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Kulhankova L, Bindels E, Kayser M, Mulugeta E. Deconvoluting multi-person biological mixtures and accurate characterization and identification of separated contributors using non-targeted single-cell DNA sequencing. Forensic Sci Int Genet 2024; 71:103030. [PMID: 38513339 DOI: 10.1016/j.fsigen.2024.103030] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/22/2023] [Revised: 02/16/2024] [Accepted: 03/04/2024] [Indexed: 03/23/2024]
Abstract
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.
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Affiliation(s)
- Lucie Kulhankova
- Department of Genetic Identification, Erasmus MC University Medical Center Rotterdam, Rotterdam, the Netherlands
| | - Eric Bindels
- Department of Haematology, Erasmus MC University Medical Center Rotterdam, Rotterdam, the Netherlands
| | - Manfred Kayser
- Department of Genetic Identification, Erasmus MC University Medical Center Rotterdam, Rotterdam, the Netherlands.
| | - Eskeatnaf Mulugeta
- Department of Cell Biology, Erasmus MC University Medical Center Rotterdam, Rotterdam, the Netherlands.
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Grgicak CM, Bhembe Q, Slooten K, Sheth NC, Duffy KR, Lun DS. Single-cell investigative genetics: Single-cell data produces genotype distributions concentrated at the true genotype across all mixture complexities. Forensic Sci Int Genet 2024; 69:103000. [PMID: 38199167 DOI: 10.1016/j.fsigen.2023.103000] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/21/2023] [Revised: 11/07/2023] [Accepted: 12/12/2023] [Indexed: 01/12/2024]
Abstract
In the absence of a suspect the forensic aim is investigative, and the focus is one of discerning what genotypes best explain the evidence. In traditional systems, the list of candidate genotypes may become vast if the sample contains DNA from many donors or the information from a minor contributor is swamped by that of major contributors, leading to lower evidential value for a true donor's contribution and, as a result, possibly overlooked or inefficient investigative leads. Recent developments in single-cell analysis offer a way forward, by producing data capable of discriminating genotypes. This is accomplished by first clustering single-cell data by similarity without reference to a known genotype. With good clustering it is reasonable to assume that the scEPGs in a cluster are of a single contributor. With that assumption we determine the probability of a cluster's content given each possible genotype at each locus, which is then used to determine the posterior probability mass distribution for all genotypes by application of Bayes' rule. A decision criterion is then applied such that the sum of the ranked probabilities of all genotypes falling in the set is at least 1-α. This is the credible genotype set and is used to inform database search criteria. Within this work we demonstrate the salience of single-cell analysis by performance testing a set of 630 previously constructed admixtures containing up to 5 donors of balanced and unbalanced contributions. We use scEPGs that were generated by isolating single cells, employing a direct-to-PCR extraction treatment, amplifying STRs that are compliant with existing national databases and applying post-PCR treatments that elicit a detection limit of one DNA copy. We determined that, for these test data, 99.3% of the true genotypes are included in the 99.8% credible set, regardless of the number of donors that comprised the mixture. We also determined that the most probable genotype was the true genotype for 97% of the loci when the number of cells in a cluster was at least two. Since efficient investigative leads will be borne by posterior mass distributions that are narrow and concentrated at the true genotype, we report that, for this test set, 47,900 (86%) loci returned only one credible genotype and of these 47,551 (99%) were the true genotype. When determining the LR for true contributors, 91% of the clusters rendered LR>1018, showing the potential of single-cell data to positively affect investigative reporting.
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Affiliation(s)
- Catherine M Grgicak
- Department of Chemistry, Rutgers University, Camden, NJ 08102, USA; Center for Computational and Integrative Biology, Rutgers University, Camden, NJ 08102, USA; Program in Biomedical Forensic Sciences, Boston University, Boston, MA 02118, USA.
| | - Qhawe Bhembe
- Center for Computational and Integrative Biology, Rutgers University, Camden, NJ 08102, USA
| | - Klaas Slooten
- Netherlands Forensic Institute, P.O. Box 24044, 2490 AA The Hague, the Netherlands; VU University Amsterdam, De Boelelaan 1081, 1081 HV Amsterdam, the Netherlands
| | - Nidhi C Sheth
- Center for Computational and Integrative Biology, Rutgers University, Camden, NJ 08102, USA
| | - Ken R Duffy
- Department of Mathematics, Northeastern University, Boston, MA 02115, USA; Department of Electrical and Computer Engineering, Northeastern University, Boston, MA 02115, USA; Hamilton Institute, Maynooth University, Ireland
| | - Desmond S Lun
- Center for Computational and Integrative Biology, Rutgers University, Camden, NJ 08102, USA; Department of Computer Science, Rutgers University, Camden, NJ 08102, USA
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Huffman K, Ballantyne J. Single cell genomics applications in forensic science: Current state and future directions. iScience 2023; 26:107961. [PMID: 37876804 PMCID: PMC10590970 DOI: 10.1016/j.isci.2023.107961] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 10/26/2023] Open
Abstract
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.
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Affiliation(s)
- Kaitlin Huffman
- Graduate Program in Chemistry, Department of Chemistry, University of Central Florida, PO Box 162366, Orlando, FL 32816-2366, USA
| | - Jack Ballantyne
- National Center for Forensic Science, PO Box 162367, Orlando, FL 32816-2367, USA
- Department of Chemistry, University of Central Florida, PO Box 162366, Orlando, FL 32816-2366, USA
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Schulte J, Marciano MA, Scheurer E, Schulz I. A systematic approach to improve downstream single-cell analysis for the DEPArray™ technology. J Forensic Sci 2023; 68:1875-1893. [PMID: 37497755 DOI: 10.1111/1556-4029.15344] [Citation(s) in RCA: 2] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/28/2023] [Revised: 06/26/2023] [Accepted: 07/12/2023] [Indexed: 07/28/2023]
Abstract
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.
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Affiliation(s)
- Janine Schulte
- Institute of Forensic Medicine, University of Basel, Basel, Switzerland
| | - Michael A Marciano
- Forensic & National Security Sciences Institute, Syracuse University, Syracuse, New York, USA
| | - Eva Scheurer
- Institute of Forensic Medicine, University of Basel, Basel, Switzerland
| | - Iris Schulz
- Institute of Forensic Medicine, University of Basel, Basel, Switzerland
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Huffman K, Kruijver M, Ballantyne J, Taylor D. Carrying out common DNA donor analysis using DBLR™ on two or five-cell mini-mixture subsamples for improved discrimination power in complex DNA mixtures. Forensic Sci Int Genet 2023; 66:102908. [PMID: 37402330 DOI: 10.1016/j.fsigen.2023.102908] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/13/2023] [Revised: 06/13/2023] [Accepted: 06/15/2023] [Indexed: 07/06/2023]
Abstract
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.
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Affiliation(s)
- Kaitlin Huffman
- Graduate Program in Chemistry, Department of Chemistry, University of Central Florida, P.O. Box 162366, Orlando, FL 32816-2366, USA
| | - Maarten Kruijver
- Institute of Environmental Science and Research Limited, Private Bag 92021, Auckland 1142, New Zealand
| | - Jack Ballantyne
- Graduate Program in Chemistry, Department of Chemistry, University of Central Florida, P.O. Box 162366, Orlando, FL 32816-2366, USA; National Center for Forensic Science, P.O. Box 162367, Orlando, FL 32816-2367, USA
| | - Duncan Taylor
- Forensic Science SA, GPO Box 2790, Adelaide, SA 5001, Australia; School of Biological Sciences, Flinders University, GPO Box 2100, Adelaide, SA 5001, Australia.
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Evidentiary evaluation of single cells renders highly informative forensic comparisons across multifarious admixtures. Forensic Sci Int Genet 2023; 64:102852. [PMID: 36934551 DOI: 10.1016/j.fsigen.2023.102852] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/16/2022] [Revised: 02/09/2023] [Accepted: 03/01/2023] [Indexed: 03/07/2023]
Abstract
The consistency between DNA evidence and person(s) of interest (PoI) is summarized by a likelihood ratio (LR): the probability of the data given the PoI contributed divided by the probability given they did not. It is often the case that there are several PoI who may have individually or jointly contributed to the stain. If there is more than one PoI, or the number of contributors (NoC) cannot easily be determined, then several sets of hypotheses are needed, requiring significant resources to complete the interpretation. Recent technological developments in laboratory systems offer a way forward, by enabling production of single cell data. Though single-cell data may be procured by next generation sequencing or capillary electrophoresis workflows, in this work we focus our attention on assessing the consistency between PoIs and a collection of single cell electropherograms (scEPGs) from diploid cells - i.e., leukocytes and epithelial cells. Specifically, we introduce a framework that: I) clusters scEPGs into collections, each originating from one genetic source; II) for each PoI, determines a LR for each cluster of scEPGs; and III) by averaging the likelihood ratios for each PoI across all clusters provides a whole-sample weight of evidence summary. By using Model Based Clustering (MBC) in step I) and an algorithm, named EESCIt for Evidentiary Evaluation of Single Cells, that computes single-cell LRs in step II), we show that 99% of the comparisons rendered log LR values > 0 for true contributors, and of these all but one gave log LR > 5, regardless of the number of donors or whether the smallest contributor donated less than 20% of the cells, greatly expanding the collection of cases for which DNA forensics provides informative results.
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Huffman K, Hanson E, Ballantyne J. Y-STR mixture deconvolution by single-cell analysis. J Forensic Sci 2023; 68:275-288. [PMID: 36183153 DOI: 10.1111/1556-4029.15150] [Citation(s) in RCA: 5] [Impact Index Per Article: 5.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/19/2022] [Revised: 09/06/2022] [Accepted: 09/13/2022] [Indexed: 12/31/2022]
Abstract
Since Y-STR typing only amplifies male Y chromosomal DNA, it can simplify the interpretation of some DNA mixtures that contain female DNA. However, if there are multiple male contributors, mixed Y-STR DNA profiles will often be obtained. Y-STR mixture analysis cases are particularly challenging though as, currently, there are no validated probabilistic genotyping (PG) software solutions commercially available to aid in their interpretation. One approach to fully deconvoluting these challenging mixtures into their individual donors is to conduct single-cell genotyping by isolating individual cells from a mixture prior to conducting DNA typing. In this work, a physical micromanipulation technique involving a tungsten needle and direct PCR with decreased reaction volume and increased cycle number was applied to equimolar 2- and 3-person buccal cell male DNA mixtures and a mock touch DNA case scenario involving the consecutive firing of a handgun by two males. A consensus DNA profiling approach was then utilized to obtain YFiler™ Plus Y-STR haplotypes. Buccal cells were used to optimize and test the direct single-cell subsampling approach, and 2-3 person male buccal cell mixtures were fully deconvoluted into their individual donor Y-STR haplotypes. Single-cell (or agglomerated cell clump) subsampling from the gun's trigger recovered single-source Y-STR profiles from both individuals who fired the gun, the owner, and the other unrelated male. Only the non-owner's DNA was found in the cells recovered from the handle. In summary, direct single-cell subsampling as described represents a potential simple way to analyze and interpret Y-STR mixtures.
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Affiliation(s)
- Kaitlin Huffman
- Graduate Program in Chemistry, Department of Chemistry, University of Central Florida, Orlando, Florida, USA
| | - Erin Hanson
- Graduate Program in Chemistry, Department of Chemistry, University of Central Florida, Orlando, Florida, USA.,National Center for Forensic Science, Orlando, Florida, USA.,Department of Chemistry, University of Central Florida, Orlando, Florida, USA
| | - Jack Ballantyne
- Graduate Program in Chemistry, Department of Chemistry, University of Central Florida, Orlando, Florida, USA.,National Center for Forensic Science, Orlando, Florida, USA.,Department of Chemistry, University of Central Florida, Orlando, Florida, USA
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Abstract
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.
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9
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Identification of individuals by RNA sequencing of low template samples. FORENSIC SCIENCE INTERNATIONAL GENETICS SUPPLEMENT SERIES 2022. [DOI: 10.1016/j.fsigss.2022.10.076] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/21/2022]
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Probabilistic Genotyping of Single Cell Replicates from Mixtures Involving First-Degree Relatives Prevents the False Inclusions of Non-Donor Relatives. Genes (Basel) 2022; 13:genes13091658. [PMID: 36140825 PMCID: PMC9498535 DOI: 10.3390/genes13091658] [Citation(s) in RCA: 4] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/18/2022] [Revised: 09/05/2022] [Accepted: 09/13/2022] [Indexed: 11/26/2022] Open
Abstract
Analysis of complex DNA mixtures comprised of related individuals requires a great degree of care due to the increased risk of falsely including non-donor first-degree relatives. Although alternative likelihood ratio (LR) propositions that may aid in the analysis of these difficult cases can be employed, the prior information required for their use is not always known, nor do these alternative propositions always prevent false inclusions. For example, with a father/mother/child mixture, conditioning the mixture on the presence of one of the parents is recommended. However, the definitive presence of the parent(s) is not always known and an assumption of their presence in the mixture may not be objectively justifiable. Additionally, the high level of allele sharing seen with familial mixtures leads to an increased risk of underestimating the number of contributors (NOC) to a mixture. Therefore, fully resolving and identifying each of the individuals present in familial mixtures and excluding related non-donors is an important goal of the mixture deconvolution process and can be of great investigative value. Here, firstly, we further investigated and confirmed the problems encountered with standard bulk analysis of familial mixtures and demonstrated the ability of single cell analysis to fully distinguish first-degree relatives (FDR). Then, separation of each of the individual donors via single cell analysis was carried out by a combination of direct single cell subsampling (DSCS), enhanced DNA typing, and probabilistic genotyping, and applied to three complex familial 4-person mixtures resulting in a probative gain of LR for all donors and an accurate determination of the NOC. Significantly, non-donor first-degree relatives that were falsely included (LRs > 102−108) by a standard bulk sampling and analysis approach were no longer falsely included using DSCS.
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Jäger R. New Perspectives for Whole Genome Amplification in Forensic STR Analysis. Int J Mol Sci 2022; 23:ijms23137090. [PMID: 35806097 PMCID: PMC9267064 DOI: 10.3390/ijms23137090] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/02/2022] [Revised: 06/23/2022] [Accepted: 06/24/2022] [Indexed: 02/04/2023] Open
Abstract
Modern PCR-based analytical techniques have reached sensitivity levels that allow for obtaining complete forensic DNA profiles from even tiny traces containing genomic DNA amounts as small as 125 pg. Yet these techniques have reached their limits when it comes to the analysis of traces such as fingerprints or single cells. One suggestion to overcome these limits has been the usage of whole genome amplification (WGA) methods. These methods aim at increasing the copy number of genomic DNA and by this means generate more template DNA for subsequent analyses. Their application in forensic contexts has so far remained mostly an academic exercise, and results have not shown significant improvements and even have raised additional analytical problems. Until very recently, based on these disappointments, the forensic application of WGA seems to have largely been abandoned. In the meantime, however, novel improved methods are pointing towards a perspective for WGA in specific forensic applications. This review article tries to summarize current knowledge about WGA in forensics and suggests the forensic analysis of single-donor bioparticles and of single cells as promising applications.
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Affiliation(s)
- Richard Jäger
- Department of Natural Sciences, Bonn-Rhein-Sieg University of Applied Sciences, von-Liebig Str. 20, 53359 Rheinbach, Germany;
- Institute for Functional Gene Analytics, Bonn-Rhein-Sieg University of Applied Sciences, Grantham Allee 20, 53757 Sankt Augustin, Germany
- Institute of Safety and Security Research, Bonn-Rhein-Sieg University of Applied Sciences, Grantham Allee 20, 53757 Sankt Augustin, Germany
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Huffman K, Hanson E, Ballantyne J. Cell Subsampling Recovers Probative DNA Profile Information from Unresolvable/Undetectable Minor Donors in Mixtures. Genes (Basel) 2022; 13:genes13071117. [PMID: 35885899 PMCID: PMC9321018 DOI: 10.3390/genes13071117] [Citation(s) in RCA: 6] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/13/2022] [Revised: 06/13/2022] [Accepted: 06/20/2022] [Indexed: 02/04/2023] Open
Abstract
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 STRmixTM and EuroForMix resulting in a significant probative gain of information, (LR ≅ 1011, compared to standard bulk mixture PG methods, LR ≅ 101–102).
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Affiliation(s)
- Kaitlin Huffman
- Graduate Program in Chemistry, Department of Chemistry, University of Central Florida, P.O. Box 162366, Orlando, FL 32816-2366, USA; (K.H.); (E.H.)
| | - Erin Hanson
- Graduate Program in Chemistry, Department of Chemistry, University of Central Florida, P.O. Box 162366, Orlando, FL 32816-2366, USA; (K.H.); (E.H.)
- National Center for Forensic Science, P.O. Box 162367, Orlando, FL 32816-2367, USA
| | - Jack Ballantyne
- Graduate Program in Chemistry, Department of Chemistry, University of Central Florida, P.O. Box 162366, Orlando, FL 32816-2366, USA; (K.H.); (E.H.)
- National Center for Forensic Science, P.O. Box 162367, Orlando, FL 32816-2367, USA
- Correspondence:
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13
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Probabilistic Genotyping of Single Cell Replicates from Complex DNA Mixtures Recovers Higher Contributor LRs than Standard Analysis. Sci Justice 2022; 62:156-163. [DOI: 10.1016/j.scijus.2022.01.003] [Citation(s) in RCA: 4] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 06/21/2021] [Revised: 12/01/2021] [Accepted: 01/16/2022] [Indexed: 12/31/2022]
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14
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Ge J, King JL, Smuts A, Budowle B. Precision DNA Mixture Interpretation with Single-Cell Profiling. Genes (Basel) 2021; 12:1649. [PMID: 34828255 PMCID: PMC8623868 DOI: 10.3390/genes12111649] [Citation(s) in RCA: 7] [Impact Index Per Article: 2.3] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/29/2021] [Revised: 10/14/2021] [Accepted: 10/14/2021] [Indexed: 11/16/2022] Open
Abstract
Wet-lab based studies have exploited emerging single-cell technologies to address the challenges of interpreting forensic mixture evidence. However, little effort has been dedicated to developing a systematic approach to interpreting the single-cell profiles derived from the mixtures. This study is the first attempt to develop a comprehensive interpretation workflow in which single-cell profiles from mixtures are interpreted individually and holistically. In this approach, the genotypes from each cell are assessed, the number of contributors (NOC) of the single-cell profiles is estimated, followed by developing a consensus profile of each contributor, and finally the consensus profile(s) can be used for a DNA database search or comparing with known profiles to determine their potential sources. The potential of this single-cell interpretation workflow was assessed by simulation with various mixture scenarios and empirical allele drop-out and drop-in rates, the accuracies of estimating the NOC, the accuracies of recovering the true alleles by consensus, and the capabilities of deconvolving mixtures with related contributors. The results support that the single-cell based mixture interpretation can provide a precision that cannot beachieved with current standard CE-STR analyses. A new paradigm for mixture interpretation is available to enhance the interpretation of forensic genetic casework.
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Affiliation(s)
- Jianye Ge
- Center for Human Identification, University of North Texas Health Science Center, Fort Worth, TX 76107, USA; (J.L.K.); (A.S.); (B.B.)
- Department of Microbiology, Immunology and Genetics, University of North Texas Health Science Center, Fort Worth, TX 76107, USA
| | - Jonathan L. King
- Center for Human Identification, University of North Texas Health Science Center, Fort Worth, TX 76107, USA; (J.L.K.); (A.S.); (B.B.)
| | - Amy Smuts
- Center for Human Identification, University of North Texas Health Science Center, Fort Worth, TX 76107, USA; (J.L.K.); (A.S.); (B.B.)
| | - Bruce Budowle
- Center for Human Identification, University of North Texas Health Science Center, Fort Worth, TX 76107, USA; (J.L.K.); (A.S.); (B.B.)
- Department of Microbiology, Immunology and Genetics, University of North Texas Health Science Center, Fort Worth, TX 76107, USA
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Diepenbroek M, Bayer B, Anslinger K. Pushing the Boundaries: Forensic DNA Phenotyping Challenged by Single-Cell Sequencing. Genes (Basel) 2021; 12:genes12091362. [PMID: 34573344 PMCID: PMC8466929 DOI: 10.3390/genes12091362] [Citation(s) in RCA: 8] [Impact Index Per Article: 2.7] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/28/2021] [Revised: 08/24/2021] [Accepted: 08/27/2021] [Indexed: 12/26/2022] Open
Abstract
Single-cell sequencing is a fast developing and very promising field; however, it is not commonly used in forensics. The main motivation behind introducing this technology into forensics is to improve mixture deconvolution, especially when a trace consists of the same cell type. Successful studies demonstrate the ability to analyze a mixture by separating single cells and obtaining CE-based STR profiles. This indicates a potential use of the method in other forensic investigations, like forensic DNA phenotyping, in which using mixed traces is not fully recommended. For this study, we collected single-source autopsy blood from which the white cells were first stained and later separated with the DEPArray™ N×T System. Groups of 20, 10, and 5 cells, as well as 20 single cells, were collected and submitted for DNA extraction. Libraries were prepared using the Ion AmpliSeq™ PhenoTrivium Panel, which includes both phenotype (HIrisPlex-S: eye, hair, and skin color) and ancestry-associated SNP-markers. Prior to sequencing, half of the single-cell-based libraries were additionally amplified and purified in order to improve the library concentrations. Ancestry and phenotype analysis resulted in nearly full consensus profiles resulting in correct predictions not only for the cells groups but also for the ten re-amplified single-cell libraries. Our results suggest that sequencing of single cells can be a promising tool used to deconvolute mixed traces submitted for forensic DNA phenotyping.
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