Whose blood is it? Application of DEPArray™ technology for the identification of individual/s who contributed blood to a mixed stain

Deconvoluting multi-person biological mixtures and accurate characterization and identification of separated contributors using non-targeted single-cell DNA sequencing

The genetic characterization and identification of individuals who contributed to biological mixtures are complex and mostly unresolved tasks. These tasks are relevant in various fields, particularly in forensic investigations, which frequently encounters crime scene stains generated by more than one person. Currently, forensic mixture deconvolution is mostly performed subsequent to forensic DNA profiling at the level of the mixed DNA profiles, which comes with several limitations. Some previous studies attempted at separating single cells prior to forensic DNA profiling. However, these approaches are biased at selection of the cells and, due to their targeted DNA analysis on low template DNA, provide incomplete and unreliable forensic DNA profiles. We recently demonstrated the feasibility of performing mixture deconvolution prior to forensic DNA profiling through the utilization of a non-targeted single-cell transcriptome sequencing (scRNA-seq). In addition to individual-specific mixture deconvolution, this approach also allowed accurate characterisation of biological sex, biogeographic ancestry and individual identification of the separated mixture contributors. However, RNA has the forensic disadvantage of being prone to degradation, and sequencing RNA - focussing on coding regions - limits the number of single nucleotide polymorphisms (SNPs) utilized for genetic mixture deconvolution, characterization, and identification. These limitations can be overcome by performing single-cell sequencing on the level of DNA instead of RNA. Here, for the first time, we applied non-targeted single-cell DNA sequencing (scDNA-seq) by applying the scATAC-seq (Assay for Transposase-Accessible Chromatin with sequencing) technique to address the challenges of mixture deconvolution in the forensic context. We demonstrated that scATAC-seq, together with our recently developed De-goulash data analysis pipeline, is capable of deconvoluting complex blood mixtures of five individuals from both sexes with varying biogeographic ancestries. We further showed that our approach achieved correct genetic characterization of the biological sex and the biogeographic ancestry of each of the separated mixture contributors and established their identity. Furthermore, by analysing in-silico generated scATAC-seq data mixtures, we demonstrated successful individual-specific mixture deconvolution of i) highly complex mixtures of 11 individuals, ii) balanced mixtures containing as few as 20 cells (10 per each individual), and iii) imbalanced mixtures with a ratio as low as 1:80. Overall, our proof-of-principle study demonstrates the general feasibility of scDNA-seq in general, and scATAC-seq in particular, for mixture deconvolution, genetic characterization and individual identification of the separated mixture contributors. Furthermore, it shows that compared to scRNA-seq, scDNA-seq detects more SNPs from fewer cells, providing higher sensitivity, that is valuable in forensic genetics.

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.

Single cell genomics applications in forensic science: Current state and future directions

Standard methods of mixture analysis involve subjecting a dried crime scene sample to a "bulk" DNA extraction method such that the resulting isolate compromises a homogenized DNA mixture from the individual donors. If, however, instead of bulk DNA extraction, a sufficient number of individual cells from the mixed stain are subsampled prior to genetic analysis then it should be possible to recover highly probative single source, non-mixed scDNA profiles from each of the donors. This approach can detect low DNA level minor donors to a mixture that otherwise would not be identified using standard methods and can also resolve rare mixtures comprising first degree relatives and thereby also prevent the false inclusion of non-donor relatives. This literature landscape review and associated commentary reports on the history and increasing interest in current and potential future applications of scDNA in forensic genomics, and critically evaluates opportunities and impediments to further progress.

Phenotype predictions of two-person mixture using single cell analysis

Over a decade after the publication of the first forensic DNA phenotyping (FDP) studies, DNA-based appearance predictions are now becoming a reality in routine crime scene investigations. The significant number of publications dedicated to the subject of FDP clearly demonstrates a sustained interest and a strong need for further method development. However, the implementation of FDP in routine work still encounters obstacles, and one of these challenges is making phenotype predictions from DNA mixtures. In this study, we examined single-cell sequencing as a potential tool to enable reliable phenotyping of contributors within mixtures. Two mock mixtures, each containing two contributors with similar and different physical appearances, were analyzed using two different workflows. In the first workflow, the mixtures were sequenced using the Ion AmpliSeq™ PhenoTrivium Panel, which includes 41 HIrisPlex-S (HPS) markers. Subsequently, the genotypes were analyzed using the HPS Deconvolution Tool to predict the phenotypes of both contributors. The second workflow involved the introduction of single-cell separation and collection using the DEPArray™ PLUS System. Two different PhenoTrivium amplification protocols were tested, and the phenotype predictions from single cells were compared with the results obtained using the HPS Tool. Our results suggest that the approach presented here allows for the obtainment of nearly complete HIrisPlex-S profiles with accurate genotypes and reliable phenotype predictions from single cells. This method proves successful in deconvoluting mixtures submitted to forensic DNA phenotyping.

Carrying out common DNA donor analysis using DBLR™ on two or five-cell mini-mixture subsamples for improved discrimination power in complex DNA mixtures

Probabilistic genotyping systems are able to analyse complex mixed DNA profiles and show good power to discriminate contributors from non-contributors. However, the abilities of the statistical analyses are still unavoidably bound by the quality of information being analysed. If a profile has a high number of contributors, or a contributor that is present in trace amounts, then the amount of information about those individuals in the DNA profile is limited. Recent work has shown the ability to gain better resolution of the genotypes of contributors to complex profiles using cell subsampling. This is the process of taking many sets of a limited number of cells and individually profiling each set. These 'mini-mixtures' can provide greater information about the genotypes of underlying contributors. In our work we take the resulting profiles from multiple subsamplings of complex DNA profiles in equal amounts and show how testing for, and then assuming, a common DNA donor can further improve the ability to resolve the genotypes of contributors. Using direct cell sub-sampling and statistical analysis software DBLR™, we were able to recover single source profiles of uploadable quality from five out of the six contributors of an equally proportioned mixture. Through the analysis of mixtures in this work we provide a template for carrying out common donor analysis for maximum effect.

Single-cell transcriptome sequencing allows genetic separation, characterization and identification of individuals in multi-person biological mixtures

Identifying individuals from biological mixtures to which they contributed is highly relevant in crime scene investigation and various biomedical research fields, but despite previous attempts, remains nearly impossible. Here we investigated the potential of using single-cell transcriptome sequencing (scRNA-seq), coupled with a dedicated bioinformatics pipeline (De-goulash), to solve this long-standing problem. We developed a novel approach and tested it with scRNA-seq data that we de-novo generated from multi-person blood mixtures, and also in-silico mixtures we assembled from public single individual scRNA-seq datasets, involving different numbers, ratios, and bio-geographic ancestries of contributors. For all 2 up to 9-person balanced and imbalanced blood mixtures with ratios up to 1:60, we achieved a clear single-cell separation according to the contributing individuals. For all separated mixture contributors, sex and bio-geographic ancestry (maternal, paternal, and bi-parental) were correctly determined. All separated contributors were correctly individually identified with court-acceptable statistical certainty using de-novo generated whole exome sequencing reference data. In this proof-of-concept study, we demonstrate the feasibility of single-cell approaches to deconvolute biological mixtures and subsequently genetically characterise, and individually identify the separated mixture contributors. With further optimisation and implementation, this approach may eventually allow moving to challenging biological mixtures, including those found at crime scenes.

Recent advances in forensic biology and forensic DNA typing: INTERPOL review 2019-2022

This review paper covers the forensic-relevant literature in biological sciences from 2019 to 2022 as a part of the 20th INTERPOL International Forensic Science Managers Symposium. Topics reviewed include rapid DNA testing, using law enforcement DNA databases plus investigative genetic genealogy DNA databases along with privacy/ethical issues, forensic biology and body fluid identification, DNA extraction and typing methods, mixture interpretation involving probabilistic genotyping software (PGS), DNA transfer and activity-level evaluations, next-generation sequencing (NGS), DNA phenotyping, lineage markers (Y-chromosome, mitochondrial DNA, X-chromosome), new markers and approaches (microhaplotypes, proteomics, and microbial DNA), kinship analysis and human identification with disaster victim identification (DVI), and non-human DNA testing including wildlife forensics. Available books and review articles are summarized as well as 70 guidance documents to assist in quality control that were published in the past three years by various groups within the United States and around the world.

Comparison of swab types & elution buffers for collection and analysis of intact cells to aid in deconvolution of complex DNA mixtures

Heightened sensitivity of forensic DNA techniques has led to an increased variety of samples tested, often yielding complex DNA mixtures, in turn making the interpretation of profiling results more complicated. Currently, there is no prescribed upstream laboratory method to separate complex DNA mixtures by their contributors; therefore, a method is needed that could reduce mixtures into their component parts. Various cell sorting applications have the potential to be this method, if intact cells can be reliably obtained from forensic samples. Here, the effects of elution buffer and swab substrate on the recovery of intact, human, white blood cells from dried blood samples were evaluated. Approximately 328,000 cells per swab were deposited onto cotton, flocked, and dissolvable swabs. The whole-cell elution of the dried samples was evaluated with water, phosphate buffered saline, and AutoMACS® elution buffers. We demonstrate that AutoMACS® buffer is superior for the elution of intact cells, compared to phosphate buffered saline and water. When swab type was considered, the highest yield of intact cells resulted from flocked swabs, as opposed to cotton or dissolvable swabs.

Cell Subsampling Recovers Probative DNA Profile Information from Unresolvable/Undetectable Minor Donors in Mixtures

When a minor DNA component to a binary mixture is present at a weight ratio of approximately 1:50 or less, the presence of this minor donor is undetectable (or barely detectable) by standard mixture deconvolution approaches. In an attempt to retrieve probative minor donor DNA profile information, multiple quintuple cell subsamples were collected from a 1:50 DNA mixture using direct single cell subsampling (DSCS) paired with probabilistic genotyping (PG), the latter validated for use with single or few cells. DSCS employs a simplified micromanipulation technique paired with an enhanced DNA profiling approach, involving direct cell lysis and a sensitive PCR process, to genotype individual cells. Multiple five-cell subsamples were used to interrogate sufficient cells from the mixture such that some of the created 5-cell “mini-mixture” subsamples contained a cell from the minor donor. The latter mini-mixture subsamples, which now comprised weight ratios of 1:4 as opposed to the bulk mixture 1:50, were analyzed with the PG systems STRmix^TM and EuroForMix resulting in a significant probative gain of information, (LR ≅ 10¹¹, compared to standard bulk mixture PG methods, LR ≅ 10¹–10²).

Three-dimensional superimposition of digital models for individual identification

Dentition is an individualizing structure in humans that may be potentially utilized in individual identification. However, research on the use of three-dimensional (3D) digital models for personal identification is rare. This study aimed to develop a method for individual identification based on a 3D image registration algorithm and assess its feasibility in practice. Twenty-eight college students were recruited; for each subject, a dental cast and an intraoral scan were taken at different time points, and digital models were acquired. The digital models of the dental casts and intraoral scans were assumed as antemortem and postmortem dentition, respectively. Additional 72 dental casts were extracted from a hospital database as a suspect pool together with 28 antemortem models. The dentition images of all of the models were extracted. Correntropy was introduced into the traditional iterative closest point algorithm to compare each postmortem 3D dentition with 3D dentitions in the suspect pool. Point-to-point root mean square (RMS) distances were calculated, and then 28 matches and 2772 mismatches were obtained. Statistical analysis was performed using the Mann-Whitney U test, which showed significant differences in RMS between matches (0.18±0.03mm) and mismatches (1.04±0.67mm) (P<0.05). All of the RMS values of the matched models were below 0.27mm. The percentage of accurate identification reached 100% in the present study. These results indicate that this method for individual identification based on 3D superimposition of digital models is effective in personal identification.

Recovery of single source DNA profiles from mixtures by direct single cell subsampling and simplified micromanipulation

Deconvolution of forensic DNA mixtures into their individual component DNA (geno)types is of great investigative value, though often complex and difficult. Two-person mixtures comprising a major and minor contributor are often easily interpreted although, when the DNA ratio of the two individuals is approximately equal (~1:1), deconvolution and interpretation becomes much more difficult. To address this issue, a physical separation of individual-, two- or three- cell subsamples prior to autosomal STR analysis was performed using a simplified micromanipulation technique paired with a decreased reaction volume and increased cycle number PCR. Using this method, single and multiple buccal epithelial cells were collected from a 1:1 two-person mixture (i.e. from individual 'A' and 'B') and directly amplified, omitting standard DNA extraction and purification steps. Single cell subsamples resulted in partial single-source profiles for both contributors while, in accordance with expectations of a quasi-binomial sampling schema, two- and three-cell subsamples resulted in single source informative partial profiles of individual A and individual B as well as complete consensus profiles, and equally mixed 1:1 (2-cell subsamples) and 2:1 (3-cell subsamples) admixed profiles of individual A and B.This proof-of-concept approach shows promise in permitting the DNA deconvolution of mixed samples where the individual contributors are present in similar amounts that would otherwise be difficult to interpret, resulting in an increase in evidentiary value. The subsampling approach can be readily investigated for DNA casework applications without additional investment in costly, new equipment, requiring only a stereo microscope and a tungsten needle.

DEPArray™ system: An automatic image-based sorter for isolation of pure circulating tumor cells

Circulating tumor cells (CTCs) are rare cells shed into the bloodstream by invasive tumors and their analysis offers a promising noninvasive tool to predict and monitor therapeutic responses. CTCs can be isolated from patient blood and their characterization at single‐cell level can inform on the genomic landscape of a tumor. All CTC enrichment methods bear a burden of contaminating normal cells, which mandate a further step of purification to enable reliable downstream genetic analysis. Here, we describe the DEPArray™ technology, a microchip‐based digital sorter, which combines precise microfluidic and microelectronic enabling precise, image‐based isolation of single CTCs, which can then be analyzed by Next Generation Sequencing (NGS) methods. © 2018 The Authors. Cytometry Part A published by Wiley Periodicals, Inc. on behalf of International Society for Advancement of Cytometry.