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Towards developing forensically relevant single-cell pipelines by incorporating direct-to-PCR extraction: compatibility, signal quality, and allele detection

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Abstract

Current analysis of forensic DNA stains relies on the probabilistic interpretation of bulk-processed samples that represent mixed profiles consisting of an unknown number of potentially partial representations of each contributor. Single-cell methods, in contrast, offer a solution to the forensic DNA mixture problem by incorporating a step that separates cells before extraction. A forensically relevant single-cell pipeline relies on efficient direct-to-PCR extractions that are compatible with standard downstream forensic reagents. Here we demonstrate the feasibility of implementing single-cell pipelines into the forensic process by exploring four metrics of electropherogram (EPG) signal quality—i.e., allele detection rates, peak heights, peak height ratios, and peak height balance across low- to high-molecular-weight short tandem repeat (STR) markers—obtained with four direct-to-PCR extraction treatments and a common post-PCR laboratory procedure. Each treatment was used to extract DNA from 102 single buccal cells, whereupon the amplification reagents were immediately added to the tube and the DNA was amplified/injected using post-PCR conditions known to elicit a limit of detection (LoD) of one DNA molecule. The results show that most cells, regardless of extraction treatment, rendered EPGs with at least a 50% true positive allele detection rate and that allele drop-out was not cell independent. Statistical tests demonstrated that extraction treatments significantly impacted all metrics of EPG quality, where the Arcturus® PicoPure™ extraction method resulted in the lowest median allele drop-out rate, highest median average peak height, highest median average peak height ratio, and least negative median values of EPG sloping for GlobalFiler™ STR loci amplified at half volume. We, therefore, conclude the feasibility of implementing single-cell pipelines for casework purposes and demonstrate that inferential systems assuming cell independence will not be appropriate in the probabilistic interpretation of a collection of single-cell EPGs.

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Abbreviations

STR:

Short tandem repeat

CE:

Capillary electrophoresis

EPG:

Electropherogram

PCR:

Polymerase chain reaction

RFU:

Relative fluorescence unit

b.p.:

Base pairs

ILS:

Internal lane standard

LoD:

Limit of detection

References

  1. Taylor D, Bright JA, Buckleton J (2013) The interpretation of single source and mixed DNA profiles. Forensic Sci Int Genet 7(5):516–528. https://doi.org/10.1016/j.fsigen.2013.05.011

    Article  CAS  PubMed  Google Scholar 

  2. Swaminathan H, Garg A, Grgicak CM, Medard M, Lun DS (2016) CEESIt: a computational tool for the interpretation of STR mixtures. Forensic Sci Int Genet 22:149–160. https://doi.org/10.1016/j.fsigen.2016.02.005

    Article  CAS  PubMed  Google Scholar 

  3. Manabe S, Morimoto C, Hamano Y, Fujimoto S, Tamaki K (2017) Development and validation of open-source software for DNA mixture interpretation based on a quantitative continuous model. PLoS One 12(11):e0188183. https://doi.org/10.1371/journal.pone.0188183

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  4. Bleka O, Storvik G, Gill P (2016) EuroForMix: an open source software based on a continuous model to evaluate STR DNA profiles from a mixture of contributors with artefacts. Forensic Sci Int Genet 21:35–44. https://doi.org/10.1016/j.fsigen.2015.11.008

    Article  CAS  PubMed  Google Scholar 

  5. Grgicak CM, Karkar S, Yearwood-Garcia X, Alfonse LE, Duffy KR, Lun DS (2020) A large-scale validation of NOCIt’s a posteriori probability of the number of contributors and its integration into forensic interpretation pipelines. Forensic Sci Int Genet 47:14. https://doi.org/10.1016/j.fsigen.2020.102296

    Article  CAS  Google Scholar 

  6. Slooten K, Caliebe A (2018) Contributors are a nuisance (parameter) for DNA mixture evidence evaluation. Forensic Sci Int Genet 37:116–125. https://doi.org/10.1016/j.fsigen.2018.05.004

    Article  CAS  PubMed  Google Scholar 

  7. Perlin MW, Legler MM, Spencer CE, Smith JL, Allan WP, Belrose JL, Duceman BW (2011) Validating TrueAllele® DNA mixture interpretation. J Forensic Sci 56(6):1430–1447. https://doi.org/10.1111/j.1556-4029.2011.01859.x

    Article  CAS  PubMed  Google Scholar 

  8. Lynch PC, Cotton RW (2018) Determination of the possible number of genotypes which can contribute to DNA mixtures: non-computer assisted deconvolution should not be attempted for greater than two person mixtures. Forensic Sci Int Genet 37:235–240. https://doi.org/10.1016/j.fsigen.2018.09.002

    Article  CAS  PubMed  Google Scholar 

  9. Peters KC, Swaminathan H, Sheehan J, Duffy KR, Lun DS, Grgicak CM (2017) Production of high-fidelity electropherograms results in improved and consistent DNA interpretation: standardizing the forensic validation process. Forensic Sci Int Genet 31:160–170. https://doi.org/10.1016/j.fsigen.2017.09.005

    Article  CAS  PubMed  Google Scholar 

  10. Taylor D, Buckleton J, Evett I (2015) Testing likelihood ratios produced from complex DNA profiles. Forensic Sci Int Genet 16:165–171. https://doi.org/10.1016/j.fsigen.2015.01.008

    Article  CAS  PubMed  Google Scholar 

  11. Barrio PA, Crespillo M, Luque JA, Aler M, Baeza-Richer C, Baldassarri L, Carnevali E, Coufalova P, Flores I, Garcia O, Garcia MA, Gonzalez R, Hernandez A, Ingles V, Luque GM, Mosquera-Miguel A, Pedrosa S, Pontes ML, Porto MJ, Posada Y, Ramella MI, Ribeiro T, Riego E, Sala A, Saragoni VG, Serrano A, Vannelli S (2018) GHEP-ISFG collaborative exercise on mixture profiles (GHEP-MIX06). Reporting conclusions: results and evaluation. Forensic Sci Int Genet 35:156–163. https://doi.org/10.1016/j.fsigen.2018.05.005

    Article  CAS  PubMed  Google Scholar 

  12. Toscanini U, Gusmao L, Narvaez MCA, Alvarez C, Baldassarri L, Barbaro A, Berardi G, Hernandez EB, Camargo M, Carreras-Carbonell J, Castro J, Costa SC, Coufalova P, Dominguez V, de Carvalho EF, Ferreira STG, Furfuro S, Garcia O, Goios A, Gonzalez R, de la Vega AG, Gorostiza A, Hernandez A, Moreno SJ, Lareu MV, Almagro AL, Marino M, Martinez G, Miozzo MC, Modesti NM, Onofri V, Pagano S, Arias BP, Pedrosa S, Penacino GA, Pontes ML, Porto MJ, Puente-Prieto J, Perez RR, Ribeiro T, Cardozo BR, Lesmes YMR, Satiage ASB, Saragoni VG, Serrano A, Streitenberger E, Morales MAT, Rey SAV, Miranda MV, Whittle MR, Fernandez K, Salas A (2016) Analysis of uni and bi-parental markers in mixture samples: lessons from the 22nd GHEP-ISFG Intercomparison Exercise. Forensic Sci Int Genet 25:63–72. https://doi.org/10.1016/j.fsigen.2016.07.010

    Article  CAS  PubMed  Google Scholar 

  13. Crespillo M, Barrio PA, Luque JA, Alves C, Aler M, Alessandrini F, Andrade L, Barretto RM, Bofarull A, Costa S, Garcia MA, Garcia O, Gaviria A, Gladys A, Gorostiza A, Hernandez A, Herrera M, Hombreiro L, Ibarra AA, Jimenez MJ, Luque GM, Madero P, Martinez-Jarreta B, Masciovecchio MV, Modesti NM, Moreno F, Pagano S, Pedrosa S, Plaza G, Prat E, Puente J, Rendo F, Ribeiro T, Sala A, Santamaria E, Saragoni VG, Whittle MR (2014) EGHEP-ISFG collaborative exercise on mixture profiles of autosomal STRs (GHEP-MIX01, GHEP-MIX02 and GHEP-MIX03): results and evaluation. Forensic Sci Int Genet 10:64–72. https://doi.org/10.1016/j.fsigen.2014.01.009

    Article  CAS  PubMed  Google Scholar 

  14. Butler JM, Kline MC, Coble MD (2018) NIST interlaboratory studies involving DNA mixtures (MIX05 and MIX13): variation observed and lessons learned. Forensic Sci Int Genet 37:81–94. https://doi.org/10.1016/j.fsigen.2018.07.024

    Article  CAS  PubMed  Google Scholar 

  15. Binder V, Bartenhagen C, Okpanyi V, Gombert M, Moehlendick B, Behrens B, Klein HU, Rieder H, Krell PFI, Dugas M, Stoecklein NH, Borkhardt A (2014) A new workflow for whole-genome sequencing of single human cells. Hum Mutat 35(10):1260–1270. https://doi.org/10.1002/humu.22625

    Article  CAS  PubMed  Google Scholar 

  16. Lu X, Huang WH, Wang ZL, Cheng HK (2004) Recent developments in single-cell analysis. Anal Chim Acta 510(2):127–138. https://doi.org/10.1016/j.aca.2004.01.014

    Article  CAS  Google Scholar 

  17. Peeters DJE, De Laere B, Van den Eynden GG, Van Laere SJ, Rothe F, Ignatiadis M, Sieuwerts AM, Lambrechts D, Rutten A, van Dam PA, Pauwels P, Peeters M, Vermeulen PB, Dirix LY (2013) Semiautomated isolation and molecular characterisation of single or highly purified tumour cells from CellSearch enriched blood samples using dielectrophoretic cell sorting. Br J Cancer 108(6):1358–1367. https://doi.org/10.1038/bjc.2013.92

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  18. Sinha N, Subedi N, Tel J (2018) Integrating immunology and microfluidics for single immune cell analysis. Front Immunol 9:16. https://doi.org/10.3389/fimmu.2018.02373

    Article  CAS  Google Scholar 

  19. Rowan KE, Wellner GA, Grgicak CM (2016) Exploring the impacts of ordinary laboratory alterations during forensic DNA processing on peak height variation, thresholds, and probability of dropout. J Forensic Sci 61(1):177–185. https://doi.org/10.1111/1556-4029.12899

    Article  CAS  PubMed  Google Scholar 

  20. Duffy KR, Gurram N, Peters KC, Wellner G, Grgicak CM (2017) Exploring STR signal in the single- and multicopy number regimes: deductions from an in silico model of the entire DNA laboratory process. Electrophoresis 38(6):855–868. https://doi.org/10.1002/elps.201600385

    Article  CAS  PubMed  Google Scholar 

  21. Weusten J, Herbergs J (2012) A stochastic model of the processes in PCR based amplification of STR DNA in forensic applications. Forensic Sci Int Genet 6(1):17–25. https://doi.org/10.1016/j.fsigen.2011.01.003

    Article  CAS  PubMed  Google Scholar 

  22. Gill P, Curran J, Elliot K (2005) A graphical simulation model of the entire DNA process associated with the analysis of short tandem repeat loci. Nucleic Acids Res 33(2):632–643. https://doi.org/10.1093/nar/gki205

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  23. Klein SB, Buoncristiani MR (2017) Evaluating the efficacy of DNA differential extraction methods for sexual assault evidence. Forensic Sci Int Genet 29:109–117. https://doi.org/10.1016/j.fsigen.2017.03.021

    Article  CAS  PubMed  Google Scholar 

  24. Ottens R, Templeton J, Paradiso V, Taylor D, Abarno D, Linacre A (2013) Application of direct PCR in forensic casework. Forensic Sci Int Genet Suppl Ser 4(1):e47–e48. https://doi.org/10.1016/j.fsigss.2013.10.024

    Article  Google Scholar 

  25. van Oorschot RAH, Phelan DG, Furlong S, Scarfo GM, Holding NL, Cummins MJ (2003) Are you collecting all the available DNA from touched objects? Progr Forensic Genet 9(1239):803–807. https://doi.org/10.1016/s0531-5131(02)00498-3

    Article  Google Scholar 

  26. Vandenberg N, vanOorschot RAH, Mitchell RJ (1997) An evaluation of selected DNA extraction strategies for short tandem repeat typing. Electrophoresis 18(9):1624–1626. https://doi.org/10.1002/elps.1150180924

    Article  CAS  PubMed  Google Scholar 

  27. Ip SC, Lin SW, Lai KM (2015) An evaluation of the performance of five extraction methods: Chelex® 100, QIAamp® DNA Blood Mini Kit, QIAamp® DNA Investigator Kit, QIAsymphony® DNA Investigator® Kit and DNA IQ™. Sci Justice 55(3):200–208. https://doi.org/10.1016/j.scijus.2015.01.005

    Article  PubMed  Google Scholar 

  28. QIAamp® DNA Investigator Handbook (2020) QIAGEN®

  29. BioRad Chelex(R) 100 and Chelex 20 Chelating Ion Exchange Resin Instruction Manual

  30. GlobalFiler™ PCR Amplification Kit (2015) Thermo Fisher Scientific Inc., version 4477604 Rev F

  31. PowerPlex® Fusion System for Use on the Applied Biosystems® Genetic Analyzers (2020) Promega Corporation, version TMD045

  32. Findlay I, Taylor A, Quirke P, Frazier R, Urquhart A (1997) DNA fingerprinting from single cells. Nature 389(6651):555–556. https://doi.org/10.1038/39225

    Article  CAS  PubMed  Google Scholar 

  33. Sanders CT, Sanchez N, Ballantyne J, Peterson DA (2006) Laser microdissection separation of pure spermatozoa from epithelial cells for short tandem repeat analysis. J Forensic Sci 51(4):748–757. https://doi.org/10.1111/j.1556-4029.2006.00180.x

    Article  CAS  PubMed  Google Scholar 

  34. Feng L, C-x L, Han J-p XC, Hu L (2015) Isolating cells from female/male blood mixtures using florescence in situ hybridization combined with low volume PCR and its application in forensic science. Int J Legal Med 129(6):1211–1215. https://doi.org/10.1007/s00414-014-1103-0

    Article  PubMed  Google Scholar 

  35. Williamson VR, Laris TM, Romano R, Marciano MA (2018) Enhanced DNA mixture deconvolution of sexual offense samples using the DEPArray (TM) system. Forensic Sci Int Genet 34:265–276. https://doi.org/10.1016/j.fsigen.2018.03.001

    Article  CAS  PubMed  Google Scholar 

  36. Fontana F, Rapone C, Bregola G, Aversa R, de Meo A, Signorini G, Sergio M, Ferrarini A, Lanzellotto R, Medoro G, Giorgini G, Manaresi N, Berti A (2017) Isolation and genetic analysis of pure cells from forensic biological mixtures: the precision of a digital approach. Forensic Sci Int Genet 29:225–241. https://doi.org/10.1016/j.fsigen.2017.04.023

    Article  CAS  PubMed  Google Scholar 

  37. Anslinger K, Graw M, Bayer B (2019) Deconvolution of blood-blood mixtures using DEPArray(TM) separated single cell STR profiling. Rechtsmedizin 29(1):30–40. https://doi.org/10.1007/s00194-018-0291-1

    Article  Google Scholar 

  38. Hedell R, Hedman J, Mostad P (2018) Determining the optimal forensic DNA analysis procedure following investigation of sample quality. Int J Legal Med 132(4):955–966. https://doi.org/10.1007/s00414-017-1635-1

    Article  PubMed  Google Scholar 

  39. Hansson O, Egeland T, Gill P (2017) Characterization of degradation and heterozygote balance by simulation of the forensic DNA analysis process. Int J Legal Med 131(2):303–317. https://doi.org/10.1007/s00414-016-1453-x

    Article  PubMed  Google Scholar 

  40. ARCTURUS® PicoPure® DNA Extraction Kit (2010) Life Technologies Corporation, version 12637–00 Rev D

  41. DEPArray™ Forensic Sample Prep Kit User Manual (2019) Menarini Silicon Biosystems S.p.A, version IFU_1005 Rev.4–2019 December

  42. DirectPCR Lysis Reagent (Cell) Protocol (2007) Viagen Biotech, Inc., https://www.viagenbiotech.com/PDF/Protocol%2D%2D-Cell-2007_Feb.pdf. Accessed 12/07/2020

  43. ZyGem Quick-Start Guide. DNA Extraction Using forensicGEM Saliva

  44. Mönich UJ, Duffy K, Médard M, Cadambe V, Alfonse LE, Grgicak C (2015) Probabilistic characterisation of baseline noise in STR profiles. Forensic Sci Int Genet 19:107–122. https://doi.org/10.1016/j.fsigen.2015.07.001

    Article  CAS  PubMed  Google Scholar 

  45. Alfonse LE, Garrett AD, Lun DS, Duffy KR, Grgicak CM (2018) A large-scale dataset of single and mixed-source short tandem repeat profiles to inform human identification strategies: PROVEDIt. Forensic Sci Int Genet 32:62–70. https://doi.org/10.1016/j.fsigen.2017.10.006

    Article  CAS  PubMed  Google Scholar 

  46. Good P (2000) Permutation tests: a practical guide to resampling methods for testing hypotheses. https://doi.org/10.1007/978-1-4757-3235-1

  47. Funes-Huacca ME, Opel K, Thompson R, McCord BR (2011) A comparison of the effects of PCR inhibition in quantitative PCR and forensic STR analysis. Electrophoresis 32(9):1084–1089. https://doi.org/10.1002/elps.201000584

    Article  CAS  PubMed  Google Scholar 

  48. Bright JA, Taylor D, Curran JM, Buckleton JS (2013) Degradation of forensic DNA profiles. Aust J Forensic Sci 45(4):445–449. https://doi.org/10.1080/00450618.2013.772235

    Article  Google Scholar 

  49. Geng T, Novak R, Mathies RA (2014) Single-cell forensic short tandem repeat typing within microfluidic droplets. Anal Chem 86(1):703–712. https://doi.org/10.1021/ac403137h

    Article  CAS  PubMed  Google Scholar 

  50. Karkar S, Alfonse LE, Grgicak CM, Lun DS (2019) Statistical modeling of STR capillary electrophoresis signal. BMC Bioinformatics 20(16):584. https://doi.org/10.1186/s12859-019-3074-0

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  51. Goor RM, Forman Neall L, Hoffman D, Sherry ST (2011) A mathematical approach to the analysis of multiplex DNA profiles. Bull Math Biol 73(8):1909–1931. https://doi.org/10.1007/s11538-010-9598-0

    Article  CAS  PubMed  Google Scholar 

  52. Bright JA, Taylor D, Curran JM, Buckleton JS (2013) Developing allelic and stutter peak height models for a continuous method of DNA interpretation. Forensic Sci Int Genet 7(2):296–304. https://doi.org/10.1016/j.fsigen.2012.11.013

    Article  CAS  PubMed  Google Scholar 

  53. Opel KL, Chung D, McCord BR (2010) A study of PCR inhibition mechanisms using real time PCR. J Forensic Sci 55(1):25–33. https://doi.org/10.1111/j.1556-4029.2009.01245.x

    Article  CAS  PubMed  Google Scholar 

  54. Hall A, Sims LM, Ballantyne J (2014) Assessment of DNA damage induced by terrestrial UV irradiation of dried bloodstains: forensic implications. Forensic Sci Int Genet 8(1):24–32. https://doi.org/10.1016/j.fsigen.2013.06.010

    Article  CAS  PubMed  Google Scholar 

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Funding

This work was partially supported by NIJ2018-DU-BX-K0185 and NIJ2014-DN-BX-K026 awarded by the National Institute of Justice, Office of Justice Programs, US Department of Justice.

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Authors and Affiliations

  1. Center for Computational and Integrative Biology, Rutgers University, Camden, NJ, 08102, USA

    Nidhi Sheth & Catherine M. Grgicak

  2. Biomedical Forensic Sciences Program, Boston University School of Medicine, Boston, MA, 02118, USA

    Harish Swaminathan & Catherine M. Grgicak

  3. Department of Chemistry, Rutgers University, 315 Penn Street R306C, Camden, NJ, 08102, USA

    Amanda J. Gonzalez & Catherine M. Grgicak

  4. Hamilton Institute, Maynooth University, Maynooth, Ireland

    Ken R. Duffy

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  1. Nidhi Sheth
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  2. Harish Swaminathan
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Correspondence to Catherine M. Grgicak.

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Sheth, N., Swaminathan, H., Gonzalez, A.J. et al. Towards developing forensically relevant single-cell pipelines by incorporating direct-to-PCR extraction: compatibility, signal quality, and allele detection. Int J Legal Med 135, 727–738 (2021). https://doi.org/10.1007/s00414-021-02503-4

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