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

Abstract

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 <1 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.

Potential applications of microRNA profiling to forensic investigations

“…Dørum et al (2019) reported using partial least squares (PLS) and linear discriminant analysis (LDA) to predict body fluids and also identified a minimum number of miRNA markers that will still provide good prediction accuracy. Other statistical approaches have been applied, such as that of Hanson et al (2014), who developed a quantitative statistical model using logistic regression to predict menstrual blood and reported a high, and measurable, degree of accuracy.…”

Expression profile analysis and stability evaluation of 18 small RNAs in the Chinese Han population

“…In the field of forensic science, RNA expression profile analysis has been proposed as a potential investigation method for various purposes, such as time of death prediction [1], monozygotic twins’ discrimination [2, 3], age prediction [4, 5], and body fluid identification (BFID) [6, 7]. During the last decade, the potential use of small RNA markers, such as micro RNA (miRNA) and piwi‐interacting RNA, in determining the composition of forensically relevant biological samples has been widely studied [8–20], and a vast number of candidates have been identified using various detection approaches, such as the CE method [21], microarray screening [8], and small RNA sequencing [14]. Nevertheless, real‐time quantitative PCR (RT‐qPCR) is still typically used to confirm the expression of selected candidates because it is generally considered as the gold standard for the quantification of gene expression [22, 23].…”

Human Organ Tissue Identification by Targeted RNA Deep Sequencing to Aid the Investigation of Traumatic Injury

“…A molecular analysis of the RNA transcriptome, the proteome, or the epigenome from a putative tissue fragment should permit assignment of its source to a specific tissue and organ, since each differentiable cell type will exhibit unique patterns of gene and protein expression, as well as DNA methylation [ 3 , 4 ]. These “-omes” are currently the subject of investigation for the purposes of secreted body fluid identification for forensic purposes, and show great promise in that regard [ 5 , 6 , 7 , 8 , 9 , 10 , 11 , 12 , 13 , 14 , 15 , 16 , 17 , 18 , 19 , 20 , 21 , 22 , 23 , 24 , 25 , 26 , 27 , 28 , 29 , 30 , 31 , 32 , 33 , 34 , 35 , 36 , 37 ]. The authors are unaware of any published or presented work yet on organ tissue identification for forensic purposes using DNA methylation, although this might be expected in the future.…”