The identification of menstrual blood in forensic samples by logistic regression modeling of miRNA expression

We report the identification of sensitive and specific miRNA biomarkers for menstrual blood, a tissue that might provide probative information in certain specialized instances. We incorporated these biomarkers into qPCR assays and developed a quantitative statistical model using logistic regression that permits the prediction of menstrual blood in a forensic sample with a high, and measurable, degree of accuracy. Using the developed model, we achieved 100% accuracy in determining the body fluid of interest for a set of test samples (i.e. samples not used in model development). The development, and details, of the logistic regression model are described. Testing and evaluation of the finalized logistic regression modeled assay using a small number of samples was carried out to preliminarily estimate the limit of detection (LOD), specificity in admixed samples and expression of the menstrual blood miRNA biomarkers throughout the menstrual cycle (25-28 days). The LOD was \u3c1 ng of total RNA, the assay performed as expected with admixed samples and menstrual blood was identified only during the menses phase of the female reproductive cycle in two donors