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

    Authors

    E. K. Hanson; M. Mirza; K. Rekab;J. Ballantyne

    Comments

    Authors: contact us about adding a copy of your work at STARS@ucf.edu

    Abbreviated Journal Title

    Electrophoresis

    Keywords

    Body fluid identification; Forensic science; Logistic regression; analysis; MicroRNA (miRNA); RNA profiling; BODY-FLUID IDENTIFICATION; MESSENGER-RNA MARKERS; COLLABORATIVE EDNAP; EXERCISE; RNA/DNA CO-ANALYSIS; QUANTITATIVE RT-PCR; REAL-TIME PCR; DNA; METHYLATION; CAPILLARY-ELECTROPHORESIS; REVERSE TRANSCRIPTION; MICRORNA; MARKERS; Biochemical Research Methods; Chemistry, Analytical

    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.

    Journal Title

    Electrophoresis

    Volume

    35

    Issue/Number

    21-22

    Publication Date

    1-1-2014

    Document Type

    Article

    Language

    English

    First Page

    3087

    Last Page

    3095

    WOS Identifier

    WOS:000345272200010

    ISSN

    0173-0835

    Share

    COinS