McCormick Place | Chicago, Illinois, USA
March 1 - 5, 2020

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Deconvolution of Mixture Samples using Single-Cell Techniques for Forensic Human Identification

  • Room: Skyline Ballroom
  • Session Number: 6-3-30P
Tuesday, March 03, 2020: 5:30 PM - 7:30 PM

Speaker(s)

Co-Author
Amanda Gonzalez
Rutgers University Camden
Co-Author
Catherine Grgicak
Rutgers University Camden
Co-Author
Desmond Lun
Rutgers University Camden
Co-Author
Harish Swaminathan
Boston University School of Medicine
Co-Author
Ken Duffy
Maynooth University, Ireland
Author
Nidhi Sheth
Rutgers University Camden

Description

Assessing the degree of consistency between genetic profiles from suspects and profiles obtained from items of evidence is undertaken by probabilistic models that consider all possible genotype combinations, making forensic interpretation an arduous task. The application of single-cell techniques has the potential to fill the gap left by a traditional interpretation but it comes with its own challenges, which must first be overcome. One challenge to overcome, for example, is the preponderance of allele drop-out, which can be described as the probability that the signal intensity is below the lower signal boundary, i.e. AT or analytical threshold, given DNA was extracted from at least one cell.

The forensic analytical pipeline consists of, in general, extraction, amplification of forensically relevant microsatellites and laser-induced fluorescent-capillary electrophoresis of the amplicons. Though known as a sensitive pipeline it is reliant upon the efficient extraction of DNA from the cell matrix.

As such, four direct-to-PCR DNA extraction methods were tested: 1) forensicGEM Saliva; 2) DEPArray LysePrep; 3) Viagen Direct and; 4) Arcturus Pico Pure. Specifically, 408 cells were vacuum-pipetted into 96-well plates and the DNA was extracted as per manufacture’s recommendations. By comparing allelic dropout rates and signal intensities, it was experimentally deduced that extraction method significantly impacts downstream signal detection; thus, informing which extraction protocol to implement.

Once the full laboratory process was finalized, an additional 556 single cells were analyzed to test if drop-out was cell-independent. We compared the experimental distributions of drop-out rates for each single-cell profile to the expected rates modeled with a binomial distribution with parameters NHet (number of heterozygous alleles) and the probability of allele drop-out across all samples. Early analysis demonstrates allele drop-out is cell dependent, suggesting full forensically relevant evaluations of single-cell profiles will require models that address this unique feature.



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