collection. Collection tubes need to be free of trace metals, which interfere with analyses of metal exposure (Vaught 2006). Anticoagulants and preservatives for sample tubes need to be selected based upon the target of interest, such as RNA or DNA, as different targets require certain stabilizers (Holland et al. 2003). However, great care needs to be taken when choosing anticoagulants and other processing additives, because some of these are known to affect the levels of particular biomarkers in serum and plasma samples. Biomarker levels reported in different investigations may not be directly comparable if proper sample collection and preparation procedures were not adhered to (Gillio-Meina et al. 2013).
After blood collection, samples need to be immediately centrifuged to create a Ficoll gradient, which separates blood into plasma, white blood cells (WBC), and RBC (Holland et al. 2005; Li, Todor, and Luo 2016). Blood samples need to be processed shortly after collection, and all samples need to be cryopreserved within 24 h of sample collection (Shabihkhani et al. 2014). Different cell types and components require unique storage conditions. For example, cells need to be separated from serum immediately after sample collection for proper analysis of cytokines. If the desired target is unknown, blood samples may be kept at 4°C for several days to protect cells against enzymatic degradation while maintaining cell viability. Depending upon the target of interest, protease inhibitors may be added to the sample to slow degradation. However, these inhibitors are toxic to live cells and should not be used if cell viability is necessary. If novel targets are being analyzed, it may be necessary to conduct a preliminary experiment to determine optimal storage conditions and times before gathering samples from subjects (Holland et al. 2003).
If possible, blood samples need to be collected at the location where samples will be analyzed to avoid sample handling issues that may affect sample integrity. However, this is typically not feasible, because samples are frequently collected in the field, and sample transportation is thus unavoidable. Shipped samples need to conform to proper guidelines and restrictions for biological media (Holland et al. 2003). Depending upon the blood component and what the sample will be used for, blood samples may be shipped at room temperature, on ice, or frozen (Belloni, Meschini, and Palitti 2008). For example, serum and plasma samples need to be stored at −80°C for analysis of soluble molecules, but plasma may be stored at room temperature for immunoglobulin analyses, as these are more stable under these conditions (Gillio-Meina et al. 2013; Holland et al. 2003). Lymphocytes need to be stored in liquid nitrogen vapor at −150°C if viability is required (Vaught 2006). When considering storage conditions, it is important to remember that apoptosis generally increases with time and temperature (Belloni, Meschini, and Palitti 2008). Therefore, transportation conditions need to be specifically selected based upon cell type or liquid and the component of interest to ensure that the sample will be properly preserved for analysis. During transportation, measures need to be taken to ensure that samples do not undergo any freeze–thaw cycles, which might damage cells and decrease sample integrity (Gillio-Meina et al. 2013; Holland et al. 2005; Holland et al. 2003; Shabihkhani et al. 2014).
Storage conditions for blood samples depend upon cell type and preservation method. Samples need to be maintained at either 4, −20, or −80°C, depending upon the medium and compound of interest (Holland et al. 2005; Vaught 2006). Blood samples may be stored in either plastic tubes or glass tubes with polytetrafluoroethylene (PTFE) or aluminum cap liners (Polkowska et al. 2004). However, the exact tube utilized depends upon the medium of interest, such as whole blood, plasma, serum, or RBC, all of which have specially designed tubes for optimal stabilization and preservation (Shabihkhani et al. 2014). Cells may be cryopreserved in small volumes for long-term storage. For cryopreservation, dimethyl sulfoxide (DMSO) is added to cells as a cryoprotectant, and samples are stored in liquid nitrogen vapor to prevent cell death (Holland et al. 2005; Vaught 2006). Ellis and Venturini (2013) also showed that blood samples may be frozen in larger volumes and that frozen aliquots removed from the stock solution without thawing the samples and without a significant loss in sample integrity, thereby decreasing the number of sample tubes needed to store multiple aliquots. While high levels of reproducibility were observed for cholesterol, triglycerides, glucose, and immunoglobulin G, the effectiveness of this technique for other analytes in blood may vary (Ellis and Venturini 2013). Blood samples are commonly stored in biorepositories, such as the European Cancer Study biorepository, which contains more than 380,000 blood samples. Nanobarcoding technology was employed to uniquely label samples in small surface areas without using removable labels that may fade or fall off with time (2005; Holland et al. 2003). Efficient sample storage and electronic databases are necessary to maintain sample integrity and ensure that samples are easily retrieved for future analyses.
Traditional Environmental Exposure Assessment
Environmental applications of blood analysis have historically focused on measuring exogenous compounds in blood circulation to detect environmental exposures and estimate relative body burden. Levels of compounds, such as heavy metals, that an individual has been exposed to may be assessed in healthy populations to detect biomarkers associated with low-level exposure, in addition to providing a baseline to which affected individuals may be compared (Pollack et al. 2015). Placental and maternal blood was examined to evaluate fetal risk to Cd exposure from dietary and tobacco sources (Piasek et al. 2014). Blood Pb levels were also measured to determine the extent of Pb exposure, and these levels may be tracked in populations to identify potential contributing factors, such as smoking status, alcohol intake, red meat consumption, geographical location, and tap-water contents (Bellinger et al. 1987; Counter, Buchanan, and Ortega 2015; Lopes et al. 2015; Ngueta et al. 2016). Trace levels of PAH from environmental exposure to diesel exhaust and markers of exposure to toluene, ethylbenzene, and m-xylene were also determined in blood samples from volunteers in controlled chamber studies (Marchand et al. 2015; Pleil et al. 2011b; Pleil et al. 2010). In addition, volatile organic compounds (VOC) released from blood were analyzed to detect changes in cellular respiration, and this technique may be utilized to detect alcohol content in blood to assess the amount of alcohol consumption (Amann et al. 2014; de Lacy Costello et al. 2014; Pleil 2016; Pleil et al. 2014).
In many cases, biomarkers of exposure are present in multiple biofluids. In such cases, investigators have predominantly turned to less invasive biofluids such as urine or breath. However, there are a number of situations where blood remains the best source of biomarkers of exposure. For example, biomarkers with long elimination halflives exhibit higher concentrations in blood than urine. Ochratoxin A, a nephrotoxic mycotoxin found in contaminated vegetables, has a serum half-life of 35 d in humans. In addition, ochratoxin A has poor elimination kinetics, and therefore the concentration of ochratoxin A in urine is much lower than found in blood (Duarte, Pena, and Lino 2011). Mercury (Hg) and Pb also have long halflives of 30 and 70 d in blood, respectively (Brodkin et al. 2007), and Cd levels in blood reveal exposure over preceding months (Kim et al. 2015). Perfluorooctanesulfonate (PFOS), perfluorohexanesulfonate (PFHS), and perfluorooctanoate (PFOA) have serum elimination half-lives of approximately 5, 8, and 3.5 yrs, respectively (Olsen et al. 2007). Therefore, urine levels of biomarkers with long elimination half-lives will be lower than the actual concentration in the body, which might lead to an erroneous conclusion that an exposure has not occurred. For biomarkers with long elimination half-lives, blood is the ideal biofluid in which to measure the concentration.
Blood biomarkers may more accurately reflect chemical levels that an individual was exposed to than urinary metabolites (Arayasiri et al. 2010; Käfferlein et al. 2005). Hemoglobin adducts of aromatic compounds have average lifetimes of approximately 120 d, whereas the respective native compound concentrations in blood display halflives ranging from minutes to a few hours, depending upon the exposure history (Käfferlein et al. 2005). Thus, Hb adducts reveal the mean exposure to a chemical that occurred over the past several months. N-Methylcarbamoylated haemoglobin (NMHb) levels, a blood biomarker of N, N-dimethylformamide (DMF) exposure, were evaluated in workers from a polyacrylic fiber plant. NMHb was capable of distinguishing between three different levels of DMF exposure with statistical significance. While urinary metabolites N-methylformamide (NMF) and N-acetyl-S(N-methylcarbamoyl)cysteine (AMCC) may identify DMF exposure, these biomarkers were not able to distinguish between high, medium, and low exposure, but this was possible with the blood biomarker (Käfferlein et al. 2005). In addition, some compounds, such as 2-butoxyacetic acid, display inconsistent levels of conjugation in urine excretions, which skews the results when measuring the level of the free compound to determine the extent of exposure to 2-butoxyethanol from inks, varnishes, and cleaning fluids (Jones and Cocker 2003).
Blood may also be the best biofluid to use for analysis when urinary biomarker metabolites are nonspecific. For biomarker analysis, measuring metabolites of the parent compound is not always sufficient for exposure assessment. A parent compound may generate several nonspecific metabolites that are excreted in urine, but analyzing these metabolites may not reveal which compound the individual was actually exposed to. Some pesticides and other contaminants have common urinary metabolites that do not reveal the identity of the parent compound (Barr et al. 2002; Needham et al. 2005; Smolders et al. 2009). The identification of 3,5,6-trichloropyridinol in urine samples might be due to exposure to either chlorpyrifos or chlorpyrifos-methyl pesticides (Barr et al. 2002; Needham et al. 2005). Chlorpyrifos metabolites are also found as natural degradation products in the environment and may not always be indicative of direct exposure to the parent pesticide (Smolders et al. 2009). Further, some biomarkers do not have any apparent known metabolites, and therefore the parent compound needs to be quantified instead; in such cases blood is frequently the best sample for measuring the most relevant biomarker (Barr et al. 2002). Other biomarkers and metabolites, such as organic Hg, are predominantly excreted in feces instead of urine, making blood the ideal biofluid for analysis (Brodkin et al. 2007; Needham et al. 2005). Thus, knowing the likely exposure route and identity of the biomarker might help the investigator or clinician select the most appropriate biofluid to use for exposure assessment.
Environmental Biomarkers of Effect
Traditional exposure assessment has been broadened philosophically to include forward mapping toward health outcome. In this case, indicators of the presence and levels of specific chemicals that an individual was exposed to are statistically linked to subclinical and preclinical effects. The measured compounds, whether exogenous or endogenous, are not necessarily toxic themselves but might trigger perturbations that lead to increased adverse health risk. These compounds are known as biomarkers of effect or bioindicators. As stated previously, biomarkers of effect are of endogenous origin and are altered by interactions with the environmental stressor, which then becomes involved in an AOP.
Biomarkers of effect include protein indicators, DNA indicators, and adducts of both proteins and DNA. Protein indicators of effect include alterations in protein expression levels and patterns. Normal protein expression may be altered by exposures to stressors that interfere with endogenous protein levels (Merrick 2008). Exposures to air pollutants and other harmful substances might lead to an inflammatory response affecting cytokine levels. Diesel exhaust, nitrogen dioxide (NO2), and ozone (O3) exposure were shown to acutely elevate levels of WBC and interleukin-8 (IL-8) in plasma samples from human volunteers (Channell et al. 2012; Steenhof et al. 2014). In a study of the effects of O3, fibrinolysis markers, such as plasminogen activator inhibitor-1 were measured in human blood samples and found to decrease after O3 exposure at moderate temperature, while the markers rose after exposure at elevated temperature, indicating that the effect of O3 on the fibrinolytic pathway is temperature dependent (Kahle et al. 2015). Ozone was also found to elevate levels of stress hormones and lipid metabolites in serum samples from subjects exposed during intermittent exercise (Miller et al. 2016). Exposure may induce protein overexpression. For example, the glycoprotein mesothelin and soluble mesothelin-related peptides become overexpressed in malignant mesothelioma as a result of asbestos inhalation (Marini et al. 2011). Some proteins may maintain normal expression levels but experience a change in activity due to exposure. The activity level of the protein β-glucuronidase was increased as a result of low-level organophosphorus insecticide exposure (Ueyama et al. 2010). Protein modifications are another common effect of exposure to stressors. Common modifications include protein oxidation and lipid peroxidation, which are signs of oxidative damage (Rossner et al. 2009).
DNA indicators of effect include changes in gene expression, mitochondrial DNA, DNA modifications, DNA damage, and miRNA (Boffetta 2010). Gene expression patterns were found to alter based upon exposure to environmental contaminants, such as ultrafine carbon particles (UFP), arsenic (As), and benzene (Huang et al. 2010; McHale et al. 2009; Mo et al. 2009). Increased or decreased gene expression in the presence of an environmental stressor may indicate that an AOP was triggered or that DNA damage repair responses and defense mechanisms against exposures were activated (Mo et al. 2009). Mitochondrial DNA was labeled as a useful biomarker of damage and exposure. Unlike genomic DNA, mitochondrial DNA does not have histone protection or repair mechanisms and may therefore reflect more severe damage (Hou et al. 2010). Exposure to airborne particulate matter (PM) and halo-alkanebased pesticides was linked to damaged mitochondrial DNA (Budnik et al. 2013; Hou et al. 2010). Environmental contaminants, such as As, nickel (Ni), and ultrafine PM, were reported to produce increased DNA SSB in exposed individuals (Avogbe et al. 2005; Basu et al. 2005; Glei et al. 2005; M’Bemba-Meka, Lemieux, and Chakrabarti 2005; Popp et al. 1997). Levels of DNA SSB may be used to distinguish affected versus unaffected populations (Basu et al. 2005). Telomere lengths have also been reported as biomarkers of DNA damage. While exposures to traffic pollution, PAHs, fine particulate matter (PM2.5), pesticides, and Pb have been associated with telomere shortening in chronically exposed individuals (Hoxha et al. 2009; Wong et al. 2014; Zhang et al. 2013a), individuals exposed to low doses of persistent organic pollutants (POP) or As exhibited telomere lengthening (Shin et al. 2010; Zhang et al. 2013a). Although telomeres tend to shorten with age and exposure, telomere lengthening in healthy individuals may be a biomarker of earlystage carcinogenesis (Shin et al. 2010). Increased MN and binucleated micronucleated cells (BNMN) in blood lymphocytes are biomarkers of exposure to genotoxic agents. The frequency of MN increases as a result of chromosome breakage or loss, indicating that DNA damage occurred. Elevations in MN and BNMN frequencies were attributed to air pollution, pesticides, and As (Martínez et al. 2004; Pedersen et al. 2006; Ramos-Chavez et al. 2015).
DNA modification through methylation is another biomarker of environmental exposure. Some common aberrant methylation patterns include global hypomethylation and gene-specific hypermethylation/hypomethylation (Bollati et al. 2007). DNA methylation mediates gene expression through protein–protein and protein–DNA interactions. Deviation from normal methylation patterns may be indicative of oxidative damage (Franco et al. 2008). While one study observed increased DNA methylation as a result of low-dose benzene exposure (Kim et al. 2010), reduced methylation was noted in a study of traffic particle exposure (Baccarelli et al. 2009). DNA methylation levels change based upon the type of environmental exposure and exposure time (Baccarelli et al. 2009). Whether an exposure results in elevated or decreased methylation may be used in the future to determine the nature of the exposure that occurred. miRNA, small noncoding RNA that regulate gene expression, were identified as biomarkers of exposure and cancer due to altered expression levels in plasma and blood samples in affected individuals (Bollati et al. 2010; Liu et al. 2012; Zhang et al. 2010). Upregulation of miR-222 has been associated with metal-contaminated air particle exposure (Bollati et al. 2010), and miR-122 was identified as a specific blood-based biomarker of liver disease (Zhang et al. 2010).
Blood levels of environmental compounds often reflect the dose to which an individual was exposed. For example, high levels of compounds are often found in blood samples of individuals exposed to higher concentrations of toxins than for those exposed to lower doses (Käfferlein et al. 2005; Lope et al. 2010; Lunder et al. 2010; Turyk et al. 2010). While some compounds with long half-lives in blood may be directly measured, other compounds have short halflives in blood, and thus the exogenous compound cannot be directly analyzed to verify exposure. For example, cyanide (CN) is rapidly metabolized and has a half-life of less than 1 h in whole blood. Thus, the level of CN cannot be accurately measured by evaluating the native compound (Fasco et al. 2007). For cases such as CN, evaluating DNA or protein adducts isa useful way to assess the effects of environmental compounds. DNA and protein adducts have longer half-lives, typically 3–4 mo, depending upon the protein lifetime. Adducts are also stable and reflect longer term exposures than urinary or breath biomarkers (weeks or months vs. hours or days) (Johnson et al. 2010). Protein and DNA adducts also rise with relevant exposure dose and may be utilized to assess total amount of exposure (Beach and Gupta 1992; Bechtold et al. 1992; Binková et al. 2000; Käfferlein et al. 2005; Magagnotti et al. 2000; Topinka et al. 2007). Benzene exposure was assessed by monitoring levels of Hb and albumin adducts with benzene oxide, as well as albumin adducts with S-phenylcysteine (SPC), a benzene metabolite (Bechtold et al. 1992; Lindstrom et al. 1998). Protein adducts are typically favored over DNA adducts for exposure assessment because they are not repaired; the concentration of DNA adducts at any given time depends on the repair rate and may not linearly rise as the dose is increased (Watson and Mutti 2004). In a previous study of policemen exposure to traffic emissions, the levels of c-PAH increased by threeto fourfold, but the amount of benzo[a] pyrene DNA adducts only rose by approximately 20%, indicating that DNA repair mechanisms were eliminating some of the adducts (Topinka et al. 2007). These biomarkers of effect indicate that an environmental stressor induced alterations in the chemical makeup of the individual. While the presence of DNA modifications and DNA and protein adducts can be verified, the long-term effects of these biomarkers may or may not lead to an AOP. Whether an AOP is triggered may depend upon the susceptibility of the individual. Some individuals may possess more efficient DNA repair mechanisms than others, allowing them to avoid some of the deleterious outcomes of DNA adducts. Other exposed individuals may display reduced DNA repair after chronic exposure (Arayasiri et al. 2010). The concentration of the compound, length of exposure, and consistency of exposure (once versus daily) all affect the severity of consequences from exposure and whether an AOP might be triggered.
Assessment of Susceptibility
The eventual path to an adverse outcome is thought to involve both environmental component (exposome) and genetic component (genome). Thus, there is some form of intrinsic individual susceptibility to different environmental stressors (Boffetta 2010). For instance, not all smokers contract lung cancer; not all sunbathers suffer from melanoma; not everyone on high-fat diets has the result of heart attack development. In general, susceptibility is difficult to assess directly but might sometimes be determined by examining trends in exposure of larger populations. In the case of asbestos, inhalation was correlated with malignant pleural mesothelioma (MPM), but some individuals with high asbestos exposure did not develop the disease, while others with lower exposures were more susceptible. Recently, three Fe metabolism genes were identified with protective properties against MPM development, explaining some of the differences in individual susceptibility (Corvella et al. 2016). In some cases, a specific genetic sequence or polymorphism was used to assign relative risk in conjunction with a specific exposure, or a host factor (e.g., age, weight, or health state) was associated with increased risk in the presence of environmental chemicals (Boffetta 2010). In general, however, it is difficult to predict individual susceptibility. Blood measurements of various endogenous biomarkers might be employed to estimate relative susceptibility via comparisons to onboard dose. Signaling chemicals, such as cytokines and various expressed proteins, may be directly interpreted to establish relative risk, and other chemicals present in blood might serve different levels of protective functions.
Biomarkers of susceptibility include antibody levels and genetic polymorphisms. Levels of Cdinduced metallothionein (MT) mRNA were used as a biomarker of renal dysfunction susceptibility in peripheral blood lymphocytes. Several studies showed that high plasma MT antibody (MT-Ab) levels correlated with a higher risk of renal tubular damage, while low MT-Ab levels indicate decreased susceptibility. MT is a small protein that binds Cd, leading to detoxification. Renal damage occurs as a result of excess Cd that is not bound to MT within the cell (Chen et al. 2005; 2006). Genetic polymorphisms, which are defined as sequence variants that affect approximately 1% or more of the population, also influence individual susceptibility to compounds found in the environment (Kelada et al. 2003). In an analysis of blood gene expression levels and clinical biomarkers using a decision-tree approach to discover correlations among asthma endotypes, polymorphisms in the gene HNF-4a that are known to affect diabetes were also implicated in enhanced asthma susceptibility (George et al. 2015). Further, single-nucleotide polymorphisms in catechol O-methyltransferase (COMT) were found to initiate increased susceptibility to neurobehavioral issues due to Hg exposure among children (Woods et al. 2014). Polymorphisms that affect DNA repair genes may also enhance susceptibility by lowering the individual’s ability to correct mutations to the genome. Polymorphisms were detected in the x-ray repair cross-complementing (XRCC1) gene, which functions in base excision repair, and these polymorphisms were found to induce deficiencies in DNA repair mechanisms (Feng et al. 2014; Topinka et al. 2007; Tuimala et al. 2002). XRCC1 mutants exhibit accumulation of SSB in DNA (Thacker and Zdzienicka 2003), and XRCC1 variant 399Gln leads to increased DNA adducts and SCE levels (Da Silva et al. 2013). Polymorphisms also produce proteins with decreased activity compared with wild-type protein. Methionine synthase reductase variant 66GG protein exhibits fourfold reduced activity and cannot perform its normal function to catalyze conversion of methionine synthase into its active form. This variant was also shown to lower genomic stability, thus increasing susceptibility of the individual to environmental exposures (Zijno et al. 2003).
Finally, vitamin and mineral deficiencies are also bioindicators of susceptibility to environmental stressors. Many vitamins and minerals are critical for cell survival, and a lack of these micronutrients leads to significant damage. Iron deficiency was linked to increased susceptibility to Mnand Cd-mediated toxicity after exposure (Kim et al. 2015), while folic acid and vitamin B12 deficiencies produced SSB and double-strand breaks in DNA, as well as oxidative lesions. Folic acid deficiency resulted in uracil incorporation into DNA instead of thymine, thereby enhancing the risk of chromosome breaks (Ames 2001; Fenech 2001). Unfortunately, approximately 10% of the U.S. population have a folate deficiency and approximately 4% suffer from a lack of vitamin B12 (Ames 2001). Vitamin and mineral deficiencies may be due to genetic defects or a lack of dietary consumption, both of which enhance susceptibility. Knowledge of these types of biomarkers might prove useful to prevent AOP in individuals who are prone to genetic damage or dietary deficiencies.
Blood Biomarkers: New Developments
Advances in Sampling Techniques: Dried Blood Spots (DBS)
Dried blood spot (DBS) sampling has become more accessible and applicable for blood sample analysis in both clinical and environmental settings. DBS sampling was initially developed in 1963 by Dr. Robert Guthrie to detect phenylketonuria in newborns by pricking their heels and collecting a blood spot on paper. Newborn DBS sampling is still performed, and several other metabolic disorders are also currently tested for (Guthrie and Susi 1963; McDade, Williams, and Snodgrass 2007). DBS sampling is less expensive than collecting large volumes of liquid blood samples, and it is less painful and less invasive for the subject. Therefore, more individuals are likely to provide DBS samples (Benyshek 2010; Ostler, Porter, and Buxton 2014). Individuals who have chosen not to provide DBS samples for investigations stated different reasons, including medical conditions, use of blood thinners, fear, or thinking it unnecessary for the study (Williams and McDade 2009). DBS samples can also be analyzed after several months or years for quantification of certain analytes. DNA has been extracted and amplified from DBS samples stored for 19 mo, and even after this length of storage at room temperature, comparable DNA levels were achieved (Kline et al. 2002).
DBS Sampling and Storage
Dried blood spot sample collection does not require a trained phlebotomist, as anyone can be easily trained to acquire the samples. In one study, more than 2000 DBS samples were collected by trained field interviewers without a medical professional (Williams and McDade 2009). Spring-loaded finger-stick lancets were developed that cut to uniform depths of 2 mm to enhance blood flow and reduce pain and bruising (Benyshek 2010; Edelbroek, Van Der Heijden, and Stolk 2009). The first blood drop is wiped away and is not applied to the filter paper. Subsequent drops are placed on the filter paper by touching the drop to the paper without the finger contacting the paper, to avoid smearing (McDade, Williams, and Snodgrass 2007; Ostler, Porter, and Buxton 2014). Filter paper with preprinted circles of approximately half an inch in diameter is utilized to control the sizes of drops collected. The circles can hold approximately 50–80 µl blood, and blood circles of 3.2–6 mm in diameter are required for most DBS assays (Benyshek 2010; Edelbroek, Van Der Heijden, and Stolk 2009; McDade, Williams, and Snodgrass 2007; Williams and McDade 2009). The quality and type of paper needs to be considered prior to sampling (Edelbroek, Van Der Heijden, and Stolk 2009; Mei et al. 2001). RBC transport easily within hardwood fiber sheets, providing higher clarity assays compared with softwood fiber papers, which have more complex pore structures (Li et al. 2014). For assessments of metal exposure, blood collection cards need to be treated prior to sample collection to remove background levels of heavy metals in the filter paper, which were found to significantly skew results (Funk et al. 2015).
The collected samples are allowed to dry for at least 15 min and for as long as 4 hr (McDade, Williams, and Snodgrass 2007; Ostler, Porter, and Buxton 2014). Once the samples are collected, these do not need to be centrifuged immediately and stored at cold temperature like traditional blood samples. DBS samples are typically stable for at least two weeks at room temperature and easy to store because they take up less space than liquid blood samples (Benyshek 2010; McDade, Williams, and Snodgrass 2007). In fact, some light-sensitive compounds demonstrated better stability stored as DBS samples than as liquid plasma or whole blood samples (Bowen et al. 2010). For best results, DBS samples need to be assayed or frozen within 2 wks after collection (Ostler, Porter, and Buxton 2014). Storing DBS samples in airtight leakproof plastic bags may generate heat and moisture accumulation due to a lack of air exchange, which leads to sample degradation (Edelbroek, Van Der Heijden, and Stolk 2009). Degradation of DBS samples might be reduced by adding vapor-phase desiccants to the plastic bags in which the DBS samples are stored or by adding humidity indicators. These storage bags can then be placed in waterproof bags for shipping and storage (Benyshek 2010; Edelbroek, Van Der Heijden, and Stolk 2009). While liquid blood samples need to be shipped on dry ice, DBS cards can remain at room temperature, lowering shipping costs (Amsterdam and Waldrop 2010). A lab freezer can store approximately 8,000–10,000 DBS samples, which is greater than the number of liquid samples that can be stored (Benyshek 2010; McDade, Williams, and Snodgrass 2007). Neonatal DBS samples can be stored at −20°C for several weeks or yrs, but sample loss was noted after 5 yrs (Edelbroek, Van Der Heijden, and Stolk 2009). Biobanking of liquid blood samples is also expensive, but DBS samples do not require cryopreservation and do not need to be stored with the same stringency, thus severely decreasing storage cost (Barbareschi, Cotrupi, and Guarrera 2008; Riegman et al. 2008).
DBS Challenges
While DBS samples have many advantages, there are several method issues that need to be addressed. The protocol for DBS sampling is not as well controlled as liquid blood sampling, which may cause some samples not to meet quality control standards (Edelbroek, Van Der Heijden, and Stolk 2009). Some sampling issues include blood spots that are too dilute, contaminated, smeared, spotted multiple times, or have too little volume (Ostler, Porter, and Buxton 2014). Samples that suffer from these issues cannot be used for analysis, as heterogeneity in sample spots may lead to erroneous results and conclusions. Differences in the type of filter paper utilized and in the paper batch may also affect the amount of blood that is absorbed (Chaudhuri et al. 2009). Thus, DBS sample analysis data may not be directly comparable to liquid blood samples using either serum or plasma due to differences in sample collection and blood composition (Benyshek 2010; McDade 2014). Since DBS samples only contain approximately 50 µl blood, assays that require more volume may not be compatible (McDade, Williams, and Snodgrass 2007). Therefore, individual methods for compound analysis may need to be developed for DBS analysis. The newly developed assays may not be immediately compatible with automated clinical assays using serum or plasma, which would delay the applicability of DBS for clinical analysis (Benyshek 2010; McDade, Williams, and Snodgrass 2007). However, some investigators have already been working to solve this problem. An immunoassay for C-reactive protein that is compatible with serum, plasma, and DBS was developed, and C-reactive protein measurements were highly correlated in the three media (Brindle et al. 2010). Multiplex analyses for DBS were also prepared to increase the number of proteins that can be assessed using a single DBS. A 25-plex multianalyte method with xMAP technology was developed to analyze inflammatory markers and growth factors (Skogstrand et al. 2005). Further, how the findings of DBS and liquid blood results relate also needs to be thoroughly assessed. Correction factors may be applied to the data to overcome some of these issues, but these factors may depend upon the method being used and population being studied (Benyshek 2010).
Current and Future DBS Applications
Several commercial assays were developed for DBS analysis, including inflammatory markers, growth factors, C-reactive protein, blood lipids, metabolism, immune response, and biomarkers (Benyshek 2010; McDade, Burhop, and Dohnal 2004; Skogstrand et al. 2005). DBS assays were also developed for a proinflammatory cytokine, IL-6, immunoglobulin E, leptin, total cholesterol, high-density lipoprotein cholesterol, and anti-Müllerian hormone with accuracies and precision similar to those of established serum-based assays (McDade et al. 2012; Miller and McDade 2012; Miller, Sharrock, and McDade 2006; Samuelsson et al. 2015; Tanner and McDade 2007). For analysis, a portion of the DBS is punched out and placed in an elution buffer to reconstitute the sample into hemolyzed liquid whole blood. For new assays, elution times, mixing times, temperature, and buffer constituents needed to obtain optimal concentrations and results need to be determined (Skogstrand et al. 2005). Recently, mass spectrometry (MS) analysis of DBS samples with off-line and on-line extractions has become more popular with improved instrumentation for surface analysis MS (Wagner et al. 2014). The first multiplexed assay using DBS was also recently reported. In this assay, 60 endogenous proteins were targeted using tryptic peptides and isotopelabeled analogs with multiple reaction monitoring (MRM). This method was found to be highly reproducible, and measured concentrations for 80% of the proteins were consistent after 10 d of storage at either −20, 4, or 37°C (Chambers et al. 2013). While these advances in DBS analysis hold promise for extension of current liquid assays to DBS applications, development of additional validated DBS enzyme immunoassays for use in field research will take time (McDade 2014; McDade, Williams, and Snodgrass 2007).
DBS samples may be employed to monitor exposure to environmental compounds over time. A study was conducted to measure the presence of perfluorinated compounds (PFC) in newborn DBS samples collected during 1997–2005. The presence of PFC in commercial products, such as surfactants, insecticides, and carpet, decreased in 2000 due to suspected liver toxicity. The presence of PFC in newborn DBS sampled during 2005–2007 was significantly decreased compared with earlier exposures, indicating that the population exposure to PFC had been reduced as a result of decreased commercial production. This analysis showed for the first time how DBS analysis might be used to measure temporal environmental exposure on the population level, as DBS samples are collected from more than 98% of newborns in the United States (Spliethoff et al. 2008). Exposure of infants to dichlorodiphenyldichloroethylene (p,p’-DDE), a breakdown product of the organochlorine pesticide dichlorodiphenyl-trichloroethane (DDT), was also assessed using DBS analysis (Burse et al. 1997). Highthroughput methods for Pb, PFC, and perchlorate analyses were also developed using MS (Hsieh et al. 2009; Kato et al. 2009; Otero-Santos et al. 2009; Schreinemachers et al. 2015). DBS samples were also utilized to measure exposure of pregnant women, newborns, and children to heavy metals, including Pb, Cd, As, and Hg (Chaudhuri et al. 2009; Funk et al. 2013; 2015; Michniewicz et al. 2015). Organochlorine pesticide and polychlorinated biphenyl (PCB) exposures have also been tracked in DBS samples from newborns to determine environmental exposures transferred from pregnant women to fetuses (Ma et al. 2014). Similarly, PCBs, chlorinated pesticides, and brominated flame retardants were investigated in a population study using DBS measurements to estimate exposure to environmental toxicants (Batterman and Chernyak 2014). Hemoglobin adducts of benzene oxide, a carcinogen, were also found in DBS samples at quantities similar to those seen in whole blood analyses (Funk et al. 2008; Rappaport, Funk, and Chaing 2008).
DBS analyses show potential for use in clinical trials. Clinical trials require multiple blood draws from patients over the course of several months to years. These repeated samplings may be tiresome for individuals and extremely costly. As DBS analysis becomes more common, expansion of the technology into clinical trials looks promising (Amsterdam and Waldrop. 2010). Another significant advantage of DBS sampling is the ability to obtain samples from populations in remote areas with lower levels of technology. Since a trained professional is not needed for sample collection, samples may be taken in areas where access to a hospital, clinic, or other sterile environment is limited (Benyshek 2010). Patients trained in proper sample collection procedures and quality control standards might also take their own samples at home and ship them to a lab or clinic for analysis to monitor their biomarker levels without needing to travel and be admitted to a hospital, saving them both time and money and increasing sampling efficiency (Edelbroek, Van Der Heijden, and Stolk 2009). These sampling advantages hold promise for further expanding DBS technology in the arena of environmental exposure assessment to reach remote populations, allowing for inclusion of isolated and chronically ill individuals who may otherwise have been overlooked. DBS sampling provides an avenue by which to signficantly increase population sizes of studies without further raising the cost of the study, since individuals can easily collect their own samples.
New Developments: Incorporation of “Omics”Technology
The range of blood analysis applications has significantly increased with the growth of “omics” technology in the past several years. The phenotype of an individual is the sum of the genome, transcriptome, proteome, and metabolome, and each “ome” branch is interconnected with the others, explaining the different traits of individuals (Titz et al. 2014; Trifonova, Lokhov, and Archakov 2013). An advantage of “omics” technologies is the ability to perform large-scale, high-throughput discovery screens without searching for a particular target compound. The ultimate goal of “omics” research is to identify every gene, protein, and chemical present within a sample (Vineis et al. 2009; Yin and Xu 2014). While the genome has been thoroughly studied, much remains to be discovered regarding the other three branches (Smith and Rappaport 2009). A current research objective is to parse out biomarkers of exposure from biomarkers of disease in “omics” experiments (Rappaport 2012a). The influences of an individual’s environment strongly affect the chemicals that are present in that person’s body. The effects of outside stressors, such as diet, pollution, pathogens, gut microflora, and drugs, can be measured in the blood metabolome using these techniques (Rappaport 2012a; Schnackenberg and Beger 2008). Data from “omics” experiments may also be applied to analyze toxicity pathways. Pathways of toxicity are cellular processes that link toxicant interactions with adverse outcomes (Bouhifd et al. 2013). These high-throughput “omics” techniques can be used to detect patterns (signatures) that are indicative of particular toxicities (Hartung 2009).
Of the four main “omics” branches, metabolomics and proteomics are perhaps the most developed and relevant for blood analysis. Although the human genome has been well studied, only 15–50% of disease was linked to genetic factors in genome-wide association studies (Vineis et al. 2009). Transcriptomics, which evaluates changes in mRNA expression levels, typically yields short term information instead of long-term. mRNA is heterogeneous and unstable, making it difficult to analyze for biomarkers of exposure (Vineis et al. 2009). Proteomics provides information regarding function, but the size of the plasma proteome is large (Anderson and Anderson 2002; Watkins and German 2002). The plasma proteome was estimated to contain approximately 50,000 proteins in different glycosylated forms, 500,000 tissue leakage proteins in a variety of splice variants with varying posttranslational modifications, and around 10,000,000 unique sequences of immunoglobulins (Anderson and Anderson 2002). While advances in characterizing the plasma proteome were accomplished, plasma consists of 55% albumin, which dominates protein detection, making it difficult to identify less abundant proteins in blood samples without first removing highly abundant proteins (Anderson and Anderson 2002). However, automated techniques that deplete the sample of highly abundant proteins prior to LCMS/MS analysis elevated throughput and coverage, helping ameliorate this issue (Dayon et al. 2014; Such-Sanmartín, Ventura-Espejo, and Jensen 2014). The metabolome is smaller than the genome or the proteome, with approximately 3,600 distinct confirmed serum/plasma metabolites (Trifonova, Lokhov, and Archakov 2013), but still contains vital information regarding exposure, creating an advantageous biomarker for discovery (Watkins and German 2002).
Blood is the predominant carrier of small molecules, such as hormones and nutrients within the body, making it an ideal biofluid for proteomic and metabolomic analyses (Trifonova, Lokhov, and Archakov 2013). Metabolic profiling is often conducted using either plasma or serum. Serum is utilized to transfer small molecules between cells and tissues, and thus contains a wealth of information about chemicals circulating within the body (Rappaport 2012a). Researchers have debated whether plasma or serum is the optimal biofluid for proteomic and metabolomic analyses. However, evidence indicates that the two biofluids are comparable in the number of detected compounds and display similar subject variability (Wedge et al. 2011; Yu et al. 2011). The reproducibility of results using the two biofluids may be analyte dependent, as some studies demonstrated serum to be more replicable than plasma for metabolite analysis (Breier et al. 2014), while another investigation obtained greater reproducibility with plasma (Yu et al. 2011). However, both studies demonstrated that metabolite concentrations are higher in serum than plasma (Breier et al. 2014; Yu et al. 2011). While this may make serum more advantageous for some applications, serum preparation requires more postprocessing steps than plasma and is more difficult to standardize. In addition, some important metabolites were only identified in plasma and not serum (Wedge et al. 2011). Therefore, selecting serum or plasma for analysis depends upon the compounds of interest, goals of the study, and preference of the researcher.
Current Challenges of “Omics” Strategies
While advances have been made in improving the applicability of “omics” sciences, several key challenges remain to be addressed. Due to a lack of standardized procedures, “omics” results using MS may be difficult to reproduce between labs (Vineis et al. 2009). However, recently, a large-scale analysis of 1000 human plasma samples was conducted using an isobaric mass tagging quantification procedure, which provided consistent results for proteins with stable expression levels across samples (Cominetti et al. 2016). Different sample handling procedures, including temperatures, timing, and humidity levels, were shown to produce variation in proteomics and metabolomics experiments, and steps need to be taken to reduce inconsistencies in sample preparation (West-Nørager et al. 2007; Yin et al. 2013). Samples are sensitive to storage conditions, and incorrect storage temperatures might lead to increased enzymatic cleavage of plasma samples (Pinto et al. 2014; Yin and Xu 2014). Improved nuclear magnetic resonance (NMR) methods with optimized sample preparation procedures for metabolomics analyses were recently developed, improving quantitation and coverage of different protein classes, such as amino acids, organic acids, and carbohydrates (Gowda, Gowda, and Raftery 2015). Further, there is a wide range of “normal” levels of peptides, proteins, and metabolites among healthy individuals, making it difficult to establish a baseline for nonaffected, healthy individuals (Bischoff and Luider 2004; Enroth et al. 2015; Vineis et al. 2009; Watkins and German 2002). However, as more metabolomics data is being published, this limitation is decreasing due to the abundance of information regarding normal and abnormal ranges of metabolites (Li, Todor, and Luo 2016).
Despite the large number of experiments performed, few new proteins have been introduced as biomarkers (Liotta, Ferrari, and Petricoin 2003), and only approximately 10–20 blood metabolites are currently assayed in clinical diagnostics (Trifonova, Lokhov, and Archakov 2013). However, some exposures are unlikely to be described by only a single protein or metabolite change; instead, these exposures may induce changes in several proteins or metabolites, resulting in characteristic patterns (Liotta, Ferrari, and Petricoin 2003). Currently, “omics” techniques still suffer from coverage issues, making it challenging to identify relevant expression patterns (Anderson and Anderson 2002). A method for analyzing the low-molecular-weight plasma proteome using top-down MS was developed, making it possible to quantify proteins that may not be detected in a typical mass spectrum (Cheon et al. 2016). Assaying all of the proteins or metabolites in a sample and correctly identifying them is extremely challenging, especially considering the (1) number to be characterized, (2) influence of mass defect on identification, (3) incompleteness of spectral libraries and databases, and (4) instrumental limitations (Pleil and Isaacs 2015; Trifonova, Lokhov, and Archakov 2013; Veenstra 2007; Yin and Xu 2014). Improved separation techniques and faster instruments might alleviate the coverage issue, but not without significantly increasing the expense of the assay (Pasikanti, Ho, and Chan 2008). Despite the current expense, “omics” research might help reduce health care costs in the long run (Anderson and Anderson 2002; Smith and Rappaport 2009; Vineis et al. 2009). The slow transfer of biomarker data from the research lab into clinical utility might seem daunting, but implementation should improve as assays become more developed and faster instruments are utilized. The main challenge facing “omics” techniques today is the lack of validation strategies for novel biomarkers. It is difficult to know if altered expression levels are a direct indicator of exposure or a downstream result due to activation of an AOP. Affinity-based tests might be used for biomarker validation when available, and single-reaction monitoring may be employed to assess a single protein in a protein mixture (Veenstra 2007). As high-throughput discovery techniques continue to evolve, their use for clinical and environmental applications is certain to increase.
Range of “Omics” Applicability and Future Directions
“Omics” strategies have been used in clinical settings for several years. Metabolite differences between controls and patients with known diseases were utilized to identify signatures of disease. Investigations of metabolomic biomarkers of diabetes, macular degeneration, asthma, Parkinson’s disease, and tuberculosis have all been conducted in clinical research (Li, Todor, and Luo 2016). NMR analyses using blood sera were found to predict a type of coronary artery disease with greater than 90% specificity (Mayr 2008). Metabolomics was also utilized to identify differences in patients with small-cell lung cancer, and metabolites that are associated with worse survival times were identified in plasma but not serum samples (Wedge et al. 2011). Further, plasma metabolites released after exercise-induced myocardial injury were also detected by metabolomics, and an elevation in compounds in the citric acid cycle were noted in myocardial ischemia, a condition in which blood flow to the heart is reduced, causing the heart to receive insufficient oxygen levels (Mayr 2008). An analysis of plasma metabolites in control subjects and patients with autosomal dominant polycystic kidney disease was conducted to determine whether mealtimes and sampling time of day exerted an effect on metabolite levels. Interestingly, higher metabolome variability was observed after morning rather than evening mealtimes (Kim et al. 2014). This study demonstrates the importance of standardizing sampling procedures during experiments to ensure that consistent and relevant data are obtained across different studies for comparison.
Recently, “omics” techniques have been utilized for environmental exposure applications. Proteomic profiles of subjects naturally exposed to different levels of As in drinking water were collected, and surface-enhanced laser desorption/ionization-timeof-flight-mass spectrometry (SELDI-TOF-MS) was utilized to detect differences in 20 proteins in serum proteome profiles between low-, medium-, and high-exposure groups (Zhao et al. 2010). Similarly, SELDI-TOF-MS was employed in a different study to determine the impact of benzene exposure in shoe-factory workers. The human serum proteome was examined, and platelet factor 4 and connective tissue activating peptide III were consistently downregulated in benzene-exposed workers (Vermeulen et al. 2005). Metabolomics was also used to investigate serum biomarkers of workers exposed to 2,3,7,8-tetrachlorodibenzo-p-dioxin (TCDD) (Hosnijeh et al. 2013). Blood is also a reliable medium for metabolomics studies in nutritional research, as blood metabolites include fat, lactate, and glucose (Savorani et al. 2013), and blood samples were employed to characterize lipid metabolic profiles after high-fat meals and to identify biomarkers of sucrose intake (Beckmann et al. 2016; Bijlsma et al. 2006). These types of studies demonstrate how “omics” experiments might be utilized to detect proteins and metabolites that are sensitive to low levels of exposure, revealing early biological effects of AOP. A strategy to incorporate metabolomics data measured in three different biofluids, plasma, urine, and saliva, was also implemented to obtain a more comprehensive picture of the complex interactions that occur within the human body as a whole (Do et al. 2015). This valuable information might be utilized to gain a better understanding of environmental exposure science.
Although not discussed in detail here, new bioinformatics tools are becoming increasingly important to assess complex data streams from metabolomics studies. Currently, large databases, computational tools, and cloud computing are addressing data interpretation challenges from an ever-increasing volume of biomarker and bioindicator information (Bonvallot et al. 2014; Chen et al. 2013; Judson 2010)
Summary
Blood is clearly an invaluable biofluid for analysis of environmental exposure, despite requiring a more invasive collection procedure than for excreted biofluids, such as breath or urine. Under the assumption of sufficient analytical sensitivity, it should be possible to characterize the majority of human exposures (regardless of source or exposure pathway) in blood as it comes in contact with all living cells. Exogenous compounds, endogenous compounds, and inflammatory, molecular modification, and damage biomarkers and bioindicators can all be analyzed in blood samples to assess exposure, effect, and susceptibility. In cases where compounds that have long elimination halflives, nonspecific urinary metabolites, or protein adducts need to be investigated, blood is the best biofluid for analysis.
One of the more promising new research areas for blood analysis is implementation of DBS as a surrogate for whole blood samples. Here, there is essentially no volumetric limit for sampling (as encountered for 10 ml whole blood samples) as each sample contains only ~50 μl. DBS sampling can be self-administered, and once dried, the sample is stable and may be shipped and stored at room temperature for a couple weeks. Although the DBS approach loses volatile constituents, including water and dissolved gases, there are advantages in that the remaining materials such as proteins and inorganics become highly concentrated. DBS assays have been performed to detect pesticides, develop multiplex protein assays, and identify protein adducts.
Metabolomics and proteomics analyses are also increasing in frequency and application to environmental assessment of blood samples. Improvements in MS have led to faster, more sensitive instruments capable of detecting small molecules and proteins in blood, serum, and plasma. High-throughput experiments are being performed to identify patterns in expression levels that are indicative of different exposures and disease states. While “omics” techniques still suffer from the overabundance of HSA, coverage issues, and need for standardization and validation, their usage in clinical and environmental fields is on the rise as technology continues to improve.
In conclusion, blood sampling in the environmental health field remains a viable and important methodology. Newer analytical technologies that require smaller volumes are closing the gap in logistical concerns in regards to acceptance, invasiveness, and need for trained personnel. Due to these advancements, blood sampling is expected to remain a standard measurement of environmental exposure assessment in the future.
Acknowledgments
The authors are grateful for advice and encouragement from Myriam Medina-Vera, Jon Sobus, Michael Madden, Timothy Buckley, and Michelle Angrish from the U.S. Environmental Protection Agency (EPA). The U.S. Environmental Protection Agency through its Office of Research and Development has subjected this article to agency administrative review and approved it for publication. Mention of trade names or commercial products does not constitute endorsement for use.
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