Author ORCID Identifier

https://orcid.org/0000-0001-8294-2381

Defense Date

2022

Document Type

Directed Research Project

First Advisor

Christopher Ehrhardt

Second Advisor

Katherine Philpott

Third Advisor

Sarah Williams

Abstract

Forensic casework relies heavily on DNA profiles which may be time-consuming to generate and difficult to interpret when biological mixtures are present. Additionally, there are significant challenges in using sub-source data alone to answer the activity-level questions often most pertinent in criminal cases. Source level information can add critical probative value, however, methods that can provide information as to the source tissue of evidentiary cell populations are limited. There remains a need for new methods that can differentiate between and classify various cell populations, particularly for vaginal and epidermal tissue, since there are no conventional serological techniques specific to these sources. One promising but unexplored approach is to use flow cytometry on the front-end of the DNA workflow. Flow cytometry is a high throughput and non-destructive method for characterizing physical and biological attributes of individual cells through autofluorescence profiles. This study aimed to develop a new forensic signature system to increase the probative value of DNA profiles generated from specific types of sexual assault evidence samples: cellular mixtures resulting from digital penetration, consisting of trace hand epidermal cells and vaginal cells, and from mouth-to-skin contact, consisting of trace hand epidermal cells and cellular components of saliva (i.e. buccal cells). Cell characterizations were performed using imaging flow cytometry (IFC) and subsequent analysis with data imaging and statistical analysis software. The data collected was used to create cell filters within the data imaging software, resulting in a method to effectively filter all of the hand cells out, potentially isolating a vaginal cell signature. Cells filtered with the image analysis filter were then run through a series of discriminant function analyses to enhance classification and to predict group membership of unknown cells from mock forensic samples. The results of this study showed that the vast majority of these unknown cells classified into the expected group, with few misclassifications. Further, correct classifications were supported by high posterior probabilities, in stark contrast to the posterior probabilities accompanying misclassifying cells. These results indicate that this method can successfully differentiate between the cell populations in question, as well as flag possible false positive misclassifications, and may provide a promising new method for front-end analysis prior to DNA profiling. The ability to identify the components of a biological mixture prior to STR analysis will improve efficacy and decrease bias that can often confuse the activity-level conclusions in trace mixture sample analysis.

Rights

© The Author(s)

Is Part Of

VCU Master of Science in Forensic Science Directed Research Projects

Date of Submission

5-8-2022

Available for download on Monday, May 08, 2023

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