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Simplification of complex DNA profiles using front end cell separation and probabilistic modeling.

ABSTRACT: Forensic samples comprised of cell populations from multiple contributors often yield DNA profiles that can be extremely 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 purpose of this study was to test a front-end cell separation workflow on complex mixtures containing as many as five contributors. Our approach involved selectively labelling certain cell populations in dried whole blood mixture samples with fluorescently labeled antibody probe targeting the HLA-A*02 allele, separating the mixture using Fluorescence Activated Cell Sorting (FACS) into two fractions that are enriched in A*02 positive and A*02 negative cells, and then generating DNA profiles for each fraction. We 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. Additionally, 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.

SUBMITTER: Stokes NA

PROVIDER: S-EPMC6120788 | biostudies-literature | 2018 Sep

REPOSITORIES: biostudies-literature

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Simplification of complex DNA profiles using front end cell separation and probabilistic modeling.

Stokes Nancy A NA Stanciu Cristina E CE Brocato Emily R ER Ehrhardt Christopher J CJ Greenspoon Susan A SA

Forensic science international. Genetics 20180717

Forensic samples comprised of cell populations from multiple contributors often yield DNA profiles that can be extremely 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 purpose of this study was to test a front-end cell separation workflow on complex mixtures containing as many as five contributors. Our approach involved selectively labelling certain cell populations in dried who ...[more]

PMID: 30055432

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Project description:The goal of this study was to survey optical and biochemical variation in cell populations deposited onto a surface through touch or contact and identify specific features that may be used to distinguish and then sort cell populations from separate contributors in a trace biological mixture. Although we were not able to detect meaningful biochemical variation in touch samples deposited by different contributors through preliminary antibody surveys, we did observe distinct differences in red autofluorescence emissions (650-670 nm), with as much as a tenfold difference in mean fluorescence intensities observed between certain pairs of donors. Results indicate that the level of red autofluorescence in touch samples can be influenced by a donor's contact with specific material prior to handling the substrate from which cells were collected. In particular, we observed increased red autofluorescence in cells deposited subsequent to handling laboratory gloves, plant material, and certain types of marker ink, which could be easily visualized microscopically or using flow cytometry, and persisted after hand washing. To test whether these observed optical differences could potentially be used as the basis for a cell separation workflow, a controlled two-person touch mixture was separated into two fractions via fluorescence-activated cell sorting (FACS) using gating criteria based on intensity of 650-670 nm emissions and then subjected to DNA analysis. Genetic analysis of the sorted fractions provided partial DNA profiles that were consistent with separation of individual contributors from the mixture suggesting that variation in autofluorescence signatures, even if driven by extrinsic factors, may nonetheless be a useful means of isolating contributors to some touch mixtures. Graphical Abstract Conceptual workflow diagram. Trace biological mixtures containing cells from multiple individuals are analyzed by flow cytometry. Cells are then physically separated into two populations based on intensity of red autofluorescence using Fluorescence Activated Cell Sorting. Each isolated cell fraction is subjected to DNA analysis resulting in a DNA profile for each contributor.

Dataset Information

Simplification of complex DNA profiles using front end cell separation and probabilistic modeling.

Publications

Simplification of complex DNA profiles using front end cell separation and probabilistic modeling.

Similar Datasets

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