Using total RNA quality metrics for time since deposition estimates in degrading bloodstains
Colin I. ElliottiD, Theresa E. StotesburyiD, Aaron B. A. ShaferiD
Abstract
Determining the time since deposition (TSD) of bloodstains would provide forensic scientists with critical information regarding the timeline of the events involving bloodshed. The physicochemical changes occurring to biomolecules as a bloodstain degrades can be used to approximate the TSD of bloodstains. Our study aims to quantify the timewise degradation trends and temperature dependence found in total RNA from bloodstains without the use of amplification, expanding the scope of the RNA TSD research which has previously targeted mRNA molecules. Whole bovine blood was stored in plastic microcentrifuge tubes at 21{degrees}C or 4{degrees}C and tested over different timepoints spanning one week. Nine RNA metrics were visually assessed and quantified using linear and mixed models; the RNA Integrity Number equivalent (RINe) and the DV200 demonstrated strong negative trends over time and statistical independence. The RINe model fit was high (R2 = 0.60), and while including the biological replicate as a random effect increased the fit for all RNA metrics, no significant differences were found between biological replicates stored at the same temperature for the RINe and DV200 metrics. Importantly, this suggests that these standardized metrics can likely be directly compared between scenarios and individuals, with DV200 having an inflection point at [~]28 hrs. This study provides a novel approach for blood TSD estimates, producing metrics that are not affected by inter-individual variation and improving our understanding of the rapid degradation occurring in bloodstains. HIGHLIGHTSO_LIAmplification-free analysis of total RNA in degrading bloodstains. C_LIO_LIShort-term RNA degradation assessment using high-resolution size measurements. C_LIO_LITotal RNA quality and quantity metrics were assessed across a one-week. C_LIO_LITotal RNA quality metrics demonstrated the strongest timewise trends. C_LIO_LIBiological replicates produced similar results for RNA quality metrics. C_LI
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