Comparing endpoint and real-time PCR for forensic body fluid identification

ABSTRACT

Identifying the body fluid source of stains can give an indication of activities, prioritize samples for DNA profiling, and provide probative evidence when the presence of DNA alone does not. Messenger RNA profiling assays using endpoint reverse-transcription polymerase-chain reaction (RT-PCR), were developed to address the issues with conventional body fluid identification and have since been used in forensic casework. However, endpoint RT-PCR has a narrow linear dynamic range (LDR), and there is currently no specific method for mRNA quantification to direct case strategy and optimize input template. Real-time reverse-transcription quantitative PCR (RT-qPCR) collects data during the amplification phase which reflects the starting quantity. The application of RT-qPCR assays as a quantification precursor to endpoint RT-PCR was investigated using previously described assays for circulatory blood, saliva, menstrual fluid, spermatozoa, seminal fluid, and vaginal material. While blood marker SLC4A1 and vaginal marker CYP2B7P showed a strong relationship, the remaining body fluid markers lacked a robust predictive relationship, likely due to differences in LDR. CYP2B7P and SLC4A1 showed high sensitivity when compared with other markers – resulting in a greater number of data points for estimation of the linear relationship. Compared with endpoint, the RT-qPCR assays had higher precision, LDR, and sensitivity.

KEYWORDS:

• mRNA profiling
• body fluid identification
• real-time RT-PCR
• forensic biology
• endpoint RT-PCR

Disclosure statement

No potential conflict of interest was reported by the author(s).

Data availability statement

Due to the nature of this research, participants of this study did not agree for their data to be shared publicly, and therefore supporting data is not available.

Supplementary material

Supplemental data for this article can be accessed online at https://doi.org/10.1080/00450618.2024.2331532

Additional information

Funding

This work was supported by the New Zealand Ministry of Business, Innovation & Employment Strategic Science Investment Fund, and the University of Auckland Doctoral Scholarship.