Article pubs.acs.org/ac
Ages at a Crime Scene: Simultaneous Estimation of the Time since Deposition and Age of Its Originator Juliana Agudelo, Lenka Halámková, Erica Brunelle, Roselyn Rodrigues, Crystal Huynh, and Jan Halámek* Department of Chemistry, University at Albany, State University of New York, 1400 Washington Avenue, Albany, New York 12222, United States ABSTRACT: Blood is a major contributor of evidence in investigations involving violent crimes because of the unique composition of proteins and low molecular weight compounds present in the circulatory system, which often serve as biomarkers in clinical diagnostics. It was recently shown that biomarkers present in blood can also identify characteristics of the originator, such as ethnicity and biological sex. A biocatalytic assay for on-site forensic investigations was developed to simultaneously identify the age range of the blood sample originator and the time since deposition (TSD) of the blood spot. For these two characteristics to be identified, the levels of alkaline phosphatase (ALP), a marker commonly used in clinical diagnostics corresponding to old and young originators, were monitored after deposition for up to 48 h to mimic a crime scene setting. ALP was chosen as the biomarker due to its age-dependent nature. The biocatalytic assay was used to determine the age range of the originator using human serum samples. By means of statistical tools for evaluation and the physiological levels of ALP in healthy people, the applicability of this assay in forensic science was shown for the simultaneous determination of the age of the originator and the TSD of the blood spot. The stability of ALP in serum allows for the differentiation between old and young originators up to 2 days after the sample was left under mimicked crime scene conditions.
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scene, but also the TSD of the particular blood spot. The identification of a suspect’s or victim’s age from biological traces, specifically blood, along with the TSD of the blood spot at crime scenes can be a tremendous help to forensic investigations to reduce the pool of suspects. In support of this work, knowing a suspect’s age, particularly if they are younger or older than the age of 18, is pertinent to distinguishing between crimes committed by a minor or an adult. Criminal offenders under the age of 18 are considered juveniles,13 and are tried, prosecuted, and sentenced differently than adult offenders who are 18 years and older.14 Moreover, it has been shown by “the age-crime curve” that criminal behavior increases in adolescence and decreases in adulthood with the mean age for committing any crime being 26 years old.14−16 In the past, genetic methods were used to determine the age of a person based on the shortening of telomeres or the accumulation of mitochondrial DNA deletions. Unfortunately, these methods showed low accuracy and therefore were deemed inappropriate for forensic applications.17 Aspartic racemization (AAR) is another method typically used to estimate the age of remains, which involves a destructive technique to examine bones, teeth, and ligaments. This approach is also extremely sensitive to temperature, thus limiting its forensic application.17 Another technique to estimate age of the originator is advanced glycation endproducts (AGEs).17 This consists of the analysis of reduced sugars and other α-carbonylic compounds formed by non-
orensic investigations have been revolutionized by the incorporation of science. The newly emerging field of forensic science has contributed immensely to criminal prosecutions with regards to providing unbiased facts about crimes. The analysis of biological samples such as blood,1,2 saliva, and sweat belong to forensic serology, a branch of forensic science that utilizes biochemical techniques for analysis.3,4 DNA obtained from those biological samples is one of the major contributors to crime investigations. With the use of molecular biology techniques, scientists have created databases, such as the Combined DNA Index System (CODIS), that allow for the identification of the sample originator.5−7 However, if the suspect’s or victim’s DNA is not stored in the database, DNA information cannot be used to determine the age of the originator.8 The current techniques implemented for the analysis of biological samples in criminal investigations depend on sophisticated instrumentation and a complex chain of custody.9 Besides genetic material, blood contains proteins and low molecular weight compounds that can be used as biological markers. Our previous study has shown that the TSD of a blood spot can be determined using biocatalytic cascades;10 however, this has not yet been transferred from an academic context to a practical context at real crime scenes. Characteristics such as biological sex and ethnicity of a sample originator have been elucidated using a similar technique.11,12 However, there is no technology like this being used at crime scenes to determine the age of the originator. Here, we present a biocatalytic assay that has been modified to characterize not only the age of the originator−who may potentially be a person of interest in the crime−from biological samples left at a crime © XXXX American Chemical Society
Received: March 24, 2016 Accepted: May 23, 2016
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DOI: 10.1021/acs.analchem.6b01169 Anal. Chem. XXXX, XXX, XXX−XXX
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Scheme 1. Conversion of p-Nitrophenol Phosphate (pNPP) by ALP into p-Nitrophenol
enzymatic reactions, such as oxidative and nonoxidative reactions, with amino groups on proteins, lipids, and nucleic acids. However, this technique is limited to bodies aged up to 45 years, and the sample can only be obtained from a corpse or from long-lived proteins. One of the most recent investigations uses T-cell DNA rearrangements to estimate human age. This technique is promising but still requires a laboratory setting and involves different biomolecular techniques. As for the ability to determine the TSD, numerous techniques have been developed as well, but none of them have been successfully applied in an on-site setting. In 1930, there was an attempt to find the relationship between the solubility of blood in water and its TSD.18 Later, applied spectrophotometric analysis was done for the first time in 1960.19 In 1983, changes in individual proteins present in bloodstains were studied.20 The latest methods include electron paramagnetic resonance (EPR),21 high performance liquid chromatography (HPLC),22 oxygen electrodes,23 RNA degradation,24 near-infrared (NIR) spectroscopy,25 and atomic force microscopy (AFM).26 Similar to the techniques developed to determine the age of the blood sample originator, these techniques require sample preparation and complex instrumentation, preventing the possibility of performing analyses directly at crime scenes. Alkaline phosphatase (ALP; E.C. 3.1.3.1) is a blood marker that is routinely used in clinical settings to identify bone diseases and hepatobiliary disorders.27,28 ALP is a membranebound enzyme that hydrolyzes monophosphates at high pH.30 There are three main sources that produce ALP in the body: liver, kidney, and bone.29,30 ALP levels in bone are age dependent, as it is released in the body predominantly during active bone growth.31−33 In females, the highest production of ALP is between 0 and 17 years old, and in males, the highest production of ALP is between the ages of 0−18 years old.34 Other studies have successfully shown the application of clinical markers in forensic and biosensing cascades.35−39
different age groups, old and young, were determined by the values that are reported in the literature and routinely used in clinical settings.45,46 R-project software was used to generate randomized concentrations of ALP based on the aforementioned average concentrations for each of the four age groups to create 100 samples, each with a different concentration of ALP. Ultimately, 25 samples represented young female ALP concentrations, 25 samples represented old female ALP concentrations, 25 samples represented young male ALP concentrations, and 25 samples represented old male ALP concentrations. It is important to note that this work did not focus on differentiating between biological sexes within these age groups. The generated ALP concertation values were used in conjunction with the biocatalytic system for the analysis of samples mimicking the real distribution of this marker’s concentration in blood. In this biocatalytic assay, ALP converts p-nitrophenol phosphate (pNPP) to p-nitrophenol, which absorbs at λ = 405 nm.47,48 The biocatalytic cascade was performed in 0.1 M carbonate buffer solution at pH 9.00, and the pNPP concentration was 2.5 mM. All measurements were taken at 37 °C to optimize the activity of the enzyme. The fact that we only performed the following experiments and statistical analyses for the ALP values of healthy individuals presents a minor challenge in terms of being able to apply this assay directly on-site. This is because there is a small percentage of the population with bone diseases that alter the ALP levels regardless of the age of the originator. This would mean that a blood sample from an originator with a certain bone disease would not be correctly categorized as old or young if the biocatalytic assay is conducted. However, given that this is the first use of this biocatalytic assay for a forensic application, it was important to base the study on what is “the majority” of the population. This way, we were able to have a strong understanding of the platform before honing in on technicalities. Blood Spot Analysis for Determination of the Time since Deposition. To mimic authentic blood samples, human serum (type AB; Sigma-Aldrich) was spiked with levels of ALP relevant for healthy females both young (0−17 years old) and old (18−60 years old) at 326.9 U/L and 100.1 U/L, respectively. The mimicked samples were left on a glass surface on a work bench and were naturally dried by the environment (approximate temperature 25 ± 3 °C). The time intervals after which the samples were analyzed were 0, 3, 6, 12, 24, 30, 36, and 48 h. Following incubation (aging process), the samples were immediately subjected to continuous optical measuring at λ = 405 nm to monitor the conversion of pNPP to pnitrophenol. The biocatalytic cascade was activated by adding the pNPP to the resuspended sample. The substrate pNPP was previously dissolved in 0.1 M carbonate buffer at pH 9.00 and had a concentration of 2.5 mM. All measurements were performed at 37 °C to improve the activity of the enzyme.
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EXPERIMENTAL SECTION Chemicals and Reagents Used. All enzymes and substrates were purchased from Sigma-Aldrich and used with no further treatment, including alkaline phosphatase from bovine intestinal mucosa (ALP; E.C. 3.1.3.1), p-nitrophenyl phosphate disodium salt hexahydrate (pNPP), and human serum (Type AB; Sigma-Aldrich). The water used for all experimental procedures was ultrapure water (18.2 MΩ cm) obtained from a PURELAB flex, ELGA water purification system. Instrumentation and Measurements. A Molecular Devices UV/vis spectrophotometer/plate reader (SpectraMax NanoDrop 384) and an Infinite F200 PRO (Tecan) microplate reader were used to take optical measurements of the samples at λ = 405 nm. Microtiter polystyrene (PS, Thermo Scientific) plates were utilized. Composition and Operation of the Model Systems. As depicted in Scheme 1,40 alkaline phosphatase (ALP) was used in a concerted manner to determine the age group of the originator as well as the time since deposition (TSD).41−44 The concentrations of ALP used for identifying the age group of the originator, as reported in the literature, were as follows: young female (0−17 years), 326.9 U/L; old female (18−60 years), 100.1 U/L; young male (0−18 years), 343.9 U/L; and old male (19−60), 111.3 U/L.45 These concentrations correspond to ALP levels from healthy individuals. The age ranges for the two B
DOI: 10.1021/acs.analchem.6b01169 Anal. Chem. XXXX, XXX, XXX−XXX
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RESULTS AND DISCUSSION Viability of ALP as a Marker. Here, the ALP biocatalytic assay was utilized for forensic age identification of the originator for a male group and a female group; it is also capable of determining the TSD along with the age group of its originator for the female group. The performance of the biocatalytic assay was evaluated by receiver operating characteristic (ROC) analysis. Our approach is noninvasive and can be performed directly on-site at a crime scene, just like point-ofcare devices routinely used in clinical settings. The dual property nature of our sensing system is especially important for the efficiency of the system, potentiating the possible utilization of our enzymatic cascade in the field. A single sensor capable of simultaneously measuring two parameters would be much more cost effective and time efficient, enabling broader use and applications of enzyme-based sensor technology. Prior to starting this investigation, we tested the viability of ALP as a marker. We measured the dependence of the biocatalytic conversion of pNPP to p-nitrophenol using different concentrations of enzyme (ALP) while keeping the substrate concentration constant. The results of this experiment are shown in Figure 1, where a concentration dependence indicates the applicability of ALP as a marker for the biocatalytic assay to determine the age range of the originator.
samples, an authentic crime scene scenario was mimicked, and the potential for this assay to distinguish between the two age groups (young and old ) was determined. Advanced statistical analysis was performed using MATLAB (MathWorks, Inc., version R2013b) and R-project software, ver. 3.0.0, to generate 25 random ALP values per age group according to the parameters given by the previously reported distribution of ALP. This resulted in 100 different samples, each containing a different concentration of ALP. As mentioned above, the samples were prepared by spiking human serum samples with the corresponding 25 randomly distributed ALP concentration values for that age group.31,49 The use of 25 concentration values per group is deemed adequate for statistical calculations as they are sufficient enough to resemble a log normal distribution reported in the study. All four sets of ALP values have been used to prepare four sets of experimental samples representing the entire distribution of the ALP marker with concentrations typical for each group. This is necessary to mimic authentic crime scene scenarios. In this biocatalytic assay, ALP converts p-nitrophenol phosphate (pNPP) to p-nitrophenol, which is monitored at 405 nm. 47,48 The absorbance is proportional to the concentration/activity of the enzyme in the sample, making it possible to determine if the originator is in an age range of young or old. The biocatalytic assay was performed in 0.1 M carbonate buffer solution at pH 9.0, and the concentration of pNPP was 2.5 mM. The conversion of pNPP to p-nitrophenol was monitored by absorbance at 405 nm (see experimental details in the Experimental Section). All responses are presented in Figure 2 as the increasing optical absorbance of
Figure 1. Standard curve of the changes in absorbance (λ = 405 nm) corresponding to the biocatalytic conversion of pNPP to p-nitrophenol via ALP. These points correspond to samples containing different concentrations (n = 3) of ALP measured after 300 s. The star represents the old female concentration, and the diamond represents the young female concentration. The Experimental Section includes the exact composition of the reactant solutions used.
Figure 2. Absorbance at λ = 405 nm, corresponding to the conversion of pNPP to p-nitrophenol by ALP, for (A) male and (B) female samples. Black traces correspond to randomized young enzyme concentrations, whereas the red traces correspond to randomized enzyme concentrations for old populations. Exact information on the chemicals used is given in the Experimental Section.
Enzymatic and Statistical Analysis of Model ALP Solutions for Age of the Originator. Given the positive results of this proof of concept experiment, we performed a detailed biochemical assay to mimic an authentic crime scene scenario. ALP distribution in blood has been previously studied; therefore, its variability and contribution to age has been reported.31 Because the study reported significantly different levels of ALP for different age groups, we used these parameters to imitate the concentration of the enzyme in serum for the age groups used throughout this work. The presented values deviated from a normal distribution and were consistent with a log-normal distribution. We followed the published study by subdividing the age groups into smaller groups according to biological sex and conducting the measurements separately. However, this study does not involve differentiating between biological sexes. ALP can be implemented in biocatalytic assays that can be performed on-site to help build a profile of the originator’s age range and provide the TSD. Using spiked human serum
p-nitrophenol for the real time response from male and female samples using the ALP biocatalytic assay. The output signals measured for females do not overlap between the young and old populations; however, there is a small overlap with the outputs measured for the young and old concentrations in males. Each figure shows the response for young and old ALP concentrations for each biological sex, which was generated from a reference range calculated according to the procedure described above. ROC analyses were performed to assess the discriminatory power of our biocatalytic assay for the distinction between young and old populations for both biological sexes separately. Again, the scope of this work does not include differentiating between biological sexes. The ROC curve was plotted as a function of sensitivity (true positive rate) versus specificity C
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characteristic of females; young females (0−17 years) at 326.9 U/L and old females (18−60 years) at 100.1 U/L. The ALP spiked human serum samples (both young and old groups, separately) were placed on a glass surface and underwent an aging process for 2 days at 25 ± 3 °C (from 0 to 48 h). The human serum samples containing average levels of young and old female ALP levels were naturally dried by the environment; they were not covered or placed under any special conditions. The samples were left on the laboratory bench, near the window to have sunlight during the day, and no light at night, to mimic a crime scene scenario as closely as possible. Then, the samples were resuspended with distilled water just before measurements were taken. The ALP concentration was determined by measuring the absorbance change corresponding to the conversion of 2.5 mM pNPP to pnitrophenol with absorbance at λ = 405 nm. Each measurement contained a set of 10 samples (n = 10) (Figure 4). After
(true negative rate) for varying thresholds of class assignment. The area under the ROC curve,50 also known as AUC, was estimated by the trapezoidal integration method, and the corresponding 95% (Confidence Interval) CI was estimated using the method described by DeLong et al.51 The AUC indicates how well the model ranks samples according to the change in absorbance assigned to the positive class. Using the ROC analysis, we identified the best thresholds (above which the change in absorbance is assigned to the positive class) for a ranking biocatalytic assay to separate young and old populations. AUC ranges from 0 to 1, where an AUC of 0.5 represents a random classifier and an AUC of 1 indicates a perfect test. ROC analyses were carried out with package pROC.52 For the assay described above, the AUC of the ROC curves was estimated as 0.99 (95% CI: 0.98−1.00) and 1.00 (95% CI: 1.00−1.00) for male and female groups, respectively (Figure 3).
Figure 3. Trade-off between sensitivity and specificity are shown by presenting the ROC curve for (A) male and (B) female groups. The AUC is 99% for males and 100% for females, which is the probability of the assay to identify a young or old group based on the ALP levels in the blood. The optimum cutoff point was chosen with sensitivity and specificity of 100% and 84%, respectively, for the male group and 100 % and 100%, respectively, for the female group. Random choice is denoted by the gray diagonal line.
Figure 4. Absorbance change at λ = 405 nm, corresponding to the conversion of pNPP to p-nitrophenol by ALP. Samples (n = 10 per data point) were resuspended after undergoing the aging process for up to 48 h. The zero time interval corresponds to the freshly prepared samples without drying. The young samples are represented by red circles, and the old samples are represented by black squares. The error bars represent the relative standard error from the change in absorbance from 10 samples. The exact information on chemicals used is given in the Experimental Section.
This means that the diagnostic test has a 99 and 100% probability of correctly differentiating between older and younger groups in males and females, respectively. Note that the ROC curves were generated from absorbance changes, and the best absorbance thresholds of 0.640 and 0.699 were determined for male and female groups, respectively, which balanced the trade-off that exists between sensitivity and specificity. These absorbance changes are the most accurate cutoff points for discrimination between older and younger populations. As shown, ROC analysis has proven the potential of this assay to differentiate between samples from older and younger groups. The similar thresholds suggest no significant difference between male and female groups with respect to the enzymatic activity of ALP. As a result, the subsequent TSD analysis was performed for female ALP levels only. Enzymatic and Statistical Analysis of Model Female ALP Samples for Time since Deposition. In the next step, we tested the use of the ALP biocatalytic assay to estimate the TSD of dried bloodstains. Because our previous experiment did not show a significant difference in ALP activity between biological sexes, TSD experiments were performed using only healthy female ALP levels. In addition, the mimicked female samples do not overlap between young and old groups, further supporting the use of only the average levels of ALP that are
conducting these experiments, it was determined that ALP has the ability to determine the age of the originator from a blood sample left at a crime scene. A support vector machine regression (SVMR) was applied to predict the TSD of the dried bloodstains from 0 to 48 h. Our model was tested with an external data set with absorbance spectra that were excluded from training data sets at the beginning of statistical analysis. To the best of our knowledge, this is the first report of the determination of the TSD of a bloodstain using an enzymatic cascade that is accompanied by further validation of prediction ability. We applied SVM in the regression form to build a quantitative model to predict the TSD of the dried blood spots of the young and old groups. SVM constructs a hyperplane in a multidimensional space, which is used for regression. The training data set is marked as values describing the TSD, and an SVM training algorithm assigns the new validation data by the predicted value for the TSD. For this purpose, recorded data were divided into two data sets, calibration and validation, by moving each fifth absorbance D
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much during the aging process, and so the output signal ratio for the young and old populations was distinct even after 48 h. It is important to reiterate that this was done only for the female group. The outcome will be very similar for both biological sexes, because biological sex did not show an effect on the outcome of the age group of the originator. Therefore, we first applied the SVM in the form of discriminant analysis (SVMDA) to test if we could differentiate the two age populations within the 48 h deposition period. First, the SVMDA model was used to differentiate young and old groups. Twenty-nine absorbance spectra were chosen for testing, and the remaining 119 spectra were used for calibration. The calibration model correctly classified all 29 spectra with 100% discrimination and no misclassification. This shows the ability of the assay and SVMDA method to correctly discriminate between young and old groups. We have proven that the differentiation between the two age group populations is possible. Thus, we were able to apply the SVMR to determine the TSD of the dried bloodstains for each group separately. SVMR was applied to provide statistical models to relate the absorbance spectra to the particular stage of the deposition time frame. SVM regression models were created for both groups (young and old population) using four latent variables (LVs). The models were trained with the training data sets of absorbance spectra (in the same manner as for SVMDA), and calibration models were then used to predict the TSD of dried bloodstains. The estimated ages for all time points were plotted against the actual age of the dried blood spots. The predictive performance of the model shows a sufficient correlation between the age predicted by the catalytic assay and actual age established by the assay. As can be seen from Figure 5A, the plot shows a wider scatter of predicted age data points for the young population than for the old population (Figure 5B). Moreover, it is evident that the best fit for the regression of predicted relative to measured values for the young population does not intersect with the origin, which suggests greater bias of the model for this case. The reason for the larger spread and greater model bias for the younger population can be explained by the greater changes occurring in the blood due to the higher rate of the enzymatic reaction, therefore causing a naturally higher variability in the responses. However, the spread of data points expressed by R2 = 89% and RMSEP = 6.20 h is sufficiently small enough for the TSD of a bloodstain to be correctly predicted with high accuracy. The SVM algorithm predicted the TSD of the dried bloodspot in the old population more accurately with an RMSEP of 6.41 h and R2 of 91%. The dual attribute nature of this biocatalytic assay is a key advantage for forensic purposes because it is possible to identify the age group of the originator from a discovered dried bloodstain and the sample’s TSD at the same time.
spectrum into a validation data set. The remaining measurement spectra were used for calibration. Our validation data set was separated from the training data set at the beginning of the statistical analysis. As a result, we predicted the TSD of the blood spots using the SVM regression models using independent validation sets. The SVMR is used with the radial basis function (RBF) as a kernel function, and it is optimized by a combined approach of 2-fold cross validation (two samples out) and a systematic grid search of the parameters. The quality of the predictions is expressed by predicted-versus-measured plots of the calibration data set and prediction models based on absorbance changes for young and old groups. We used an internal cross-validation step in the calibration process to determine the number of latent variables. The effectiveness of SVMRs was then calculated using the root mean squared error (RMSE) and the coefficient of determination (R2). The red line represents the prediction, and the green line is perfect agreement between measured and predicted values for both groups, using four latent variables (Figure 5).
Figure 5. SVM regression plot for samples of the (A) young and (B) old group up to 48 h showing measured TSD values versus predicted TSD values of calibration (black stars) and test (red circles) data sets. The red line demonstrates the actual fit, and the green line is the ideal fit (1:1).
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CONCLUSIONS Using ALP as a biomarker, we have performed experiments and statistical analyses that corroborate the assay’s viability for use at crime scenes. As a result, we have come to the conclusion that the ALP biocatalytic assay can be a beneficial tool for forensic investigations. As previously mentioned, this assay is not focused on identifying the biological sex of the originator. Our main interest is to assist in building a profile where the age group of the originator can be approximated along with the TSD of the bloodstain; this system also has the potential to be used on-site. At crime scenes, the ALP sample will be found in
Figure 4 demonstrates the results of our assay for TSD estimation. Even after 48 h of the biological samples being left at the mimicked crime scene conditions, it can be determined whether the sample came from a young or old originator along with the TSD by monitoring the activity and biological levels of ALP. The output signals measured for the aged samples show a distinguishable difference for both young and old populations. The difference between the two populations did not change E
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(18) Schwarzacher, P. D. Am. J. Police Sci. 1930, 1, 374−380. (19) Patterson, D. Nature 1960, 187, 688−689. (20) Tsutsumi, A.; Ishizu, H. Jpn. J. Legal Med. 1983, 37, 770. (21) Fujita, Y.; Tsuchiya, K.; Abe, S.; Takiguchi, Y.; Kubo, S.; Sakurai, H. Forensic Sci. Int. 2005, 152, 39−43. (22) Inoue, H.; Takabe, F.; Iwasa, M.; Maeno, Y.; Seko, Y. Forensic Sci. Int. 1992, 57, 17−27. (23) Matsuoka, T.; Taguchi, T.; Okuda, J. Biol. Pharm. Bull. 1995, 18, 1031−1035. (24) Bauer, M.; Polzin, S.; Patzelt, D. Forensic Sci. Int. 2003, 138, 94− 103. (25) Botonjic-Sehic, E.; Brown, C. W.; Lamontagne, M.; Tsaparikos, M. Spectroscopy 2009, 2, 42−48. (26) Strasser, S.; Zink, A.; Kada, G.; Hinterdorfer, P.; Peschel, O.; Heckl, W. M.; Nerlich, A. G.; Thalhammer, S. Forensic Sci. Int. 2007, 170, 8−14. (27) Gordon, T. Arch. Pathol. Lab. Med. 1993, 117, 187−190. (28) Yam, L. T. Am. J. Med. 1974, 56, 604−616. (29) Nanji, A. A. Am. J. Med. Technol. 1983, 49, 241−245. (30) Orimo, H. J. Nippon Med. Sch. 2010, 77, 4−12. (31) Farley, J. R.; Baylink, D. J. Clin. Chem. 1995, 4, 1551−1553. (32) Crofton, P. Clin. Chem. 1992, 38, 663−670. (33) Tobiume, H.; Kanzaki, S.; Hida, S.; Ono, T.; Moriwake, T.; Yamauchi, S.; Seino, Y. J. Clin. Endocrinol. Metab. 1997, 82, 2056− 2061. (34) Eastman, J. R.; Bixler, D. Clin. Chem. 1977, 23, 1769−1770. (35) May, E. E.; Dolan, P. L.; Crozier, P. S.; Brozik, S.; Manginell, M. IEEE Sens. J. 2008, 8, 1011−1019. (36) Zhou, J.; Halámek, J.; Bocharova, V.; Wang, J.; Katz, E. Talanta 2011, 83, 955−959. (37) Halámek, J.; Bocharova, V.; Chinnapareddy, S.; Windmiller, J. R.; Strack, G.; Chuang, M.-C.; Zhou, J.; Santhosh, P.; Ramirez, G. V.; Arugula, M. A.; Wang, J.; Katz, E. Mol. BioSyst. 2010, 6, 2554−2560. (38) Halámek, J.; Windmiller, J. R.; Zhou, J.; Chuang, M.-C.; Santhosh, P.; Strack, G.; Arugula, M. A.; Chinnapareddy, S.; Bocharova, V.; Wang, J.; Katz, E. Analyst 2010, 135, 2249−2259. (39) Halámková, L.; Halámek, J.; Bocharova, V.; Wolf, S.; Mulier, K. E.; Beilman, G.; Wang, J.; Katz, E. Analyst 2012, 137, 1768−1770. (40) SIGMAFAST p-Nitrophenyl phosphate tablets. Sigma-Aldrich Co. LLC.http://www.sigmaaldrich.com/catalog/product/sigma/ n1891?lang=en®ion=US (accessed Apr 4, 2016). (41) Bilal, M.; Tariq, A.; Khan, S.; Quratulain, T. A.; Shahid, M. F.; Khan, M. W.; Shah, A. R.; Naveed, A. K. J. Ayub. Med. Coll. Abbottabad 2011, 23, 70−72. (42) Lee, J. K.; Shim, J. H.; Lee, H. C.; Lee, S. H.; Kim, K. M.; Lim, Y.-S.; Chung, Y.-H.; Lee, Y. S.; Suh, D. J. Hepatology 2010, 51, 1577− 1583. (43) Prati, D.; Taioli, E.; Zanella, A.; Torre, E. D.; Butelli, S.; Vecchio, E. D.; Vianello, L.; Zanuso, F.; Mozzi, F.; Milani, S.; Conte, D.; Colombo, M.; Sirchia, G. Ann. Int. Med. 2002, 137, 1−9. (44) Deuster, P. A., O’Connor, F. G., Kenney, K., Heled, Y., Muldoon, S., Contreras-Sesvold, C., Campbell, W. W. Creatine Kinase Clinical Considerations: Ethnicity, Gender and Genetics; abstract at the conference: RTO-MP-HFM-181- Human Performance Enhancement for NATO Military Operations (Science, Technology and Ethics). (45) Eastman, J. R.; Bixler, D. Clin. Chem. 1977, 9, 1769−1770. (46) ALP - Blood Test: MedlinePlus Medical Encyclopedia. U.S. National Library of Medicine. https://www.nlm.nih.gov/medlineplus/ ency/article/003470.htm (accessed Apr 21, 2015). (47) Galindo, S. Idaho State University. http://www.isu.edu/ ~galisusa/alp_sop.html (accessed Apr 21, 2015). (48) Yam, L. T. Am. J. Med. 1974, 56, 604−616. (49) Tietz, N. W.; Wekstein, D. R.; Shuey, D. F.; Brauer, G. A. J. Am. Geriatr. Soc. 1984, 32, 563−570. (50) Zweig, M. H.; Campbell, G. Clin. Chem. 1993, 39, 561−577. (51) DeLong, E. R.; DeLong, D. M.; Clarke-Pearson, D. L. Biometrics 1988, 44, 837−845. (52) Robin, X.; Turck, N.; Hainard, A. BMC Bioinf. 2011, 7, 77.
blood spots, which is why the experiments were performed by spiking human serum with the ALP levels of interest. Taking into account that enzymatic activity will be affected by time and environmental conditions, aged samples (0 to 48 h) were tested, which showed identifiable activity between young and old originators even after 2 days. The age groups have been assigned according to literature45 and clinical46 ALP values, also taking into account that criminal behavior increases in adolescence according to “the age-crime curve”.14−16 This ultimately carries over into the criminal justice system where adolescents, or juveniles, are tried, prosecuted, and sentenced differently than adults in the court room.13,14 The ALP assay can be incorporated into a novel forensic serology field that is still under development as it is able to identify the age range of a blood sample originator and the TSD of a blood spot. This system has the potential to be part of a forensic field kit and be utilized by all law enforcement personnel. If this method is made portable and able to be brought directly to the crime scene, blood samples can be rapidly analyzed directly at the crime scene to obtain preliminary information about the person or persons of interest in the crime. Ultimately, increasing the turnaround time on a suspect’s physical characteristics can help avoid spending excess time looking into someone who does not fit within the identified age range. Additional forensic serology assays that can identify other originator characteristics, and ultimately aid in criminal investigations, are currently under development in our laboratory.
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AUTHOR INFORMATION
Corresponding Author
*E-mail:
[email protected]. Notes
The authors declare no competing financial interest.
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REFERENCES
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DOI: 10.1021/acs.analchem.6b01169 Anal. Chem. XXXX, XXX, XXX−XXX
