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Simplification of Complex DNA Profiles Using Front End Cell Separation and Probabilistic Modeling

NCJ Number
253495
Date Published
Author(s)
Nancy A. Stokes, Cristina E. Stanciu, Emily R. Brocato, Christopher J. Ehrhardt, Susan A. Greenspoon
Agencies
NIJ-Sponsored
Annotation
This article reports on a study that tested a front-end cell separation workflow on complex mixtures containing as many as five contributors.
Abstract
Forensic samples composed of cell populations from multiple contributors often yield DNA profiles that can be challenging to interpret. This frequently results in decreased statistical strength of an individual’s association to the mixture and the loss of probative data. The current study involved selectively labelling certain cell populations in dried whole blood mixture samples with fluorescently labeled antibody probe that targeted the HLA-A*02 allele, separating the mixture using Fluorescence Activated Cell Sorting (FACS) into two fractions that were enriched in A*02 positive and A*02 negative cells, and then generating DNA profiles for each fraction. The study then tested whether antibody labelling and cell sorting effectively reduced the complexity of the original cell mixture by analyzing STR profiles quantitatively using the probabilistic modeling software, TrueAllele® Casework. Results showed that antibody labelling and FACS separation of target populations yielded simplified STR profiles that could be more easily interpreted using conventional procedures. In addition, TrueAllele® analysis of STR profiles from sorted cell fractions increased statistical strength for the association of most of the original contributors interpreted from the original mixtures. (publisher abstract modified)
Date Created: December 21, 2020