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Using methylation patterns to determine origin of biological material and age

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In this QIAGEN sponsored webinar, our guest speakers from the San Francisco Police Department (SFPD) Crime Lab and Florida International University (FIU) discuss their research on the potential of epigenetic methylation as a procedure for body fluid identification and age estimation from DNA left at crime scenes. Several approaches have been studied, including an analysis of methyl array data and an initial validation of procedures such as pyrosequencing and real-time PCR. The presentation focuses on a number of tissue-specific epigenetic markers for body fluid and age determination with a promise of future integration of these markers into the forensic lab due to the simplicity of analysis and the ease of application.

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Using methylation patterns to determine origin of biological material and age

  1. 1. Sample to Insight QIAGEN is pleased to present a webinar titled “Using Methylation Patterns to Determine Origin of Biological Material and Age”
  2. 2. Sample to Insight QIAGEN would like to thank our first speaker, Dr. Bruce McCord, Professor of Analytical/Forensic Chemistry, Department of Chemistry, Florida International University, for his presentation. Disclaimer: This is a QIAGEN sponsored webinar. QIAGEN is not affiliated with the Florida International University. The views expressed herein are those of the speaker, and do not necessarily express the views of QIAGEN.
  3. 3. Forensic Epigenetics, Methods to discriminate body fluids as well as age and phenotype Bruce McCord Florida International University Miami, FL mccordb@fiu.edu
  4. 4. Scenario  In the early 90s, A woman is found murdered. Trace DNA is found under her fingernails.  Ex husband (custody dispute) cant be excluded from intimate mixture. (presumptive for blood)  Suspect argues, not blood - DNA match is from secondary skin cells transferred during hand off of child.  Critical to determination is the question? Rust or blood? Human or animal, etc. Could this sample be tested years later?
  5. 5.  current procedures for the body fluid id date from the 1940s.  Chemical and enzymatic tests are not specific and lack sensitivity when compared to the PCR.  Forensic DNA detect subnanogram levels of DNA, but cannot tell you the source of the material..  Many labs no longer do sperm searches, yet qPCR Y based methods cannot identify the presence of sperm The Problem:
  6. 6. New methods for body fluid analysis exploit the process of transcription  Proteins form the functions of the cell  RNA templates are translated to form the scaffolding for proteins.  DNA templates are transcribed to produce RNA  Epigentic loci control gene transcription Genome Transcriptome Proteome Epigenome
  7. 7. Epigenetics  It is obvious to anyone that the human body has many different kinds of cells; skin, hair, teeth, blood, etc  Yet our DNA is all the same, How then does our body differentiate cells? Why do twins have different fingerprints? DNA methylation patterns in young and older twins. Why do identical twins begin to appear different with age?
  8. 8. Epigenetics  The answer is that there are heritable differences in our DNA that are not related to base pairing.  Instead these differences are controlled by patterns of methylation in cytosine and in post translational modifications of histones.  Epigenetics is the study of heritable changes in gene expression unrelated to DNA base pairing.
  9. 9. Methylation  Methyl residues are covalently bound to the 5’ carbon position of cytosine pyrimidine ring via DNA methyltransferases (Dnmt) forming 5-methylcytosines  Observed at CpG dinucleotides (70% of CpGs are methylated in vertebrates but distinct patterns are seen  “CpG islands” – areas of high CpG density usually mapping to promoter regions  Methylation  gene silencing http://www.hgu.mrc.ac.uk/people/r.meehan_researchb.html
  10. 10. How to detect CpG sites?  Primers are designed to encompass regions of interest  Candidate CpGs are then assessed to detect differential levels of methylation
  11. 11. Global differences exist between methylation levels of different tissues Note differences occurring between sperm, keratinocytes (skin cells), and lymphocytes(white blood cells) (Eckhardt et al. 2006).
  12. 12. How to exploit this forensically?  Find locations near genes that target expression of cellular proteins or examine whole genome array studies  Look in the genome for tissue specific methylated CpG sites (methylation based differences occur in what are called CpG islands.)  Measure differences in methylation that are dependent on cell type!
  13. 13. But PCR erases methylation differences- So use Bisulfite Modified PCR to lock in place
  14. 14. Result following Bisulfite modified PCR http://en.wikipedia.org/wiki/File:Wiki_Bisulfite_sequencing
  15. 15. Objective  Locate sites where tissue specific gene expression occurs. Design primers to encompass CpG islands. Extract DNA  Use Pyrosequencing and/or Real time PCR with (HRM) to detect methylation differences  Differentiate biofluids commonly found at crime scenes (Blood, Saliva, Sperm, Vaginal Epithelial Cells) by examining methylation patterns at specific loci
  16. 16. Sample to Insight QIAGEN would like to thank our second speaker, Dr. Ruth Kläver, Senior Scientist, Product Development, Pyrosequencing, QIAGEN, for her presentation.
  17. 17. Sample to Insight Pyrosequencing – principle and key features Using Methylation Patterns to Determine Origin of Biological Material and Age .Based on SEQUENCING-by-SYNTHESIS Principle  4 enzymes present in the system at all time  DNA-Polymerase  ATP-Sulfurylase  Luciferase  Apyrase  Enzyme cascade generates a light signal upon incorporation of nucleotides  Only one nucleotide (dNTP) is added at a time ⇒ Detected peaks demonstrate sequence
  18. 18. Sample to Insight Pyrosequencing – principle and key features .Based on SEQUENCING-by-SYNTHESIS Principle  Stepwise synthesis of DNA by addition of nucleotides Using Methylation Patterns to Determine Origin of Biological Material and Age Template preparation Sequencing primer
  19. 19. Sample to Insight Pyrosequencing – assay modes Using Methylation Patterns to Determine Origin of Biological Material and Age Sequencing through unknown regions Single Nucleotide Polymorhism (SNP)
  20. 20. Sample to Insight Pyrosequencing – assay modes Using Methylation Patterns to Determine Origin of Biological Material and Age A: 44% C: 0% G: 56% T: 0% Di-, tri- and tetra allelic mutations Insertions / Deletions - - - - - - - : 56% ATCTGCC: 44% C: 57% T: 43%
  21. 21. Sample to Insight Pyrosequencing – assay modes DNA methylation of multiple CpG sites Using Methylation Patterns to Determine Origin of Biological Material and Age
  22. 22. Sample to Insight Measuring DNA methylation after bisulfite conversion Using Methylation Patterns to Determine Origin of Biological Material and Age Example: DNA methylation analysis .A G T T A C G A C A.Sequence to be analyzed: .After bisulfite conversion: .A G T T A C G A C A .A G T T A C m G A C A.and .A G T T A T G A T A .A G T T A C m G A T A.and .Analyzed sequence: X .A .G .T .A .A.T/C .T.T G .A Ratio T:C .A .G .T.A .A.T .C .C .T.G .27% .Nucleotides added: .A
  23. 23. Sample to Insight Measuring DNA methylation after bisulfite conversion Using Methylation Patterns to Determine Origin of Biological Material and Age Example: DNA methylation analysis Ratio T:C .A G T T A C G A C A .A G T T A C G A C A .A G T T A C m G A C A.and .A G T T A T G A T A .A G T T A C m G A T A.and .A .G .T.A .A.T .C .C .T.G .27% X .A .G .T .A .A.T/C .T.T G .A .A.Nucleotides added: .After bisulfite conversion: .Analyzed sequence: .Sequence to be analyzed: .Built-in quality control: successful bisulfite conversion C = 0% T = 100% 
  24. 24. Sample to Insight QIAGEN’s Pyrosequencing solutions for HID Pyrosequencing workflow for genetic and epigenetic marker analysis • PAXgene Blood DNA Tube • QIAamp Kits • AllPrep RNA/ DNA Kits • EpiTect Fast DNA Kits • EpiTect Fast LyseAll Kits • EpiTect Fast FFPE Kits • PyroMark Assay Design SW • PyroMark PCR Kit • PyroMark Q24 Advanced • PyroMark Q48 Autoprep • PyroMark Q24 Advanced Reagents • PyroMark Q48 Advanced Reagents Sample collection &/ stabilization DNA purification Assay design Bisulfite conversion Pre- amplification Pyro- sequencing For epigentics only Using Methylation Patterns to Determine Origin of Biological Material and Age
  25. 25. Sample to Insight Pyrosequencing – workflow Load reagents, nucleotides, buffers Load PCR product & beads Manual Template Preparation with VPWS Anneal Seq- primer Pyro- sequencing Wash Cartridge & VPWS Load reagents, nucleotides, buffers Load PCR product & magnetic beads Automatic Template Preparation Anneal Seq- primer Pyro- sequencing PyroMark Q24/Q24 Advanced PyroMark Q48 Autoprep manual automated manual/automated Wash Cartridge Using Methylation Patterns to Determine Origin of Biological Material and Age PyroMark Assays Design SW 2.0 PyroMark CpG Assays 1) Assay Design 2) PCR~ 5 min 120 min 3) Pyrosequencing PyroMark PCR Kit
  26. 26. Sample to Insight PyroMark Q48 Autoprep – Protocol PyroMark Q48 Autoprep workflow – fully integrated automatic template preparation manual automated manual/automated Load & run files via USB or ethernet Load PCR product & magnetic beads Load reagents, nucleotides and buffers Automatic template preparation Anneal sequencing primer Pyro- sequencing Wash cartridge Using Methylation Patterns to Determine Origin of Biological Material and Age
  27. 27. Sample to Insight PyroMark Q48 Autoprep Performance Methylation analysis (CpG mode) Using Methylation Patterns to Determine Origin of Biological Material and Age unmethylated50%methylatedHistogram
  28. 28. Sample to Insight Once again, QIAGEN would like to thank Dr. Bruce McCord, for his presentation.
  29. 29. Methodology 29 DNA Extraction Bisulfite Modification PCR Pyrosequencing Real-time PCR/ HRM STR Analysis Easily fits into the general flow of the forensic laboratory
  30. 30. Selection of Loci - ZC3H12D "ZC3H12D, also known as MCPIP4, is a member of a family of novel CCCH-zinc finger proteins. Hypomethylated in sperm cells. It is a known fact that levels of Zn are high in human sperm “The total zinc content in semen from mammals is high, and zinc has been found to be critical to spermatogenesis.” Mol. Hum. Reprod. (1999) 5 (4):331-337. Tania Madi
  31. 31. In ZC3H12D Blood is hypermethylated while Semen is hypomethylated Dr. Balamurgen
  32. 32. Discrimination: blood, semen, saliva, vaginal epi * PFN 3A Vaginal Epi methylated methylated unmethylated intermediate
  33. 33. Our Current Multiplex for Body Fluid ID simultaneous amplification of loci to preserve DNA extracts Sohee Cho Quentin Gaither
  34. 34. Validation Studies based on SWGDAM guidelines  Sensitivity  Age  Species Specificity  Mixture ratios  Degradation
  35. 35. 20-year old samples – blood and semen George Duncan D. Silva, J. Antunes, K. Balamurugan; G. Duncan, C. S. Alho, B. McCord. Forensic Sci Int Genet. 2016 Jul;23:55-63.
  36. 36. Non-human samples fail to show peaks for C20orf117 and three other loci
  37. 37. 0 10 20 30 40 50 60 70 80 Mean%Methylation Vag.Epi.(n=11) VE 75%VE:25%S 50%VE:50%S 25%VE:75%S S Semen(n=12) Mixtures of Vaginal Epithelia and Semen.
  38. 38. In a real time PCR detection system fluorescence will be a function of the amount of dsDNA. If the temperature is increases the two strands melt and the fluorescence is altered. Melt Curves (HRM)
  39. 39. Target loci are labeled with fluorescent intercalatating dyes. The temperature of strand separation is measured by a change in fluorescence as dye is released. Melt Curves (the temperature @ which the 2 strands separate) AT Rich GC Rich Dye DNA DNA methylated unmethylated
  40. 40. Realtime PCR/HRM works well for identification of semen when a large difference exists between methylation levels.. Due to primer design, DNA not bisulfite modified does not amplify sperm Blood_1,2 saliva_1,2 Positive control Antunes; Silva; Balamurugan; Duncan; Alho; McCord; Analytical Biochemistry, 2016, 494: 40-45 Semen Unmethylated Blood (methylated) Saliva methylated Joana Antunes
  41. 41. Epigenetic phenotyping (its not just body fluid typing  Age  Environment  Behavior  Diet  Smoker/ non-smoker  Body Mass index  Hair color  Drug abuse  Because certain epigenetic effects are a response to environment there may be advantages over genetic phenotyping
  42. 42. The importance of age determination in forensics DNA based facial reconstruction must be artificially aged http://www.nytimes.com/2015/02/24/science/b uilding-face-and-a-case-on-dna.html Melanie McCord
  43. 43. Several genetic loci, including GRIA2, NPTX2, KLF14, and SCGN, previously identified in a whole methylome studies were examined and primers were designed to explore the regions. We analyzed saliva and blood samples from volunteers with ages ranging from 5 to 72 years So how to determine age with epigenetics and pyrosequencing? Deborah Silva D. Silva, J. Antunes, K. Balamurugan; G. Duncan, C. S. Alho, B. McCord., Electrophoresis, 2015, 36, 1775-1780. Alghanim H, Antunes J, Silva DSBS, Alho CS, Balamurugan K, McCord B. Forensic Sci Int Genet. 2017 Nov;31:81-88
  44. 44. Results from individual loci at GRIA2, KLF14 and SCGN provided good correlations with age GRIA NPTX2 Difference between predicted and observed: 6.9 years years 7.1 years KLF14+SCGN Our results show good correlation with age using CpG sites from 1-2 amplicons – quick and simple.
  45. 45. Epigenetics can also be used to detect suspect lifestyle. Here we show a marker for smoking status Hussain Alghanim 47% 81% 92%
  46. 46. Conclusions  Epigenetics methods can be used to to discriminate blood, saliva semen, and vaginal epithelia.  Methods fit easily into current forensic laboratory flow and body fluid markers can be multiplexed  Epigenetic methods are human specific and show great stability in samples stored for up to 20 years  Mixtures of 2 different body fluids produce intermediate levels of methylation  Age, smoking status, and other phenotypic loci can also be defined using this technique
  47. 47. Publications 1. Madi,T.; Balamurugan,K; Bombardi,R.;Duncan, G.; McCord,B. The determination of tissue specific DNA methylation patterns in forensic biofluids using bisulfite modification and pyrosequencing Electrophoresis, 2012, 33(12) 1736-1745. 2. Balamurugan,K.; Bombardi,R.; Duncan,G.; McCord, B., The identification of spermatozoa by tissue specific differential DNA methylation using bisulfite modification and pyrosequencing, Electrophoresis, 2014, 35, 3079-3086. 3. Deborah S.B.S. Silva, Joana Antunes, K. Balamurugan; G. Duncan, C. S. Alho, B. McCord. Evaluation of DNA methylation markers and their potential to predict human aging, Electrophoresis, 2015, 36, 1775-1780. 4. Joana Antunes1, Kuppareddi Balamurugan2, George Duncan1, Bruce McCord1 Tissue specific DNA methylation patterns in forensic samples detected by pyrosequencing, Jörg Tost and Ulrich Lehmann (eds) Methods in Microbiology, Springer, 2015. 5. Deborah S.B.S. Silva, Joana Antunes, K. Balamurugan; G. Duncan, C. S. Alho, B. McCord. Developmental validation studies of epigenetic DNA methylation markers for the detection of blood, semen and saliva samples, Forensic Science International: Genetics, 2016,23:55–63. 6. Joana Antunes, Deborah S.B.S. Silva, K. Balamurugan3; G. Duncan, C. S. Alho2, B. McCord. High Resolution Melt analysis of DNA methylation to discriminate semen in biological stains, Analytical Biochemistry, 2016, 494: 40-45 7. Sang-Eun Jung; Sohee Cho; Joana Antunes; Iva Gomes; Mari L. Uchimoto; Yu Na Oh; Lisa Di Giacomo; Peter M. Schneider; Min Sun Park; Dieudonne van der Meer; Graham Williams; Bruce McCord; Hee-Jung Ahn; Dong Ho Choi; Yang-Han Lee; Soong Deok Lee; Hwan Young Lee. A collaborative exercise on DNA methylation based body fluid typing, Electrophoresis, 2016, 37, 2759-2766. 8. Antunes, J. Deborah S.B.S. Silva K. Balamurugan; G. Duncan, C. S. Alho, B. McCord, Epigenetic discrimination of vaginal epithelia using bisulfite modified PCR and pyrosequencing, Electrophoresis,2016, 37, 2751-2758. 9. Alghanim H; Antunes J;Silva D; Alho C, Balamurugan K; McCord B. Detection and evaluation of DNA methylation markers found at SCGN and KLF14 loci to estimate human age, FSI Genetics, 2017,31 81-88. 10. Alghanim, H. , Wu, W. and McCord, B. (2018), DNA methylation assay based on pyrosequencing for determination of smoking status. Electrophoresis 2018, in press. .
  48. 48. Acknowledgements Award 2012-DN-BX-K018 Major support for this work was provided by: The National Institute of Justice Points of view in the document are those of the authors and do not necessarily represent the official view of the U.S. Department of Justice Tania Madi, Kuppareddi Balamurugan, Joana Antunes, Deborah Silva, Clarice Alho, Hussain Alghanim, Quentin Gaither, Sohee Cho Florida International University (USA) University of Southern Mississippi (USA) Catholic University of Rio Grande do Sol (Brazil) Broward Sheriff’s Office Ft Lauderdale, FL (USA) San Francisco Police Department (USA) CNPq - Brazil- Conselho Nacional de Desenvolvimento Cientifico e Tecnologico Institute of Forensic Science, Seoul National University”. Qiagen
  49. 49. Sample to Insight QIAGEN would like to thank our last two speakers, Ms. Amy S. Lee and Mr. Peter St. Andre, Criminalist II, SFPD Crime Lab, for their presentation. Disclaimer: This is a QIAGEN sponsored webinar. QIAGEN is not affiliated with the San Francisco Police Department (SFPD) Crime Laboratory. The views expressed herein are those of the speakers, and do not necessarily express the views of QIAGEN.
  50. 50. PYROSEQUENCING IN FORENSICS Peter St. Andre, MFS Amy S. Lee, MFS SFPD Criminalistics Laboratory
  51. 51. Laboratory Workflow 1) Determine which DNA extracts need to be tested. 2) Perform bisulfite conversion on the remaining DNA extracts using the EpiTect Fast Bisulfite kit (~15-20 ul) and incubate on a thermal cycler (~1 hour) 3) Clean-up converted DNA using QIAcube protocol (~1 hour) 4) Amplify the purified DNA using the PyroMark PCR kit and the proper tissue- specific PCR primers (~4 hours)
  52. 52. Laboratory Workflow (cont.) 5) Create a Pyrosequencing assay setup file using the PyroMark Q48 Autoprep software and load the file onto the instrument 6) Set up a Pyrosequencing reaction disc and place the disc onto the Pyromark Q48 Autoprep for sequencing (~1.5 hours) 7) Analyze the data using the PyroMark Q48 Autoprep software W x D x H: 9.8” x 11.8” x 11.8” with chamber lid and injector cover closed; 9.8” x 22” x 15.4” with chamber lid and injector cover open
  53. 53. Preliminary testing by SFPD lab Testing performed using only the semen primer (ZC3H12D) 1) Determined semen primer is specific to sperm 2) Developed more sensitive PCR amplification protocol by adjusting ramp rates 3) Performed basic tests to confirm concepts discussed in other published papers
  54. 54. Confirmation of sperm as ZC3H12D target 85-95% 0-4% 32-38%
  55. 55. Effects of ramp rates during PCR amplification
  56. 56. 50 ng input – Neat Semen 0.05 ng input – Neat Semen 0.01 ng input – Neat Semen Sensitivity test for ZC3H12D
  57. 57. Traditional screening methods • How does the pyrosequencing protocol compare to the traditional screening methods, i.e. sperm searching? 1) Sensitivity limits in detecting male DNA (and observing sperm) 2) Mixture ratios that allow for deductions Went through all casework reports from 2017 where the lab was using traditional screening methods and compiled data for the following categories: 1) Substrate 2) Total human quant 3) Total male quant, 4) Whether a male DNA profile was detected 5) Whether said male profile could be deduced
  58. 58. Finding the benchmarks • Compiled data from 432 sperm fractions from female victims • Quick facts: • 216 sperm positive samples • 181 male profiles detected • 139 male profiles deduced • Smallest male quant w/ deduced male profile: 0.0105 ng total input • Smallest male quant w/ male detected in EPG: 0.0045 ng • Smallest male:female ratio w/ deduced male profile: 1:33 • Smallest male:female ratio w/ male detected in EPG: 1:100
  59. 59. Sensitivity benchmarks 1) Visual detection of sperm more sensitive than quant 2) More than half of samples with greater than 0.2 ng total input yield deducible profiles 3) Large dropoff in deducible profiles seen at 0.1 ng total input DNA 0.0% 10.0% 20.0% 30.0% 40.0% 50.0% 60.0% 70.0% 80.0% 90.0% 100.0% >1 ng 0.5-1.0 ng 0.2-0.5 ng 0.05-0.2 ng <0.05 ng 0 ng Sensitivity based on male quant Sperm Detected Male Detected Male Deduced
  60. 60. Sensitivity benchmarks 0.0% 20.0% 40.0% 60.0% 80.0% 100.0% >10:1 4:1 - 10:1 2:1 - 4:1 1:1 - 2:1 1:2 - 1:1 1:4 - 1:2 1:10 - 1:4 <1:10 Deduction based on male:female ratio <0.1 ng 0.1 ng 0.2 ng 1) Large dropoff at ratios of less than 1:10 2) Large dropoff at quants less than 0.1 ng total input 3) Likely to deduce foreign male as a minor contributor in two person mixtures Note: Samples used where sperm was detected.
  61. 61. Testing of various tissues using semen primer • Need to examine methylation values of non-sperm samples using semen primer • Differential samples containing sperm are often accompanied with different tissue types including blood or vaginal epithelial cells among others. • Methylation values are the only differentiating information • Important to know that different tissue types amp similarly using the semen primer • The goal is to find the thresholds where we can be confident in confirming the presence of semen in a mixture sample
  62. 62. Pyrosequencing benchmarks We used a two-tiered strategy to identify sensitivity limits based on reproducibility • Part A: • Test a dilution series of non-semen samples with the semen primer* • Identify sensitivity limits • Part B: • Once limit is identified, test replicates to look at reproducibility around that sensitivity range identified in Part A *Testing done on blood, saliva, vaginal epithelial samples with range 0.05ng – 50ng
  63. 63. Pyrosequencing sensitivity results 1) All biological material tested with semen primer amped comparable to semen 2) Dependable methylation results required two conditions: a) at least 0.2ng of total input DNA, b) proper quality flags by pyrosequencing software. With both conditions met, we were able to positively ID sperm in samples using pyrosequencing. • At 0.1ng of total input DNA, methylation results become less reproducible • At 0.05ng of total input DNA, methylation results are not usable • Q48 software program begins to flag data for quality around 0.1ng • At 0.05ng, almost all data is flagged as a fail 0.2ng serves as both sensitivity and amp fidelity thresholds
  64. 64. Sample dilution series w/ semen SampleID Pos. 1 (%) Pos. 2 (%) Pos. 3 (%) Pos. 4 (%) Pos. 5 (%) Semen B 0.5 4.73 1.88 3.2 4.79 3.12 Semen B 0.5 5.59 3.94 6.32 9.04 6.55 Semen B 0.5 4.25 6.99 2.96 6.6 4.34 Semen C 0.5 3.5 3.11 2.79 5.62 3.26 Semen C 0.5 3.61 2.34 3.62 6.13 4.66 Semen C 0.5 4.16 4.38 3.63 5.23 4.89 Semen B 0.4 3.48 2.75 3.66 6.35 3.82 Semen B 0.4 5.36 4.01 4.05 5.31 3.63 Semen B 0.4 5.67 3.78 4.17 6.93 5.96 Semen C 0.4 4.68 1.92 4.39 3.88 5.56 Semen C 0.4 3.68 4 5.06 5.44 4 Semen C 0.4 5.15 2.04 3.75 6.23 3.58 Semen B 0.3 4.66 3.05 3.44 5.23 3.52 Semen B 0.3 5.01 4.41 7.1 6.79 7.34 Semen B 0.3 5.92 5.47 4.64 6.43 5.66 Semen C 0.3 9 3.14 5.34 7.76 5.46 Semen C 0.3 4.35 2.9 4.06 8.58 6.42 Semen C 0.3 4.45 4.32 5.61 7.14 5.12 Semen B 0.2 10.94 6.72 6.05 13.39 10.14 Semen B 0.2 5.85 4.31 4.34 6.63 6.9 Semen B 0.2 5.37 2.35 4.22 5.78 3.72 Semen C 0.2 3.59 2.31 3.03 7.44 4.31 Semen C 0.2 4.78 4.05 4.75 8.61 7 Semen C 0.2 3.52 5.88 5.3 6.72 6.65 Semen B 0.1 8 6.2 5.33 8.11 6.26 Semen B 0.1 6.33 4.47 5.24 8.37 8.58 Semen B 0.1 5.21 3.99 3.37 4.38 5.36 Semen C 0.1 10.1 8.18 9.42 10.75 12.31 Semen C 0.1 5.96 6.62 7.61 9.74 7.18 Semen C 0.1 - - - - - SampleID Pos. 1 (%) Pos. 2 (%) Pos. 3 (%) Pos. 4 (%) Pos. 5 (%) Semen B 0.5 4.73 1.88 3.2 4.79 3.12 Semen B 0.5 5.59 3.94 6.32 9.04 6.55 Semen B 0.5 4.25 6.99 2.96 6.6 4.34 Semen C 0.5 3.5 3.11 2.79 5.62 3.26 Semen C 0.5 3.61 2.34 3.62 6.13 4.66 Semen C 0.5 4.16 4.38 3.63 5.23 4.89 Semen B 0.4 3.48 2.75 3.66 6.35 3.82 Semen B 0.4 5.36 4.01 4.05 5.31 3.63 Semen B 0.4 5.67 3.78 4.17 6.93 5.96 Semen C 0.4 4.68 1.92 4.39 3.88 5.56 Semen C 0.4 3.68 4 5.06 5.44 4 Semen C 0.4 5.15 2.04 3.75 6.23 3.58 Semen B 0.3 4.66 3.05 3.44 5.23 3.52 Semen B 0.3 5.01 4.41 7.1 6.79 7.34 Semen B 0.3 5.92 5.47 4.64 6.43 5.66 Semen C 0.3 9 3.14 5.34 7.76 5.46 Semen C 0.3 4.35 2.9 4.06 8.58 6.42 Semen C 0.3 4.45 4.32 5.61 7.14 5.12 Semen B 0.2 10.94 6.72 6.05 13.39 10.14 Semen B 0.2 5.85 4.31 4.34 6.63 6.9 Semen B 0.2 5.37 2.35 4.22 5.78 3.72 Semen C 0.2 3.59 2.31 3.03 7.44 4.31 Semen C 0.2 4.78 4.05 4.75 8.61 7 Semen C 0.2 3.52 5.88 5.3 6.72 6.65 Semen B 0.1 8 6.2 5.33 8.11 6.26 Semen B 0.1 6.33 4.47 5.24 8.37 8.58 Semen B 0.1 5.21 3.99 3.37 4.38 5.36 Semen C 0.1 10.1 8.18 9.42 10.75 12.31 Semen C 0.1 5.96 6.62 7.61 9.74 7.18 Semen C 0.1 - - - - -
  65. 65. Example: Vaginal Epithelial Blue = Strong amp, no quality flags Yellow = Moderate amp, check data Red = Weak amp/control failure, fail Green = Methylation % w/in 2 SD of position-specific mean (per SFPD testing) Pyrogram Quality Acceptable methylation range [55.25% - 100%] SampleID Pos. 1 Pos. 2 Pos. 3 Pos. 4 Pos. 5 Vag 50 68.35 68.48 89.66 72.98 77.02 Vag 0.5 92.45 94.94 74.97 72.44 68.06 Vag 0.25 93.28 63.78 99.61 100 93.43 Vag 0.1 94.84 91.37 100 100 94.98 Vag 0.1 93.31 91.17 100 100 90.52 Vag 0.1 92.22 3.2 99.43 99.09 93 Vag 0.05 3.2 2.41 98.9 10.72 90.81 Vag 0.05 92.26 89.62 99.68 100 91.5 Vag 0.05 - - - - - Vag 0.01 94.1 91.74 99.93 100 96.13 Vag 0.005 - - - - - SampleID Pos. 1 Pos. 2 Pos. 3 Pos. 4 Pos. 5 Vag 50 68.35 68.48 89.66 72.98 77.02 Vag 0.5 92.45 94.94 74.97 72.44 68.06 Vag 0.25 93.28 63.78 99.61 100 93.43 Vag 0.1 94.84 91.37 100 100 94.98 Vag 0.1 93.31 91.17 100 100 90.52 Vag 0.1 92.22 3.2 99.43 99.09 93 Vag 0.05 3.2 2.41 98.9 10.72 90.81 Vag 0.05 92.26 89.62 99.68 100 91.5 Vag 0.05 - - - - - Vag 0.01 94.1 91.74 99.93 100 96.13 Vag 0.005 - - - - -
  66. 66. Application to mixtures Ratio Pos. 1 (%) Pos. 2 (%) Pos. 3 (%) Pos. 4 (%) Pos. 5 (%) 9:1 16.12 12.51 16.41 16.75 15.54 9:1 15.25 10.49 15.87 18.53 17.37 9:1 31.38 20.43 31.43 24.57 30.85 9:1 21.14 20.32 22.2 25.83 21.75 4:1 19.17 16.79 25.61 20.75 21.11 4:1 18.16 17.58 21.84 24.91 20.46 4:1 25.62 23.4 28.3 27.46 23.68 4:1 17.72 17.39 24.79 20.01 22.79 2:1 26.36 25.11 30.65 28.96 29.19 2:1 24.7 22.23 26.6 26.74 25.29 2:1 23.83 34.93 41.21 39.33 39.17 2:1 21.56 21.01 32.78 33.67 31.42 1:1 33.81 34.87 40.9 36.28 37.91 1:1 37.58 34.15 43.37 37.47 37.96 1:1 20.59 25.08 39.72 28.38 37.86 1:1 33.07 30.69 36.02 36.65 35.31 1:2 44.18 43.56 52.69 51.3 47.42 1:2 47.8 39.63 53.44 51.84 48.54 1:2 46.16 36.57 64.69 49.07 52.73 1:2 39.4 53.76 55.52 48.92 60.72 1:4 54.42 52.13 68.22 65.32 58.21 1:4 58.68 51.19 73.2 57.5 64.63 1:4 61.86 54.91 66.23 57.78 61.94 1:4 40.93 23.9 49.02 36.19 37.13 1:9 56.8 57.16 71.3 62 64.09 1:9 53.21 50.72 71.89 59.57 61.67 1:9 54.32 55.34 58.85 50.69 57.49 1:9 89.26 86.44 89.31 84.76 94.25 • Mixtures of extracted DNA from vaginal epithelial cells and liquid semen were made. • Ratios ranged from 9:1 to 1:9, semen to vaginal epithelial • Includes two sets of data: 10 ng of total input and 1 ng of total input. No significant difference between the two data sets. • Acceptable range used was <50%. No non-sperm samples with input of greater than 0.2 ng and passing quality flags had a methylation percentage below 50%.
  67. 67. GlobalFiler sensitivity • Dilution sets were run concurrently with Globalfiler to compare the sensitivity ranges between the Pyrosequencing amp kit and an STR kit • Full profiles from single source samples were obtained down to 0.1 ng total input • Dropout was observed in nearly every sample with 0.05 ng total input • Pyrosequencing results were concordant with Globalfiler. Results were obtained down to 0.1 ng, but at 0.05 ng failure rates spiked. SampleID Meth. % Meth. % Meth. % Meth. % Meth. % Full profile Semen 0.5 ng 3.05 2.49 3.73 4.13 3.69 Yes Semen 0.5 ng 3.72 2.75 5.35 4.18 5 Yes Semen 0.1 ng 4.21 2.53 2.91 4.02 4.25 No Semen 0.1 ng 4.22 2.49 3.88 4.48 5.13 No Semen 0.1 ng 2.7 3.57 52.57 6.03 4.24 No Semen 0.05 ng 6.02 4.67 5.72 10.01 10.41 No Semen 0.05 ng 3.46 3.74 3.68 5.41 5.41 No Semen 0.05 ng 11.66 5.28 14.5 13.26 25.03 No Figure: Data from one dilution set.
  68. 68. Pyrosequencing vs. Sperm search • Currently our lab only sperm searches at the request of the DA. • Only searching cases where a probative male profile has been deduced and the case is going to court. • In 2017, we had 216 sperm positive samples; 139 of those had a male profile deduced. • Assuming 0.2 ng of total male input DNA and a male:female ratio greater than 1:2, pyrosequencing should yield sperm positive results. • How many of the 139 male deduced male profiles identified as sperm positive would also be sperm positive with pyrosequencing? • Pyrosequencing: 88/139 = 63% (88 of the 139 samples fall within the conditions) • How many male profiles were deduced that were negative for sperm? • Traditional: 12/208 = 6% (possibly from other body fluids?)
  69. 69. Are there stochastic effects? From the beginning we would see outlier methylation values at low levels in samples where data quality was acceptable. Why? • Naue et al. ran simulations using models to predict methylation % of biological material at varying input levels • Methylation is a binary event. The position is either methylated or it is non-methylated. • Consider the tissue as a total population. The methylation % is the average for thousands of cells. • At low levels, we are only examining a couple of cells. • This is not a true sampling of the cell population
  70. 70. Main Takeaways 1) Reliable results are dependent on both the amount of DNA and the robustness of the amp. 2) We have shown that it can be possible to confirm the presence of sperm in a mixture sample. Next… • More data necessary for all body fluids, especially vaginal epithelial. • Need to conduct population studies to show that the methylation % holds true across different populations. • Multiplex could provide greater specificity (but may lose sensitivity). A probabilistic approach could greatly improve the ability for confirmation of a specific biological material in a mixture.
  71. 71. References • Silva, D. et al. Developmental validation studies of epigenetic DNA methylation markers for the detection of blood, semen and saliva samples. FSIG 23(2016) 55-63. • QIAGEN. "Pyrosequencing Technology and Platform Overview.” • Fischinger, F. Pyrosequencing Workflow for DNA Methylation Analysis. QIAGEN. • Naue, Jana et al. Forensic DNA Methylation profiling from minimal traces: How low can we go?. FSIG 33(2018) 17-23.
  72. 72. Thanks to: Mark Powell, SFPD Eleanor Salmon, SFPD Fabiola Siordia, SFPD John Haley, QIAGEN Sim Winitz, QIAGEN Frank Fischinger, QIAGEN Bruce McCord, Florida International University
  73. 73. Sample to Insight All QIAGEN products mentioned here are intended for molecular biology applications. The products are not intended for diagnosis, prevention, or treatment of a disease. The PAXgene product is for research use only and not for use in diagnostic procedures. Thank you for your attention! Trademarks: QIAGEN®, Sample to Insight®, QIAamp®, AllPrep®, EpiTect®, PyroMark®, Pyrosequencing® (QIAGEN Group), PAXgene® (PreAnalytiX GmbH). Registered names, trademarks, etc. used in this document, even when not specifically marked as such, are not to be considered unprotected by law. PROM-12928-001 08/2018 © 2018, QIAGEN, all rights reserved. For more information, contact your QIAGEN sales representative or visit www.qiagen.com

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