Evaluating the use of hypoxia sensitive markers for body fluid stain age prediction

To augment DNA profiling and body fluid identification techniques efforts are being made to increase the amount of information available from a crime scene stain, which includes efforts to identify externally visible characteristics through phenotypic analysis. A key question surrounding crime scene stains is the length of time between deposition of the stain and its subsequent recovery, in that is the stain recovered related to the incident in question or from a previously deposited stain number of weeks earlier? The inability to answer this fundamental question has a detrimental effect upon the successful completion of a criminal investigation. Once a body fluid leaves the body, the oxygen concentration in the environment changes; therefore, it may be that this change could cause a change in the expression of hypoxia-sensitive biomarkers. Here, a range of bloodstains, liquid saliva and liquid semen samples were collected at 0 days, 7 days, 14 days, 21 days and 28 days of degrading at room temperature (19-22 °C), before undergoing total RNA extraction and cDNA synthesis. Blood was recovered from filter paper with 3 mm², with saliva and semen being left in their tubes and swabbed at the appropriate times. All samples then underwent quantitative PCR targeting Vascular Endothelial Growth Factor A (VEGFA) and Hypoxia-Inducible Factor 1 Alpha (HIF1A), with B-Actin (ACTB) as a reference gene. A range of linear and quadratic correlation values was obtained from the qPCR data and used to develop a predictive model with a mean absolute deviation (MAD) of 4.2, 2.1, and 5 days for blood, saliva, and semen respectively. Blind testing indicated that a stain age prediction model based upon VEGFA with ACTB as a reference gene could be used on samples up to four weeks old with a margin of error ranging from 2 days through to 5 days. While a sizeable potential time frame exists using this model; this represents a significant step towards the target of having an accurate stain age prediction model.