Amber C. W. Vandepoele*, Jonathan Hogg, Nori Zaccheo, Morgan Frank, Haley Crooks, and Michael Marciano | Syracuse University Janine Schulte and Iris Schulz | University of Basel Jeremy Dubois | Acadiana Crime Laboratory Abstract: Mixture interpretation remains a central challenge in the forensic DNA field. Much research and development has focused on methods to improve the interpretation of complex samples, including software solutions (probabilistic genotyping1,2,3,4 and statistical/machine learning-based methods5,6). These methods are used to improve mixture interpretation on the “back-end” rather than address the separation of the individual biological components (e.g., cells) to wholly avoid mixtures. This project will address this need in the forensic biology field, aiming to evaluate an emerging method of targeted male cell analysis in mixtures of like-cells (e.g., male and female epithelial cells collected on a vaginal swab). This method will either eliminate or significantly ease the complexity of mixture interpretation for specific case types where male and female like-cells are collected, improve laboratory efficiency, and lower the cost of processing such cases. The central aim of this project is to develop and optimize a method to identify and recover male cells from a mixture of male and female like-cells (e.g., epithelial cells); for example, the identification of a vasectomized male in a sexual assault of a female victim. The method will adapt two well- characterized methods into a single unified protocol for use in forensic DNA analyses—Y chromosome targeting via the Abbott Molecular Vysis™ CEP Y DYZ1 probe and male cell recovery using the Menarini-Silicon Biosystems DEPArray™ NxT or PLUS. Separating male from female cells in like-cell mixtures will result in single-source male autosomal profiles and provide unprecedented levels of resolution that will lead to stronger statistical support for the comparisons. It may also allow the profiles to be uploaded to CODIS in cases that yielded profiles that were not previously able to be uploaded. The proposed method would also simplify the data interpretation because probabilistic genotyping would not be required to deconvolute a mixture. The benefits extend to any sample with a male and female mixture of like-cells. This study is in progress. References 1. Taylor, Duncan, Jo-Ann Bright, and John Buckleton. “The Interpretation of Single Source and Mixed DNA Profiles.” Forensic Science International: Genetics 7 (2013): 516–528. https://doi.org/10.1016/J.FSIGEN.2013.05.011. 2. Perlin, Mark W., Matthew M. Legler, Cara E. Spencer, Jessica L. Smith, William P. Allan, Jamie L. Belrose, and Barry W. Duceman. “Validating TrueAllele® DNA Mixture Interpretation.” Journal of Forensic Science 56 (2011): 1430–1447. https://doi.org/10.1111/j.1556-4029.2011.01859.x 3. Brenner, Charles H. “DNAVIEW Product Information.” Accessed May 11, 2020. http://dna-view.com/dnaview.htm. 4. Bleka, Øyvind, Geir Storvik, and Peter Gill. “EuroForMix: An Open Source Software Based on a Continuous Model to Evaluate STR DNA Profiles From a Mixture of Contributors with Artefacts.” Forensic Science International: Genetics 21 (2016): 35–44. https://doi.org/10.1016/j.fsigen.2015.11.008. 5. Alfonse, Lauren E., Genesis Tejada, Harish Swaminathan, Desmond S. Lun, and Catherine M. Grgicak, “Inferring the Number of Contributors to Complex DNA Mixtures Using Three Methods: Exploring the Limits of Low-Template DNA Interpretation.” Journal of Forensic Science 62 (2017): 308–316. https://doi.org/10.1111/1556-4029.13284. 6. Marciano, Michael A., and John D. Adelman. “PACE: Probabilistic Assessment for Contributor Estimation— A Machine Learning-Based Assessment of the Number of Contributors in DNA mixtures.” Forensic Science International: Genetics 27 (2017): 82–91. https://doi.org/10.1016/j.fsigen.2016.11.006.