Targeted recovery of male cells in a male and female same-cell mixture.

DNA mixture deconvolution in the forensic DNA community has been addressed in a variety of ways. "Front-end" methods that separate the cellular components of mixtures can provide a significant benefit over computational methods as there is no need to rely on models with inherent uncertainty to generate conclusions. Historically, cell separation methods have been investigated but have been largely ineffective due to high cost, unreliability, and the lack of proper instrumentation. However, the last decade has given rise to more innovative technology that can target and recover cells more effectively. This study focuses on the development and optimization of a method to selectively label and recover male cells in a mixture of male and female epithelial cells using a Y-chromosome labeling kit with DEPArray™ technology, whereby male cells are labeled and recovered into a single extraction-ready tube. Labeling efficiency was tested using freshly collected and aged buccal swabs where 70%-75% and 38% of male cells were labeled, respectively, with less than 1% false positives. DEPArray™ detection was assessed using single buccal epithelial cells where approximately 80% of labeled cells were identified as male. Mixtures (1:1, 1:10, male to female) yielded profiles that were predominantly single source male or those in which the male component was more easily interpreted. The male-specific labeling method was demonstrated to be both robust and reliable when used on freshly collected cells. While the DEPArray™ meditated detection and recovery had notable limitations, it still improved the interpretation of the male component in same-cell mixtures in more recently collected samples.

DEPArray™ single-cell technology: A validation study for forensic applications.

In forensics investigations, it is common to encounter biological mixtures consisting of homogeneous or heterogeneous components from multiple individuals and with different genetic contributions. One promising mixture deconvolution strategy is the DEPArray™ technology, which enables the separation of cell populations before genetic analysis. While technological advances are fundamental, their reliable validation is crucial for successful implementation and use for casework. Thus, this study aimed to 1) systematically validate the DEPArray™ system concerning specificity, sensitivity, repeatability, and contamination occurrences for blood, epithelial, and sperm cells, and 2) evaluate its potential for single-cell analysis in the field of forensic science. Our findings confirmed the effective identification of different cell types and the correct assignment of successfully genotyped single cells to their respective donor(s). Using the NGM Detect™ Amplification Kit, the average profile completeness for diploid cells was approximately 80%, with ∼ 290 RFUs. In contrast, haploid sperm analysis yielded an average completeness of 51% referring to the haploid reference profile, accompanied by mean peak heights of ∼ 176 RFUs. Although certain alleles of heterozygous loci in diploid cells showed strong imbalances, the overall peak balances yielded acceptable values above ≥ 60% with a mean value of 72% ± 0.21, a median of 77%, but with a maximum imbalance of 9% between heterozygous peaks. Locus dropouts were considered stochastic events, exhibiting variations among donors and cell types, with a notable failure incidence observed for TH01. Within the wet-lab experimentation with >500 single cells for the validation, profiling was performed using the consensus approach, where profiles were selected randomly from all data to better mirror real casework results. Nevertheless, complete profiles could be achieved with as few as three diploid cells, while the average success rate increased to 100% when using profiles of 6-10 cells. For sperms, however, a consensus profile with completeness >90% of the autosomal diploid genotype could be attained using ≥15 cells. In addition, the robustness of the consensus approach was evaluated in the absence of the respective reference profile without severe deterioration. Here, increased stutter peaks (≥ 15%) were found as the main artifact in single-cell profiles, while contamination and drop-ins were ascertained as rare events. Lastly, the technique's potential and limitations are discussed, and practical guidance is provided, particularly valuable for cold cases, multiple perpetrator rapes, and analyses of homogeneous mixed evidence.

A systematic approach to improve downstream single-cell analysis for the DEPArray™ technology.

Most commercially available STR amplification kits have never been fully validated for low template DNA analysis, highlighting the need for testing different PCR kits and conditions for improving single-cell profiling. Here, current strategies rely mainly on adjusting PCR cycle number and analytical threshold settings, with a strong preference for using 30 amplification cycles and thresholds at 30-150 RFU for allele detection. This study aimed to (1) determine appropriate conditions for obtaining informative profiles utilizing a dilution series, and (2) test the outcome on single cells using the DEPArray™ technology. Four routinely applied forensic STR kits were compared by using three different amplification volumes and DNA dilutions down to 3.0 pg, while two well-performing kits were used for single/pooled leucocyte and sperm cell genotyping. Besides reduced costs, the results demonstrate that a 50%-75% PCR volume reduction was beneficial for peak height evaluation. However, this was counteracted by an increased artifact generation in diluted DNA volumes. Regarding profile completeness, the advantage of volume reduction was only prominent in samples processed with Fusion 6C. For single and pooled cells, ESIFast and NGMDetect provided a solid basis for consensus profiling regarding locus failure, although locus dropouts were generally observed as stochastic events. Amplification volume of 12.5 µL was confirmed as appropriate in terms of peak heights and stutter frequencies, with increased stutter peaks being the main artifact in single-cell profiles. Limitations associated with these analyses are discussed, providing a solid foundation for further studies on low template DNA.

Racial/ethnic discrimination, ADH1B*3, and coping-motivated drinking among Black college students.

BACKGROUND AND OBJECTIVES: Discrimination due to race and/or ethnicity can be a pervasive stressor for Black college students in the United States beyond general negative life events and has demonstrated associations with adverse health and alcohol outcomes. Genetics may confer individual differences in the risk of drinking to cope with discrimination-related stress. This study tested whether associations of racial/ethnic discrimination with coping drinking motives and alcohol use differ as a function of a well-documented variant in the alcohol dehydrogenase 1B gene (ADH1B*3). METHODS: Cross-sectional data were obtained from 241 Black students (Mage = 20.04 [range = 18-53]; 66% female) attending a predominantly White university in the northeastern United States. Participants provided a saliva sample for genotyping and self-reported on their racial/ethnic discrimination experiences, coping drinking motives, and past-month total alcohol quantity. RESULTS: Path models demonstrated that associations of discrimination with alcohol quantity directly or indirectly through coping drinking motives did not differ as a function of ADH1B*3, after controlling for gender, age, negative life events, and potential confounding interactions of covariates with model predictors. Regardless of ADH1B*3, greater experience of negative life events was associated with higher coping drinking motives, which in turn were associated with greater alcohol quantity. CONCLUSION AND SCIENTIFIC SIGNIFICANCE: Findings represent a novel investigation into gene-environment interplay in associations of alcohol use with racial/ethnic discrimination. Findings demonstrate coping-motivated drinking associated with negative life events within Black college drinkers regardless of ADH1B*3. Future research should leverage longitudinal designs to characterize associations of genetics, stressful experiences, and coping-motivated drinking over time.

Assessing non-LUS stutter in DNA sequence data.

Forensic DNA analysis is among the most well-recognized and well-developed forensic disciplines. The field's use of DNA markers known as short tandem repeats (STRs) offer a robust means of discriminating individuals while also introducing challenges to the analysis. One of these challenges, stutter, is the result of a non-biological artifact introduced during PCR. The formation and amplification of these stutter products can occur at rates as high as 15-20% of the parent allele. The challenge inherent in this process is differentiating stutter artifacts from true alleles, particularly in the presence of a minor contributor. Traditionally, DNA profiles are obtained using capillary electrophoresis (CE), where amplified DNA fragments are separated by size, not sequence, and the identification of stutter is performed on a locus-specific level. The use of CE-based fragment data rather than sequence-based data, has limited the community's understanding of the precise behavior of stutter. Massively parallel sequencing (MPS) data provides an opportunity to better characterize stutter, permitting a more accurate means of detecting both size- or longest uninterrupted stretch (LUS)-based stutter but also allele and motif-specific stutter characteristics. This study sheds light on the value of characterizing motif- and allele-specific stutter, including non-LUS stutter, when using MPS methods. Analysis and characterization of stutter sequences was performed using data generated from 539 samples amplified with the ForenSeq and PowerSeq 46GY library preparation kit and sequenced on the Illumina MiSeq FGx. Assessment of non-LUS stutter begins with calculating stutter rates for all potential stutter products at a given locus (and allele), additionally, the occurrence of these discrete stutter products were quantified. Results show that although the LUS sequence stutters at a higher rate than non-LUS motifs, the non-LUS stutter products do occur at detectable levels and potentially influence sequence-based mixture analysis. The data indicate that the stutter from one motif or allele can be distinguished from another motif or allele based on their unique stutter rates; however, the number of stutter products from each motif or allele may similarly make up the overall pool of stutter products. Motif- and allele-specific stutter models provide the most comprehensive analysis of sequence stutter rates and provide the ability to differentiate stutter sequences more accurately from true allele stutter. This information provides a foundation for including the characterization of non-LUS stutter products when analyzing DNA profiles, specifically mixtures with potential low-level contributors.

Revisiting single cell analysis in forensic science.

Forensic science has yet to take full advantage of single cell analysis. Its greatest benefit is the ability to alleviate the challenges associated with DNA mixture analysis, which remains a significant hurdle in forensic science. Many of the factors that cause complexity in mixture interpretation are absent in single cell analyses-multiple contributors, varied levels of contribution, and allele masking. This study revisits single cell analyses in the context of forensic identification, introducing previously unseen depth to the characterization of data generated from single cells using a novel pipeline that includes recovery of single cells using the DEPArray NxT and amplification using the PowerPlex Fusion 6c kit with varied PCR cycles (29, 30, and 31). The resulting allelic signal was assessed using analytical thresholds of 10, 100, and 150RFU. The mean peak heights across the sample sets generally increased as cycle number increased, 75.0 ± 85.3, 147.1 ± 172.6, and 226.1 ± 298.2 RFU, for 29, 30, and 31 cycles, respectively. The average proportion of allele/locus dropout was most significantly impacted by changes in the detection threshold, whereas increases in PCR cycle number had less impact. Overall data quality improved notably when increasing PCR from 29 to 30 cycles, less improvement and more volatility was introduced at 31 cycles. The average random match probabilities for the 29, 30, and 31 cycle sets at 150RFU are 1 in 2.4 × 1018 ± 1.46 × 1019, 1 in 1.49 × 1025 ± 5.8 × 1025, and 1 in 1.83 × 1024 ± 8.09 × 1024, respectively. This demonstrates the current power of single cell analysis in removing the need for complex mixture analysis.

Match statistics for sequence-based alleles in profiles from forensic PCR-mps kits.

The first autosomal sequence-based allele (aka SNP-STR haplotype) frequency database for forensic massively parallel sequencing (MPS) has been published, thereby removing one of the remaining barriers to implementing MPS in casework. The database was developed using a specific set of flank trim sites. If different trim sites or different kits with different primers are used for casework, then SNP-STR haplotypes may be detected that do not have frequencies in the database. We describe a procedure to address calculation of match probabilities when casework samples are generated using an MPS kit with different trim sites than those present in the relevant population frequency database. The procedure provides a framework for comparison of any MPS kit or database combination while also accommodating comparison of MPS and CE profiles.

Developmental validation of PACE™: Automated artifact identification and contributor estimation for use with GlobalFiler™ and PowerPlex® fusion 6c generated data.

DNA mixture interpretation remains one of the major challenges in forensic DNA analysis. DNA mixture samples are inherently complex due to several factors including the variations in the quantity of DNA, the presence of non-allelic artifactual peaks and the presence of multiple contributors with variable levels of allele sharing. The Probabilistic Assessment for Contributor Estimation (PACE) is a fully continuous probabilistic machine learning-based method to predict the number of contributors (n) in a sample, and was previously developed for use with the Identifiler amplification kit. This system required manual preprocessing of data and was limited, exclusively, to samples amplified using said kit. This study introduces PACE™ v1.3.7 for use with both the GlobalFiler and PowerPlex Fusion 6c amplification kits. An automated artifact identification and management system has been added to accompany the rapid estimation of the number of donors in a given mixture. The artifact management module, when evaluated using previously unseen data, identified true allelic peaks and removed artifacts such as elevated baseline noise, stutter, and pull-up with accuracy over 93.5%. The systems yield the correct n classifications in over 90% of the samples, and demonstrate consistent accuracies as the number of donors and the overall mixture complexity increase. Misclassified samples generally exhibited high levels of allele sharing among donors, low DNA template amounts and high incidence of allelic dropout. This system offers a means for both artifact management and n estimation as well as a quantitative and reproducible method of assessing the quality of a profile.

Automated detection and removal of capillary electrophoresis artifacts due to spectral overlap.

While DNA detection using capillary electrophoresis has enabled improvements in both resolution and throughput, the use of CE - particularly with multiple dye channels - can introduce artifacts that can complicate analyses. Undetected pull-up artifacts can pose a challenge to investigators, especially in low-level samples, while partial pull-up peaks can distort peak height balance within a locus and impact the downstream likelihood ratio. Current methods for addressing pull-up are typically manually implemented. This study presents an effective alternative: a series of mathematical models, created using symbolic regression achieved through genetic programming. The models estimate the amount of pull-up expected in a peak from a true allele for a given dye-dye relationship and instrument type. This leads to the removal of artifactual pull-up peaks and peak height corrections when pull-up is present within true alleles. When models are used in conjunction with a dynamic threshold, pull-up peaks were automatically detected and removed with an accuracy rate of 96.1%. The removal of partial pull-up from true allele peaks led to a more accurate heterozygote balance for the affected locus. These models have been optimized for use with any analytical threshold and can be implemented by any lab using a 3100 or 3500 instrument series.

Interaction Effects between the Cumulative Genetic Score and Psychosocial Stressor on Self-Reported Drinking Urge and Implicit Attentional Bias for Alcohol: A Human Laboratory Study.

AIMS: The current candidate gene and environment interaction (cGxE) study examined whether the effects of an experimentally manipulated psychosocial stressor on self-reported drinking urge and implicit attentional bias for alcohol cues differ as a function of a cumulative genetic score of 5-HTTLPR, MAO-A, DRD4, DAT1 and DRD2 genotypes. The current study also examined whether salivary alpha-amylase level or self-reported anxiety state mediate these cGxE effects. SHORT SUMMARY: Individuals with high cumulative genetic risk score of the five monoamergic genotypes showed greater attentional bias toward alcohol cues when exposed to a psychosocial stressor than when not exposed. METHODS: Frequent binge-drinking Caucasian young adults (N = 105; mean age = 19; 61% male) completed both the control condition and stress condition (using the Trier Social Stress Test) in order. RESULTS: Regarding attentional bias, individuals with high and medium cumulative genetic risk scores showed greater attentional bias toward alcohol stimuli in the stress condition than in the control condition, whereas, those with low genetic risk scores showed greater attentional bias toward alcohol stimuli in the control condition than in the stress condition. No mediating roles of salivary alpha-amylase and anxiety state in the cGxE effect were found. Regarding self-reported drinking urge, individuals with high cumulative genetic score reported greater drinking urge than those with low genetic score regardless of experimental conditions. CONCLUSIONS: Although replication is necessary, the findings suggest that the association of a psychosocial stressor on implicit (but not explicit, self-reported) alcohol outcomes may differ as a function of the collective effects of five monoamine genes.

A hybrid approach to increase the informedness of CE-based data using locus-specific thresholding and machine learning.

The interpretation of genetic profiles require a robust and reliable method to discriminate true allelic information from noise, regardless of the instrumentation or methods used. Traditionally, static peak detection thresholds (analytical thresholds) have been applied to capillary electrophoresis generated data to distinguish the true allelic peaks from noise. While the rigid nature of these thresholds attempts to conservatively account for baseline variability across instrument runs, samples, capillaries, dye-channels, injection times, and voltage, its static nature is unable to adapt, leading to a loss of allelic information that exists below the threshold. The method described herein is able to account for this variability by collectively minimizing the incorrect detection of non-allelic artifacts (false positives) and the threshold-induced dropout of true allelic information (false negatives). This is accomplished by using a dynamic locus and sample specific analytical threshold and a machine learning-derived probabilistic artifact detection model. The system produced an allele detection accuracy of 97.2%, an 11.4% increase from the lowest static threshold (50 RFU), with a low incidence of incorrectly identified artifacts (0.79%). This adaptive method outperformed static thresholds in the retention of allelic information content at minimal cost.

Enhanced DNA mixture deconvolution of sexual offense samples using the DEPArray™ system.

The interpretation of DNA mixtures remains a significant challenge in the analysis of forensic evidence. The ability to selectively identify, collect, and analyze single cells or groups of cells has wide implications in the analysis of forensic samples and the subsequent deconvolution of DNA mixtures, particularly in the processing and interpretation of sexual offense evidence where the deconvolution of heterogeneous sources is essential. Single cell separation technology can be used to address this mixture separation challenge, specifically using the DEPArray™ system from Menarini Silicon Biosystems. We propose that the DEPArray™ will enable enhancements to the standard workflow for forensic biology/DNA analytical laboratories. We have demonstrated that the DEPArray™ workflow will lead to fewer mixture samples, enable purification of sperm and epithelial cell fractions without the need for differential extraction, improve the amplification success rate of samples and improve the interpretation of low template DNA samples. Sperm profiles were identified in 27 of 32 DEPArray™ processed samples, with 26 of 27 (96.2%) yielding single source profiles. In contrast, single source profiles were obtained from 9 of 28 (32.1%) differentially extracted samples. The use of the DEPArray™ also eliminates the need for additional confirmatory tests for the presence of human sperm and permits direct identification of the type and number of cells being analyzed eliminating the need for qPCR-based DNA quantification.

Development of a Method to Extract Opium Poppy (Papaver somniferum L.) DNA from Heroin.

This study is the first to report the successful development of a method to extract opium poppy (Papaver somniferum L.) DNA from heroin samples. Determining of the source of an unknown heroin sample (forensic geosourcing) is vital to informing domestic and foreign policy related to counter-narcoterrorism. Current profiling methods focus on identifying process-related chemical impurities found in heroin samples. Changes to the geographically distinct processing methods may lead to difficulties in classifying and attributing heroin samples to a region/country. This study focuses on methods to optimize the DNA extraction and amplification of samples with low levels of degraded DNA and inhibiting compounds such as heroin. We compared modified commercial-off-the-shelf extraction methods such as the Qiagen Plant, Stool and the Promega Maxwell-16 RNA-LEV tissue kits for the ability to extract opium poppy DNA from latex, raw and cooked opium, white and brown powder heroin and black tar heroin. Opium poppy DNA was successfully detected in all poppy-derived samples, including heroin. The modified Qiagen stool method with post-extraction purification and a two-stage, dual DNA polymerase amplification procedure resulted in the highest DNA yield and minimized inhibition. This paper describes the initial phase in establishing a DNA-based signature method to characterize heroin.

PACE: Probabilistic Assessment for Contributor Estimation- A machine learning-based assessment of the number of contributors in DNA mixtures.

The deconvolution of DNA mixtures remains one of the most critical challenges in the field of forensic DNA analysis. In addition, of all the data features required to perform such deconvolution, the number of contributors in the sample is widely considered the most important, and, if incorrectly chosen, the most likely to negatively influence the mixture interpretation of a DNA profile. Unfortunately, most current approaches to mixture deconvolution require the assumption that the number of contributors is known by the analyst, an assumption that can prove to be especially faulty when faced with increasingly complex mixtures of 3 or more contributors. In this study, we propose a probabilistic approach for estimating the number of contributors in a DNA mixture that leverages the strengths of machine learning. To assess this approach, we compare classification performances of six machine learning algorithms and evaluate the model from the top-performing algorithm against the current state of the art in the field of contributor number classification. Overall results show over 98% accuracy in identifying the number of contributors in a DNA mixture of up to 4 contributors. Comparative results showed 3-person mixtures had a classification accuracy improvement of over 6% compared to the current best-in-field methodology, and that 4-person mixtures had a classification accuracy improvement of over 20%. The Probabilistic Assessment for Contributor Estimation (PACE) also accomplishes classification of mixtures of up to 4 contributors in less than 1s using a standard laptop or desktop computer. Considering the high classification accuracy rates, as well as the significant time commitment required by the current state of the art model versus seconds required by a machine learning-derived model, the approach described herein provides a promising means of estimating the number of contributors and, subsequently, will lead to improved DNA mixture interpretation.