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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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Diepenbroek M, Bayer B, Anslinger K. Phenotype predictions of two-person mixture using single cell analysis. Forensic Sci Int Genet 2023; 67:102938. [PMID: 37832204 DOI: 10.1016/j.fsigen.2023.102938] [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: 08/01/2023] [Revised: 09/19/2023] [Accepted: 09/27/2023] [Indexed: 10/15/2023]
Abstract
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.
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Affiliation(s)
- Marta Diepenbroek
- Institute of Legal Medicine LMU Munich, Nussbaumstrasse 26, 80336 Munich, Germany.
| | - Birgit Bayer
- Institute of Legal Medicine LMU Munich, Nussbaumstrasse 26, 80336 Munich, Germany
| | - Katja Anslinger
- Institute of Legal Medicine LMU Munich, Nussbaumstrasse 26, 80336 Munich, Germany
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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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Butler JM. Recent advances in forensic biology and forensic DNA typing: INTERPOL review 2019-2022. Forensic Sci Int Synerg 2022; 6:100311. [PMID: 36618991 PMCID: PMC9813539 DOI: 10.1016/j.fsisyn.2022.100311] [Citation(s) in RCA: 16] [Impact Index Per Article: 8.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/28/2022]
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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Affiliation(s)
- John M. Butler
- National Institute of Standards and Technology, Special Programs Office, 100 Bureau Drive, Mail Stop 4701, Gaithersburg, MD, USA
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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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