Description of original award (Fiscal Year 2017, $667,774)
As submitted by the proposer:
The ability to identify menstrual blood has important implication in the criminal justice system where blood stains at a crime scene may be ascribed to a female victims period, or where a violent sexual assault with vaginal trauma may be claimed as consensual intercourse with a woman during menses. However, the ability to distinguish menstrual from circulating blood poses distinct problems for forensic scientists.
Compared to the more commonly tested forensic body fluids -- blood, saliva and semen -- which have easily identifiable and abundant marker proteins due to the biological functions these proteins perform in their respective body fluids, menstrual blood is a mixture of the uterine endometrium, vaginal fluid and mostly blood. Consequently, menstrual blood is similar to a body fluid mixture with all the attendant difficulties of discerning small amounts of unique or enriched markers in a field of other body fluid abundant markers - and where markers are sometimes shared. Making things even more difficult is that menses is a bodily function that changes over the days of a womans period, and it is vital that any final test can identify menstrual blood at all times.
In their previous NIJ grant, the researchers employed mass spectrometry to evaluate the menstrual blood proteomes of 45 women during all days of menses. The researchers generated extremely large proteomic datasets to search for menstrual blood markers, and were able to identify five unique menstrual blood markers, but not found at all times, as well as four additional menstrual blood markers found at all times but shared with a small number of other body fluids. However, with the use of bioinformatic computational analysis on all proteins detected in menstrual and venous blood, the researchers demonstrated that the proteomes of these two body fluids segregated into two groups - effectively distinguishing menstrual and venous blood.
The goal of this project is to use Q-TOF mass spectrometry with menstrual and venous bloods from 100 new volunteers to generate large menstrual and venous blood proteomic databases on which the researchers will employ computational bioinformatic approaches to generate a predictive model for menstrual blood identification.
This project has three specific aims: i) to collect and analyze menstrual and venous blood samples from 100 women by Q-TOF MS; ii) to establish a predictive model; and iii) to determine the models limit of detection with menstrual blood and evaluate it on other body fluid mixtures to demonstrate its accuracy.
Note: This project contains a research and/or development component, as defined in applicable law, and complies with Part 200 Uniform Requirements - 2 CFR 200.210(a)(14).