Rapid differentiation of epithelial cell types in aged biological samples using autofluorescence and morphological signatures

Classification of epidermal, buccal, penile and vaginal epithelial cells using morphological characteristics measured by imaging flow cytometry

As a result of the increased sensitivity of forensic DNA techniques, which can generate informative results from as little as a few cells, developing an understanding of the anatomical region these cells originate from is becoming more pertinent. Imaging Flow Cytometry (IFC) represents a promising method for identifying epithelial cells from different anatomical regions. This project aimed to determine whether IFC could be used to distinguish epithelial cells collected from different forensically relevant anatomical regions based on their morphology and autofluorescence. Penile, vaginal, buccal, and epidermal epithelial cells were collected in triplicate from 15 male and 15 female participants, in three different age groups: 18-39, 40-59, and 60+ years. Using the high statistical output from the IFC, 234 morphological measurements were collected for 571,546 single cells. Using a linear discriminate analysis with a minimum posterior probability threshold, the four epithelial cell types could be identified and distinguished with a 72-83 % classification accuracy. The results showed that the age and biological sex of the individual had no effect on the morphology of the four epithelial cell types. These data provide insights into the ability of IFC to identify and distinguish penile, buccal, vaginal, and epidermal epithelial cells and identifies further avenues for improvement and optimisation.

The efficacy of Diamond™ nucleic acid dye-stained cell counting techniques for forensic application

Touch DNA is one of the most common types of biological material collected during criminal investigations. Diamond™ Nucleic Acid Dye (DD) has been shown to aid in touch sample visualisation and target sampling. It has also been used as a method of shedder categorisation that is cheaper and quicker than DNA methods. However, the DD method routinely involves manual cell counting, which can result in intra and inter-person variability similar to other manual techniques used in forensic science, for example, fingerprint identification. Additionally, DD based shedder categorisation involves counting cells in a portion of the touch deposit to extrapolate an individual's shedder status, and the sampling effect of such estimations is currently unknown. The present study tested different data analysis aspects of the DD method, including counting variability within and between people, shedder classification differences based on different counting methods (entire thumbprint, sub-section of a print with most cells, sub-section of a print deemed most representative of the entire thumbprint, and random sections), the use of ImageJ software to semi-automate counting and the use and extension of the DD method for investigating DNA Transfer, Persistence, Prevalence and Recovery (DNA-TPPR). The results of this study show that there are meaningful differences observed during counting processes both between and within people. These differences tended to increase as the factor of time, or the duration of counting, rather than the complexity of cell deposits being assessed. Investment in cell counting software that eliminates personal factors, such as boredom fatigue, can remedy most of these issues, however, will require optimisation, such as fibre recognition. Shedder testing was shown to be affected by the choice of sampling and categorisation methods, and suggested that using an entire finger or larger section size can provide increased precision. Finally, inverted worn gloves stained with DD may provide an acceptable alternative for hands in DNA-TPPR investigations, providing an interesting alternative for future research.

Targeted recovery of male cells in a male and female same-cell mixture

DNA mixture deconvolution in the forensic DNA community has been addressed in a variety of ways. "Front-end" methods that separate the cellular components of mixtures can provide a significant benefit over computational methods as there is no need to rely on models with inherent uncertainty to generate conclusions. Historically, cell separation methods have been investigated but have been largely ineffective due to high cost, unreliability, and the lack of proper instrumentation. However, the last decade has given rise to more innovative technology that can target and recover cells more effectively. This study focuses on the development and optimization of a method to selectively label and recover male cells in a mixture of male and female epithelial cells using a Y-chromosome labeling kit with DEPArray™ technology, whereby male cells are labeled and recovered into a single extraction-ready tube. Labeling efficiency was tested using freshly collected and aged buccal swabs where 70%-75% and 38% of male cells were labeled, respectively, with less than 1% false positives. DEPArray™ detection was assessed using single buccal epithelial cells where approximately 80% of labeled cells were identified as male. Mixtures (1:1, 1:10, male to female) yielded profiles that were predominantly single source male or those in which the male component was more easily interpreted. The male-specific labeling method was demonstrated to be both robust and reliable when used on freshly collected cells. While the DEPArray™ meditated detection and recovery had notable limitations, it still improved the interpretation of the male component in same-cell mixtures in more recently collected samples.

Preliminary assessment of three quantitative approaches for estimating time-since-deposition from autofluorescence and morphological profiles of cell populations from forensic biological samples

Determining when DNA recovered from a crime scene transferred from its biological source, i.e., a sample's 'time-since-deposition' (TSD), can provide critical context for biological evidence. Yet, there remains no analytical techniques for TSD that are validated for forensic casework. In this study, we investigate whether morphological and autofluorescence measurements of forensically-relevant cell populations generated with Imaging Flow Cytometry (IFC) can be used to predict the TSD of 'touch' or trace biological samples. To this end, three different prediction frameworks for estimating the number of day(s) for TSD were evaluated: the elastic net, gradient boosting machines (GBM), and generalized linear mixed model (GLMM) LASSO. Additionally, we transformed these continuous predictions into a series of binary classifiers to evaluate the potential utility for forensic casework. Results showed that GBM and GLMM-LASSO showed the highest accuracy, with mean absolute error estimates in a hold-out test set of 29 and 21 days, respectively. Binary classifiers for these models correctly binned 94-96% and 98-99% of the age estimates as over/under 7 or 180 days, respectively. This suggests that predicted TSD using IFC measurements coupled to one or, possibly, a combination binary classification decision rules, may provide probative information for trace biological samples encountered during forensic casework.

Differentiation of vaginal cells from epidermal cells using morphological and autofluorescence properties: Implications for sexual assault casework involving digital penetration

Analysis of DNA mixtures from sexual assault evidence is an ongoing challenge for DNA casework laboratories. To assist the forensic scientist address source and activity level propositions there is a significant need for new techniques that can provide information as to the source of DNA, particularly for sexual assault samples that do not involve semen. The goal of this study was to develop a new biological signature system that provides additional probative value to samples comprised of mixtures of epidermal and vaginal cells, as may be observed in cases involving digital penetration. Signatures were based on morphological and autofluorescence properties of individual cells collected through Imaging Flow Cytometry (IFC). Comparisons to reference cell populations from vaginal tissue and epidermal cells collected from hands showed strong multivariate differences across > 80 cellular measurements. These differences were used to build a predictive framework for classifying unknown cell populations as originating from epithelial cells associated with digital penetration or epidermal tissue. As part of the classification scheme, posterior probabilities of specific tissue group membership were calculated for each cell, along with multivariate similarity to that tissue type. We tested this approach on cell populations from reference tissue as well as mock casework samples involving hand swabbings following digital vaginal penetration. Many more cells classifying as non-epidermal tissue were detected in digital penetration hand swab samples than control hand swabbings. Minimum interpretation thresholds were developed to minimize false positives; these thresholds were also effective when screening licked hands, indicating the potential utility of this method for a variety of biological mixture types and depositional events relevant to forensic casework. Results showed that samples collected subsequent to digital penetration possessed markedly higher numbers of cells classifying as vaginal tissue as well as higher posterior probabilities for vaginal tissue (≥ 0.90) compared to cell populations collected from hands without prior contact with vaginal tissue. Additionally, digital penetration cell populations may be resolved from saliva cell populations and other non-target tissue types.

Differentiation of vaginal cells from epidermal cells using morphological and autofluorescence properties: Implications for sexual assault casework involving digital penetration

Analysis of DNA mixtures from sexual assault evidence is an ongoing challenge for DNA casework laboratories. There is a significant need for new techniques that can provide information as to the source of DNA, particularly for sexual assault samples that do not involve semen. The goal of this study was to develop a new biological signature system that provides additional probative value to samples comprised of mixtures of epidermal and vaginal cells, as may be observed in cases involving digital penetration. Signatures were based on morphological and autofluorescence properties of individual cells collected through Imaging Flow Cytometry (IFC). Comparisons to reference cell populations from vaginal tissue and epidermal cells collected from hands showed strong multivariate differences across >80 cellular measurements. These differences were used to build a predictive framework for classifying unknown cell populations as originating from epithelial cells associated with digital penetration or epidermal tissue. As part of the classification scheme, posterior probabilities of specific tissue group membership were calculated for each cell, along with multivariate similarity to that tissue type. We tested this approach on cell populations from reference tissue as well as mock casework samples involving digital penetration. Many more cells classifying as non-epidermal tissue were detected in digital penetration samples than control hand swabbings. Minimum interpretation thresholds were developed to minimize false positives; these thresholds were also effective when screening licked hands, indicating the potential utility of this method for a variety of biological mixture types and depositional events relevant to forensic casework. Results showed that samples collected subsequent to digital penetration possessed markedly higher numbers of cells classifying as vaginal tissue as well as higher posterior probabilities for vaginal tissue (≥ 0.90) compared to cell populations collected from hands without prior contact with vaginal tissue. Additionally, digital penetration cell populations may be resolved from saliva cell populations and other non-target tissue types.

Accumulation of endogenous and exogenous nucleic acids in "Touch DNA" components on hands

Successful forensic DNA profiling from handled items is increasingly routine in casework. This "touch DNA" is thought to contain both cellular and acellular nucleic acid sources. However, there is little clarity on the origins or characteristics of this material. The cellular component consists of anucleate, terminally differentiated corneocytes (assumed to lack DNA), and the occasional nucleated cell. The acellular DNA source is fragmentary, presumably cell breakdown products. This study examines the relative contributions each component makes to the hand-secretions (endogenous) and hand-accumulations (exogenous) by recovering rinses from the inside and outside of worn gloves. Additionally, cellular and acellular DNA was measured at timepoints up to 2 h after hand washing, both with and without interim contact. Microscopic examination confirmed cell morphology and presence of nucleic acids. Following the novel application of a hair keratinocyte lysis method and plasma-DNA fragment purification to hand rinse samples, DNA profiles were generated from both fractions. Exogenous cell-free DNA is shown to be a significant source of touch DNA, which reaccumulates quickly, although its amplifiable nuclear alleles are limited. Endogenous DNA is mostly cellular in origin and provides more allelic information consistently over time.

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

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.