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Research Article| Volume 64, 102856, May 2023

One-step endpoint RT-PCR assays for confirmatory body fluid identification

  • Author Footnotes
    1 https://orcid.org/0000-0002-2840-9196
    Courtney Lynch
    Footnotes
    1 https://orcid.org/0000-0002-2840-9196
    Affiliations
    Forensic Science Programme, School of Chemical Sciences, The University of Auckland, Auckland, New Zealand

    Forensic Research and Development Team, Institute of Environmental Science and Research Ltd, Auckland, New Zealand
    Search for articles by this author
  • Author Footnotes
    2 https://orcid.org/0000-0002-9321-1020
    Rachel Fleming
    Correspondence
    Correspondence to: Institute of Environmental Science and Research Ltd.,120 Mt Albert Road, Auckland 1025, New Zealand.
    Footnotes
    2 https://orcid.org/0000-0002-9321-1020
    Affiliations
    Forensic Research and Development Team, Institute of Environmental Science and Research Ltd, Auckland, New Zealand
    Search for articles by this author
  • Author Footnotes
    1 https://orcid.org/0000-0002-2840-9196
    2 https://orcid.org/0000-0002-9321-1020

      Highlights

      • One-step RT-PCR assays were developed for confirmatory body fluid identification.
      • Sensitivity and specificity across body fluids was assessed.
      • ROC curves were used to determine specific peak height cut-offs.
      • The assays successfully detected body fluid mixture components.

      Abstract

      Messenger RNA (mRNA) expression analysis is increasingly used in casework, in the form of multiplex two-step reverse transcriptase PCR (RT-PCR) assays such as CellTyper 2 (CT2), developed by the Institute of Environmental Science and Research (ESR). This paper presents the development of a one-step endpoint RT-PCR workflow to improve the efficiency and precision of confirmatory body fluid identification. A comparative study of commercial one-step RT-PCR kits was undertaken, with the highest performing kit (RNA to CT) retained for further development.
      Sensitivity, specificity across body fluids, and precision was assessed simultaneously using receiver operating characteristic (ROC) curves. An optimal RFU cut-off value which maximised sensitivity and specificity was determined for each marker. All assays performed significantly better when compared to the equivalent of a completely uninformative test (area under the curve of 0.5) for their target body fluid. Sensitivity varied between different donors, but the limit of detectionss were estimated as follows; saliva markers HTN3: 1 in 100 dilution of a whole buccal swab and FDCSP: 1 in 10 dilution of a whole buccal swab, circulatory blood marker SLC4A1: 0.1 µL blood, menstrual fluid markers STC1, MMP10: 1 in 10 dilution of a whole menstrual swab, spermatozoa markers PRM1, TNP1: 0.1 µL semen, seminal fluid markers KLK2: 0.1 µL semen and MSMB: 0.01 µL semen, and vaginal material marker CYP2B7P: 1 in 1000 dilution of a whole vaginal swab. The method successfully detected most body fluids in a range of simple mixtures with 77 out of 80 markers observed when expected.
      The developed one-step endpoint RT-PCR assays lack the sensitivity and precision required for forensic casework and provide little benefit when compared with standard two-step endpoint RT-PCR, other than minimal time and cost savings, similar sensitivity, and improved precision for some markers. As both methods utilise endpoint RT-PCR, they have the same narrow linear dynamic range. The novel method is therefore similarly susceptible to varied RNA input, a major disadvantage of this approach. The limited sensitivity and precision consistently encountered with endpoint RT-PCR - regardless of cDNA synthesis strategy - could be addressed by a real-time PCR approach.

      Keywords

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      References

        • Fleming R.
        • Harbison S.
        The development of a mRNA multiplex RT-PCR assay for the definitive identification of body fluids.
        Forensic Sci. Int Genet. 2010; 4: 244-256https://doi.org/10.1016/j.fsigen.2009.10.006
        • Albani P.P.
        • Fleming R.
        Developmental validation of an enhanced mRNA-based multiplex system for body fluid and cell type identification.
        Sci. Justice. 2019; 59: 217-227https://doi.org/10.1016/j.scijus.2019.01.001
        • Albani P.P.
        • Fleming R.
        Novel messenger RNAs for body fluid identification.
        Sci. Justice. 2018; 58: 145-152https://doi.org/10.1016/j.scijus.2017.09.002
        • Haas C.
        • Hanson E.K.
        • Ballantyne J.
        Capillary electrophoresis of a multiplex reverse transcription-polymerase chain reaction to target messenger RNA markers for body fluid identification.
        Methods Mol. Biol. 2012; 830: 169-183https://doi.org/10.1007/978-1-61779-461-2_12
        • Juusola J.
        • Ballantyne J.
        Messenger RNA profiling: a prototype method to supplant conventional methods for body fluid identification.
        Forensic Sci. Int. 2003; 135: 85-96https://doi.org/10.1016/S0379-0738(03)00197-X
        • Lindenbergh A.
        • de Pagter M.
        • Ramdayal G.
        • et al.
        A multiplex (m)RNA-profiling system for the forensic identification of body fluids and contact traces.
        Forensic Sci. Int. Genet. 2012; 6: 565-577https://doi.org/10.1016/j.fsigen.2012.01.009
        • Roeder A.D.
        • Haas C.
        mRNA profiling using a minimum of five mRNA markers per body fluid and a novel scoring method for body fluid identification.
        Int J. Leg. Med. 2013; 127: 707-721https://doi.org/10.1007/s00414-012-0794-3
        • Yang Q.
        • Liu B.
        • Zhou Y.
        • et al.
        Evaluation of one-step RT-PCR multiplex assay for body fluid identification.
        Int J. Leg. Med. 2021; 135: 1727-1735https://doi.org/10.1007/s00414-021-02535-w
      1. Albani P.P. Enhancement of MRNA-Based Methods for Body Fluid and Cell Type Identification. 2018.

        • Nolan T.
        • Hands R.E.
        • Bustin S.A.
        Quantification of mRNA using real-time RT-PCR.
        Nat. Protoc. 2006; 1: 1559-1582https://doi.org/10.1038/nprot.2006.236
        • Ståhlberg A.
        • Kubista M.
        • Pfaffl M.
        Comparison of reverse transcriptases in gene expression analysis.
        Clin. Chem. 2004; 50: 1678-1680https://doi.org/10.1373/clinchem.2004.035469
        • Hansen K.D.
        • Brenner S.E.
        • Dudoit S.
        Biases in Illumina transcriptome sequencing caused by random hexamer priming.
        Nucleic Acids Res. 2010; 38 (e131-e131)https://doi.org/10.1093/nar/gkq224
      2. van Gurp T.P., McIntyre L.M., Verhoeven K.J.F. Consistent errors in first strand cDNA due to random hexamer mispriming. Gibas C., ed. PLoS One. 2013;8(12):e85583. doi:10.1371/journal.pone.0085583.

        • Bustin S.A.
        • Nolan T.
        Pitfalls of quantitative real- time reverse-transcription polymerase chain reaction..
        J. Biomol. Tech. 2004; 15: 155-166
        • Lynch C.
        • Fleming R.
        A comparative study of commercial real-time reverse transcription PCR kits for forensic body fluid identification.
        Aust. J. Forensic Sci. Publ. 2022; 13: 1-14https://doi.org/10.1080/00450618.2022.2058610
        • Bowden A.
        • Fleming R.
        • Harbison S.
        A method for DNA and RNA co-extraction for use on forensic samples using the Promega DNA IQ™ system..
        Forensic Sci. Int. Genet. 2011; 5: 64-68https://doi.org/10.1016/j.fsigen.2009.11.007
      3. Illumina. MiSeqTM System Denature and Dilute Libraries Guide.; 2019.

        • Ihaka R.
        • Gentleman R.
        R: a language for data analysis and graphics..
        J. Comput. Graph. Stat. 1996; 5: 299https://doi.org/10.2307/1390807
      4. Biology Team. BIO/SOP/023: Methods for the Identification of Body Fluids Using Messenger RNA V5.; 2018.

      5. Biology Team. BIO/SOP/006: Analysis of Profiles - Methods V13.; 2020.

      6. Harbison S., Patel J., Lemalu A., et al. Internal Report: Body Fluid Detection by MRNA Profiling.; 2019.

      7. Free Software Foundation. Bash (3.2. 48) [Unix shell program]. Published online 2007..

      8. Andrews S. FastQC: a quality control tool for high throughput sequence data. Published online 2010.

      9. Krueger F. A wrapper tool around Cutadapt and FastQC to consistently apply quality and adapter trimming to FastQ files, with some extra functionality for MspI-digested RRBS-type (Reduced Representation Bisufite-Seq) libraries. Available online at: http://www.bioinformatics.babraham.ac.uk/projects/trim_galore/. Published online 2012. http://http//www. bioinformatics. babraham. ac. uk/projects/trim%5C_galore/.

        • Kim D.
        • Paggi J.M.
        • Park C.
        • Bennett C.
        • Salzberg S.L.
        Graph-based genome alignment and genotyping with HISAT2 and HISAT-genotype.
        Nat. Biotechnol. 2019; 37: 907-915https://doi.org/10.1038/s41587-019-0201-4
        • Kim D.
        • Langmead B.
        • Salzberg S.L.
        HISAT: A fast spliced aligner with low memory requirements.
        Nat. Methods. 2015; 12: 357-360https://doi.org/10.1038/nmeth.3317
        • Li H.
        • Handsaker B.
        • Wysoker A.
        • et al.
        The sequence alignment/map format and SAMtools.
        Bioinformatics. 2009; 25: 2078-2079https://doi.org/10.1093/bioinformatics/btp352
        • Robinson J.T.
        • Thorvaldsdóttir H.
        • Winckler W.
        • et al.
        Integrative genomics viewer.
        Nat. Biotechnol. 2011; 29: 24-26https://doi.org/10.1038/nbt.1754
        • R Development Core Team
        • .
        R: A language and environment for statistical computing.
        R. Found. Stat. Comput. 2019; (https://www.R--project.org. http://www.r-project.org): 2
        • Swets J.A.
        Indices of discrimination or diagnostic accuracy: their ROCs and implied models.
        Psychol. Bull. 1986; 99: 100-117
        • Hanley J.A.
        • McNeil B.J.
        The meaning and use of the area under a receiver operating characteristic (ROC) curve.
        Radiology. 1982; 143: 29-36https://doi.org/10.1148/radiology.143.1.7063747
        • Nutz S.
        • Döll K.
        • Karlovsky P.
        Determination of the LOQ in real-time PCR by receiver operating characteristic curve analysis: application to qPCR assays for Fusarium verticillioides and F. proliferatum.
        Anal. Bioanal. Chem. 2011; 401: 717-726https://doi.org/10.1007/s00216-011-5089-x
        • Youden W.J.
        Index for rating diagnostic tests.
        Cancer. 1950; 3: 32-35https://doi.org/10.1002/1097-0142(1950)3:1<32::AID-CNCR2820030106>3.0.CO;2-3
        • Robin X.
        • Turck N.
        • Hainard A.
        • et al.
        pROC: an open-source package for R and S+ to analyze and compare ROC curves.
        BMC Bioinforma. 2011; 12: 77https://doi.org/10.1186/1471-2105-12-77
        • López-Ratón M.
        • Rodríguez-Álvarez M.X.
        • Suárez C.C.
        • Sampedro F.G.
        OptimalCutpoints: an R package for selecting optimal cutpoints in diagnostic tests.
        J. Stat. Softw. 2014; 61: 1-36https://doi.org/10.18637/jss.v061.i08
        • Sachs M.C.
        plotROC: a tool for plotting ROC curves.
        J. Stat. Softw. 2017; 79: 1-19https://doi.org/10.18637/jss.v079.c02
        • Mason S.J.
        • Graham N.E.
        Areas beneath the relative operating characteristics (ROC) and relative operating levels (ROL) curves: Statistical significance and interpretation.
        Q. J. R. Meteorol. Soc. 2002; 128: 2145-2166https://doi.org/10.1256/003590002320603584
        • Mann H.B.
        • Whitney D.R.
        On a test of whether one of two random variables is stochastically larger than the other.
        Ann. Math. Stat. 1947; 18: 50-60https://doi.org/10.1214/aoms/1177730491
        • Pasqualotto A.C.
        • Denning D.W.
        • Anderson M.J.
        A cautionary tale: Lack of consistency in allele sizes between two laboratories for a published multilocus microsatellite typing system.
        J. Clin. Microbiol. 2007; 45: 522-528https://doi.org/10.1128/JCM.02136-06
        • Taylor D.
        • Buckleton J.S.
        • Bright J.A.
        Factors affecting peak height variability for short tandem repeat data.
        Forensic Sci. Int Genet. 2016; 21: 126-133https://doi.org/10.1016/j.fsigen.2015.12.009
        • Ståhlberg A.
        • Håkansson J.
        • Xian X.
        • Semb H.
        • Kubista M.
        Properties of the reverse transcription reaction in mRNA quantification.
        Clin. Chem. 2004; 50: 509-515https://doi.org/10.1373/clinchem.2003.026161
      10. Biology Team. BIO/SOP/047: Case File Completion, Reporting, and Technical Review V15.; 2020.

        • Børsting C.
        • Morling N.
        Next generation sequencing and its applications in forensic genetics.
        Forensic Sci. Int Genet. 2015; 18: 78-89https://doi.org/10.1016/j.fsigen.2015.02.002
        • Cossu C.
        • Germann U.
        • Kratzer A.
        • Bär W.
        • Haas C.
        How specific are the vaginal secretion mRNA-markers HBD1 and MUC4?.
        Forensic Sci. Int Genet Suppl. Ser. 2009; 2: 536-537https://doi.org/10.1016/j.fsigss.2009.08.063
        • Yang H.
        • Zhou B.
        • Prinz M.
        • Siegel D.
        Proteomic analysis of menstrual blood.
        Mol. Cell. Proteom. 2012; 11: 1024-1035https://doi.org/10.1074/mcp.M112.018390
        • Mallidis C.
        • Howard E.J.
        • Baker H.W.G.
        Variation of semen quality in normal men.
        Int J. Androl. 1991; 14: 99-107https://doi.org/10.1111/j.1365-2605.1991.tb01071.x
        • Baker H.W.G.
        • Burger H.G.
        • Kretser D.M.
        • Lording D.W.
        • McGowan P.
        • Rennie G.G.
        Factors affecting the variability of semen analysis results in infertile men.
        Int J. Androl. 1981; 4: 609-622https://doi.org/10.1111/j.1365-2605.1981.tb00743.x
        • Poland M.L.
        • Moghissi K.S.
        • Giblin P.T.
        • Ager J.W.
        • Olson J.M.
        Variation of semen measures within normal men.
        Fertil. Steril. 1985; 44: 396-400https://doi.org/10.1016/S0015-0282(16)48866-7
        • Keel B.A.
        Within- and between-subject variation in semen parameters in infertile men and normal semen donors.
        Fertil. Steril. 2006; 85: 128-134https://doi.org/10.1016/j.fertnstert.2005.06.048
        • Melé M.
        • Ferreira P.G.
        • Reverter F.
        • et al.
        The human transcriptome across tissues and individuals.
        Science (1979). 2015; 348 (660 LP - 665)https://doi.org/10.1126/science.aaa0355
        • Bieber F.R.
        • Buckleton J.S.
        • Budowle B.
        • Butler J.M.
        • Coble M.D.
        Evaluation of forensic DNA mixture evidence: protocol for evaluation, interpretation, and statistical calculations using the combined probability of inclusion.
        BMC Genet. 2016; 17: 125https://doi.org/10.1186/s12863-016-0429-7
        • Reed G.F.
        • Lynn F.
        • Meade B.D.
        Use of coefficient of variation in assessing variability of quantitative assays.
        Clin. Diagn. Lab Immunol. 2002; 9: 1235-1239https://doi.org/10.1128/cdli.9.6.1235-1239.2002
        • Haas C.
        • Neubauer J.
        • Salzmann A.P.
        • Hanson E.K.
        • Ballantyne J.
        Forensic transcriptome analysis using massively parallel sequencing.
        Forensic Sci. Int Genet. 2021; : 52https://doi.org/10.1016/j.fsigen.2021.102486
        • Harteveld J.
        • Lindenbergh A.
        • Sijen T.
        RNA cell typing and DNA profiling of mixed samples: Can cell types and donors be associated?.
        Sci. Justice. 2013; 53: 261-269https://doi.org/10.1016/j.scijus.2013.02.001
        • Salzmann A.P.
        • Bamberg M.
        • Courts C.
        • et al.
        mRNA profiling of mock casework samples: Results of a FoRNAP collaborative exercise.
        Forensic Sci. Int Genet. 2021; 50102409https://doi.org/10.1016/j.fsigen.2020.102409
        • Sijen T.
        • Harbison S.
        On the identification of body fluids and tissues: a crucial link in the investigation and solution of crime.
        Genes. 2021; 12: 1728https://doi.org/10.3390/genes12111728
        • Gray D.
        • Frascione N.
        • Daniel B.
        Development of an immunoassay for the differentiation of menstrual blood from peripheral blood.
        Forensic Sci. Int. 2012; 220: 12-18https://doi.org/10.1016/j.forsciint.2012.01.020
      11. Waltke H., LaPorte G., Weiss D., Schwarting D., Nguyen M., Scott F. Sexual Assault Cases: Exploring the Importance of Non-DNA Forensic Evidence. Vol 279.; 2017. http://www.hpdwv.com/initiatives_sexual-assault-kit-testing.php.%0Ahttp://www.hpdwv.com/initiatives_sexual-assault-kit-testing.php.%0Ahttps://www.nij.gov/journals/279/Pages/non-dna-evidence-in-sexual-assault-cases.aspx.

        • Virkler K.
        • Lednev I.K.
        Analysis of body fluids for forensic purposes: from laboratory testing to non-destructive rapid confirmatory identification at a crime scene.
        Forensic Sci. Int. 2009; 188: 1-17https://doi.org/10.1016/j.forsciint.2009.02.013
        • Hanson E.K.
        • Ballantyne J.
        Highly specific mRNA biomarkers for the identification of vaginal secretions in sexual assault investigations.
        Sci. Justice. 2013; 53: 14-22https://doi.org/10.1016/j.scijus.2012.03.007
        • Torres Y.
        • Flores I.
        • Prieto V.
        • et al.
        DNA mixtures in forensic casework: a 4-year retrospective study.
        Forensic Sci. Int. 2003; 134: 180-186https://doi.org/10.1016/S0379-0738(03)00161-0
        • Fraser I.S.
        • McCarron G.
        • Markham R.
        • Resta T.
        Blood and total fluid content of menstrual discharge.
        Obstet. Gynecol. 1985; 65: 194-198
        • Tozzo P.
        • Nespeca P.
        • Spigarolo G.
        • Caenazzo L.
        The importance of distinguishing menstrual and peripheral blood in forensic casework: a case report.
        Am. J. Forensic Med Pathol. 2018; 39 (https://journals.lww.com/amjforensicmedicine/Fulltext/2018/12000/The_Importance_of_Distinguishing_Menstrual_and.7.aspx)
        • Ingold S.
        • Dørum G.
        • Hanson E.K.
        • Ballantyne J.
        • Haas C.
        Assigning forensic body fluids to donors in mixed body fluids by targeted RNA/DNA deep sequencing of coding region SNPs.
        Int. J. Leg. Med. Publ. 2020; : 1-13https://doi.org/10.1007/s00414-020-02252-w
        • Dørum G.
        • Ingold S.
        • Hanson E.K.
        • Ballantyne J.
        • Snipen L.
        • Haas C.
        Predicting the origin of stains from next generation sequencing mRNA data.
        Forensic Sci. Int. Genet. 2018; 34: 37-48https://doi.org/10.1016/j.fsigen.2018.01.001
        • Ingold S.
        • Dørum G.
        • Hanson E.K.
        • et al.
        Body fluid identification using a targeted mRNA massively parallel sequencing approach – results of a EUROFORGEN/EDNAP collaborative exercise.
        Forensic Sci. Int. Genet. 2018; 34: 105-115https://doi.org/10.1016/j.fsigen.2018.01.002
        • McKiernan H.E.
        • Danielson P.B.
        • Brown C.O.
        • Signaevsky M.
        • Westring C.G.
        • Legg K.M.
        Developmental validation of a multiplex proteomic assay for the identification of forensically relevant biological fluids.
        Forensic Sci. Int. 2021; 326110908https://doi.org/10.1016/j.forsciint.2021.110908
        • Wang D.Y.
        • Chang C.W.
        • Lagacé R.E.
        • Calandro L.M.
        • Hennessy L.K.
        Developmental validation of the AmpFlSTR® Identifiler® Plus PCR Amplification Kit: an established multiplex assay with improved performance.
        J. Forensic Sci. 2012; 57: 453-465https://doi.org/10.1111/j.1556-4029.2011.01963.x
      12. Butler J.M., Press R., Taylor M.K., Vall 1 P.M., Willis S. DNA Mixture Interpretation: A NIST Scientific Foundation Review (NISTIR 8351-DRAFT).; 2021.

        • Ludeman M.J.
        • Zhong C.
        • Mulero J.J.
        • et al.
        Developmental validation of GlobalFiler™ PCR amplification kit: a 6-dye multiplex assay designed for amplification of casework samples.
        Int J. Leg. Med. 2018; 132: 1555-1573https://doi.org/10.1007/s00414-018-1817-5
        • Ensenberger M.G.
        • Lenz K.A.
        • Matthies L.K.
        • et al.
        Developmental validation of the PowerPlex® Fusion 6C System.
        Forensic Sci. Int Genet. 2016; 21: 134-144https://doi.org/10.1016/j.fsigen.2015.12.011
        • Bright J.A.
        • Taylor D.
        • Gittelson S.
        • Buckleton J.S.
        The paradigm shift in DNA profile interpretation.
        Forensic Sci. Int Genet. 2017; 31: e24-e32https://doi.org/10.1016/j.fsigen.2017.08.005
        • van der Gaag K.J.
        • de Leeuw R.H.
        • Hoogenboom J.
        • et al.
        Massively parallel sequencing of short tandem repeats—Population data and mixture analysis results for the PowerSeq™ system.
        Forensic Sci. Int. Genet. 2016; 24: 86-96https://doi.org/10.1016/j.fsigen.2016.05.016
      13. Scientific Working Group on DNA Analysis Methods. SWGDAM Validation Guidelines for DNA Analysis Methods. Scientific Working Group on DNA Analysis Methods; 2012. https://docs.wixstatic.com/ugd/4344b0_813b241e8944497e99b9c45b163b76bd.pdf.

        • Setzer M.
        • Juusola J.
        • Ballantyne J.
        Recovery and stability of RNA in vaginal swabs and blood, semen, and saliva stains.
        J. Forensic Sci. 2008; 53: 296-305https://doi.org/10.1111/j.1556-4029.2007.00652.x
        • Zubakov D.
        • Hanekamp E.
        • Kokshoorn M.
        • van IJcken W.
        • Kayser M.
        Stable RNA markers for identification of blood and saliva stains revealed from whole genome expression analysis of time-wise degraded samples.
        Int J. Leg. Med. 2008; 122: 135-142https://doi.org/10.1007/s00414-007-0182-6
        • Juusola J.
        • Ballantyne J.
        mRNA profiling for body fluid identification by multiplex quantitative RT-PCR.
        J. Forensic Sci. 2007; 52: 1252-1262https://doi.org/10.1111/j.1556-4029.2007.00550.x