New strategies and recommendations for front end separation of compromised biological mixtures using cellular fluorescence profiling and flow cytometry
Year: 2018
Type: other
Abstract: : Previous studies have demonstrated that hybridizing cell mixtures with fluorescently-labeled antibody probes and then isolating fluorescent cells using flow cytometry can be an effective technique for separating certain types of cell mixtures. However, mixture samples that have been compromised and/or degraded show a significant decrease in the efficiency of antibody probe binding as well as an overall loss of intact cell targets. This presents an ongoing obstacle for adopting a cell separation workflow for forensic applications. Therefore, the goal of this study was to investigate the molecular dynamics of cellular decomposition in simulated forensic samples and develop new strategies for mitigating non-specific probe interactions and subsequent inefficiencies in cell sorting. A series of two-person blood mixtures that varied in drying time (between 24 hours and 128 hours), contributor ratio (10:1, 5:1, 3:1, 1:1), and total starting volume of blood (between 20 µl and 500 µl) were created and analyzed using antibody hybridization and flow cytometry. We found that non-specific interactions with antibody probe were a significant factor in blood mixtures that had been dried for more than 48 hours, such that target cell populations were difficult to identify against non-target populations. Subsequent sorting experiments performed on two person mixtures with varying contributor ratios showed that the STR profile of the minor contributor was successfully enriched in each sorted cell fraction when compared to the STR profile of the unsorted mixture. We also observed that the position of the sorting gate had a significant effect on the quality of the resulting STR profile, such that gates designed to capture cells at the tail ends of the fluorescence distribution of the mixture showed the highest enrichment for the target cell population.
Institution: Virginia Commonwealth University
Language: English
Subfield: Genetics
Domain: Life Sciences
Open Access status: gold
OpenAlex ID: W2794741110